FinalProject_group146.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project  \"Tick Tock Shot Clock\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every year, a new NBA team is pushed to improve more than the year prior. Marginal increases in efficiency can help the underdog team make a push an dethrone the reigning champs. This is especially true in the last two decades, as NBA player salaries have risen exponentially compared to other sports, such as the NFL. Because of this increased monetary incentive, the NBA has been acquiring the best athletic talents in the world, pushing the limits of what a single team can accomplish. This accumulation of world class talent means that every team has to perform to their very best ability for the entire 48 minute span of a basketball game. Field goal percentage and pace are the most important factors of a game. Field goal percentage (fg%) is a recording of how frequently the ball is going into the hoop and pace is the frequency of possessions a team has within the 48 minute game. Both of these factors account for how many shots a team takes and how many shots they make. With our research question, we are analyzing an intersection between fg% and pace. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names and Contributions\n",
    "\n",
    "- Huikai (Kyle) Wang - Data Wrangling and Visualization\n",
    "- Yanxi (Thomas) Tang - Database, Data Wrangling and Visualization\n",
    "- Safa Raffo - Data Analysis & Results, Discussion, Peer Review\n",
    "- Julian Chelsey - Overview, Background, Hypothesis, Data Analysis, Conclusion\n",
    "- Jason Monroy - Discussion, Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Key Terms:\n",
    "__Field Goal Percentage:__ (How many shots are made) / (How many shots are attempted) = fg%  \n",
    "__Pace:__ The pace factor is used to estimate the number of possessions a team has per game.  \n",
    "__Shot Clock:__ A 24 second timer that begins at the start of every possession. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question\n",
    "How does shooting the ball earlier in the shot clock (having a faster pace) affect field goal percentage versus shooting later in the shot clock? \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset we plan on analyzing contains information regarding the time that a shot is taken during a possession of an NBA (National Basketball Association) basketball game. In a basketball game, everytime the ball changes possession and is brought to the other side of the court, the shot clock is reset to 24 seconds. The shot clock is a timer that counts down, leaving each team with 24 seconds to take their shot. Our data specifically tracks the success of these shots, while tracking how much time was left out of the 24 seconds when the shot was made or missed. \n",
    "\n",
    "Everyone in our group is an avid NBA fan, and thus we all know the rules of basketball and have holistic understandings of what good basketball statistics look like in comparison to bad basketball statistics. Our understanding of basketball allowed us to pick a niche dataset to explore, such as shot success in the shot clock, which more casual fans might not see the importance of. \n",
    "\n",
    "Similarly to our project, a team from the WI18 class analyzed the shot success rate between 3PT shots and 2PT shots in an attempt to determine which one had a greater positive impact on winning the game. In comparison, our project aims to analyze the shot success rate between shots taken early in the shot clock versus late in the shot clock and determine whether one has a greater impact on winning the game. The other team gleaned their data from some of the same sites as our team will be, as the NBA makes its data public, and they also aimed to answer the broader question of win rate based off of a niche statistic. The other team found multiple strong correlations, which suggested that a certain style of play might be marginally preferable over another. Our team also hopes to find data that can provide useful insights into an improved way to play the game, rather than inconclusive and scattered data. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We expect that teams who take their time in shooting the ball (slower pace) will have a higher field goal percentage. Taking time to shoot the ball typically means that an isolation play is being run or that many passes are being made in an attempt to find an open shot, both of which are gold standard plays in the NBA. It is important for our hypothesis to note that we believe a slower pace will correlate to a higher field goal percentage, but won’t necessarily correlate with winning the game, as a slower pace leads to less possessions, which can be detrimental to the outcome of the game despite higher field goal percentage. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our dataset is a set created by DanB from Kraggle.com, which has data on every shot taken in the 2014-2015 NBA season, can be found at https://www.kaggle.com/dansbecker/nba-shot-logs. It is a very detailed dataset containing every imaginable information on every single specific shot taken during that season, and comes available as a CSV file. We will be using this dataset to explore the corrolation between the shot clock and the efficiency of shots taken."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe your data cleaning steps here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the CSV file and store into shots\n",
    "shots = pd.read_csv('shot_logs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>FEB 27, 2015 - CHA @ BOS</td>\n",
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       "      <th>15</th>\n",
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       "      <td>FEB 27, 2015 - CHA @ BOS</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10:38</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>24.7</td>\n",
       "      <td>3</td>\n",
       "      <td>made</td>\n",
       "      <td>Young, James</td>\n",
       "      <td>203923</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21400859</td>\n",
       "      <td>FEB 27, 2015 - CHA @ BOS</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8:27</td>\n",
       "      <td>17.6</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3</td>\n",
       "      <td>missed</td>\n",
       "      <td>Jerebko, Jonas</td>\n",
       "      <td>201973</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>21400859</td>\n",
       "      <td>FEB 27, 2015 - CHA @ BOS</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>10:55</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>25.6</td>\n",
       "      <td>3</td>\n",
       "      <td>missed</td>\n",
       "      <td>Crowder, Jae</td>\n",
       "      <td>203109</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21400859</td>\n",
       "      <td>FEB 27, 2015 - CHA @ BOS</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10:29</td>\n",
       "      <td>20.8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.2</td>\n",
       "      <td>3</td>\n",
       "      <td>made</td>\n",
       "      <td>Thomas, Isaiah</td>\n",
       "      <td>202738</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21400845</td>\n",
       "      <td>FEB 25, 2015 - CHA @ CHI</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3:35</td>\n",
       "      <td>17.5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>25.4</td>\n",
       "      <td>3</td>\n",
       "      <td>missed</td>\n",
       "      <td>Brooks, Aaron</td>\n",
       "      <td>201166</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21400845</td>\n",
       "      <td>FEB 25, 2015 - CHA @ CHI</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0:33</td>\n",
       "      <td>19.5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>19.1</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Hinrich, Kirk</td>\n",
       "      <td>2550</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21400845</td>\n",
       "      <td>FEB 25, 2015 - CHA @ CHI</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8:50</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Gibson, Taj</td>\n",
       "      <td>201959</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>21400845</td>\n",
       "      <td>FEB 25, 2015 - CHA @ CHI</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8:24</td>\n",
       "      <td>15.7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.2</td>\n",
       "      <td>3</td>\n",
       "      <td>missed</td>\n",
       "      <td>Hinrich, Kirk</td>\n",
       "      <td>2550</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21400845</td>\n",
       "      <td>FEB 25, 2015 - CHA @ CHI</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5:32</td>\n",
       "      <td>11.2</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>22.8</td>\n",
       "      <td>3</td>\n",
       "      <td>made</td>\n",
       "      <td>Gibson, Taj</td>\n",
       "      <td>201959</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>21400845</td>\n",
       "      <td>FEB 25, 2015 - CHA @ CHI</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0:04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.2</td>\n",
       "      <td>3</td>\n",
       "      <td>missed</td>\n",
       "      <td>Butler, Jimmy</td>\n",
       "      <td>202710</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>21400845</td>\n",
       "      <td>FEB 25, 2015 - CHA @ CHI</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>10:13</td>\n",
       "      <td>17.1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>24.6</td>\n",
       "      <td>3</td>\n",
       "      <td>made</td>\n",
       "      <td>Noah, Joakim</td>\n",
       "      <td>201149</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>21400845</td>\n",
       "      <td>FEB 25, 2015 - CHA @ CHI</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>5:34</td>\n",
       "      <td>15.4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>10.6</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Brooks, Aaron</td>\n",
       "      <td>201166</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>21400827</td>\n",
       "      <td>FEB 22, 2015 - CHA @ DAL</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2:25</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Harris, Devin</td>\n",
       "      <td>2734</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>21400827</td>\n",
       "      <td>FEB 22, 2015 - CHA @ DAL</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-11</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>9:08</td>\n",
       "      <td>18.2</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>24.9</td>\n",
       "      <td>3</td>\n",
       "      <td>missed</td>\n",
       "      <td>Barea, Jose Juan</td>\n",
       "      <td>200826</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>21400827</td>\n",
       "      <td>FEB 22, 2015 - CHA @ DAL</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-11</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8:53</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8.3</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Barea, Jose Juan</td>\n",
       "      <td>200826</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>brian roberts</td>\n",
       "      <td>203148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128039</th>\n",
       "      <td>21400075</td>\n",
       "      <td>NOV 07, 2014 - BKN vs. NYK</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>9:05</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>10.7</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Larkin, Shane</td>\n",
       "      <td>203499</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128040</th>\n",
       "      <td>21400075</td>\n",
       "      <td>NOV 07, 2014 - BKN vs. NYK</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11:13</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Smith, Jason</td>\n",
       "      <td>201160</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128041</th>\n",
       "      <td>21400075</td>\n",
       "      <td>NOV 07, 2014 - BKN vs. NYK</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4:23</td>\n",
       "      <td>10.3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Larkin, Shane</td>\n",
       "      <td>203499</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128042</th>\n",
       "      <td>21400075</td>\n",
       "      <td>NOV 07, 2014 - BKN vs. NYK</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0:06</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Wear, Travis</td>\n",
       "      <td>204037</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128043</th>\n",
       "      <td>21400060</td>\n",
       "      <td>NOV 05, 2014 - BKN vs. MIN</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11:48</td>\n",
       "      <td>12.1</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>16.2</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Williams, Mo</td>\n",
       "      <td>2590</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128044</th>\n",
       "      <td>21400060</td>\n",
       "      <td>NOV 05, 2014 - BKN vs. MIN</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10:05</td>\n",
       "      <td>8.4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>17.7</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Williams, Mo</td>\n",
       "      <td>2590</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128045</th>\n",
       "      <td>21400060</td>\n",
       "      <td>NOV 05, 2014 - BKN vs. MIN</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9:34</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Dieng, Gorgui</td>\n",
       "      <td>203476</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128046</th>\n",
       "      <td>21400060</td>\n",
       "      <td>NOV 05, 2014 - BKN vs. MIN</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4:01</td>\n",
       "      <td>19.3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Martin, Kevin</td>\n",
       "      <td>2755</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128047</th>\n",
       "      <td>21400060</td>\n",
       "      <td>NOV 05, 2014 - BKN vs. MIN</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3:05</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Williams, Mo</td>\n",
       "      <td>2590</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128048</th>\n",
       "      <td>21400060</td>\n",
       "      <td>NOV 05, 2014 - BKN vs. MIN</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>11:01</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Brewer, Corey</td>\n",
       "      <td>201147</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128049</th>\n",
       "      <td>21400060</td>\n",
       "      <td>NOV 05, 2014 - BKN vs. MIN</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7:27</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Pekovic, Nikola</td>\n",
       "      <td>201593</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128050</th>\n",
       "      <td>21400060</td>\n",
       "      <td>NOV 05, 2014 - BKN vs. MIN</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6:41</td>\n",
       "      <td>22.7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Williams, Mo</td>\n",
       "      <td>2590</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128051</th>\n",
       "      <td>21400044</td>\n",
       "      <td>NOV 03, 2014 - BKN vs. OKC</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0:16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Telfair, Sebastian</td>\n",
       "      <td>2742</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128052</th>\n",
       "      <td>21400044</td>\n",
       "      <td>NOV 03, 2014 - BKN vs. OKC</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10:29</td>\n",
       "      <td>19.3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>18.9</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Telfair, Sebastian</td>\n",
       "      <td>2742</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128053</th>\n",
       "      <td>21400044</td>\n",
       "      <td>NOV 03, 2014 - BKN vs. OKC</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6:15</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>13.7</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Adams, Steven</td>\n",
       "      <td>203500</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128054</th>\n",
       "      <td>21400044</td>\n",
       "      <td>NOV 03, 2014 - BKN vs. OKC</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3:17</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>11.1</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Telfair, Sebastian</td>\n",
       "      <td>2742</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128055</th>\n",
       "      <td>21400044</td>\n",
       "      <td>NOV 03, 2014 - BKN vs. OKC</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>11:22</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Jackson, Reggie</td>\n",
       "      <td>202704</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128056</th>\n",
       "      <td>21400033</td>\n",
       "      <td>NOV 01, 2014 - BKN @ DET</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1:50</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Jennings, Brandon</td>\n",
       "      <td>201943</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128057</th>\n",
       "      <td>21400033</td>\n",
       "      <td>NOV 01, 2014 - BKN @ DET</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10:02</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.1</td>\n",
       "      <td>3</td>\n",
       "      <td>missed</td>\n",
       "      <td>Augustin, D.J.</td>\n",
       "      <td>201571</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128058</th>\n",
       "      <td>21400033</td>\n",
       "      <td>NOV 01, 2014 - BKN @ DET</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>10:57</td>\n",
       "      <td>14.3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Augustin, D.J.</td>\n",
       "      <td>201571</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128059</th>\n",
       "      <td>21400033</td>\n",
       "      <td>NOV 01, 2014 - BKN @ DET</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8:34</td>\n",
       "      <td>19.8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.7</td>\n",
       "      <td>3</td>\n",
       "      <td>missed</td>\n",
       "      <td>Augustin, D.J.</td>\n",
       "      <td>201571</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128060</th>\n",
       "      <td>21400006</td>\n",
       "      <td>OCT 29, 2014 - BKN @ BOS</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1:59</td>\n",
       "      <td>11.4</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>12.6</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Rondo, Rajon</td>\n",
       "      <td>200765</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128061</th>\n",
       "      <td>21400006</td>\n",
       "      <td>OCT 29, 2014 - BKN @ BOS</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10:10</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Bradley, Avery</td>\n",
       "      <td>202340</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128062</th>\n",
       "      <td>21400006</td>\n",
       "      <td>OCT 29, 2014 - BKN @ BOS</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7:46</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>14.5</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Smart, Marcus</td>\n",
       "      <td>203935</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128063</th>\n",
       "      <td>21400006</td>\n",
       "      <td>OCT 29, 2014 - BKN @ BOS</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5:05</td>\n",
       "      <td>15.3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Sullinger, Jared</td>\n",
       "      <td>203096</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128064</th>\n",
       "      <td>21400006</td>\n",
       "      <td>OCT 29, 2014 - BKN @ BOS</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1:52</td>\n",
       "      <td>18.3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Smart, Marcus</td>\n",
       "      <td>203935</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128065</th>\n",
       "      <td>21400006</td>\n",
       "      <td>OCT 29, 2014 - BKN @ BOS</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>11:28</td>\n",
       "      <td>19.8</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Turner, Evan</td>\n",
       "      <td>202323</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128066</th>\n",
       "      <td>21400006</td>\n",
       "      <td>OCT 29, 2014 - BKN @ BOS</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>11:10</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Thornton, Marcus</td>\n",
       "      <td>201977</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128067</th>\n",
       "      <td>21400006</td>\n",
       "      <td>OCT 29, 2014 - BKN @ BOS</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2:37</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>18.3</td>\n",
       "      <td>2</td>\n",
       "      <td>missed</td>\n",
       "      <td>Bradley, Avery</td>\n",
       "      <td>202340</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128068</th>\n",
       "      <td>21400006</td>\n",
       "      <td>OCT 29, 2014 - BKN @ BOS</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0:12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2</td>\n",
       "      <td>made</td>\n",
       "      <td>Bradley, Avery</td>\n",
       "      <td>202340</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>jarrett jack</td>\n",
       "      <td>101127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128069 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         GAME_ID                     MATCHUP LOCATION  W  FINAL_MARGIN  \\\n",
       "0       21400899    MAR 04, 2015 - CHA @ BKN        A  W            24   \n",
       "1       21400899    MAR 04, 2015 - CHA @ BKN        A  W            24   \n",
       "2       21400899    MAR 04, 2015 - CHA @ BKN        A  W            24   \n",
       "3       21400899    MAR 04, 2015 - CHA @ BKN        A  W            24   \n",
       "4       21400899    MAR 04, 2015 - CHA @ BKN        A  W            24   \n",
       "5       21400899    MAR 04, 2015 - CHA @ BKN        A  W            24   \n",
       "6       21400899    MAR 04, 2015 - CHA @ BKN        A  W            24   \n",
       "7       21400899    MAR 04, 2015 - CHA @ BKN        A  W            24   \n",
       "8       21400899    MAR 04, 2015 - CHA @ BKN        A  W            24   \n",
       "9       21400890  MAR 03, 2015 - CHA vs. LAL        H  W             1   \n",
       "10      21400890  MAR 03, 2015 - CHA vs. LAL        H  W             1   \n",
       "11      21400890  MAR 03, 2015 - CHA vs. LAL        H  W             1   \n",
       "12      21400890  MAR 03, 2015 - CHA vs. LAL        H  W             1   \n",
       "13      21400882    MAR 01, 2015 - CHA @ ORL        A  W            15   \n",
       "14      21400859    FEB 27, 2015 - CHA @ BOS        A  L            -8   \n",
       "15      21400859    FEB 27, 2015 - CHA @ BOS        A  L            -8   \n",
       "16      21400859    FEB 27, 2015 - CHA @ BOS        A  L            -8   \n",
       "17      21400859    FEB 27, 2015 - CHA @ BOS        A  L            -8   \n",
       "18      21400859    FEB 27, 2015 - CHA @ BOS        A  L            -8   \n",
       "19      21400845    FEB 25, 2015 - CHA @ CHI        A  W            12   \n",
       "20      21400845    FEB 25, 2015 - CHA @ CHI        A  W            12   \n",
       "21      21400845    FEB 25, 2015 - CHA @ CHI        A  W            12   \n",
       "22      21400845    FEB 25, 2015 - CHA @ CHI        A  W            12   \n",
       "23      21400845    FEB 25, 2015 - CHA @ CHI        A  W            12   \n",
       "24      21400845    FEB 25, 2015 - CHA @ CHI        A  W            12   \n",
       "25      21400845    FEB 25, 2015 - CHA @ CHI        A  W            12   \n",
       "26      21400845    FEB 25, 2015 - CHA @ CHI        A  W            12   \n",
       "27      21400827    FEB 22, 2015 - CHA @ DAL        A  L           -11   \n",
       "28      21400827    FEB 22, 2015 - CHA @ DAL        A  L           -11   \n",
       "29      21400827    FEB 22, 2015 - CHA @ DAL        A  L           -11   \n",
       "...          ...                         ...      ... ..           ...   \n",
       "128039  21400075  NOV 07, 2014 - BKN vs. NYK        H  W            11   \n",
       "128040  21400075  NOV 07, 2014 - BKN vs. NYK        H  W            11   \n",
       "128041  21400075  NOV 07, 2014 - BKN vs. NYK        H  W            11   \n",
       "128042  21400075  NOV 07, 2014 - BKN vs. NYK        H  W            11   \n",
       "128043  21400060  NOV 05, 2014 - BKN vs. MIN        H  L            -7   \n",
       "128044  21400060  NOV 05, 2014 - BKN vs. MIN        H  L            -7   \n",
       "128045  21400060  NOV 05, 2014 - BKN vs. MIN        H  L            -7   \n",
       "128046  21400060  NOV 05, 2014 - BKN vs. MIN        H  L            -7   \n",
       "128047  21400060  NOV 05, 2014 - BKN vs. MIN        H  L            -7   \n",
       "128048  21400060  NOV 05, 2014 - BKN vs. MIN        H  L            -7   \n",
       "128049  21400060  NOV 05, 2014 - BKN vs. MIN        H  L            -7   \n",
       "128050  21400060  NOV 05, 2014 - BKN vs. MIN        H  L            -7   \n",
       "128051  21400044  NOV 03, 2014 - BKN vs. OKC        H  W            31   \n",
       "128052  21400044  NOV 03, 2014 - BKN vs. OKC        H  W            31   \n",
       "128053  21400044  NOV 03, 2014 - BKN vs. OKC        H  W            31   \n",
       "128054  21400044  NOV 03, 2014 - BKN vs. OKC        H  W            31   \n",
       "128055  21400044  NOV 03, 2014 - BKN vs. OKC        H  W            31   \n",
       "128056  21400033    NOV 01, 2014 - BKN @ DET        A  W            12   \n",
       "128057  21400033    NOV 01, 2014 - BKN @ DET        A  W            12   \n",
       "128058  21400033    NOV 01, 2014 - BKN @ DET        A  W            12   \n",
       "128059  21400033    NOV 01, 2014 - BKN @ DET        A  W            12   \n",
       "128060  21400006    OCT 29, 2014 - BKN @ BOS        A  L           -16   \n",
       "128061  21400006    OCT 29, 2014 - BKN @ BOS        A  L           -16   \n",
       "128062  21400006    OCT 29, 2014 - BKN @ BOS        A  L           -16   \n",
       "128063  21400006    OCT 29, 2014 - BKN @ BOS        A  L           -16   \n",
       "128064  21400006    OCT 29, 2014 - BKN @ BOS        A  L           -16   \n",
       "128065  21400006    OCT 29, 2014 - BKN @ BOS        A  L           -16   \n",
       "128066  21400006    OCT 29, 2014 - BKN @ BOS        A  L           -16   \n",
       "128067  21400006    OCT 29, 2014 - BKN @ BOS        A  L           -16   \n",
       "128068  21400006    OCT 29, 2014 - BKN @ BOS        A  L           -16   \n",
       "\n",
       "        SHOT_NUMBER  PERIOD GAME_CLOCK  SHOT_CLOCK  DRIBBLES  ...  SHOT_DIST  \\\n",
       "0                 1       1       1:09        10.8         2  ...        7.7   \n",
       "1                 2       1       0:14         3.4         0  ...       28.2   \n",
       "2                 3       1       0:00         NaN         3  ...       10.1   \n",
       "3                 4       2      11:47        10.3         2  ...       17.2   \n",
       "4                 5       2      10:34        10.9         2  ...        3.7   \n",
       "5                 6       2       8:15         9.1         2  ...       18.4   \n",
       "6                 7       4      10:15        14.5        11  ...       20.7   \n",
       "7                 8       4       8:00         3.4         3  ...        3.5   \n",
       "8                 9       4       5:14        12.4         0  ...       24.6   \n",
       "9                 1       2      11:32        17.4         0  ...       22.4   \n",
       "10                2       2       6:30        16.0         8  ...       24.5   \n",
       "11                3       4      11:32        12.1        14  ...       14.6   \n",
       "12                4       4       8:55         4.3         2  ...        5.9   \n",
       "13                1       4       9:10         4.4         0  ...       26.4   \n",
       "14                1       1       0:48         6.8         0  ...       22.8   \n",
       "15                2       2      10:38         6.4         3  ...       24.7   \n",
       "16                3       2       8:27        17.6         6  ...       25.0   \n",
       "17                4       4      10:55         8.7         1  ...       25.6   \n",
       "18                5       4      10:29        20.8         0  ...       24.2   \n",
       "19                1       1       3:35        17.5         2  ...       25.4   \n",
       "20                2       1       0:33        19.5         5  ...       19.1   \n",
       "21                3       2       8:50         6.0         6  ...       14.8   \n",
       "22                4       2       8:24        15.7         0  ...       25.2   \n",
       "23                5       2       5:32        11.2        16  ...       22.8   \n",
       "24                6       2       0:04         NaN         0  ...       25.2   \n",
       "25                7       4      10:13        17.1         7  ...       24.6   \n",
       "26                8       4       5:34        15.4         1  ...       10.6   \n",
       "27                1       1       2:25        12.3         0  ...       20.2   \n",
       "28                2       2       9:08        18.2         7  ...       24.9   \n",
       "29                3       2       8:53        11.9         1  ...        8.3   \n",
       "...             ...     ...        ...         ...       ...  ...        ...   \n",
       "128039            2       2       9:05         7.9         6  ...       10.7   \n",
       "128040            3       4      11:13         8.8         0  ...        2.5   \n",
       "128041            4       4       4:23        10.3         3  ...        7.5   \n",
       "128042            5       4       0:06         4.0        20  ...        6.8   \n",
       "128043            1       2      11:48        12.1        16  ...       16.2   \n",
       "128044            2       2      10:05         8.4         3  ...       17.7   \n",
       "128045            3       2       9:34         9.0         0  ...        3.6   \n",
       "128046            4       3       4:01        19.3         2  ...       17.0   \n",
       "128047            5       3       3:05         7.3         6  ...       15.5   \n",
       "128048            6       4      11:01         9.0         0  ...       20.2   \n",
       "128049            7       4       7:27        11.0         2  ...        9.4   \n",
       "128050            8       4       6:41        22.7         1  ...        2.3   \n",
       "128051            2       1       0:16         NaN         3  ...        2.6   \n",
       "128052            3       2      10:29        19.3         2  ...       18.9   \n",
       "128053            4       3       6:15         3.2         5  ...       13.7   \n",
       "128054            5       3       3:17         4.1         2  ...       11.1   \n",
       "128055            6       4      11:22        13.8         0  ...        5.1   \n",
       "128056            1       1       1:50         7.1         2  ...       15.5   \n",
       "128057            2       2      10:02         7.3         0  ...       22.1   \n",
       "128058            3       4      10:57        14.3         2  ...       18.2   \n",
       "128059            4       4       8:34        19.8         0  ...       22.7   \n",
       "128060            1       1       1:59        11.4        16  ...       12.6   \n",
       "128061            2       2      10:10        19.0         0  ...        7.4   \n",
       "128062            3       2       7:46         7.0         1  ...       14.5   \n",
       "128063            4       2       5:05        15.3         2  ...        8.9   \n",
       "128064            5       3       1:52        18.3         5  ...        8.7   \n",
       "128065            6       4      11:28        19.8         4  ...        0.6   \n",
       "128066            7       4      11:10        23.0         2  ...       16.9   \n",
       "128067            8       4       2:37         9.1         4  ...       18.3   \n",
       "128068            9       4       0:12         NaN         5  ...        5.1   \n",
       "\n",
       "        PTS_TYPE  SHOT_RESULT    CLOSEST_DEFENDER CLOSEST_DEFENDER_PLAYER_ID  \\\n",
       "0              2         made      Anderson, Alan                     101187   \n",
       "1              3       missed   Bogdanovic, Bojan                     202711   \n",
       "2              2       missed   Bogdanovic, Bojan                     202711   \n",
       "3              2       missed       Brown, Markel                     203900   \n",
       "4              2       missed     Young, Thaddeus                     201152   \n",
       "5              2       missed     Williams, Deron                     101114   \n",
       "6              2       missed       Jack, Jarrett                     101127   \n",
       "7              2         made      Plumlee, Mason                     203486   \n",
       "8              3       missed      Morris, Darius                     202721   \n",
       "9              3       missed    Ellington, Wayne                     201961   \n",
       "10             3       missed         Lin, Jeremy                     202391   \n",
       "11             2         made         Lin, Jeremy                     202391   \n",
       "12             2         made        Hill, Jordan                     201941   \n",
       "13             3       missed       Green, Willie                       2584   \n",
       "14             3       missed       Smart, Marcus                     203935   \n",
       "15             3         made        Young, James                     203923   \n",
       "16             3       missed      Jerebko, Jonas                     201973   \n",
       "17             3       missed        Crowder, Jae                     203109   \n",
       "18             3         made      Thomas, Isaiah                     202738   \n",
       "19             3       missed       Brooks, Aaron                     201166   \n",
       "20             2         made       Hinrich, Kirk                       2550   \n",
       "21             2         made         Gibson, Taj                     201959   \n",
       "22             3       missed       Hinrich, Kirk                       2550   \n",
       "23             3         made         Gibson, Taj                     201959   \n",
       "24             3       missed       Butler, Jimmy                     202710   \n",
       "25             3         made        Noah, Joakim                     201149   \n",
       "26             2         made       Brooks, Aaron                     201166   \n",
       "27             2       missed       Harris, Devin                       2734   \n",
       "28             3       missed    Barea, Jose Juan                     200826   \n",
       "29             2       missed    Barea, Jose Juan                     200826   \n",
       "...          ...          ...                 ...                        ...   \n",
       "128039         2       missed       Larkin, Shane                     203499   \n",
       "128040         2       missed        Smith, Jason                     201160   \n",
       "128041         2         made       Larkin, Shane                     203499   \n",
       "128042         2         made        Wear, Travis                     204037   \n",
       "128043         2         made        Williams, Mo                       2590   \n",
       "128044         2       missed        Williams, Mo                       2590   \n",
       "128045         2       missed       Dieng, Gorgui                     203476   \n",
       "128046         2       missed       Martin, Kevin                       2755   \n",
       "128047         2         made        Williams, Mo                       2590   \n",
       "128048         2         made       Brewer, Corey                     201147   \n",
       "128049         2         made     Pekovic, Nikola                     201593   \n",
       "128050         2         made        Williams, Mo                       2590   \n",
       "128051         2         made  Telfair, Sebastian                       2742   \n",
       "128052         2       missed  Telfair, Sebastian                       2742   \n",
       "128053         2       missed       Adams, Steven                     203500   \n",
       "128054         2       missed  Telfair, Sebastian                       2742   \n",
       "128055         2       missed     Jackson, Reggie                     202704   \n",
       "128056         2       missed   Jennings, Brandon                     201943   \n",
       "128057         3       missed      Augustin, D.J.                     201571   \n",
       "128058         2         made      Augustin, D.J.                     201571   \n",
       "128059         3       missed      Augustin, D.J.                     201571   \n",
       "128060         2       missed        Rondo, Rajon                     200765   \n",
       "128061         2       missed      Bradley, Avery                     202340   \n",
       "128062         2         made       Smart, Marcus                     203935   \n",
       "128063         2         made    Sullinger, Jared                     203096   \n",
       "128064         2       missed       Smart, Marcus                     203935   \n",
       "128065         2         made        Turner, Evan                     202323   \n",
       "128066         2         made    Thornton, Marcus                     201977   \n",
       "128067         2       missed      Bradley, Avery                     202340   \n",
       "128068         2         made      Bradley, Avery                     202340   \n",
       "\n",
       "        CLOSE_DEF_DIST  FGM  PTS    player_name player_id  \n",
       "0                  1.3    1    2  brian roberts    203148  \n",
       "1                  6.1    0    0  brian roberts    203148  \n",
       "2                  0.9    0    0  brian roberts    203148  \n",
       "3                  3.4    0    0  brian roberts    203148  \n",
       "4                  1.1    0    0  brian roberts    203148  \n",
       "5                  2.6    0    0  brian roberts    203148  \n",
       "6                  6.1    0    0  brian roberts    203148  \n",
       "7                  2.1    1    2  brian roberts    203148  \n",
       "8                  7.3    0    0  brian roberts    203148  \n",
       "9                 19.8    0    0  brian roberts    203148  \n",
       "10                 4.7    0    0  brian roberts    203148  \n",
       "11                 1.8    1    2  brian roberts    203148  \n",
       "12                 5.4    1    2  brian roberts    203148  \n",
       "13                 4.4    0    0  brian roberts    203148  \n",
       "14                 5.3    0    0  brian roberts    203148  \n",
       "15                 5.6    1    3  brian roberts    203148  \n",
       "16                 5.4    0    0  brian roberts    203148  \n",
       "17                 5.1    0    0  brian roberts    203148  \n",
       "18                11.1    1    3  brian roberts    203148  \n",
       "19                 3.5    0    0  brian roberts    203148  \n",
       "20                 4.0    1    2  brian roberts    203148  \n",
       "21                 5.4    1    2  brian roberts    203148  \n",
       "22                 3.7    0    0  brian roberts    203148  \n",
       "23                 5.1    1    3  brian roberts    203148  \n",
       "24                 3.6    0    0  brian roberts    203148  \n",
       "25                 6.0    1    3  brian roberts    203148  \n",
       "26                 0.7    1    2  brian roberts    203148  \n",
       "27                 3.5    0    0  brian roberts    203148  \n",
       "28                 4.5    0    0  brian roberts    203148  \n",
       "29                 3.5    0    0  brian roberts    203148  \n",
       "...                ...  ...  ...            ...       ...  \n",
       "128039             1.2    0    0   jarrett jack    101127  \n",
       "128040             5.1    0    0   jarrett jack    101127  \n",
       "128041             0.9    1    2   jarrett jack    101127  \n",
       "128042             0.9    1    2   jarrett jack    101127  \n",
       "128043             2.5    1    2   jarrett jack    101127  \n",
       "128044             1.8    0    0   jarrett jack    101127  \n",
       "128045             1.5    0    0   jarrett jack    101127  \n",
       "128046             3.6    0    0   jarrett jack    101127  \n",
       "128047             2.4    1    2   jarrett jack    101127  \n",
       "128048            11.1    1    2   jarrett jack    101127  \n",
       "128049             6.0    1    2   jarrett jack    101127  \n",
       "128050             7.7    1    2   jarrett jack    101127  \n",
       "128051             5.4    1    2   jarrett jack    101127  \n",
       "128052             3.8    0    0   jarrett jack    101127  \n",
       "128053             2.8    0    0   jarrett jack    101127  \n",
       "128054             4.2    0    0   jarrett jack    101127  \n",
       "128055             2.1    0    0   jarrett jack    101127  \n",
       "128056             3.1    0    0   jarrett jack    101127  \n",
       "128057             2.9    0    0   jarrett jack    101127  \n",
       "128058             1.1    1    2   jarrett jack    101127  \n",
       "128059             4.0    0    0   jarrett jack    101127  \n",
       "128060             4.8    0    0   jarrett jack    101127  \n",
       "128061             2.7    0    0   jarrett jack    101127  \n",
       "128062             3.1    1    2   jarrett jack    101127  \n",
       "128063             5.7    1    2   jarrett jack    101127  \n",
       "128064             0.8    0    0   jarrett jack    101127  \n",
       "128065             0.6    1    2   jarrett jack    101127  \n",
       "128066             4.2    1    2   jarrett jack    101127  \n",
       "128067             3.0    0    0   jarrett jack    101127  \n",
       "128068             2.3    1    2   jarrett jack    101127  \n",
       "\n",
       "[128069 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print/Display the original shot_log dataset\n",
    "shots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop all columns that we won't be using\n",
    "shots.drop(['GAME_ID', 'MATCHUP', 'LOCATION', 'SHOT_NUMBER','DRIBBLES', 'TOUCH_TIME',\n",
    "            'CLOSEST_DEFENDER', 'CLOSEST_DEFENDER_PLAYER_ID', 'CLOSE_DEF_DIST', 'SHOT_RESULT', 'player_name', 'player_id'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>FINAL_MARGIN</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>GAME_CLOCK</th>\n",
       "      <th>SHOT_CLOCK</th>\n",
       "      <th>SHOT_DIST</th>\n",
       "      <th>PTS_TYPE</th>\n",
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       "      <th>PTS</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1:09</td>\n",
       "      <td>10.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0:14</td>\n",
       "      <td>3.4</td>\n",
       "      <td>28.2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>11:47</td>\n",
       "      <td>10.3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>10:34</td>\n",
       "      <td>10.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>8:15</td>\n",
       "      <td>9.1</td>\n",
       "      <td>18.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>10:15</td>\n",
       "      <td>14.5</td>\n",
       "      <td>20.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>8:00</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>5:14</td>\n",
       "      <td>12.4</td>\n",
       "      <td>24.6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11:32</td>\n",
       "      <td>17.4</td>\n",
       "      <td>22.4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6:30</td>\n",
       "      <td>16.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11:32</td>\n",
       "      <td>12.1</td>\n",
       "      <td>14.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8:55</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>W</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>9:10</td>\n",
       "      <td>4.4</td>\n",
       "      <td>26.4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>1</td>\n",
       "      <td>0:48</td>\n",
       "      <td>6.8</td>\n",
       "      <td>22.8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>2</td>\n",
       "      <td>10:38</td>\n",
       "      <td>6.4</td>\n",
       "      <td>24.7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>2</td>\n",
       "      <td>8:27</td>\n",
       "      <td>17.6</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>4</td>\n",
       "      <td>10:55</td>\n",
       "      <td>8.7</td>\n",
       "      <td>25.6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>4</td>\n",
       "      <td>10:29</td>\n",
       "      <td>20.8</td>\n",
       "      <td>24.2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>3:35</td>\n",
       "      <td>17.5</td>\n",
       "      <td>25.4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0:33</td>\n",
       "      <td>19.5</td>\n",
       "      <td>19.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>8:50</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>8:24</td>\n",
       "      <td>15.7</td>\n",
       "      <td>25.2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>5:32</td>\n",
       "      <td>11.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0:04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>10:13</td>\n",
       "      <td>17.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>5:34</td>\n",
       "      <td>15.4</td>\n",
       "      <td>10.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>L</td>\n",
       "      <td>-11</td>\n",
       "      <td>1</td>\n",
       "      <td>2:25</td>\n",
       "      <td>12.3</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>L</td>\n",
       "      <td>-11</td>\n",
       "      <td>2</td>\n",
       "      <td>9:08</td>\n",
       "      <td>18.2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>L</td>\n",
       "      <td>-11</td>\n",
       "      <td>2</td>\n",
       "      <td>8:53</td>\n",
       "      <td>11.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128039</th>\n",
       "      <td>W</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9:05</td>\n",
       "      <td>7.9</td>\n",
       "      <td>10.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128040</th>\n",
       "      <td>W</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>11:13</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128041</th>\n",
       "      <td>W</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>4:23</td>\n",
       "      <td>10.3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128042</th>\n",
       "      <td>W</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0:06</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128043</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>2</td>\n",
       "      <td>11:48</td>\n",
       "      <td>12.1</td>\n",
       "      <td>16.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128044</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>2</td>\n",
       "      <td>10:05</td>\n",
       "      <td>8.4</td>\n",
       "      <td>17.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128045</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>2</td>\n",
       "      <td>9:34</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128046</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>3</td>\n",
       "      <td>4:01</td>\n",
       "      <td>19.3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128047</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>3</td>\n",
       "      <td>3:05</td>\n",
       "      <td>7.3</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128048</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>11:01</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128049</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>7:27</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128050</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>6:41</td>\n",
       "      <td>22.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128051</th>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0:16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128052</th>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>10:29</td>\n",
       "      <td>19.3</td>\n",
       "      <td>18.9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128053</th>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>6:15</td>\n",
       "      <td>3.2</td>\n",
       "      <td>13.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128054</th>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>3:17</td>\n",
       "      <td>4.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128055</th>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>11:22</td>\n",
       "      <td>13.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128056</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1:50</td>\n",
       "      <td>7.1</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128057</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>10:02</td>\n",
       "      <td>7.3</td>\n",
       "      <td>22.1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128058</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>10:57</td>\n",
       "      <td>14.3</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128059</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>8:34</td>\n",
       "      <td>19.8</td>\n",
       "      <td>22.7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128060</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1:59</td>\n",
       "      <td>11.4</td>\n",
       "      <td>12.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128061</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>2</td>\n",
       "      <td>10:10</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128062</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>2</td>\n",
       "      <td>7:46</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128063</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>2</td>\n",
       "      <td>5:05</td>\n",
       "      <td>15.3</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128064</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>3</td>\n",
       "      <td>1:52</td>\n",
       "      <td>18.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128065</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>4</td>\n",
       "      <td>11:28</td>\n",
       "      <td>19.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128066</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>4</td>\n",
       "      <td>11:10</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128067</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>4</td>\n",
       "      <td>2:37</td>\n",
       "      <td>9.1</td>\n",
       "      <td>18.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128068</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>4</td>\n",
       "      <td>0:12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128069 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        W  FINAL_MARGIN  PERIOD GAME_CLOCK  SHOT_CLOCK  SHOT_DIST  PTS_TYPE  \\\n",
       "0       W            24       1       1:09        10.8        7.7         2   \n",
       "1       W            24       1       0:14         3.4       28.2         3   \n",
       "2       W            24       1       0:00         NaN       10.1         2   \n",
       "3       W            24       2      11:47        10.3       17.2         2   \n",
       "4       W            24       2      10:34        10.9        3.7         2   \n",
       "5       W            24       2       8:15         9.1       18.4         2   \n",
       "6       W            24       4      10:15        14.5       20.7         2   \n",
       "7       W            24       4       8:00         3.4        3.5         2   \n",
       "8       W            24       4       5:14        12.4       24.6         3   \n",
       "9       W             1       2      11:32        17.4       22.4         3   \n",
       "10      W             1       2       6:30        16.0       24.5         3   \n",
       "11      W             1       4      11:32        12.1       14.6         2   \n",
       "12      W             1       4       8:55         4.3        5.9         2   \n",
       "13      W            15       4       9:10         4.4       26.4         3   \n",
       "14      L            -8       1       0:48         6.8       22.8         3   \n",
       "15      L            -8       2      10:38         6.4       24.7         3   \n",
       "16      L            -8       2       8:27        17.6       25.0         3   \n",
       "17      L            -8       4      10:55         8.7       25.6         3   \n",
       "18      L            -8       4      10:29        20.8       24.2         3   \n",
       "19      W            12       1       3:35        17.5       25.4         3   \n",
       "20      W            12       1       0:33        19.5       19.1         2   \n",
       "21      W            12       2       8:50         6.0       14.8         2   \n",
       "22      W            12       2       8:24        15.7       25.2         3   \n",
       "23      W            12       2       5:32        11.2       22.8         3   \n",
       "24      W            12       2       0:04         NaN       25.2         3   \n",
       "25      W            12       4      10:13        17.1       24.6         3   \n",
       "26      W            12       4       5:34        15.4       10.6         2   \n",
       "27      L           -11       1       2:25        12.3       20.2         2   \n",
       "28      L           -11       2       9:08        18.2       24.9         3   \n",
       "29      L           -11       2       8:53        11.9        8.3         2   \n",
       "...    ..           ...     ...        ...         ...        ...       ...   \n",
       "128039  W            11       2       9:05         7.9       10.7         2   \n",
       "128040  W            11       4      11:13         8.8        2.5         2   \n",
       "128041  W            11       4       4:23        10.3        7.5         2   \n",
       "128042  W            11       4       0:06         4.0        6.8         2   \n",
       "128043  L            -7       2      11:48        12.1       16.2         2   \n",
       "128044  L            -7       2      10:05         8.4       17.7         2   \n",
       "128045  L            -7       2       9:34         9.0        3.6         2   \n",
       "128046  L            -7       3       4:01        19.3       17.0         2   \n",
       "128047  L            -7       3       3:05         7.3       15.5         2   \n",
       "128048  L            -7       4      11:01         9.0       20.2         2   \n",
       "128049  L            -7       4       7:27        11.0        9.4         2   \n",
       "128050  L            -7       4       6:41        22.7        2.3         2   \n",
       "128051  W            31       1       0:16         NaN        2.6         2   \n",
       "128052  W            31       2      10:29        19.3       18.9         2   \n",
       "128053  W            31       3       6:15         3.2       13.7         2   \n",
       "128054  W            31       3       3:17         4.1       11.1         2   \n",
       "128055  W            31       4      11:22        13.8        5.1         2   \n",
       "128056  W            12       1       1:50         7.1       15.5         2   \n",
       "128057  W            12       2      10:02         7.3       22.1         3   \n",
       "128058  W            12       4      10:57        14.3       18.2         2   \n",
       "128059  W            12       4       8:34        19.8       22.7         3   \n",
       "128060  L           -16       1       1:59        11.4       12.6         2   \n",
       "128061  L           -16       2      10:10        19.0        7.4         2   \n",
       "128062  L           -16       2       7:46         7.0       14.5         2   \n",
       "128063  L           -16       2       5:05        15.3        8.9         2   \n",
       "128064  L           -16       3       1:52        18.3        8.7         2   \n",
       "128065  L           -16       4      11:28        19.8        0.6         2   \n",
       "128066  L           -16       4      11:10        23.0       16.9         2   \n",
       "128067  L           -16       4       2:37         9.1       18.3         2   \n",
       "128068  L           -16       4       0:12         NaN        5.1         2   \n",
       "\n",
       "        FGM  PTS  \n",
       "0         1    2  \n",
       "1         0    0  \n",
       "2         0    0  \n",
       "3         0    0  \n",
       "4         0    0  \n",
       "5         0    0  \n",
       "6         0    0  \n",
       "7         1    2  \n",
       "8         0    0  \n",
       "9         0    0  \n",
       "10        0    0  \n",
       "11        1    2  \n",
       "12        1    2  \n",
       "13        0    0  \n",
       "14        0    0  \n",
       "15        1    3  \n",
       "16        0    0  \n",
       "17        0    0  \n",
       "18        1    3  \n",
       "19        0    0  \n",
       "20        1    2  \n",
       "21        1    2  \n",
       "22        0    0  \n",
       "23        1    3  \n",
       "24        0    0  \n",
       "25        1    3  \n",
       "26        1    2  \n",
       "27        0    0  \n",
       "28        0    0  \n",
       "29        0    0  \n",
       "...     ...  ...  \n",
       "128039    0    0  \n",
       "128040    0    0  \n",
       "128041    1    2  \n",
       "128042    1    2  \n",
       "128043    1    2  \n",
       "128044    0    0  \n",
       "128045    0    0  \n",
       "128046    0    0  \n",
       "128047    1    2  \n",
       "128048    1    2  \n",
       "128049    1    2  \n",
       "128050    1    2  \n",
       "128051    1    2  \n",
       "128052    0    0  \n",
       "128053    0    0  \n",
       "128054    0    0  \n",
       "128055    0    0  \n",
       "128056    0    0  \n",
       "128057    0    0  \n",
       "128058    1    2  \n",
       "128059    0    0  \n",
       "128060    0    0  \n",
       "128061    0    0  \n",
       "128062    1    2  \n",
       "128063    1    2  \n",
       "128064    0    0  \n",
       "128065    1    2  \n",
       "128066    1    2  \n",
       "128067    0    0  \n",
       "128068    1    2  \n",
       "\n",
       "[128069 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>FINAL_MARGIN</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>GAME_CLOCK</th>\n",
       "      <th>SHOT_CLOCK</th>\n",
       "      <th>SHOT_DIST</th>\n",
       "      <th>PTS_TYPE</th>\n",
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       "      <td>10.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>0:14</td>\n",
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       "      <td>28.2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>11:47</td>\n",
       "      <td>10.3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2</td>\n",
       "      <td>10:34</td>\n",
       "      <td>10.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>8:15</td>\n",
       "      <td>9.1</td>\n",
       "      <td>18.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>10:15</td>\n",
       "      <td>14.5</td>\n",
       "      <td>20.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>8:00</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>5:14</td>\n",
       "      <td>12.4</td>\n",
       "      <td>24.6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11:32</td>\n",
       "      <td>17.4</td>\n",
       "      <td>22.4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6:30</td>\n",
       "      <td>16.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11:32</td>\n",
       "      <td>12.1</td>\n",
       "      <td>14.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8:55</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>W</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>9:10</td>\n",
       "      <td>4.4</td>\n",
       "      <td>26.4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>1</td>\n",
       "      <td>0:48</td>\n",
       "      <td>6.8</td>\n",
       "      <td>22.8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>2</td>\n",
       "      <td>10:38</td>\n",
       "      <td>6.4</td>\n",
       "      <td>24.7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>2</td>\n",
       "      <td>8:27</td>\n",
       "      <td>17.6</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>4</td>\n",
       "      <td>10:55</td>\n",
       "      <td>8.7</td>\n",
       "      <td>25.6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>4</td>\n",
       "      <td>10:29</td>\n",
       "      <td>20.8</td>\n",
       "      <td>24.2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>3:35</td>\n",
       "      <td>17.5</td>\n",
       "      <td>25.4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0:33</td>\n",
       "      <td>19.5</td>\n",
       "      <td>19.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>8:50</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>8:24</td>\n",
       "      <td>15.7</td>\n",
       "      <td>25.2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>5:32</td>\n",
       "      <td>11.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>10:13</td>\n",
       "      <td>17.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>5:34</td>\n",
       "      <td>15.4</td>\n",
       "      <td>10.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>L</td>\n",
       "      <td>-11</td>\n",
       "      <td>1</td>\n",
       "      <td>2:25</td>\n",
       "      <td>12.3</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>L</td>\n",
       "      <td>-11</td>\n",
       "      <td>2</td>\n",
       "      <td>9:08</td>\n",
       "      <td>18.2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>L</td>\n",
       "      <td>-11</td>\n",
       "      <td>2</td>\n",
       "      <td>8:53</td>\n",
       "      <td>11.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>L</td>\n",
       "      <td>-11</td>\n",
       "      <td>4</td>\n",
       "      <td>11:37</td>\n",
       "      <td>11.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>1</td>\n",
       "      <td>1:18</td>\n",
       "      <td>10.3</td>\n",
       "      <td>24.1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128037</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>7:19</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128038</th>\n",
       "      <td>W</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9:46</td>\n",
       "      <td>13.6</td>\n",
       "      <td>20.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128039</th>\n",
       "      <td>W</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9:05</td>\n",
       "      <td>7.9</td>\n",
       "      <td>10.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128040</th>\n",
       "      <td>W</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>11:13</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128041</th>\n",
       "      <td>W</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>4:23</td>\n",
       "      <td>10.3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128042</th>\n",
       "      <td>W</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0:06</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128043</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>2</td>\n",
       "      <td>11:48</td>\n",
       "      <td>12.1</td>\n",
       "      <td>16.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128044</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>2</td>\n",
       "      <td>10:05</td>\n",
       "      <td>8.4</td>\n",
       "      <td>17.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128045</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>2</td>\n",
       "      <td>9:34</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128046</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>3</td>\n",
       "      <td>4:01</td>\n",
       "      <td>19.3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128047</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>3</td>\n",
       "      <td>3:05</td>\n",
       "      <td>7.3</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128048</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>11:01</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128049</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>7:27</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128050</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>6:41</td>\n",
       "      <td>22.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128052</th>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>10:29</td>\n",
       "      <td>19.3</td>\n",
       "      <td>18.9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128053</th>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>6:15</td>\n",
       "      <td>3.2</td>\n",
       "      <td>13.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128054</th>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>3:17</td>\n",
       "      <td>4.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128055</th>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>11:22</td>\n",
       "      <td>13.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128056</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1:50</td>\n",
       "      <td>7.1</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128057</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>10:02</td>\n",
       "      <td>7.3</td>\n",
       "      <td>22.1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128058</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>10:57</td>\n",
       "      <td>14.3</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128059</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>8:34</td>\n",
       "      <td>19.8</td>\n",
       "      <td>22.7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128060</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1:59</td>\n",
       "      <td>11.4</td>\n",
       "      <td>12.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128061</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>2</td>\n",
       "      <td>10:10</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128062</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>2</td>\n",
       "      <td>7:46</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128063</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>2</td>\n",
       "      <td>5:05</td>\n",
       "      <td>15.3</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128064</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>3</td>\n",
       "      <td>1:52</td>\n",
       "      <td>18.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128065</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>4</td>\n",
       "      <td>11:28</td>\n",
       "      <td>19.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128066</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>4</td>\n",
       "      <td>11:10</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128067</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>4</td>\n",
       "      <td>2:37</td>\n",
       "      <td>9.1</td>\n",
       "      <td>18.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122502 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        W  FINAL_MARGIN  PERIOD GAME_CLOCK  SHOT_CLOCK  SHOT_DIST  PTS_TYPE  \\\n",
       "0       W            24       1       1:09        10.8        7.7         2   \n",
       "1       W            24       1       0:14         3.4       28.2         3   \n",
       "3       W            24       2      11:47        10.3       17.2         2   \n",
       "4       W            24       2      10:34        10.9        3.7         2   \n",
       "5       W            24       2       8:15         9.1       18.4         2   \n",
       "6       W            24       4      10:15        14.5       20.7         2   \n",
       "7       W            24       4       8:00         3.4        3.5         2   \n",
       "8       W            24       4       5:14        12.4       24.6         3   \n",
       "9       W             1       2      11:32        17.4       22.4         3   \n",
       "10      W             1       2       6:30        16.0       24.5         3   \n",
       "11      W             1       4      11:32        12.1       14.6         2   \n",
       "12      W             1       4       8:55         4.3        5.9         2   \n",
       "13      W            15       4       9:10         4.4       26.4         3   \n",
       "14      L            -8       1       0:48         6.8       22.8         3   \n",
       "15      L            -8       2      10:38         6.4       24.7         3   \n",
       "16      L            -8       2       8:27        17.6       25.0         3   \n",
       "17      L            -8       4      10:55         8.7       25.6         3   \n",
       "18      L            -8       4      10:29        20.8       24.2         3   \n",
       "19      W            12       1       3:35        17.5       25.4         3   \n",
       "20      W            12       1       0:33        19.5       19.1         2   \n",
       "21      W            12       2       8:50         6.0       14.8         2   \n",
       "22      W            12       2       8:24        15.7       25.2         3   \n",
       "23      W            12       2       5:32        11.2       22.8         3   \n",
       "25      W            12       4      10:13        17.1       24.6         3   \n",
       "26      W            12       4       5:34        15.4       10.6         2   \n",
       "27      L           -11       1       2:25        12.3       20.2         2   \n",
       "28      L           -11       2       9:08        18.2       24.9         3   \n",
       "29      L           -11       2       8:53        11.9        8.3         2   \n",
       "30      L           -11       4      11:37        11.6       10.0         2   \n",
       "31      L            -7       1       1:18        10.3       24.1         3   \n",
       "...    ..           ...     ...        ...         ...        ...       ...   \n",
       "128037  W             8       4       7:19        11.2        2.2         2   \n",
       "128038  W            11       2       9:46        13.6       20.7         2   \n",
       "128039  W            11       2       9:05         7.9       10.7         2   \n",
       "128040  W            11       4      11:13         8.8        2.5         2   \n",
       "128041  W            11       4       4:23        10.3        7.5         2   \n",
       "128042  W            11       4       0:06         4.0        6.8         2   \n",
       "128043  L            -7       2      11:48        12.1       16.2         2   \n",
       "128044  L            -7       2      10:05         8.4       17.7         2   \n",
       "128045  L            -7       2       9:34         9.0        3.6         2   \n",
       "128046  L            -7       3       4:01        19.3       17.0         2   \n",
       "128047  L            -7       3       3:05         7.3       15.5         2   \n",
       "128048  L            -7       4      11:01         9.0       20.2         2   \n",
       "128049  L            -7       4       7:27        11.0        9.4         2   \n",
       "128050  L            -7       4       6:41        22.7        2.3         2   \n",
       "128052  W            31       2      10:29        19.3       18.9         2   \n",
       "128053  W            31       3       6:15         3.2       13.7         2   \n",
       "128054  W            31       3       3:17         4.1       11.1         2   \n",
       "128055  W            31       4      11:22        13.8        5.1         2   \n",
       "128056  W            12       1       1:50         7.1       15.5         2   \n",
       "128057  W            12       2      10:02         7.3       22.1         3   \n",
       "128058  W            12       4      10:57        14.3       18.2         2   \n",
       "128059  W            12       4       8:34        19.8       22.7         3   \n",
       "128060  L           -16       1       1:59        11.4       12.6         2   \n",
       "128061  L           -16       2      10:10        19.0        7.4         2   \n",
       "128062  L           -16       2       7:46         7.0       14.5         2   \n",
       "128063  L           -16       2       5:05        15.3        8.9         2   \n",
       "128064  L           -16       3       1:52        18.3        8.7         2   \n",
       "128065  L           -16       4      11:28        19.8        0.6         2   \n",
       "128066  L           -16       4      11:10        23.0       16.9         2   \n",
       "128067  L           -16       4       2:37         9.1       18.3         2   \n",
       "\n",
       "        FGM  PTS  \n",
       "0         1    2  \n",
       "1         0    0  \n",
       "3         0    0  \n",
       "4         0    0  \n",
       "5         0    0  \n",
       "6         0    0  \n",
       "7         1    2  \n",
       "8         0    0  \n",
       "9         0    0  \n",
       "10        0    0  \n",
       "11        1    2  \n",
       "12        1    2  \n",
       "13        0    0  \n",
       "14        0    0  \n",
       "15        1    3  \n",
       "16        0    0  \n",
       "17        0    0  \n",
       "18        1    3  \n",
       "19        0    0  \n",
       "20        1    2  \n",
       "21        1    2  \n",
       "22        0    0  \n",
       "23        1    3  \n",
       "25        1    3  \n",
       "26        1    2  \n",
       "27        0    0  \n",
       "28        0    0  \n",
       "29        0    0  \n",
       "30        0    0  \n",
       "31        0    0  \n",
       "...     ...  ...  \n",
       "128037    1    2  \n",
       "128038    0    0  \n",
       "128039    0    0  \n",
       "128040    0    0  \n",
       "128041    1    2  \n",
       "128042    1    2  \n",
       "128043    1    2  \n",
       "128044    0    0  \n",
       "128045    0    0  \n",
       "128046    0    0  \n",
       "128047    1    2  \n",
       "128048    1    2  \n",
       "128049    1    2  \n",
       "128050    1    2  \n",
       "128052    0    0  \n",
       "128053    0    0  \n",
       "128054    0    0  \n",
       "128055    0    0  \n",
       "128056    0    0  \n",
       "128057    0    0  \n",
       "128058    1    2  \n",
       "128059    0    0  \n",
       "128060    0    0  \n",
       "128061    0    0  \n",
       "128062    1    2  \n",
       "128063    1    2  \n",
       "128064    0    0  \n",
       "128065    1    2  \n",
       "128066    1    2  \n",
       "128067    0    0  \n",
       "\n",
       "[122502 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delete rows with faulty data\n",
    "shots = shots[np.isfinite(shots['SHOT_CLOCK'])]\n",
    "shots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/Cellar/ipython/7.3.0/libexec/vendor/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# convert type Strings in the GAME_CLOCK column to type time\n",
    "shots['GAME_CLOCK'] = pd.to_datetime(shots['GAME_CLOCK'], format='%M:%S' ).dt.minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>FINAL_MARGIN</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>GAME_CLOCK</th>\n",
       "      <th>SHOT_CLOCK</th>\n",
       "      <th>SHOT_DIST</th>\n",
       "      <th>PTS_TYPE</th>\n",
       "      <th>FGM</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>W</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>28.2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>22.8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>19.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.3</td>\n",
       "      <td>24.1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>L</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.9</td>\n",
       "      <td>23.7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>W</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>24.1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>W</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18.2</td>\n",
       "      <td>16.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>L</td>\n",
       "      <td>-5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>23.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>L</td>\n",
       "      <td>-23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>24.8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>L</td>\n",
       "      <td>-27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>L</td>\n",
       "      <td>-30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>12.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>L</td>\n",
       "      <td>-5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>L</td>\n",
       "      <td>-5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>24.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>L</td>\n",
       "      <td>-5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>21.7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>L</td>\n",
       "      <td>-25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>20.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.6</td>\n",
       "      <td>19.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>18.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>21.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>L</td>\n",
       "      <td>-3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.4</td>\n",
       "      <td>12.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>W</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>W</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>L</td>\n",
       "      <td>-5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.5</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>L</td>\n",
       "      <td>-5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>127653</th>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>16.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127663</th>\n",
       "      <td>L</td>\n",
       "      <td>-9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.9</td>\n",
       "      <td>20.4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>127664</th>\n",
       "      <td>L</td>\n",
       "      <td>-9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127683</th>\n",
       "      <td>W</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127701</th>\n",
       "      <td>L</td>\n",
       "      <td>-11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>20.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127702</th>\n",
       "      <td>L</td>\n",
       "      <td>-11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127714</th>\n",
       "      <td>L</td>\n",
       "      <td>-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127715</th>\n",
       "      <td>L</td>\n",
       "      <td>-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127728</th>\n",
       "      <td>L</td>\n",
       "      <td>-5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.1</td>\n",
       "      <td>17.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127796</th>\n",
       "      <td>W</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127804</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.2</td>\n",
       "      <td>12.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127818</th>\n",
       "      <td>L</td>\n",
       "      <td>-25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127843</th>\n",
       "      <td>W</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127844</th>\n",
       "      <td>W</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127845</th>\n",
       "      <td>W</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127901</th>\n",
       "      <td>W</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>13.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127902</th>\n",
       "      <td>W</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127917</th>\n",
       "      <td>L</td>\n",
       "      <td>-22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>20.9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127921</th>\n",
       "      <td>L</td>\n",
       "      <td>-23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127928</th>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127946</th>\n",
       "      <td>L</td>\n",
       "      <td>-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.6</td>\n",
       "      <td>17.7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127947</th>\n",
       "      <td>L</td>\n",
       "      <td>-18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>18.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127959</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>15.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127973</th>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127988</th>\n",
       "      <td>L</td>\n",
       "      <td>-4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127997</th>\n",
       "      <td>L</td>\n",
       "      <td>-12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>18.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127998</th>\n",
       "      <td>L</td>\n",
       "      <td>-12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>23.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128006</th>\n",
       "      <td>L</td>\n",
       "      <td>-10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128056</th>\n",
       "      <td>W</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.1</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128060</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.4</td>\n",
       "      <td>12.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4676 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        W  FINAL_MARGIN  PERIOD  GAME_CLOCK  SHOT_CLOCK  SHOT_DIST  PTS_TYPE  \\\n",
       "0       W            24       1           1        10.8        7.7         2   \n",
       "1       W            24       1           0         3.4       28.2         3   \n",
       "14      L            -8       1           0         6.8       22.8         3   \n",
       "20      W            12       1           0        19.5       19.1         2   \n",
       "31      L            -7       1           1        10.3       24.1         3   \n",
       "44      L            -1       1           0        11.3       20.0         2   \n",
       "59      L            -8       1           1        10.9       23.7         3   \n",
       "70      W             7       1           0         5.3       24.1         3   \n",
       "138     W             9       1           1        18.2       16.1         2   \n",
       "147     L            -5       1           0        11.8       23.2         2   \n",
       "164     W             4       1           0         1.0       11.4         2   \n",
       "195     L           -23       1           0        16.3       24.8         3   \n",
       "232     L           -27       1           0         1.9       26.0         3   \n",
       "255     L           -30       1           0        13.5       12.1         2   \n",
       "261     L            -5       1           1        12.7        9.3         2   \n",
       "262     L            -5       1           1        12.0        3.8         2   \n",
       "274     L            -8       1           0        10.8       24.6         3   \n",
       "303     L            -5       1           0         9.9       21.7         2   \n",
       "319     L           -25       1           0         4.5       20.6         2   \n",
       "331     W             8       1           1        14.6       19.2         2   \n",
       "332     W             8       1           0        14.8       18.1         2   \n",
       "333     W             8       1           0         3.6       21.1         2   \n",
       "360     L            -3       1           1        16.4       12.2         2   \n",
       "374     W            15       1           1         4.0       17.3         2   \n",
       "438     W            28       1           1        14.0        2.0         2   \n",
       "446     W             1       1           1        14.5        1.7         2   \n",
       "447     W             1       1           0        12.6        2.8         2   \n",
       "451     L            -7       1           0        10.6        7.7         2   \n",
       "458     L            -5       1           1        11.5        6.1         2   \n",
       "459     L            -5       1           0         6.6        0.6         2   \n",
       "...    ..           ...     ...         ...         ...        ...       ...   \n",
       "127653  W             3       1           0        18.2       16.2         2   \n",
       "127663  L            -9       1           1        12.9       20.4         2   \n",
       "127664  L            -9       1           0         6.2        1.0         2   \n",
       "127683  W            22       1           1        12.3       17.2         2   \n",
       "127701  L           -11       1           0        19.5       20.1         2   \n",
       "127702  L           -11       1           0         5.0        7.3         2   \n",
       "127714  L           -14       1           1        14.4        5.9         2   \n",
       "127715  L           -14       1           1        11.5        9.2         2   \n",
       "127728  L            -5       1           1        19.1       17.8         2   \n",
       "127796  W            14       1           1         7.7        8.7         2   \n",
       "127804  W             8       1           1        16.2       12.6         2   \n",
       "127818  L           -25       1           1        16.0       15.9         2   \n",
       "127843  W             6       1           1        16.0       11.5         2   \n",
       "127844  W             6       1           0         9.0       17.4         2   \n",
       "127845  W             6       1           0         5.5        7.2         2   \n",
       "127901  W            18       1           1         1.3       13.4         2   \n",
       "127902  W            18       1           0        15.1       18.2         2   \n",
       "127917  L           -22       1           0         5.8       20.9         2   \n",
       "127921  L           -23       1           0        17.0       19.8         2   \n",
       "127928  W             2       1           0        14.0        6.9         2   \n",
       "127946  L           -18       1           1        16.6       17.7         2   \n",
       "127947  L           -18       1           0         6.2       18.9         2   \n",
       "127959  W             8       1           1         4.6       15.2         2   \n",
       "127973  W             2       1           0        17.2        3.6         2   \n",
       "127988  L            -4       1           1        19.6        3.6         2   \n",
       "127997  L           -12       1           1         8.5       18.8         2   \n",
       "127998  L           -12       1           0        13.8       23.6         2   \n",
       "128006  L           -10       1           1         9.5       15.1         2   \n",
       "128056  W            12       1           1         7.1       15.5         2   \n",
       "128060  L           -16       1           1        11.4       12.6         2   \n",
       "\n",
       "        FGM  PTS  \n",
       "0         1    2  \n",
       "1         0    0  \n",
       "14        0    0  \n",
       "20        1    2  \n",
       "31        0    0  \n",
       "44        1    2  \n",
       "59        0    0  \n",
       "70        0    0  \n",
       "138       0    0  \n",
       "147       1    2  \n",
       "164       1    2  \n",
       "195       1    3  \n",
       "232       1    3  \n",
       "255       0    0  \n",
       "261       1    2  \n",
       "262       1    2  \n",
       "274       1    3  \n",
       "303       1    2  \n",
       "319       0    0  \n",
       "331       0    0  \n",
       "332       0    0  \n",
       "333       1    2  \n",
       "360       0    0  \n",
       "374       0    0  \n",
       "438       1    2  \n",
       "446       1    2  \n",
       "447       1    2  \n",
       "451       0    0  \n",
       "458       1    2  \n",
       "459       1    2  \n",
       "...     ...  ...  \n",
       "127653    1    2  \n",
       "127663    1    2  \n",
       "127664    0    0  \n",
       "127683    0    0  \n",
       "127701    1    2  \n",
       "127702    0    0  \n",
       "127714    1    2  \n",
       "127715    1    2  \n",
       "127728    0    0  \n",
       "127796    1    2  \n",
       "127804    0    0  \n",
       "127818    0    0  \n",
       "127843    1    2  \n",
       "127844    1    2  \n",
       "127845    0    0  \n",
       "127901    0    0  \n",
       "127902    0    0  \n",
       "127917    0    0  \n",
       "127921    1    2  \n",
       "127928    1    2  \n",
       "127946    1    2  \n",
       "127947    1    2  \n",
       "127959    1    2  \n",
       "127973    1    2  \n",
       "127988    1    2  \n",
       "127997    1    2  \n",
       "127998    0    0  \n",
       "128006    0    0  \n",
       "128056    0    0  \n",
       "128060    0    0  \n",
       "\n",
       "[4676 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store data on shots taken in the first two minutes of games\n",
    "first_two_min = shots.loc[(shots['GAME_CLOCK'] >= 0) & (shots['GAME_CLOCK'] <= 1) & (shots['PERIOD'] == 1)]\n",
    "first_two_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>FINAL_MARGIN</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>GAME_CLOCK</th>\n",
       "      <th>SHOT_CLOCK</th>\n",
       "      <th>SHOT_DIST</th>\n",
       "      <th>PTS_TYPE</th>\n",
       "      <th>FGM</th>\n",
       "      <th>PTS</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>11</th>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>12.1</td>\n",
       "      <td>14.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>L</td>\n",
       "      <td>-11</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>11.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.9</td>\n",
       "      <td>7.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>W</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>8.7</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>L</td>\n",
       "      <td>-5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>5.8</td>\n",
       "      <td>19.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.8</td>\n",
       "      <td>24.8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>L</td>\n",
       "      <td>-23</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.4</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>L</td>\n",
       "      <td>-5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>21.7</td>\n",
       "      <td>23.8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>13.2</td>\n",
       "      <td>10.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>L</td>\n",
       "      <td>-1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>10.9</td>\n",
       "      <td>25.4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>18.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>L</td>\n",
       "      <td>-5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>L</td>\n",
       "      <td>-27</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>15.4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>W</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1369</th>\n",
       "      <td>W</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1577</th>\n",
       "      <td>L</td>\n",
       "      <td>-2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1602</th>\n",
       "      <td>L</td>\n",
       "      <td>-15</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1604</th>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>L</td>\n",
       "      <td>-3</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>14.4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1633</th>\n",
       "      <td>L</td>\n",
       "      <td>-2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>11.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>L</td>\n",
       "      <td>-15</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>10.9</td>\n",
       "      <td>19.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>14.2</td>\n",
       "      <td>23.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1686</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>14.0</td>\n",
       "      <td>24.1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>19.7</td>\n",
       "      <td>25.6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708</th>\n",
       "      <td>L</td>\n",
       "      <td>-1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>11.6</td>\n",
       "      <td>26.1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749</th>\n",
       "      <td>L</td>\n",
       "      <td>-9</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>14.1</td>\n",
       "      <td>25.1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1750</th>\n",
       "      <td>L</td>\n",
       "      <td>-9</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>19.4</td>\n",
       "      <td>24.8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126345</th>\n",
       "      <td>L</td>\n",
       "      <td>-6</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>5.7</td>\n",
       "      <td>20.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126366</th>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126367</th>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>21.6</td>\n",
       "      <td>23.7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126535</th>\n",
       "      <td>L</td>\n",
       "      <td>-2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>7.9</td>\n",
       "      <td>17.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126620</th>\n",
       "      <td>W</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>6.2</td>\n",
       "      <td>18.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126832</th>\n",
       "      <td>L</td>\n",
       "      <td>-12</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>11.6</td>\n",
       "      <td>24.8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127041</th>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127060</th>\n",
       "      <td>L</td>\n",
       "      <td>-4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127090</th>\n",
       "      <td>L</td>\n",
       "      <td>-12</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127139</th>\n",
       "      <td>L</td>\n",
       "      <td>-37</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>14.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127172</th>\n",
       "      <td>L</td>\n",
       "      <td>-11</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>23.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127195</th>\n",
       "      <td>L</td>\n",
       "      <td>-9</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127281</th>\n",
       "      <td>W</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>12.5</td>\n",
       "      <td>13.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127282</th>\n",
       "      <td>W</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>14.6</td>\n",
       "      <td>24.8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127291</th>\n",
       "      <td>W</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0.9</td>\n",
       "      <td>12.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127338</th>\n",
       "      <td>L</td>\n",
       "      <td>-18</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>7.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127388</th>\n",
       "      <td>L</td>\n",
       "      <td>-10</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>10.5</td>\n",
       "      <td>24.8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127473</th>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>8.7</td>\n",
       "      <td>23.6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127526</th>\n",
       "      <td>L</td>\n",
       "      <td>-6</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>18.8</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127915</th>\n",
       "      <td>L</td>\n",
       "      <td>-25</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>16.1</td>\n",
       "      <td>14.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127934</th>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127942</th>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127984</th>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128010</th>\n",
       "      <td>L</td>\n",
       "      <td>-10</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128030</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128040</th>\n",
       "      <td>W</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128048</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128055</th>\n",
       "      <td>W</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>13.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128065</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>19.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128066</th>\n",
       "      <td>L</td>\n",
       "      <td>-16</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2078 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        W  FINAL_MARGIN  PERIOD  GAME_CLOCK  SHOT_CLOCK  SHOT_DIST  PTS_TYPE  \\\n",
       "11      W             1       4          11        12.1       14.6         2   \n",
       "30      L           -11       4          11        11.6       10.0         2   \n",
       "33      L            -7       4          11         9.9        7.6         2   \n",
       "77      W             7       4          11         8.7       24.0         3   \n",
       "150     L            -5       4          11         5.8       19.2         2   \n",
       "167     W             4       4          11         9.8       24.8         3   \n",
       "198     L           -23       4          11         9.4       16.5         2   \n",
       "224     L            -5       4          11        21.7       23.8         3   \n",
       "251     L            -7       4          11        13.2       10.8         2   \n",
       "301     L            -1       4          11        10.9       25.4         3   \n",
       "351     W             3       4          11        14.2        2.0         2   \n",
       "452     L            -7       4          11        18.8        1.3         2   \n",
       "462     L            -5       4          11         2.6        5.3         2   \n",
       "478     L           -27       4          11        24.0        1.3         2   \n",
       "485     W             8       4          11        15.4        5.2         2   \n",
       "761     W            25       4          11         7.0       16.5         2   \n",
       "1369    W            18       4          11        16.0       15.6         2   \n",
       "1577    L            -2       4          11         5.5        3.5         2   \n",
       "1602    L           -15       4          11        24.0        1.0         2   \n",
       "1604    W             3       4          11         9.0       21.5         2   \n",
       "1625    L            -3       4          11        14.4        4.9         2   \n",
       "1633    L            -2       4          11        11.7        3.0         2   \n",
       "1649    L           -15       4          11        10.9       19.5         2   \n",
       "1685    L            -7       4          11        14.2       23.6         2   \n",
       "1686    L            -7       4          11        14.0       24.1         3   \n",
       "1700    W             2       4          11        19.7       25.6         3   \n",
       "1708    L            -1       4          11        13.0       12.4         2   \n",
       "1731    W             4       4          11        11.6       26.1         3   \n",
       "1749    L            -9       4          11        14.1       25.1         3   \n",
       "1750    L            -9       4          11        19.4       24.8         3   \n",
       "...    ..           ...     ...         ...         ...        ...       ...   \n",
       "126345  L            -6       4          11         5.7       20.3         2   \n",
       "126366  W             4       4          11         9.0        8.1         2   \n",
       "126367  W             4       4          11        21.6       23.7         3   \n",
       "126535  L            -2       4          11         7.9       17.8         2   \n",
       "126620  W            14       4          11         6.2       18.9         2   \n",
       "126832  L           -12       4          11        11.6       24.8         3   \n",
       "127041  W             3       4          11         4.6        5.5         2   \n",
       "127060  L            -4       4          11        24.0        3.8         2   \n",
       "127090  L           -12       4          11         6.7        2.7         2   \n",
       "127139  L           -37       4          11        14.0       23.9         3   \n",
       "127172  L           -11       4          11        23.4        1.6         2   \n",
       "127195  L            -9       4          11         8.0       25.6         3   \n",
       "127281  W            27       4          11        12.5       13.5         2   \n",
       "127282  W            27       4          11        14.6       24.8         3   \n",
       "127291  W            18       4          11         0.9       12.8         2   \n",
       "127338  L           -18       4          11         7.8        4.3         2   \n",
       "127388  L           -10       4          11        10.5       24.8         3   \n",
       "127473  W             2       4          11         8.7       23.6         3   \n",
       "127526  L            -6       4          11        18.8       15.5         2   \n",
       "127915  L           -25       4          11        16.1       14.4         2   \n",
       "127934  W             2       4          11        10.0       14.2         2   \n",
       "127942  W             5       4          11         9.5        7.4         2   \n",
       "127984  W             2       4          11         6.0       20.3         2   \n",
       "128010  L           -10       4          11         3.4        8.6         2   \n",
       "128030  L            -8       4          11        11.0       16.2         2   \n",
       "128040  W            11       4          11         8.8        2.5         2   \n",
       "128048  L            -7       4          11         9.0       20.2         2   \n",
       "128055  W            31       4          11        13.8        5.1         2   \n",
       "128065  L           -16       4          11        19.8        0.6         2   \n",
       "128066  L           -16       4          11        23.0       16.9         2   \n",
       "\n",
       "        FGM  PTS  \n",
       "11        1    2  \n",
       "30        0    0  \n",
       "33        1    2  \n",
       "77        0    0  \n",
       "150       0    0  \n",
       "167       1    3  \n",
       "198       1    2  \n",
       "224       1    3  \n",
       "251       0    0  \n",
       "301       0    0  \n",
       "351       0    0  \n",
       "452       1    2  \n",
       "462       0    0  \n",
       "478       1    2  \n",
       "485       0    0  \n",
       "761       1    2  \n",
       "1369      1    2  \n",
       "1577      0    0  \n",
       "1602      1    2  \n",
       "1604      0    0  \n",
       "1625      1    2  \n",
       "1633      0    0  \n",
       "1649      0    0  \n",
       "1685      1    2  \n",
       "1686      0    0  \n",
       "1700      0    0  \n",
       "1708      0    0  \n",
       "1731      1    3  \n",
       "1749      0    0  \n",
       "1750      1    3  \n",
       "...     ...  ...  \n",
       "126345    0    0  \n",
       "126366    0    0  \n",
       "126367    1    3  \n",
       "126535    0    0  \n",
       "126620    1    2  \n",
       "126832    1    3  \n",
       "127041    0    0  \n",
       "127060    1    2  \n",
       "127090    1    2  \n",
       "127139    1    3  \n",
       "127172    0    0  \n",
       "127195    1    3  \n",
       "127281    0    0  \n",
       "127282    1    3  \n",
       "127291    1    2  \n",
       "127338    1    2  \n",
       "127388    0    0  \n",
       "127473    0    0  \n",
       "127526    0    0  \n",
       "127915    0    0  \n",
       "127934    1    2  \n",
       "127942    0    0  \n",
       "127984    0    0  \n",
       "128010    1    2  \n",
       "128030    0    0  \n",
       "128040    0    0  \n",
       "128048    1    2  \n",
       "128055    0    0  \n",
       "128065    1    2  \n",
       "128066    1    2  \n",
       "\n",
       "[2078 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store data on shots taken in the last two minutes of games\n",
    "last_two_min = shots.loc[(shots['GAME_CLOCK'] >= 11) & (shots['GAME_CLOCK'] <= 12) & (shots['PERIOD'] == 4)]\n",
    "last_two_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>FINAL_MARGIN</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>GAME_CLOCK</th>\n",
       "      <th>SHOT_CLOCK</th>\n",
       "      <th>SHOT_DIST</th>\n",
       "      <th>PTS_TYPE</th>\n",
       "      <th>FGM</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>12.1</td>\n",
       "      <td>14.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.9</td>\n",
       "      <td>7.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>W</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>8.7</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>L</td>\n",
       "      <td>-5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>5.8</td>\n",
       "      <td>19.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.8</td>\n",
       "      <td>24.8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>L</td>\n",
       "      <td>-5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>21.7</td>\n",
       "      <td>23.8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>13.2</td>\n",
       "      <td>10.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>L</td>\n",
       "      <td>-1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>10.9</td>\n",
       "      <td>25.4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>18.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>L</td>\n",
       "      <td>-5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>15.4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1577</th>\n",
       "      <td>L</td>\n",
       "      <td>-2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1604</th>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>L</td>\n",
       "      <td>-3</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>14.4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1633</th>\n",
       "      <td>L</td>\n",
       "      <td>-2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>11.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>14.2</td>\n",
       "      <td>23.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1686</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>14.0</td>\n",
       "      <td>24.1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>19.7</td>\n",
       "      <td>25.6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708</th>\n",
       "      <td>L</td>\n",
       "      <td>-1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>11.6</td>\n",
       "      <td>26.1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.4</td>\n",
       "      <td>24.4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1789</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>11.9</td>\n",
       "      <td>24.6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1790</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>8.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.8</td>\n",
       "      <td>19.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>18.2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1830</th>\n",
       "      <td>L</td>\n",
       "      <td>-4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>W</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>L</td>\n",
       "      <td>-5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>17.4</td>\n",
       "      <td>11.7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>L</td>\n",
       "      <td>-6</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>11.2</td>\n",
       "      <td>19.4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124499</th>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.8</td>\n",
       "      <td>24.2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124550</th>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>17.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124593</th>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>14.7</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124604</th>\n",
       "      <td>L</td>\n",
       "      <td>-4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>5.6</td>\n",
       "      <td>24.4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124622</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124623</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125054</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>10.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125063</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125064</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>23.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125211</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>7.8</td>\n",
       "      <td>15.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125520</th>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>8.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125568</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125668</th>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>18.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125704</th>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125872</th>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125922</th>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>15.9</td>\n",
       "      <td>23.8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126095</th>\n",
       "      <td>L</td>\n",
       "      <td>-4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>17.3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126345</th>\n",
       "      <td>L</td>\n",
       "      <td>-6</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>5.7</td>\n",
       "      <td>20.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126366</th>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126367</th>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>21.6</td>\n",
       "      <td>23.7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126535</th>\n",
       "      <td>L</td>\n",
       "      <td>-2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>7.9</td>\n",
       "      <td>17.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127041</th>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127060</th>\n",
       "      <td>L</td>\n",
       "      <td>-4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127473</th>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>8.7</td>\n",
       "      <td>23.6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127526</th>\n",
       "      <td>L</td>\n",
       "      <td>-6</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>18.8</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127934</th>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127942</th>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127984</th>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128030</th>\n",
       "      <td>L</td>\n",
       "      <td>-8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128048</th>\n",
       "      <td>L</td>\n",
       "      <td>-7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1007 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        W  FINAL_MARGIN  PERIOD  GAME_CLOCK  SHOT_CLOCK  SHOT_DIST  PTS_TYPE  \\\n",
       "11      W             1       4          11        12.1       14.6         2   \n",
       "33      L            -7       4          11         9.9        7.6         2   \n",
       "77      W             7       4          11         8.7       24.0         3   \n",
       "150     L            -5       4          11         5.8       19.2         2   \n",
       "167     W             4       4          11         9.8       24.8         3   \n",
       "224     L            -5       4          11        21.7       23.8         3   \n",
       "251     L            -7       4          11        13.2       10.8         2   \n",
       "301     L            -1       4          11        10.9       25.4         3   \n",
       "351     W             3       4          11        14.2        2.0         2   \n",
       "452     L            -7       4          11        18.8        1.3         2   \n",
       "462     L            -5       4          11         2.6        5.3         2   \n",
       "485     W             8       4          11        15.4        5.2         2   \n",
       "1577    L            -2       4          11         5.5        3.5         2   \n",
       "1604    W             3       4          11         9.0       21.5         2   \n",
       "1625    L            -3       4          11        14.4        4.9         2   \n",
       "1633    L            -2       4          11        11.7        3.0         2   \n",
       "1685    L            -7       4          11        14.2       23.6         2   \n",
       "1686    L            -7       4          11        14.0       24.1         3   \n",
       "1700    W             2       4          11        19.7       25.6         3   \n",
       "1708    L            -1       4          11        13.0       12.4         2   \n",
       "1731    W             4       4          11        11.6       26.1         3   \n",
       "1763    W             2       4          11         9.4       24.4         3   \n",
       "1789    W             8       4          11        11.9       24.6         3   \n",
       "1790    W             8       4          11         8.3        6.4         2   \n",
       "1796    W             4       4          11         9.8       19.7         2   \n",
       "1821    W             8       4          11        18.2       24.9         3   \n",
       "1830    L            -4       4          11         5.9        1.0         2   \n",
       "1878    W             7       4          11         6.0       12.3         2   \n",
       "1917    L            -5       4          11        17.4       11.7         2   \n",
       "1949    L            -6       4          11        11.2       19.4         2   \n",
       "...    ..           ...     ...         ...         ...        ...       ...   \n",
       "124499  W             5       4          11         9.8       24.2         3   \n",
       "124550  W             5       4          11        17.5        2.3         2   \n",
       "124593  W             2       4          11        14.7       22.0         2   \n",
       "124604  L            -4       4          11         5.6       24.4         3   \n",
       "124622  L            -8       4          11         9.3        8.0         2   \n",
       "124623  L            -8       4          11         2.9       17.0         2   \n",
       "125054  L            -8       4          11        10.7        2.6         2   \n",
       "125063  W             8       4          11        24.0        1.8         2   \n",
       "125064  W             8       4          11        23.2        3.5         2   \n",
       "125211  W             8       4          11         7.8       15.6         2   \n",
       "125520  W             3       4          11         8.4       22.0         3   \n",
       "125568  L            -8       4          11         5.5        5.8         2   \n",
       "125668  W             5       4          11        18.4        4.1         2   \n",
       "125704  W             8       4          11        13.0        6.6         2   \n",
       "125872  W             2       4          11         9.1        3.7         2   \n",
       "125922  W             2       4          11        15.9       23.8         3   \n",
       "126095  L            -4       4          11        17.3       24.0         3   \n",
       "126345  L            -6       4          11         5.7       20.3         2   \n",
       "126366  W             4       4          11         9.0        8.1         2   \n",
       "126367  W             4       4          11        21.6       23.7         3   \n",
       "126535  L            -2       4          11         7.9       17.8         2   \n",
       "127041  W             3       4          11         4.6        5.5         2   \n",
       "127060  L            -4       4          11        24.0        3.8         2   \n",
       "127473  W             2       4          11         8.7       23.6         3   \n",
       "127526  L            -6       4          11        18.8       15.5         2   \n",
       "127934  W             2       4          11        10.0       14.2         2   \n",
       "127942  W             5       4          11         9.5        7.4         2   \n",
       "127984  W             2       4          11         6.0       20.3         2   \n",
       "128030  L            -8       4          11        11.0       16.2         2   \n",
       "128048  L            -7       4          11         9.0       20.2         2   \n",
       "\n",
       "        FGM  PTS  \n",
       "11        1    2  \n",
       "33        1    2  \n",
       "77        0    0  \n",
       "150       0    0  \n",
       "167       1    3  \n",
       "224       1    3  \n",
       "251       0    0  \n",
       "301       0    0  \n",
       "351       0    0  \n",
       "452       1    2  \n",
       "462       0    0  \n",
       "485       0    0  \n",
       "1577      0    0  \n",
       "1604      0    0  \n",
       "1625      1    2  \n",
       "1633      0    0  \n",
       "1685      1    2  \n",
       "1686      0    0  \n",
       "1700      0    0  \n",
       "1708      0    0  \n",
       "1731      1    3  \n",
       "1763      0    0  \n",
       "1789      0    0  \n",
       "1790      0    0  \n",
       "1796      0    0  \n",
       "1821      0    0  \n",
       "1830      0    0  \n",
       "1878      0    0  \n",
       "1917      1    2  \n",
       "1949      1    2  \n",
       "...     ...  ...  \n",
       "124499    0    0  \n",
       "124550    0    0  \n",
       "124593    1    2  \n",
       "124604    0    0  \n",
       "124622    0    0  \n",
       "124623    0    0  \n",
       "125054    0    0  \n",
       "125063    0    0  \n",
       "125064    1    2  \n",
       "125211    1    2  \n",
       "125520    0    0  \n",
       "125568    0    0  \n",
       "125668    0    0  \n",
       "125704    0    0  \n",
       "125872    1    2  \n",
       "125922    0    0  \n",
       "126095    1    3  \n",
       "126345    0    0  \n",
       "126366    0    0  \n",
       "126367    1    3  \n",
       "126535    0    0  \n",
       "127041    0    0  \n",
       "127060    1    2  \n",
       "127473    0    0  \n",
       "127526    0    0  \n",
       "127934    1    2  \n",
       "127942    0    0  \n",
       "127984    0    0  \n",
       "128030    0    0  \n",
       "128048    1    2  \n",
       "\n",
       "[1007 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store data on shots taken in the last two minutes of close games\n",
    "# A close game is a game in which the final point differential is less than or equal to 8\n",
    "\n",
    "last_two_min_close = shots.loc[(shots['GAME_CLOCK'] >= 11) & (shots['GAME_CLOCK'] <= 12) & (shots['PERIOD'] == 4) \n",
    "                                & (shots['FINAL_MARGIN'] >= -8) & (shots['FINAL_MARGIN'] <= 8)]\n",
    "last_two_min_close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time passed (s)')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph average shot frequencies based on time on the shot clock\n",
    "shots['SHOT_CLOCK'].plot.hist(bins=25)\n",
    "plt.xlabel('Time passed (s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time passed (s)')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph shot frequencies in the first two minutes of games based on time on the shot clock\n",
    "first_two_min['SHOT_CLOCK'].plot.hist(bins=25)\n",
    "plt.xlabel('Time passed (s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time passed (s)')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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laXKNJCSSvIAmIC6oqkvb5geS7NEu3wPYOIraJEnPGcXdTQHOAdZU1Wd6Fl0OHN1OHw1cNuzaJEnPN4q7mw4G/iNwW5Jb2rZTgVXAxUmOA+4BjhhBbZKkHkMPiar6ByAdiw8dZi2SpC3ziWtJUidDQpLUyZCQJHUyJCRJnUZxd9NYWExjq0jSoNiTkCR1MiQkSZ0MCUlSJ0NCktTJkJAkdTIkJEmdDAlJUidDQpLUyZCQJHUyJCRJnQwJSVInQ0KS1MmQkCR1MiQkSZ3GLiSSHJbkriRrk5w86nokaZKNVUgk2RH4c+AdwBuAo5K8YbRVSdLkGquQAA4E1lbV3VX1FHARsHLENUnSxBq3N9PtCdzbM78e+NXeFZIcDxzfzj6W5K557ms34J/m+dnFYJKPf5KPHSb7+BfNsedTc/5I77H/Yr8fGreQ2KqqWg2s3tbtJJmuqqkFKGm7NMnHP8nHDpN9/B773I993E43bQD26plf1rZJkkZg3ELiRmDfJPskeSFwJHD5iGuSpIk1VqebqurpJB8G/h7YETi3qu4Y0O62+ZTVdm6Sj3+Sjx0m+/g99jlKVS10IZKkRWLcTjdJksaIISFJ6jSRITHJQ38kWZfktiS3JJkedT2DluTcJBuT3N7T9ookVyT5Qfv75aOscVA6jv30JBva7/+WJO8cZY2DkmSvJFcnuTPJHUk+2rZPynffdfxz/v4n7ppEO/THPwJvo3lY70bgqKq6c6SFDUmSdcBUVS2KB4q2JslbgMeA86tqv7btvwAPVtWq9j8JL6+qk0ZZ5yB0HPvpwGNV9elR1jZoSfYA9qiqm5O8FLgJeC9wDJPx3Xcd/xHM8fufxJ6EQ39MkKq6Fnhws+aVwHnt9Hk0f3kWnY5jnwhVdV9V3dxOPwqsoRnRYVK++67jn7NJDInZhv6Y1x/edqqAbye5qR3iZBLtXlX3tdP3A7uPspgR+HCSW9vTUYvydEuvJMuBA4DrmcDvfrPjhzl+/5MYEpPuzVW1gmak3RPaUxITq5rzrZN0zvVs4NXAm4D7gD8dbTmDleQlwCXAx6rqkd5lk/Ddz3L8c/7+JzEkJnroj6ra0P7eCHyd5vTbpHmgPWe76dztxhHXMzRV9UBVPVNVzwJfYBF//0leQPMP5AVVdWnbPDHf/WzHP5/vfxJDYmKH/kjy4vYiFkleDLwduH3Ln1qULgeObqePBi4bYS1DtekfyNb7WKTff5IA5wBrquozPYsm4rvvOv75fP8Td3cTQHvb12d5buiPM0Zc0lAkeRVN7wGaIVn+erEfe5ILgUNohkl+ADgN+AZwMbA3cA9wRFUtugu8Hcd+CM2phgLWAR/sOUe/aCR5M/DfgduAZ9vmU2nOy0/Cd991/Ecxx+9/IkNCktSfSTzdJEnqkyEhSepkSEiSOhkSkqROhoQkqZMhoe1Wklf2jGZ5/2ajW/7PUdc3CEmuSTLry+yTfK29zbnrs59O8tbBVafFaKxeXyrNRVX9hOae74kZ3bRLkl8Cdqyqu7ew2udpnrL9znCq0mJgT0KLUpLH2t+HJPluksuS3J1kVZL3J7mhfa/Gq9v1lia5JMmN7c/Bs2zzmHY717TvIzitZ9k32kET79g0cGKSHZN8Ocnt7b7+oG0/sR3n/9YkF7VtL24HXLshyfeSrGzbd05yUZI1Sb4O7NxxyO+nfXq4a79VdQ/wyiS/sDB/ypoE9iQ0Cd4IvJ5m2Oy7gS9W1YHti1g+AnwMOAs4s6r+IcnewN+3n9ncgcB+wOPAjUm+WVXTwLFV9WCSndv2S4DlwJ4973LYtd3GycA+VfVkT9snge9U1bFt2w1JrgQ+CDxeVa9Psj9wc8cxHgxc2E6/qWO/tJ8/mGZMH2mr7EloEtzYjq//JPB/gG+37bfR/EMO8OvAnyW5hWZ8n5e1I2hu7oqq+klVPQFcCry5bT8xyfeB62gGkNyXJpBeleTzSQ4DNo1CeitwQZLfAZ5u294OnNzu/xpgJ5qhI94CfAWgqm5tPzubPYCZdrprv9AMaPevOrYh/Qx7EpoET/ZMP9sz/yzP/R3YATioqv7vVra1+Tg2leQQmpD5t1X1eJJrgJ2q6qEkbwR+A/gQzVvBjgUOp/nH/93AJ5P8MhDgN6vqrt6NN+O09eUJmmBhC/ulXeeJfjcq2ZOQGt+mOfUEQJI3daz3tjTvSd6Z5q1m/wPYBXioDYjXAQe129gN2KGqLgH+CFiRZAdgr6q6Gjip/exLaE5vfaQdvZMkB7T7uxb47bZtP2D/jrrWAK/p2m/Pev+aRTryqwbDnoTUOBH48yS30vy9uJbmf+Gbu4HmfP4y4CtVNZ3kNuBDSdYAd9GccoLmjYdfaoMB4BSakYe/kmQXmt7D56rq4ST/mWZk4lvb9X8IvIvmJTFfare9huZdxbP5Js0Ir1d27HfT+wVeA0zP6U9GE81RYKU+JTkGmKqqD4+6ls21PZurgYOr6pmOdd4HrKiq/zTU4rRd83STtAi0F9JPY8vva1/CIn9dqRaePQlJUid7EpKkToaEJKmTISFJ6mRISJI6GRKSpE7/H+InWg6PwH10AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph shot frequencies in the last two minutes of games based on time on the shot clock\n",
    "last_two_min['SHOT_CLOCK'].plot.hist(bins=25)\n",
    "plt.xlabel('Time passed (s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time passed (s)')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph shot frequencies in the first two minutes of close games based on time on the shot clock\n",
    "last_two_min_close['SHOT_CLOCK'].plot.hist(bins=25)\n",
    "plt.xlabel('Time passed (s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Partition shots into shot clock ranges, with each range being 1 second of the 24 seconds of the shot clock\n",
    "shot_clock_ranges = []\n",
    "# Loop through zero to 23 seconds to partition shots to fit into each second of the shot clock\n",
    "for i in range(23):\n",
    "    shot_clock_ranges.append(shots.loc[(shots['SHOT_CLOCK'] >= i) & (shots['SHOT_CLOCK'] < (i + 1))])\n",
    "\n",
    "\n",
    "# Calculate field goal percentages, points per shot, and average shot distance for each shot clock range\n",
    "field_goal_percentages = []\n",
    "points_per_shots = []\n",
    "shot_dist = []\n",
    "for shot_range in shot_clock_ranges:\n",
    "    field_goal_percentages.append(shot_range['FGM'].sum() / shot_range['FGM'].count())\n",
    "    points_per_shots.append(shot_range['PTS'].sum() / shot_range['PTS'].count())\n",
    "    shot_dist.append(shot_range['SHOT_DIST'].sum() / shot_range['SHOT_DIST'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Partition shots into shot clock ranges, with each range being 1 second of the 24 seconds of the shot clock\n",
    "# For shots taken in the last two minutes of games\n",
    "shot_clock_ranges_last_two = []\n",
    "# Loop through zero to 23 seconds to partition shots taken in the last two minutes of games \n",
    "# to fit into each second of the shot clockfor i in range(23):\n",
    "    shot_clock_ranges_last_two.append(last_two_min.loc[(last_two_min['SHOT_CLOCK'] >= i) & (last_two_min['SHOT_CLOCK'] < (i + 1))])\n",
    "\n",
    "# Calculate field goal percentages, points per shot, and average shot distance for each shot clock range\n",
    "field_goal_percentages_last_two = []\n",
    "points_per_shots_last_two = []\n",
    "shot_dist_last_two = []\n",
    "for shot_range in shot_clock_ranges_last_two:\n",
    "    field_goal_percentages_last_two.append(shot_range['FGM'].sum() / shot_range['FGM'].count())\n",
    "    points_per_shots_last_two.append(shot_range['PTS'].sum() / shot_range['PTS'].count())\n",
    "    shot_dist_last_two.append(shot_range['SHOT_DIST'].sum() / shot_range['SHOT_DIST'].count())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Partition shots into shot clock ranges, with each range being 1 second of the 24 seconds of the shot clock\n",
    "# For shots taken in the last two minutes of close games\n",
    "shot_clock_ranges_last_two_close = []\n",
    "# Loop through zero to 23 seconds to partition shots taken in the last two minutes of close games \n",
    "# to fit into each second of the shot clock\n",
    "for i in range(23):\n",
    "    shot_clock_ranges_last_two_close.append(last_two_min_close.loc[(last_two_min_close['SHOT_CLOCK'] >= i) & (last_two_min_close['SHOT_CLOCK'] < (i + 1))])\n",
    "\n",
    "# Calculate field goal percentages, points per shot, and average shot distance for each shot clock range\n",
    "field_goal_percentages_last_two_close = []\n",
    "points_per_shots_last_two_close = []\n",
    "shot_dist_last_two_close = []\n",
    "for shot_range in shot_clock_ranges_last_two_close:\n",
    "    field_goal_percentages_last_two_close.append(shot_range['FGM'].sum() / shot_range['FGM'].count())\n",
    "    points_per_shots_last_two_close.append(shot_range['PTS'].sum() / shot_range['PTS'].count())\n",
    "    shot_dist_last_two_close.append(shot_range['SHOT_DIST'].sum() / shot_range['SHOT_DIST'].count())\n",
    "\n",
    "\n",
    "#print (field_goal_percentages_last_two_close)\n",
    "#print (points_per_shots_last_two_close)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will analyze the data by plotting columns pertaining to field goal percentages, points per shot, and distance traveled against time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PZ8iQIURHRxMeHs57772Xea9uuukm7r77bho2bMh9992HtZYJEyawf/9+2rdvT/v27QHo378/UVFRhISE8NJLL2XGOGfOHBo2bEiTJk0YOHBgvu79lClTaNGiBd27d8/cFhoaSp8+ffIc45NPPuHOO++kc+fO1KtXj2effTbz/Pnz59OiRQsiIyPp2bMnSUlJgLOibOjQoURGRvL111/z/vvvEx0dTUREBHfddZfbKrxEREREREREcnJNPG0tq5d/2Mim/ady3b/mjwTOpGdk25ZyNp1np69j6oo/cjwnuGoZXro9JM95U1JScDgcANSqVYtvv/022/5XX32Vm2++mY8++oiEhASaNm1Khw4dssf+8su0bt2aF198kdmzZ/Phhx9eMM/GjRuJiIjITBydb8aMGcTGxrJ27VqOHj1KdHQ0bdu2JTAwkG+//ZYyZcpw9OhRmjdvTvfu3S+pD05aWhpz586lc+fObN68mWnTprF06VK8vLz4+9//zpQpU3jwwQdJTk6mWbNmvP7665w5c4aGDRsybdo0oqOjOXXqFL6+vnz44YeULVuWlStXcvr0aVq1akXHjh0BWLNmDRs3bqRq1aq0atWKpUuXMnDgQN544w1++eUXKlasmHkvAwICSE9P55ZbbmHdunXUr1+ffv36sXjxYmrVqkXv3r0veu/9/Pyy3dfIyMhc70Fer19sbCxr1qyhZMmSNGjQgAEDBuDr68uoUaNYsGABfn5+/Pvf/+aNN97gxRdfBKBChQqsXr0acCYNH3vsMQBGjBjBhx9+yIABAy76uoiIiIiIiIgUhWsueXQx5yeOLrb9Up1b4pWb+fPnM3PmzMweNqmpqfzxR/Zk1eLFi5kxYwYAXbt2pXz58hed99VXX+Xrr7/m8OHD7N+/n19//ZXevXvj6elJ5cqVadeuHStXrqRLly4MHz6cxYsX4+HhQXx8PIcOHSIoKCjXsbMmxNq0acMjjzzC5MmTWbVqFdHR0ZnHVKpUCXD2SLrrrrsA2Lp1K1WqVMk8rkyZMpn3Yd26dUyfPh2AkydPsn37dry9vWnatCnVq1cHyOwb1bp16wvi+uqrr5g8eTJpaWkcOHCATZs2kZGRQe3atTMfQ9+7d28mT56c571v1KhRrtd+xx13sH37durXr8+MGTPyfP1uueUWypYtC0BwcDB79+4lISGBTZs20apVKwDOnDlDixYtMsfv1atX5s8bNmxgxIgRJCQkkJSURKdOnXKNS0RERERERKSoXXPJo4tVCLUa/TPxCSkXbK9Wzpdp/VrkcEbBsNbyzTff0KBBg2zbDx06lK9xgoODWbt2LRkZGXh4ePD888/z/PPP59r0+pwpU6Zw5MgRVq1ahZeXFzVr1rzoY9tzSohZa/nb3/7Gv/71rwuO9/HxwdPTM88xrbVMnDjxggTJwoULKVmyZObvnp6epKWlXXD+7t27GTduHCtXrqR8+fL06dPnoteR273PKiQkJFtz7G+//ZaYmBieeeaZPMdYvnx5jnFba7n11luZOnVqjvNlrXrq06cP3333HREREXzyyScsXLgwz+sRERERERERKUrXXc+jIZ0a4OuVPcHh6+XJkE65JxYKQqdOnZg4cWJmH6M1a9ZccEzbtm354osvAJg7dy4nTpy44Ji6desSFRXFiBEjMnsNpaamZo7bpk0bpk2bRnp6OkeOHGHx4sU0bdqUkydPUqlSJby8vPjll1/Yu3fvZV3HLbfcwvTp0zl8+DAAx48fz3GsBg0acODAAVauXAlAYmIiaWlpdOrUif/85z+cPXsWgG3btpGcnJznnP7+/iQmJgJw6tQp/Pz8KFu2LIcOHWLu3LmZ8+3atYs9e/YAZPZ6gku79/feey9Lly5l5syZmduy9h66lDGyat68OUuXLs3sE5WcnMy2bdtyPDYxMZEqVapw9uxZpkyZkue4IiIiIiIiIkXtmqs8upgejasBMHbeVvYnpFC1nC9DOjXI3F5YXnjhBZ588knCw8PJyMigVq1azJo1K9sxL730Er179yYkJISWLVty44035jjWBx98wJAhQ6hbty4VKlTA19eXMWPGAM7lVr/99hsREREYYxgzZgxBQUHcd9993H777YSFhREVFUXDhg0v6zqCg4MZNWoUHTt2JCMjAy8vLyZNmkSNGjWyHeft7c20adMYMGAAKSkp+Pr6smDBAh599FH27NlDZGQk1loCAwP57rvv8pyzb9++dO7cmapVq/LLL7/QuHFjGjZsyA033JC5LMzX15d33nmHzp074+fnl7lcDi7t3vv6+jJr1iwGDx7Mk08+SeXKlfH392fEiBGXPEZWgYGBfPLJJ/Tu3ZvTp08DMGrUKOrXr3/Bsf/85z9p1qwZgYGBNGvWLDNRJiIiIiIiIlIcmJye6FWcRUVF2ZiYmGzbNm/enGf/Grk+JCUlUbp0aay1PPHEE9SrV4+nnnrK3WEVK/pbERERERERkZwYY1ZZa6Ny2nfdLVuTa9f777+Pw+EgJCSEkydP0q9fP3eHJCIiIiIiInLVu+6Wrcm166mnnlKlkYiIiIiIyFXkuzXxRd5WRvJPySMRERERERERKXLfrYnnuRnrSTnrfBhUfEIKz81YD6AEUjGjZWsiIiIiIiIiUuTGztuamTg6J+VsOmPnbXVTRJIbJY9EREREREREpMjtT0jJ13ZxHyWPRERERERERKRInfzzLF6eOackqpbzLeJo5GKUPCogpUuXvqLz9+zZwxdffHHB9vXr1+NwOHA4HAQEBFCrVi0cDgcdOnS4ovmyql69Ou3bt8+2LTQ0FIfDAcDy5cuvqBH1Rx99xMGDB68oRhEREREREbk2xCekcPe7y0jPyMDL01yw/4mb67ghKsnL9Zk8WvcVvBkKI8s5v6/7yt0R5Zo8CgsLIzY2ltjYWLp3787YsWOJjY1lwYIFBTp/QkIC+/fvB5wJqxIl/tdLvVmzZrz55puXPbaSRyIiIiIiIgKwaf8p7nxnKQdPpfL5o80Ze3cE1cr5YoDA0iUxwOq9Ce4OU85z/SWP1n0FPwyEk/sA6/z+w8BCSSD98MMPNGvWjMaNG9OhQwcOHToEwKJFizKriRo3bkxiYiLDhg1jyZIlOByOS07U9OvXjzlz5gBw++2307dvXwAmT57MSy+9BMCYMWMIDQ0lNDSUiRMn5jpWz549+eor5z2YOnUqvXv3zty3YMECevToAcCIESN45JFHaNeuHbVr12bSpEkA7NixI7NSCWD06NGMGjWKadOmERsbS69evXA4HJw5c4aVK1fSrl07mjRpQpcuXTLvy5tvvklwcDDh4eHcf//9l3QPRERERERE5OqwdMdR7nnvNzyMYfrjLWlRpwI9Gldj6bCb2T26KytHdODv7eswfVUcP20+5O5wJYsSFz/kKjN3GBxcn/v+uJWQfjr7trMp8P0/YNWnOZ8TFAZdRuc7lNatW/P7779jjOGDDz5gzJgxvP7664wbN45JkybRqlUrkpKS8PHxYfTo0YwbN45Zs2Zd8vht2rRhyZIlmQmYo0ePArBkyRL69OnD8uXLmTJlCitXriQtLY2mTZty0003ERYWdsFYPXv25KGHHuLJJ59k9uzZTJkyhalTp+Y477Zt2/jpp59ISEigUaNGPP7447nG2KtXLyZOnMjbb7+Nw+Hg9OnTDBo0iJkzZ1KxYkWmTJnCCy+8wOTJkxkzZgx79+7F29ubhARlmkVERERERK4V362JZ8j0tdSuWJpPHo6mStmc+xoNvKUeCzYd5rkZ65n/VHnKlfIu4kglJ9df5dH5iaOLbb8CcXFxdOrUibCwMMaOHcvGjRsBaNWqFYMHD2bChAkkJCRkWyKWH23atGHx4sWsX7+e8PBwKlSowJEjR/j9999p3rw5v/76K3fddRe+vr74+/vTo0cPlixZkuNYgYGB+Pn58eWXXxIREYGPj0+u83br1g1vb28qVapEQEAAR44cueSYN2/ezMaNG+nQoQMOh4PRo0ezb98+AEJCQrj//vuZMmUKXl5e+bsZIiIiIiIiUuxYa/nPwp08OS2WJjXK89XjLXJNHAGULOHJ6/dEcCz5DC//sKkII5W8XHuVRxerEHoz1LVk7Txlb4CHZhdoKAMGDGDw4MF0796dhQsXMnLkSACGDRtG165dmTNnDq1atWLevHmXNX6NGjU4fPgw8+fPp23btuzfv5+pU6dSoUIF/Pz88j1er169eOKJJ/j888/zPK5kyZKZP3t6epKWlkaJEiXIyMjI3J6amppjUsxaS3h4eI5JrHnz5rFo0SJmzpzJa6+9xrp16/D09Mz3dYiIiIiIiIj7pWdYXv5hI//3215uj6jKuJ7hlCxx8c94odXK8kT7ukz4aTudQ4PoFBJUBNFKXq6/yqNbXgSv87KcXr7O7QXs5MmTVKtWDYBPP/3fkridO3cSFhbG0KFDiY6OZsuWLfj7+5OYmJjvOZo1a8aECRNo27Ytbdq0YezYsbRp0wZwViZ9++23pKSkkJSUxPfff5+5Lyd33XUXzz77LLfeemu+4wgKCmL//v2cOHGC1NRUZs/+XyIu67UFBwcTHx/PihUrADhz5gwbN24kPT2duLg4br75ZsaMGcPRo0f5888/8x2HiIiIiIiIuF/q2XT+PmUV//fbXvq1rc34Xo5LShyd84/2dWlUpQzPf7ue48lnCjFSuRTXX/Io/B64fYKz0gjj/H77BOf2K/Dnn39SvXr1zK833niDkSNH0rNnT5o0aULFihUzj33rrbcIDQ0lPDwcLy8vunTpQnh4OJ6enkREROTryWbnkkE1a9YkOjqao0ePZm5r2rQpvXv3Jjo6mubNm9O/f/8c+x2dU7ZsWYYOHXpZy+h8fHwYPnw4UVFRdOzYkeDg4Mx9Dz30EI8++igOhwNjDNOnT2fw4MGEh4fTuHFjli9fTlpaGvfeey/h4eFERkbyzDPP4O/vn+84RERERERExL1OJJ/hvg+WM3/TIV66PZjnbmuEh4fJ1xjeJTx4vWcECX+e5aWZGwspUrlUxlrr7hjyJSoqysbExGTbtnnzZho1auSmiESuHvpbERERERGRwrTv+J/87eMVxJ1IYXwvB13CqlzReBN+2s4bP27jP/dFXvFYkjdjzCprbVRO+66/yiMRERERERERKXAb4k9yxzvLOJZ0himPNiuQZE//m+oQWq0MI77bwLGkgn/QlVwaJY9ERERERERE5Ios2naEXu/9RskSHnzTvwXRNQMKZFwvTw9e7+ngVOpZXvxey9fcpVCTR8aYzsaYrcaYHcaYYbkcc48xZpMxZqMx5ovCjEdERERERERECtbXMft45JOV3FjBjxl/b0ndSgXbv7ZBkD9PdqjP7PUHmLVuf4GOLZem0JJHxhhPYBLQBQgGehtjgs87ph7wHNDKWhsCPFlY8YiIiIiIiIhIwbHWMvGn7QyZvo7mtSvwVb/mVC7jUyhz9Wtbm4jqZXnhuw0cSdTytaJWmJVHTYEd1tpd1tozwJfAX8475jFgkrX2BIC19nAhxiMiIiIiIiIiBSAtPYPh327g9R+3cWfjanzUJxp/H69Cm6+EpwfjekaQfCadEd+t52p7+NfVrjCTR9WAfVl+j3Nty6o+UN8Ys9QY87sxpnNOAxlj+hpjYowxMUeOHCmkcEVERERERETkYv48k0a/z1YxdcUf/P2mOrx+TwTeJQq/pXK9yv48fWt95m08xMy1Wr5WlNzdMLsEUA+4CegNvG+MKXf+QdbaydbaKGttVGBgYBGHeGlKly59Refv2bOHL764sOXT+vXrcTgcOBwOAgICqFWrFg6Hgw4dOlzRfFlVr16d9u3bZ9sWGhqKw+EAYPny5Tz11FOXPf5HH33EwYMHryjGy43j1KlTPPbYY9SpU4cmTZrQvn17Vq5cSVpaGuXKXfBWKzDWWsaNG0fDhg0JDw8nIiKCZ555hvT09EKbU0REREREpLAdSzpN7/eX88vWw/yzRyjPdm6IMabI5n+0TW0a31iOF7/fyOFTqUU27/WuMJNH8cANWX6v7tqWVRww01p71lq7G9iGM5lUqGbvmk3H6R0J/zScjtM7MnvX7MKe8qJySx6FhYURGxtLbGws3bt3Z+zYscTGxoS+6JUAACAASURBVLJgwYICnT8hIYH9+52Z2/Xr11OiRInMfc2aNePNN9+87LELKnl0OXE8/PDDBAUFsWPHDlatWsUHH3zA0aNHrziWi5k0aRK//PILy5cvZ926daxYsYKAgABOn9baXBERERERuTrtPZbMXf9ZxpYDp3j3/iY80LxGkcfg6WEY1zOC1LPpDP9Wy9eKSmEmj1YC9YwxtYwx3sBfgZnnHfMdzqojjDEVcS5j21WIMTF712xGLhvJgeQDWCwHkg8wctnIQkkg/fDDDzRr1ozGjRvToUMHDh06BMCiRYsyq4kaN25MYmIiw4YNY8mSJTgcjktOkPTr1485c+YAcPvtt9O3b18AJk+ezEsvvQTAmDFjCA0NJTQ0lIkTJ+Y6Vs+ePfnqq68AmDp1Kr17987ct2DBAnr06AHAiBEjeOSRR2jXrh21a9dm0qRJAOzYsSOzUglg9OjRjBo1imnTphEbG0uvXr1wOBycOXOGlStX0q5dO5o0aUKXLl0y78ubb75JcHAw4eHh3H///RfEeClxZLV161ZiY2N5+eWXMzPhderUoUuXLtmOy8jIYPDgwYSGhhIWFsb06dMBiI+Pp3Xr1jgcDkJDQ1m2bBkAc+fOpUWLFkRGRtKrVy+Sk5MvmPu1117j3XffpWzZsgCULFmS4cOHU6pUKQD69u1LVFQUISEhvPLKK5nnVa9eneHDhxMREUF0dDSrV6+mY8eO1KlTh/fffz/b/W3atCnh4eGZ5ycmJtKlSxciIiIIDQ3NvA4REREREZErtXZfAne+s4yTKWf54rHmdAwJclssdQJLM6RTAxZsPsyM1efXqEhhKHHxQy6PtTbNGPMPYB7gCXxkrd1ojHkFiLHWznTt62iM2QSkA0OstceuZN5/r/g3W45vyXX/uiPrOJNxJtu21PRUXlz6ItO35fxhu2FAQ4Y2HZrvWFq3bs3vv/+OMYYPPviAMWPG8PrrrzNu3DgmTZpEq1atSEpKwsfHh9GjRzNu3DhmzZp1yeO3adOGJUuWZCZgzlXULFmyhD59+rB8+XKmTJmSuUyradOm3HTTTYSFhV0wVs+ePXnooYd48sknmT17NlOmTGHq1Kk5zrtt2zZ++uknEhISaNSoEY8//niuMfbq1YuJEyfy9ttv43A4OH36NIMGDWLmzJlUrFiRKVOm8MILLzB58mTGjBnD3r178fb2JiEh4aLXn1Mcnp6emfs3btxI48aN8fDIO0f69ddfs3nzZtauXcuRI0eIjo6mbdu2fP7559x+++0MHTqU9PR0UlJSOHz4MKNHj+ann36iVKlSvPrqq4wfP57hw4dnjnf8+HHOnj3LDTfckOuco0ePJiAggLS0NNq3b8/dd99NcLDzYYS1atVi7dq1DBgwgEceeYRff/2VpKQkIiIieOyxx5gzZw5//PEHy5cvx1rLbbfdxrJly9i3bx81a9Zk7ty5AJw8efKi91BERERERORift5yiCemrKGivzefPtSU2oFX1ralIDzUqhb/3XCQkT9spFXdigSVLZynvIlTofY8stbOsdbWt9bWsda+6tr2oitxhHUabK0NttaGWWu/LMx4gAsSRxfbfiXi4uLo1KkTYWFhjB07lo0bNwLQqlUrBg8ezIQJE0hISMi2RCw/2rRpw+LFi1m/fj3h4eFUqFCBI0eO8Pvvv9O8eXN+/fVX7rrrLnx9ffH396dHjx4sWbIkx7ECAwPx8/Pjyy+/JCIiAh+f3P/wunXrhre3N5UqVSIgIID8NDHfvHkzGzdupEOHDjgcDkaPHs2+fc6+6iEhIdx///1MmTIFL6+Ld+m/kjiy+vXXX+nduzeenp4EBQXRunVrYmJiiI6O5oMPPuDll19mw4YNlC5dmmXLlrFp0yZatmyJw+FgypQp7NmzJ8/x58yZg8PhoEaNGqxYsQJwVndFRkYSGRnJ5s2b2bRpU+bx3bt3B5xLFps3b46fnx+VK1fGw8ODpKQk5s+fz9y5c2ncuDGRkZHs2LGDbdu2ER4ezn//+1+GDRvG0qVLM6ueRERERERELteXK/7gsf9bRd1Kpfmmf8tikTgC5/K1sT0jOJuewbAZ67R8rZAVWuWRu1ysQqjj9I4cSD5wwfYqflX4uPPHBRrLgAEDGDx4MN27d2fhwoWMHDkSgGHDhtG1a1fmzJlDq1atmDdv3mWNX6NGDQ4fPsz8+fNp27Yt+/fvZ+rUqVSoUAE/P798j9erVy+eeOIJPv/88zyPK1myZObPnp6epKWlUaJECTIyMjK3p6am5pgUs9YSHh6eYxJr3rx5LFq0iJkzZ/Laa6+xbt26bJVElxJHViEhIcTGxpKRkXHR6qOc3HzzzSxcuJDZs2fz4IMP8uyzz1KqVCk6d+7MZ599lut5AQEBlChRgj/++IMbb7yR2267jdtuu43OnTtz5swZtm/fzvjx41mxYgXlypXj/vvvJzX1f43ezl2Xh4dHtmv08PAgLS0Na23msr3zxcTEMGfOHIYNG0aXLl2yVUSJiIiIiIhcKmstby7YzoSfttOufiDv3BeJX8nilUKoVdGPoZ0b8vIPm/h6VRz3ROW++kOujLuftlbkBkUOwscze1WNj6cPgyIHFfhcJ0+epFq1agB8+umnmdt37txJWFgYQ4cOJTo6mi1btuDv709iYmK+52jWrBkTJkygbdu2tGnThrFjx9KmTRvAWZn07bffkpKSQlJSEt9//33mvpzcddddPPvss9x66635jiMoKIj9+/dz4sQJUlNTmT37fz2ksl5bcHAw8fHxmRU4Z86cYePGjaSnpxMXF8fNN9/MmDFjOHr0KH/++We+48iqQYMGhIWF8corr2RmoXfv3p25rOucNm3a8OWXX5KRkcGhQ4dYunQpUVFR7N27l6CgIPr27ctDDz3EmjVraNmyJYsWLWLXLmdrruTkZLZv337B3M899xz9+/fPXDpmrc1MEJ06dQp/f3/KlCnDgQMH8p087NSpEx9++GFmr6W4uDiOHj1KfHw8pUuX5oEHHuDpp59m9erV+bthIiIiIiIC676CN0NhZDnn93VfuTuiInc2PYOh36xjwk/b6dmkOh/8LarYJY7O+VuLmjStFcA/f9jE/oQUd4dzzSqer34h6lq7KwDjV4/nYPJBgvyCGBQ5KHP75frzzz+pXr165u+DBw9m5MiR9OzZk/Lly3PzzTeze/duAN566y1++eUXPDw8CAkJoUuXLnh4eODp6UlERAR9+vS55EfSn1u6VrNmTapWrcrRo0czE0RNmzald+/eREdHA9C/f/8c+x2dU7ZsWYYOzX9vJwAfHx+GDx9OVFQU1apVy+zfA/DQQw/x6KOP4uvry4oVK5g+fToDBw7k1KlTpKen8/TTT1O3bl3uvfdeEhMTycjI4JlnnsHf3/+yYsnq448/ZvDgwdStWxdfX18CAwMZN25ctmPuvvtufv/9d8LDwzHG8MYbb1CpUiU++ugj3njjDby8vPD39+ezzz6jcuXKfPjhh/Tq1YszZ5xLHV977TXq1cv+kMABAwaQkpJCdHQ0Pj4+lC5dmjZt2hAREUHp0qUJDg6mYcOG1KhRg1atWuXrmm677Ta2bNlC8+bNAWdy7osvvmDTpk0MGzYMDw8PvL29effdd6/gzomIiIiIXIfWfQU/DISzriTEyX3O3wHC73FfXEUo+XQaf5+ymkXbjjDwlno81aFe5gOIiiMPD8O4uyPoPH4xQ79Zx/893LRYx3u1MlfbusCoqCgbExOTbdvmzZtp1KiRmyISuXrob0VEREREJA9vhjoTRucrewM8taHo4yliRxJP8/AnK9l04BSjeoTSu+mN7g7pkn322x5e+H4j/7oz7KqKuzgxxqyy1kbltO+6W7YmIiIiIiIikqOTcfnbfg3ZdSSJO/+zlB2Hk3j/wSZXXQLmvmY1aFmnAqNmbSLuxJW1QJELKXkkIiIiIiIiAlC2ev62XyNW7T3BXf9Zxp+n05natzk3N6zs7pDyzcPD8O+7wgF4dvo6MjKurlVWxZ2SRyIiIiIiIiIAt7wIniWzb/PydW6/Rs3feJB73/+dsr5efNO/JY4byrk7pMt2Q0Apnu8azLKdx5iy4g93h3NNuWaSR1db7yaRoqa/ERERERGRiwi/B4K7Z98W1uuabZb92e97efzzVTSsUoZv+rekZkU/d4d0xXo3vYE29Sryrzmb+eNYIS5fu86eyndNJI98fHw4duyYPhyL5MJay7Fjx/Dx8XF3KCIiIiIixVtGOpSpDi8lQJUI2L0I0tPcHVWBstYy5r9beOG7DbRvUImpjzWjQumSFz/xKmCMc/mapzEMmb62cJavnXsq38l9gP3fU/mu4QRSCXcHUBCqV69OXFwcR44ccXcoIsWWj48P1atf22u1RURERESuWHwMVG8CxkC7ofDlvbBhOkT81d2RFYgzaRkM+2YdM9bE07vpjfzzLyGU8Lwm6koyVS3nywvdgnn2m3X832976NOqVsEMfDYFEv6A/z7n/Pn8fT+9cs1WqV0TySMvLy9q1SqgN4OIiIiIiIhcn5KOOJMD0Y85f29wG1QOg8VjIawneHi6N74rlJh6lv6fr+bXHUd5+tb6/OPmuhhj3B1WoegZVZ05Gw4w+r9buKlBpUtbkmctJB2GE3vO+9rt/J54IO/zr+Gn8l0TySMRERERERGRKxYf4/xePcr53RhoNwS+ehA2fgthd7svtit06FQqD328kq2HEhlzdzj3RN3g7pAKlTGG0XeGc+ubixgyfS3T+rbAw8PA2VRI2JtDgsj1dfa8PkllqkH5mlDnZuf38jVh3vOQfPjCSa/hp/IpeSQiIiIiIiICEL8KjKez19E5DW+HwEawaAyE3AkeV98Srx2HE/nbRys58ecZPuoTTbv6ge4OqfBYC8lH4Phugk7s4csGa9m8aT2HJyQRlH4QEvdnP96rFJSv5fyq3f5/CaLyNaHcjeCVS9/YHwZmX7p2jT+VT8kjEREREREREYC4GKgUDN5Zljh5eDirj6Y/DJu/h5A73BffZVi55ziPfhqDl6cH0/q2IKx6WXeHlN26r5y9gk7GOSt3bnnx4n2DzqY6lxdmXVKWS/VQCFDFuyI7T1TEr1Fr/KvUy54g8gt0Vpjlx7n48hv3VUzJIxEREREREZGMDIhfDaE5JIeCe0DF0bBoLDT6y1VTfTR3/QEGTYulejlfPn24KTcElHJ3SNmde2rZuQqec08tA6h9U/aE0PEsSaIcq4dqOr9q3+SqJKqZWT2UlgKPvrmYOsf9+Pqelnh6FECfp/B7rulk0fmUPBIRERERERE5tgNOn4RqURfu8/CEtkNgxmOwdTY0ur3o48unj5fu5pVZm2h8Qzk++Fs0AX7e7g7pQj+9kvNTy2b0BWz27f5VsySHajq/AmpdUvVQJS945S8hDPoylg9/3UXftnUK8iquC0oeiYiIiIiIiJzfLPt8IXfCwtGw6N/QsFv+lzoVsu/WxDN23lb2J6TgV7IESafT6BhcmQm9G+PjVUyfEpfr08ksdBlzXu8h3yuaqntEVeasP8C4+du4uWEl6lbyv6LxrjdXR62diIiIiIiISGGKXwXe/lCxfs77PUtA22fg4HrY9t+ije0ivlsTz3Mz1hOfkIIFkk6n4elh6BwSVHwTR5D708nK3gDN+kH9ThDY4IoTR+B8+tqoHmH4eXvy9NfrSEvPuOIxrydKHomIiIiIiIjExUC1xs4larkJu8dZCbNwtPOpXsXE2HlbSTmbnm1beobl9R+3uSmiS3TTMOC8Cq5CfGpZoH9JXvlLKGv3JTB5ya5CmeNapeSRiIiIiIiIXN/OpsChDVCtSd7HeZaANs/AgVjY/mPRxHYJ9iek5Gt7sZF0CLDOnkUYZ8XR7RMKtRF1t/Aq3BYWxFs/bmfrwcRCm+dao+SRiIiIiIiIXN8OrIOMtJybZZ8v4q9Q9kZn76NiUn1U0b9kjturlrvy5V6FJukwLHkDGtwGQ3bAyAR4akOhP8HMGMM//xKKv08Jnvl6LWe1fO2SKHkkIiIiIiIi17eLNcvOytML2gx2nrPz58KN6xIknU7DZlyYAPH18mRIpwZuiOgS/TwK0lLh1n8W+dQVSpdkVI9Q1sef5N2FO4t8/quRkkciIiIiIiJyfYuLgTLVwT/o0o533Os83s3VR9Zahs9Yz4mUNAbcXJdq5XwxQLVyvvzrzjB6NK7mttjydHADrPkMoh+DinXdEkKXsCrcHlGVCT9vZ9P+U26J4WpSwt0BiIiIiIiIiLhV/CqofpF+R1mVKAmtn4Q5z8DuxVC7XeHFlodpK/cxc+1+hnRqwBPt6/J0x2JcaXSOtTD/eShZBto969ZQXukewm87j/HM12v5/h+t8PJUfU1udGdERERERETk+pV8FBL2XrxZ9vkaPwD+VWDRmMKJ6yK2HkzkpZkbaVOvIv3b1XFLDJdl+3zYtdD5pLVSAW4NpbyfN6/dEcqmA6eY9MsOt8ZS3Cl5JCIiIiIiItevOFe/o0tplp2Vlw+0ehL2/gp7fi34uPLw55k0nvhiNf4+XrxxjwMPD3Pxk4qD9LMwfwQE1IGoR9wdDQAdQ4K4o3E13v55BxviT7o7nGJLySMRERERERG5fsXHgPGEqo78n9vkb+BXqcirj176fiM7jyQx/q8OAnN50lqxFPMxHN0GHUdBCW93R5PppduDCfDz5pmv13ImTU9fy4mSRyIiIiIiInL9iouBSsHg7Zf/c718odUg2L0I/vi94GPLwYzVcXy9Ko4B7evSqm7FzO2zd82m4/SOhH8aTsfpHZm9a3aRxHPJUk7Awn9BzTbQoEvm5uIQd7lS3vzrzjC2HExk4s/bi3z+q4GSRyIiIiIiInJ9ysiA+NX5a5Z9vqiHwS/Q+eS1QrbjcBIjvttA01oBDLylXub22btmM3LZSA4kH8BiOZB8gJHLRhavBNLicc4EUqfXwDiX2RWnuG9pVJm7m1TnnYU7WReXUOTzF3fGuvGxgpcjKirKxsTEuDsMERERERERudod3Q5vR0H3tyHygcsfZ+l4+PFFeGQB3BBdcPFlkXo2nR6TlnI48TRzBrYhqKxP5r6O0ztyIPnABef4ePpw0w03FUo8+XImCbb/COVuzNaYfOG+haSmp15weBW/Ksy/e35RRgjAyZSzdHpzMf4+JZg1sDUlS3gWeQzuZIxZZa3NsflXiaIORkRERERERKRYyGyWfQWVR+Bs/vzrW7B4DNz39ZXHlYN/ztrEloOJfPxQdLbEEcDB5IM5npOansqW41sKJZ58STwA3t5Q0geyxJNT4ghyv57CVtbXi9F3hdHn45W8tWA7Qzs3dEscxZGSRyIiIiICOJcPjF89noPJBwnyC2JQ5CC61u7q7rBERApPfAx4l4bABlc2TsnS0PIf8NMrzmVw1SILJj6XWev2M2X5H/RrV5v2DSpl22etpZRXKZLPJl9wXhW/Kvxwxw8FGku+7fkVPukK7UdAuyHZduVWMRXkF1RU0V3gpgaV+Gv0Dby3aCcdgyvT+MbyboulOFHPIxEREREpVn0nRESKTFwMVG0MHgWwPCn6MfApB4vHXvlYWew9lsywb9bT+MZyPNMxe5IrLSONl5a9RPLZZDxN9mvw8fRhUOSgAo0l3zIyYN5wKFMNWjxxwe5BkYPw8cxeRVUc4n6+ayOCyvjwzNdrST2b7tZYigslj0RERESuY0dTjvLTHz/xz9//ecHygdT0VMavHu+myERECtnZFDi0Aarn2OIl/3zKOBMkW+fAgbUFMuTptHT+8cUaPD0ME3s3xsvzfx/hU9NSGbxwMN/u+Jb+Ef0Z1WoUVfyqYDBU8avCyJYj3V89uu5L573oMBK8S12wu2vtroxsOZIqflUytz0Q/IDb4/b38WLM3RHsPJLMGz9uc2ssxYWWrYmIiIhcJ9Iz0tmRsIO1R9YSeziW2COx7Evcl+c57uo7ISJS6A6sg4w0qFZAySOApn1h2dvO6qNen1/xcKPnbmF9/EkmP9CE6uX/l3xJPJPIgJ8HsPrQap5r+hz3NroXgG51ul3xnAXmTLJzGV/VSAi9O9fDutbuStfaXUlJS+GmaTdxPPV4EQaZu9b1KnJfsxt5f8kuOoVUpkmNAHeH5FZKHomIiIhcoxLPJLL+yHpij8QSeziWdUfXZfbECPAJoHGlxtxT/x4clRwMWTwkx0SRO/tOiIgUqvhVzu8FVXkE4FsOmj8Oi/4NhzZC5ZDLHmrexoN8vHQPD7WqSceQ//23+GjKUR7/8XF2ntzJv9v+my61uhRE5AVv2URno+yen4DHxRc9+ZbwpUONDszfM5/nmj1HSc+ShR/jRTx3WyMWbj3CM1+vY87ANvh6X19PX8tKySMRERGRa4C1ln2J+zITRbFHYtlxYgcWi4fxoF65enSr3Y2IwAgclRxUL10dY0zm+U9GPsnIZSOzLV0rDn0nREQKTXyMsxePfwEnyZs9Dr+946w+6vnJZQ0Rd+JPhny9lrBqZRnW5X9P/Np3ah99f+zLsdRjTLp5Ei2rtSygoAvYqf2wdDwE94Abm1/yaV1rd2XmzpksjlvMrTVuLcQAL03pkiUYe3c4936wnHHzt/JCt2B3h+Q2Sh6JiIiIXIVS01LZdGxTZrJo7ZG1maX+pb1KExEYwa01bsUR6CCsYhilvUvnOd65/hLjV4/nQPIBPIwHL7V4ye19J0RECk1cDFRrUvDjlgqAZv1gyevQbgtUyt/j3s+mZzBw6hoyLLx9b2NKlnBWu2w9vpV+P/YjzabxQccPCA8ML/jYC8pP/3QuCewwMl+nNQtqRkXfiszaOatYJI8AWtatyIMtavDR0t10Cgmiaa3rc/makkciIiIiV4HDfx7OrChae3gtm45vIi0jDYAaZWrQulprHJUcOAId1C5bG8/LeHLQub4TU7dM5bXlrxFRKaKgL0NEpHhIPgoJeyH6kcIZv8UT8Pt/nNVHd3+Yr1PHzd/K6j8SePvextSo4AdAzMEYBvw8AD8vPz669SNql6tdGFEXjP1rYO0X0GoQBNTK16meHp50qdWFL7d8ycnTJylbsmwhBZk/Qzs3ZOHWIwyZvpa5g9pQyvv6S6Vcf1csIiIiUsylZaSx7cS2bMmi/cn7ASjpWZKQCiE8GPwgjkAHEZUiCPAp2P8L2qSy8//Erz60mhv8byjQsUVEioW4GOf3gmyWnVWpAGj6mHPpVruhEFj/kk77Zeth3lu0i3ub3Ui38KrObX/8wpDFQ6hauirvdXiPKqWrXGQUN7IW5j0PpSpCm6cva4hutbvx2abPmL93Pj3r9yzgAC+Pn2v5Wq/JvzPmv1sZ2f3ye1ldrZQ8EhEREXGzk6dPZj4Bbe2Rtaw/up6UtBQAKvlWwlHJwf3B9+MIdNAwoCFenl6FGk/dcnUp412G1YdX85e6fynUuURE3CJ+FRhPqOoovDla/ANWTHYuX7vzvYsefvBkKk9/tZaGQf686Oqt8+32b3n5t5cJrhDMpFsmUd6nfOHFWxC2zIK9S6HrG+BzeVVDjQIaUbtsbWbtnFVskkcAzWpX4KFWNfl46R46hQTRok4Fd4dUpJQ8EhERESlgs3fNZvzq8RxMPkiQXxCDIgdl9g6y1rL71G7WHl6b2a9o18ldAHgaTxoENOCOundkLkEL8gvK1ti6KHgYDyIrRbLq0KoinVdEpMjEx0ClYPD2K7w5SgdC1MPw+zvQ7lmoUCfXQ9PSMxj45RpSz6bz9r2R+Hh58vGGj3lj1Ru0qNKCt9q/RSmvUoUXa0FIOw3zX4DAhhD5t8sexhhD19pdmbhmIvFJ8VQrXa0Ag7wyz3ZqyC9bDjNk+lrmPdkWv5LXT0rl+rlSERERkSIwe9fsbE8tO5B8gBeXvsgvf/xCSnoKa4+s5eTpkwCU8S6Do5KDbrW74ajkIKRCSLH5cBBZOZKFcQs5mnKUir4V3R2OiEjBychwVh4F9yj8uVoOhJUfwJI3oMekXA+b8PMOVuw+zhv3RFAn0I/XY17nk42f0LlmZ15r/VqhV5wWiBXvw4ndcN834HllqYbbat3GxDUTmbNrDo+FP1ZAAV45X29PxvWMoOd7v/GvuZsZ1SPM3SEVGSWPRERERArQ+NXjsz3uHuBMxhnm7Z1H7bK1ueXGWzJ7FdUsUxMP4+GmSPMWWTkSgFWHVtGpZic3RyMiUoCO74TUk1C9kPodZeVfGZo85Fy+1m4IlK95wSFLdxxl4s/bubtJdbo7ghixdAQzd87krw3+yrCmwy7rAQhFLvkYLBoDdTtAvQ5XPFx1/+pEVopk1q5ZPBr2aJFX4OYlqmYAj7auxftLdjN3/UGOJ5+hajlfhnRqQI/GxadKqqAVz3+tiIiIiFylDiYfzHG7wfB9j+95ueXL3FHvDmqXrV1sE0cAwQHB+JbwZfWh1e4ORUSkYBV2s+zztRoEHiWc1UfnOZJ4mkFfxlK7oh/Du9bhqV+eYubOmfw94u8Mbzb86kgcASwaDWeSoOOoAhuya+2u7Dq5iy3HtxTYmAWlfmV/DHAs+QwWiE9I4bkZ6/luTby7Qys0xfdfLCIiIiJXoSC/oHxtL668PL0IrxjO6sNKHonINSY+BrxLQ2CDopmvTBWIfBBiv4CEPzI3Z2RYnpoWS2LqWcbcU4+nFj3BorhFPN/sefo7+heraps8HdkKKz+EJn2gUqMCG7ZTzU6U8CjBrF2zCmzMgvLWgu38P3t3Hh5Vef5//H1msu8J2cOShC1skSQKAUHc2FMFq621rdrNroraWrfWWn9Wra21ttXW7la/2lq1tkLCoggKQkICJGFJWDIBsu/7JJnJnN8fh2CAAJNkZs5Mcr96eUVmznJjIZm557k/j3rOY2ZLH7/YVKpLPa4gzSMhhBBCCAdal74Oo3L2J8V+Rj/Wpa/TqaLhS49Jp7SplLbeNr1LEUIIx6ksgPg0cOWqnkX3gaLAXcIapwAAIABJREFUjl+feej324+z41gDP1gZx1P77qGooYhnlzzLrSm3uq4uR9j8Yy14/JpHHHrZUN9QFicsJtuUTZ+tz6HXHqmqFvOQHh8NpHkkhBBCCOFAKxJX4O/lj5/RDwWFuMA4Hl/4+Jnd1jxJRkwGKir76/brXYoQQjiGpRtqDrgm72ig0ARI+xLsexVaK8kzNfHc5lKuTzXyds1DnGo/xYvXvciKxBWurWukjm+Fo5vgqh9AoOM3V8hKzqLB3EBuTa7Drz0S8WH+Q3p8NJDmkRBCCCGEA+2p3UOHpYMnFz1J0R1FbL55s0c2jgBSo1LxUrwk90gIMXrUFIHNAgkZrr/3ovtAtdG97TnueWMf8dGNHDU+Q6elk78s+wsL4xe6vqaRsPXBph9B2CSY902n3GLJhCUEeQexoWyDU64/XA8sn46/99kr1/y9jTyw3EWjkDqQ5pEQQgghhAPlmHII8ApgyfglepcyYv5e/swcN5OC2gK9SxFCCMdwdVj2QGETUS+7DeO+f4Atj96ol/AxevPKyleYE+WBW77vexXqDsLSn4K3n1Nu4Wv0ZVniMt4/8T5mq/uMhK1JS+Dpm+aQEOaPAiSE+fP0TXNG9W5rXnoXIIQQQggxWvT29bLlxBaunXgtfl7OeSHtahkxGbx6+FW6rd2j5vckhBjDKvMhJEELsdbBv/xuYZz/W/TGvMGEoCReXvqyx22oAEB3G2x9EiZkwsw1Tr1VVnIW7xx9h22ntrEyaaVT7zUUa9ISRnWz6Fyy8kgIIYQQwkF2Vu6kvbfdrV7cjlR6TDpWm5XihmK9SxFCiJGryNdnZA3Yd7KZn+z7kO/HRJLS28sri37hmY0jgB3PQ2c9rHhKCwJ3ooyYDGICYtxy17WxRJpHQgghhBAOkmPKIcw3jAXxC/QuxWHSotNQUGR0TQjh+ToboOWE68OygZbOXr7x31/gE/cW8yIv4881dYTt/YfL63CIlpOw60VI/bxLGnEGxcCq5FXsrNxJU3eT0+8nBifNIyGEEEIIB+iydLGtYhtLJy3F2+CtdzkOE+obypTwKRKaLYTwfJWnm+AuXnnUZ+vj8289Sk/Ie2RGX8/vV/6NgNk3w56/aA0tT/P+49pqo+sec9kts5Kz6FP72FS+yWX3FGeT5pEQQgghhANsO7UNs9U8qkbW+qVHp7O/fj9Wm1XvUoQQYvgq8kExQNxcl93SYrNw27v3U8VGUkNW8/KK5/A2esPiH4DFDLt+57JaHOJUHhx4GxbeDaHjXXbbaeHTmBY+TUbXdCTNIyGEEEIIB8gx5RAdEE1GjD5ZGs50eczlmK1mSppK9C5FCCGGrzIfomeCb5BLbme2mvl6zvc41L6VBHUN/7jhKQzK6bfgUdNg9k2Q9yfo8pBRLFWFTY9AUAxcea/Lb786eTVF9UWcbDvp8nsLaR4JIYQQQoxYa08rO6p2sCJxxadvDEaR9Jh0AMk9EkJ4LptNG1tz0chaa08rX990F3vrd+HTfAuv3/JjjMZzfj5c9QD0dsLul1xS04gdeBsq9sC1P3ZZA26gVUmrUFDYULbB5fcW0jwSQgghhBix90+8j9VmZVXSKr1LcYrogGjGB42X5pEQwnM1HYfuVpeEZdd11fGVjV+huKGYnqrb+MOa7xER6HP+gdEzYOaNkPsymJudXteIWMxa1lHsHJh7my4lxAbGckXsFWwwbUBVVV1qGMukeSSEEEIIMUI5phwmBk9k5riZepfiNBkxGeyr24dNteldihBCDN2ZsGznNo9OtJ3g9pzbOdFWQeeJO1m34GauSIy48AlXPQA9bbD7D06ta8R2vwStp2DZz8Bg1K2MrOQsTrSd4EDDAd1qGKukeSSEEEIIMQL1XfXk1eSxMmkliqLoXY7TZMRk0NLTgqnVpHcpQggxdBX54BMEUdOddotDjYe4Ped22no66Sz/OgsTFvDtJZMvflLsbEjJgt2/11ZGuaOOOvj4VzB9FSQv0bWU6yddj4/BR4KzdSDNIyGEEEKIEdhUvgkVddSOrPWT3CMhhEerzIf4NKetmsmrzuOrm76Kj8EH77rvEaQk86vPzcVgsONDhSU/hJ5WyP2jU2obsa1PgrUblv4/vSsh2CeYJROWsLF8IxabRe9yxhRpHgkhhBBCjECOKYfp4dNJDkvWuxSnmhg8kUj/SGkeCSE8j6Ubag44LSz7/RPv8633v0VsQCzTbY9wsjaIF26dS1Swr30XiLsMpq2EXb+Dnnan1DhsNQdg36twxTcgcore1QDa6FpTdxO7qnbpXcqYIs0jIYQQQohhOtV+iqKGIlYmrdS7FKdTFIX06HQKagskqFQI4VlqisBmcUpY9ltH3uL727/PzHEz+Wzc06zf18Xd107lyimRQ7vQkgeguwXy/uTwGodNVWHzo+Aboq2OchOLExYT6hsqu665mDSPhBBCCCGGaaNpI8CYaB6BlntU21VLVWeV3qUIIYT9nBCWraoqfy7+Mz/d9VMWxi/kobnP8/SGE8xPimDddVOHfsGEDJiy9PTqow6H1TkiRzdD2Ta4+iEIuEjot4t5G71ZPmk5W09updPSqXc5Y4Y0j4QQQgghhinblM3cqLnEB8XrXYpLZMRoIx97a/fqXIkQQgxBRT6EJEBInEMuZ1NtPLvnWV7Y+wKrk1fzi8XP84M3D+PnbeSFW9Mw2pNzNJglD0JXI+T/1SF1jkifBTb/CMZNgSu+rnc158manEV3XzdbT27Vu5QxQ5pHQgghhBDDcLT5KMdajo2ZVUcAU8KmEOwdLLlHQgjPUpkPCekOuZTFZuHRHY/y2uHX+NKML/HUoqd4KvsoJTXtPPe5y4gN9Rv+xSdcAZOvhU9+A71dDql32PL/Bg1HtJBso7e+tQxibtRcEoISZNc1F5LmkRBCCCHEMOSYcjAoBpYlLtO7FJcxGoykxaRJ80gI4Tk6G6C53CEja2armXVb17G+bD33pN3DD6/4IRuKang99yTfXJLMNdOjR17vkgehsx4K/j7yaw2XuRm2PQ2Ji2G6e35AoigKq5JWsbt6Nw3mBr3LGROkeSSEEEIIMUSqqpJjymF+7Hwi/YcYiurh0qPTKW8rp9HcqHcpQghxaf15RyMMy27taeWuzXexs2onjy14jG+kfoOTTV08/E4x6RPD+MGy6Q4oFpiYCUlXwc5fg8XsmGsO1Ue/1BpIy58CZZgjeC6QlZyFTbWRY8rRu5QxQZpHQgghxBBsKNvAsreWkfpKKsveWiY7fYxRxQ3FVHRUjKmRtX79uUf76vbpXIkQQtihIh8UA8TNHfYlajtruXPjnRxsPMgvl/ySW6bdQo+1j++9vg+jQeE3X0jD2+jAt9ZLHoSOWtj7D8dd016NxyH3ZUj7IsSluv7+Q5AclszMcTNldM1FvPQuQAghhPAUG8o28Pgnj9Pd1w1AdWc1j3/yOACrk1frWJlwtRxTDt4Gb66bdJ3epbjcrHGz8DX6UlBbwPWTrte7HCGEuLjKAoieCb5Bdp+yoWwDL+x9gZrOGiL9I7H0Wei19fL763/P/Lj5ADyTU0JxZSt//HIG48MDHFtz4iKYdCXseB7S7wDvEeQoDdX7PwGjD1z7Y9fdcwSykrN4ds+zlLWUkRyWrHc5o5qsPBJCCCHs9MLeF840jvp193Xzwt4XdKpI6KHP1sfG8o0sTlhMiE+I3uW4nLfRm9SoVMk9EkK4P1XVmkcJGXaf0v9BUXVnNSoq9eZ6Wnpb+Orsr55pHG06WMPfdpbzlSsTWTYr1jm1L/khtFfD/tecc/3BlO+Aw+/Bovsg2Em/LwdbmbQSg2KQ1UcuIM0jIYQQwk41nTVDelyMTvm1+TSYG1iZPPZG1vqlR6dT2lxKR2+H3qUIIcSFNR6H7pYhNY8G+6AI4O2jbwNQ0dzFA/8uZE5CKA+tTHFYqedJWgIT5sPHz4O113n36WezwaZHICQBFnzX+fdzkEj/SDLjMsk2ZaOqqt7ljGrSPBJCCCHsFBs4+KdwF3pcjE45phwCvAJYMn6J3qXoJiMmA5tqY3/9fr1LEUKIC6vM174OISz7Yh8UWfps3P3GPmwq/O62NHy9jI6ocnCKoq0+aquAwtedd59+Rf+E6kK4/nHwcfAYnpNlJWdR2VEpP5OcTJpHQgghhJ3Wpa/DqJz9QtHX6Mu69HU6VSRczdJnYcuJLVwz8Rr8vfz1Lkc3l0VdhlExsrd2r96lCCHEhVXkg08QRNm/QuhiHxT9cnMp+0628Mxn5zBpXKCjqrywyddpq6Y+fg76LM67T28nfPAExKfD7Juddx8nuW7idfh7+bP+uIyuOZM0j4RwU7KjkxDuZ3XyasL9wvE1+qKc/l9sQCyrklbpXZpwkZ1VO2nrbRvz/58HeAcwI2KG5B4JIdxbZT7Ep4HB/hVC69LX4W3wPusxP6Mf18feycvby/ji/IlkpcY7utLBKQoseQhaTkLRv5x3n52/0fKVVjwNBs9rEQR4B3D1hKvZWL4RizObbGOc5/3JEGIMODeor39HJ2kgCaGv1p5WGswN3JV6F0V3FPHYgsc40X6CDSb5uzlWZJuyCfUNZUHcAr1L0V1GTAbFDcX09PXoXYoQQpzP0g01B4aUdwTaB0Vp0WlnPiSKC4zj3rmP8M8Po0iJDebHWTOdVPAFTF0KcXPho19Cn9Xx12+rgp0vwMw1MDHT8dd3kazkLNp62/i48mO9Sxm1pHkkhBuSHZ2EcE/FDcWANrIDcNPUm5gTOYfn8p+jvbddz9KEC3RZuth2ahtLJy3F2+h96RNGufSYdCw2CwcaDuhdihBCnK+mGGyWIeUd9avurGbJ+CUU3VFE9tqN/HdHLN2WPn53Wzp+3k7MORqMosCSB6HZBAfecvz1P/h/oPZpWUcebEH8AiL8IuTDdieS5pEQbkh2dBLCPRXVF2FQDMyOnA2AQTHw6PxHaTQ38tL+l3SuTjjb9ortmK3mMT+y1i89Oh1ARteEEO6pPyx7iCuPqjqqONV+ivlx8wH4zQdHyStv4sk1s5kSHeToKu0zfSXEzIGPfgG2Psddt2qfFsad+W2ISHLcdXXgbfBmReIKtp3aJh/oOYk0j4RwQ7KjkxDuqbC+kClhUwj0/jQkc1bkLG6edjNvlLzBkeYjOlYnnC3blE10QDQZMUN7IzJahfmFMSVsioRmCyHcU0U+BMdDyNDyiXKrcwH4XTYkPrSB32w9xrzEcG5KH++MKu3Tv/Na4zE48I5jrqmqsOlRCIiExd93zDV1lpWcRa+tl/dPvK93KaOSNI+EcEPr0tfha/Q96zE/o5/s6CSEjmyqjeL64jMjawPdk3YPwT7B/Gz3z1BVVYfqhLO19rSyo3IHKxJXYFDk5VO/9Oh09tfvx2pzQg6HEEKMRGU+jB96s//tQ9tQrYHUNISeeayospV391U6srqhS8mC6JmOW310+D04sROueQT8Qi99vAeYHTmbicETWV8mu645g7z6EcINrU5eze0zbz/zay+DF48vfJzVyat1rEqIsc3UaqLd0k5qVOp5z4X5hbEufR176/bKC5ZR6oOTH2C1WWVk7RwZMRl0WjopbS7VuxQhRreiN+H52fB4mPa16E29K3JvnQ3QXA4JQ8s7UlWVosYCrJ2TGfhWudti4xebdP4+ZzDAVQ9AQykc+u/IrmXtgS2PQdQMSL/DMfW5AUVRyErOYk/NHon7cAJpHgnhpiL8IgD40owvYbVZz8xdCyH0UVhfCDDoyiP4NDz7VwW/kln7USjblM3E4InMHOfiXXbcXHqMlnsko2tCOFHRm/DePdB6ClC1r+/dIw2ki6k8/T1piGHZJY3HUY2t9HVNPu+5qhazIyobmZk3QuT006uPbMO/Tt6ftADu5U+C0ctx9bmB1cmrUVHJMeXoXcqoI80jIdxUSVMJ4/zGkTU5C4Dd1bt1rkiIsa2wvpAQnxASQxIHfV7Cs0ev+q568qrzWJm0EkVR9C7HrcQGxpIQlCDNIyGc6YMnwHJO48Ji1h4Xg6vMB8WgbXFvp4+P1nPHG68DYO2cct7z8WH+Ditv2AxGbfVR3SEoGeZK585G2P4sTLle+2eUmRgykdSoVFkJ7gTSPBLCTZU2l5ISkUJKeAqhvqHsrpLmkRB6KqovIjUq9aLNAwnPHp02lW9CRZWRtQvIiMlgb91eyfsSwllaK4b2uNDCsqNmgO+ld0dr6uzl/n/t58t/yaPP9yiBhkj8iDrrGH9vIw8sn+6saodm9k0wborWABrO993tz0BvByx70vG1uYms5CyONB+htElGqh1JmkdCuCFLn4VjLcdIiUjBaDAyL3Yeu6t3ywtzIXTS3tvO8ZbjFxxZG0jCs0efHFMO08OnkxyWrHcpbik9Op2m7iZMbSa9SxFidLrQbmGhOu7+5c5UFSoLLhmWraoq/9lXwfW/2s7/Cqv43jXJ+AebuD7pSp6+KZWEMH8UICHMn6dvmsOatATX1H8p/auPaouhdIijWfWlsOcvkHEnRM9wSnnuYHnicoyKkQ2mDXqXMqpI80gIN3S89ThWm5WUiBQAFsQvoLarlvK2cn0LE2KMKq4vRkUdNCz7XGF+Ydybfq+EZ48Sp9pPUdRQxMqklXqX4rb6c48Kagt0rkSIUSp2kJ893v5w3WOur8UTNB6H7paLhmWfbOzi9r/mcd+/Cpk0LoAN9yxm9eXQ2tvK/Lj5rElLYOdD12J6ZjU7H7rWfRpH/WbfDOFJsP3nQ1t9tPnH4BOo7bA2ikX4RXBlwpVkl2VjU0eQDSXOIs0jIdxQSVMJANMjtOWxmXGZgOQeCaGXwoZCFBRSIy/dPAJYO3UtcyLn8Fz+cxKe7eE2mjYCSPPoIhJDEonwi5DcIyGcoaMOTNth/DwInaA9ZvCCz/wGUj+nb23uqvJ0I3uQsGxrn40/fnScZb/ezr6TLTxx4yze+tZCpscGk1udC8C82HmurHZ4jF5w1Q+gej8c3WLfOce3wtFN2nmBkc6tzw1kJWdR21UrH2w4kFObR4qirFAUpVRRlGOKojw0yPN3KopSryjK/tP/fN2Z9QjhKUqbSvH38mdi8EQAJgRPICEogV1Vu3SuTIixqbC+kMlhkwnyuXR2Anwant3U3STh2R4u25TN3Ki5xAddYGxEoCiKlnskzSMhHO/j57Rt1df+Ae47AKt+CTYrxM7RuzL3VZkP3oEQlXLWw8UVrdz44k6eyi5h0ZQottx/FbcvSMRo0LIMc2tySQxJJCYwRo+qhy718xA20b7VR7Y+2PQjCJsE877pmvp0dvWEqwnwCpBV4A7ktOaRoihG4EVgJTAT+IKiKIPtb/svVVXnnv7nz86qRwhPUtJUwtTwqRgNxjOPZcZlsqdmD1abVcfKhBh7bKqNovoiu/KOBpoVOYtbpt0i4dke7GjzUY61HJNVR3bIiMmgqrOK6o5qvUsRYvRoOQX5f4W0L8K401vHz7hB20Xs4Lv61ubOKvIhPk3LBgK6eq08uf4QN764g7r2Hn7/xXT+dHsGcaGf7p5msVkoqC1gftx8vaoeOqM3LP6+1iw7/sHFj933KtQdhKU/BW8/19SnM38vf66fdD2byzfT09ejdzmjgjNXHs0DjqmqWqaqai/wT+BGJ95PiFFBVVVKm0pJCT/705IF8QvosHRwsPGgTpUJMTaVt5XT3ts+5OYRwD3pEp7tyXJMORgUA8sSl+ldittLjz6de1Qn4wFCOMz2n2tfr/rhp48Fx8CkK+HgO8PbaWu0s3RDTfGZsOxtpXUs/dVH/HmHiVvnTeT9+5ewck7ceTunHmg4gNlq9qzmEcBlt0HIeNh2kdVH3W2w9UmYkAkz17i2Pp2tTl5Nh6WD7ae2613KqODM5lECcGrArytOP3auzyqKUqQoyluKokwY7EKKotylKEq+oij59fX1zqhVCLdR1VlFu6X9TN5Rv3mx81BQ2F0luUdCuFJhXSHAsJpHob6hEp7toVRVJceUw/zY+UT6j/5siJGaFj6NIO8gGV0TwlEajsH+1+Hyr0HYOW+RZq2FhiNQd0if2txZTTHYLLSNu4x1/9zHnX/bg5+3gX9/awFPrZ1DqL/3oKflVueioHBFzBUuLniEvHxg8X1QkadlYw1mx/PQWQ8rnoJzmmajXf/P8A1lsuuaI+gdmP0ekKiqaiqwBXhlsINUVf2jqqqXq6p6eVRUlEsLFMLV+sOy+3da6xfuF05KRAq7qiX3SAhXKqwvJNgnmMTQxGGdL+HZnqm4oZiKjgoZWbOT0WBkbvRcCSYVwlG2PQVevrD4/vOfOzO69h/X1+Xm1Io9ANz8Xi/ZxdXce/1Ustct5orEiIuel1udS0pECmF+Ya4o07HSvgzB8bD92fOfaz4Bu17U8pESMlxfm86MBiOrklbxUeVHtPa06l2Ox3Nm86gSGNgmH3/6sTNUVW1UVbV/APHPwNj7Ey3EOUqaSjAoBqaGTz3vucz4TArrC+mydOlQmRBjU1FDEamRqRiU4f3IlPBsz5RjysHb4M11k67TuxSPkRGTQVlrGc3dzXqXIoRnqymGA29D5rchKPr854OiIHGx1jyS0bUzyhs6+eSjzVSrEYRETyJn3WLuvX4avl7Gi55ntpoprC/0jF3WBuPlC4vuhRM7oXzH2c998FOt0XjdY/rU5gaykrOw2qxsKt+kdykez5nNoz3AVEVRkhRF8QFuBf438ABFUeIG/PIG4LAT6xHCI5Q0lZAYkoi/l/95z2XGZWK1WeWTXSFcpKO3g2PNx4Y1sjbQwPDs0qZSB1UnnKXP1sfG8o0sTlhMiE+I3uV4jP7co711MromxIhs/Rn4hsLCuy98zKy10HgMag+4ri43Zemz8dK2Yyz/9UdM6DqEJTaNN7+5gCnRwXadv79uPxabxfPyjgZKvwOCYj/NyQI4lac1IRfeDaHj9atNZykRKSSHJsvomgM4rXmkqqoV+B6wCa0p9KaqqgcVRXlCUZQbTh92j6IoBxVFKQTuAe50Vj1CeIrSptLz8o76pUen42PwYXe15B4J4QrFDcWoqKRGpY74Wv3h2U/lPiXh2W4uvzafBnMDK5NlZG0oZkfOxsfgIx9wCDESp/bAkRy48h7wD7/wcTNuAMUIB95xXW1uaP+pFj7z2x08u7GUrCm+TFRqmThnMQaD/dk+udW5eCleZMR48BCMtx9cuQ5MH8GJXdqKtE2PQFCM9vgYpigKWclZ7K3bS2VH5aVPEBfk1MwjVVWzVVWdpqrqZFVVf3b6scdUVf3f6X9/WFXVWaqqXqaq6jWqqpY4sx4h3F1rTyvVndXn5R318/PyIy0mTZpHQrhIUX0RAHOi5oz4WhKe7TlyTDkEeAWwZPwSvUvxKD5GH+ZEzZHQbCFGYuv/g8AomP+tix8XOA6Sl4zZ0bWOHis/fe8ga1/aSUuXhZe/nMFzC63akwmXD+laeTV5zImaQ4B3gBMqdaGMO8EnGF69EX4aBhV7YNoK8A3SuzLdrUpeBUB2WbbOlXg2vQOzhRAD9I+zpIQP3jwCbXTtSPMRGswNripLiDGrsL6QyaGTHTa6JOHZ7s/SZ2HLiS1cM/GaQceHxcWlR6dT0lRCp6VT71KE8Dxl27Udsxbdb98b/llrodkE1YXOr82NbC2pZdmvtvP3T8r5cuYkttx/FctnxUJlvpbvE59m97Xae9s52HjQc/OOBipZD9ZusPZ8+ljxm1D0pn41uYmEoATSo9N5r+w9Wf09AtI8EsKN9O+0Ni1i2gWPWRC3ANCW2AohnEdVVS0s2wEja/0kPNv97azaSVtvG6uSVuldike6POZy+tQ+CuvG1ptZIUZMVbVVRyEJcPlX7TsnJQsMXmNm17W69m6++/pevvr3fIL8vHjrWwt54sbZBPt5awdU5EPUjCGttMmvycem2jw776jfB0+AzXL2Yxaz9rgga3IWplYTh5skZnm4pHkkhBspbS4lyj+KSP/ICx6TEpFCiE+IjK4J4WQn2k7Q2tM64rDsc0l4tnvLNmUT6ht6plE/5hS9Cc/PhsfDtK9D/MT6sujLMCgGCuok90iIITmyURszWvJDLb/GHgERkHz1qB9dU1WVf+ad5PrntrPlYC3fXzqN9XcvJmNS+MCDoLIAxg8ttyivJg9fo6/Df9brorViaI+PMcsmLcPL4CXRASMgzSMh3EhJU8kFw7L7GQ1G5sfNZ3f1bll2KYQTFdZrKyec8YJSwrPdU5eli22ntrF00lK8jd56l+N6RW/Ce/dA6ylA1b6+d8+QGkiB3oHMiJghodlCDIXNBlufhIhkmPvFoZ076yZoOQFVozNr7Hh9B7f+cTcPvVPMjLgQcu5dzN3XTcXH65y3sU1l0N0y5Lyj3dW7SYtOw8fo48CqdXKhHdXG8E5rA4X6hnJVwlXkmHLos/XpXY5HkuaREG6it6+XspYyZkTMuOSxmXGZ1HTWUN5W7vzChBijCusLCfIOIjks2eHXlvBs97S9Yjtmq3lsjqypKmz+kTbiMNAwRh7SY9Ipri+mt6/XgQUKMYodfAdqD8DVj8BQG9cpq8DgPepG13qtNn77wVFWvvAxh6vbeOamObzxjUwmR11gJK0iX/uaYP/KowZzA8dajo2OkTWA6x4D73Oy+rz9tccFoI2uNZgbyK2R+I/hkOaREG7iWMsxrKr1kiuP4NPcIxldE8J5iuqLmBM5B4PinB+VEp7tfrJN2UT7R5Mena53Ka5h7YFj78OGH2gjah21gx83xJGHjOgMem29HGw86IAihRjl+qzw4VMQPQtmf3bo5/uHw+Rr4eC7o2Z0reBEM1m//Zjnthxh6cwY3v/+Em6dNxGDQbnwSZX54B0I0Zf+ELZffo3WcJofO0qaR6mfg8/8BkInAIr29TO/0R4XAFw1/iqCvYPZULZB71I8kjSPhHATZ3Zai7jwTmv9xgePJyEogd1V0jwSwhk6LZ0cbTnKZdHOy0AwKAbMN3SfAAAgAElEQVQezZTwbHfR2tPKjsodLE9ajtFg1Lsc5+lshP1vwL++DM8mw2ufhX2vQdxl2pvQwQxx5CEtRtvpSEbXhLBD4evQdByufRQMw3xrNmutNmZa6dl/59q7LTz23wPc/IdP6Oi28pc7LufF29KJDrYjA6oiX9tlbQjfv3dX7ybIO4gZ4+xvOLm91M/BfQfg8RbtqzSOzuJr9GVp4lLeP/E+XZYuvcvxONI8EsJNlDSV4O/lz4TgCZc8VlEUMuMy2VOzB6vN6oLqhBhbDjQcwKbaSI103E5rg5k1TgvPfr3kdQnP1tkHJz/AarOOvpE1VYX6I7Dj1/DXFfDLKfDut7Rg3jk3w21vwoMm+MLrsPLZ80cejL5DHnmI8IsgOTRZmkdCXIq1B7b9XBu1mj6C7z0pq8Do49Gja5sP1rD0Vx/x6u4T3Lkwkc33L+G6GTH2nWztgZriYYVlXx57OV4Gr2FULDxVVnIWXVYt41AMjTSPhHATJU0lTA+fbveITGZ8Ju2WdhkLEMIJiuqLAEiNcm7zCLTw7BCfEAnP1lm2KZsJwROYNW6W3qWMXJ8VTB/Dpkfht+nw4hXw/k+gtxOuegC+8SHcdwg+8wJMW/5pw+jckQcUiJ0zrE+u02PS2V+3X0JJhbiY/L9BWwVc+2NQLjKSdSl+oTDlem10zWZzXH0uUNvWzbdfK+CuVwsIC/DmP9+5kp98ZhZBvkNo6NQUa1vUDyEsu6qjilPtp0bPyJqwW0ZMBjEBMWwwyejaUEnzSAg3YFNtlDaX2pV31K//h52MrgnheIX1hSSFJhHqG+r0e4X6hnJfxn0Snq2jBnMDe2r2sDJpJcpI3sDpydwCxW/B21+HXyTDK1mQ90dt96bVz8F9B+FbH8M1j0BC+oXHYwaOPCy8W9vBqfnEkMvJiMmgw9LBkeYjI/yNCTFK9XbCx7+ExMWQfPXIrzdrrdaIqtgz8mu5gM2m8truE1z/3Ha2ltTxwxXTee/uRcydEDb0iw0jLDu3WgtMnhc3b+j3Ex7NoBhYnbyanZU7aepu0rscjyLNIyHcQGVHJZ2WTrvyjvqF+4UzI2KGhGYL4WCqqlJUX+T0kbWB1kxZQ2pkqoRn62RT+SZsqs3zRtaaTLD79/DKZ+AXk+Htr8HxrZCSBZ97FX5YBl96G674+vC2ap7/TUDRmlBDlBGtvYnbWzc6tw8XYsRy/wCd9dpYqCOa1tNWaGOmHjC6dqyunc//cRc/evcAc8aHsuneq/jO1VPwNg7zrWllPgTHQWiC3afk1eQR4RfB1LCpw7un8GhZyVn0qX1sNG3UuxSPIgOeQriBoYRlD5QZl8mrh1+ly9JFgHeAM0oTYsw51X6K5p5mp4Zln8ugGHgk8xG+sP4LvLT/JR6c96DL7i20kbVp4dOYHDZZ71IuztanBeKWZkPpRqg/rD0elQILvqdlpoy/fEiBsRcVOl5bzVDwCix5EPxC7D41LiiOuMA4CmoL+OKMLzqmHiFGC3ML7HwBpi6HCQ5a+eIXAlOXwqF3YflTww/fdrB391Xyi02lVLWYiQv1Y05CKFtL6wj09eIXN6dyc8b4ka/4rMgf0qojVVXJrc5lXuw8z11tKkZkavhUpoVPY0PZBm6bcZve5XgM9/iuIsQYd7jpMEbFyJSwKUM6LzM+E6vNKp/sCuFAhfWFAFwW5brmEWjh2Z+b/jkJz3axivYKiuqLWJm0Uu9SBtfTAYffg3e/C7+cBn9ZCjt/A4GRsPxpuGcffDcXlv4UJs53XOOo34LvQm+7tiPbEGXEZFBQWyBZXkKca9fvoLsVrv2RY687ay20V8OpXMded5je3VfJw+8UU9liRgWqWrvZdKiWOQmhvH//Em65fMLImzedjdBs0hrndjK1mag31zM/TvKOxrKs5CyKGoo40Tb00eyxSppHQriB0qZSkkKT8POyYyvSAdKj0/Ex+LCrapeTKhNi7CmsLyTQO5DJoa5fhXJ32t0Snu1iG8u1Jetu1TxqrYQ9f4HXboZnk+FfX9IaSMlXw2f/Aj88DneuhwXf0TKNnCkhHSYuhNzfa0HcQ5Aek05Td5O8MBdioI562PWS1uiJc/B49LQV4OXnNqNrP99Ygtlyfmh+bVsPkUG+jrlJ1ekPUIcQlp1XnQcgYdlj3MqklSgoZJdl612Kx5DmkRBuoKSpZEhh2f38vPxIi06T3CMhHKiovojZkbMxOnoFhx0kPNv1sk3ZXBZ1GQlB9mdlOJyqQtV++PBpePkqeH4mbLgfGo/BFV+DO97TGkY3/wXm3Az+4a6tb8F3oeUklAztz2RGjOQeCXGeHc+D1QzXPOr4a/sGwdRl2uiaDjsdqqrKkdp2Xt5+nM+/vIvq1u5Bj6tqMTvuphX5oBggPs3uU3Krc4kLjGN88DCy4MSoERsYy7zYeawvWy8f2NlJmkdC6Ky5u5narlpSwoeWd9QvMz6TI81HaDA3OLgyIcaeLksXR5qPuHxkbSAJz3adY83HONp8VJ9VR5ZuOLIZ1t8Hv5oJf1wC238OXv5w/ePwnVxtJG3F05B0FRi9XV9jv+krITwJdr04pNOSQpKI8IugoLbASYUJ4WFaK2HPn+Gy2yDSSUHNs9ZCRy2cdM2q9K5eKx8cruXR/xSz6Ocfsuz5j3g6p4S2bitBvoPH68aH+TuugMp8LffNN8iuw22qjbyaPObHzZe8I8Hq5NWcbD9JcUOx3qV4BAnMFkJnpc1atslwVh4BLIhbwAu8QF51HquSPWynICHczMHGg/Spfbo2jyQ823WyTdkYFAPLE5c7/uJFb8IHT0BrhRY8fd1j2tjZkU1wZKO2K5qlC7wDYcq1MP3H2oqBwEjH1zJSBiNkfgdyHoBTeXYH/CqKQlp0mjSPhOj30bOg2uBqJ35fn7Zca0If/A8kLnLKLU40drK1pI4PS+vZXdZIr9VGgI+RRVMi+d61U7h6ehRxof5nMo8Gjq75ext5YPnwXvOeR1W1TQRSsuw+paSphLbeNubFOiioXHi06yddz5O7n2R92XpSo1y3y66nkuaREDrrD8YdbvMoJSKFEJ8QdlXvkuaRECPUH5adGqnvC4iB4dlrpqwZ9vcHcWGqqpJjymFe7Dwi/R3csCl6E967ByynRzNaT8F/vqm9aQQISYC5t8G0ldqbO++h5d3pYu5t8OGT2uqjIewOlRGTwQcnP6Cms4bYwFgnFiiEm2s8Dntf1UZRwyY67z4+gVoD6dB/YeWzDgnR77H2kWdq4sOSeraV1lHW0AlAclQgX86cxLUp0VyeGI6v19n3WpOmjQP377YWH+bPA8unn3l8xJrKwNw8pLDsM3lHEpYtgGCfYK6ecDUbTRt54IoH8DbouMrXA0jzSAidlTSVEB0QTYRfxLDONxqMzI+bz+7q3aiqKktwhRiBwvpCEkMSCfML07sU7k67m03lm3gq9yn+vuLv8nfbwQ40HKCio4K7Uu9y/MU/eOLTxlE/1Qa+IXDnBoidA572/6dvEGTcCZ/8FppPQPgku05Lj0kHYG/tXvmAQ4xt254Bow8s/oHz7zVrrZZ7dGKnNvY6DFUtZraV1vNhaR07jzXQ1duHj5eBBcnjuGNhIldPj2LSuMBLXmdNWoLjmkXnqsjXvg4hLDu3Jpek0CSiA6KdU5PwOFnJWWw+sZldVbu4avzw/r6MFZJ5JITOSppKSIkYXt5Rv8y4TGo6a2RHGyFGQFVViuqL3GbZsoRnO1e2KRtvgzfXTbrO8RdvrRj88Z52bXclT2sc9Zv3TS2YNvdlu0+ZHj6dAK8ACc0WY1vtISj+N8z/JgTHOP9+U5dpI7EH3rH7FGufjTxTEz/fWMKKX3/Ewme28sh/ijlU1cZn08fz1zsvp/CxZbzy1XncsTDRrsaR01UWaL/P6Bl2HW7ps1BQWyAja+IsixIWEeobKq+17CArj4TQUbe1G1OriWsnXjui62TGZQKwu3o3iaGJDqhMiLGnoqOCpu4mXfOOzrVmyhrePvI2v8z/JUsmLCHEJ0TvkkaFPlsfm8o3sShhkeP/m9r6tDG0c1cegZZ95MlCE7QVDXv/AVc/BH6X/m/nZfCS3CMhPvwZ+AbDletccz+fAJi+Ag7/D1b9EoyDv+Vr6Og5s7ro4yP1tHVb8TIoXJEYwSOrUrhmejRTooPcd+VrZb62y5qdo3kHGg9gtprPvG4WAsDb6M3yScv53/H/0WnpJNDbDRqjbkpWHgmho+Mtx+lT+0a88mhC8AQSghLYXb3bQZUJMfb05x25U/OoPzy7ubuZl/a/pHc5o0ZBbQH15npWJTl4jMpmg//drTWOzs1N8PbXQrM9XeZ3oLcd9r1q9ynpMekcazlGS3eLEwsTwk1VFEDJelh4NwQML6JgWGatha5GKP/4zEM2m8r+Uy08v+UIN/5uB5c/+T4/+HcheaYmVsyO5fdfTGffY0t5465M7rpqMlNjgt23cWTtgZpiSEi3+5Tc6lwUFC6PsX/MTYwNWZOz6O7r5oOTH+hdiluTlUdC6KikqQSAlPCRNY8URSEzLpPN5Zux2qx4GeSvthBDVVRfhL+XP5PDJutdyln6w7PfKHmDtVPWSni2A2SbsvH38mfJhCWOu6iqaruR7f8/WPIgjJty/m5rqZ9z3P30kpAOk66E3X/QxtgusKJhoPRo7c3dvrp9XDPxGmdXKIR72fr/IGAcZH7btfedcj34BNFb+DabOqbzYWkd20vraezsRVEgbUIYP1g2jaunRzMzLgSDwU2bRBdSUwx9vUMKy86tziUlIsUtcg2Fe5kbNZeEoAQ2lG3ghsk36F2O25J3mELoqKSphEDvQBKCRx4kmBmXydtH3+ZQ4yG3yWwRwpMU1hcyJ3KOWzZf7067m83lm/lZ7s94ZcUr7vtJsAew9FnYcmIL10y4Bn8vf8dcVFVh849gz59h4T1w9cNartFoaBYNZsF34Z+3Qcl72uqGS5gTNQdvgzcFtQXSPBJji+ljKPsQlp0eW3MBVVUpqWnnw9I65hiuYFbhf7gvbynBAf4smRbFNSnRLJ4aRUSgj0vqcZohhmWbrWYK6wv54owvOrEo4akURWF18mr+XPxn6rvqiQqI0rsktyRja0LoqLS5lOnh0zEoI/+rOC9OC/+T0TUhhs5sNXOk6YjbNl5DfUO5N+Ne9tXt472y9/Qux6N9UvUJbb1tjh1Z+/Ap2PU7mHcXLH3CcwOx7TVtBUQkw64X7Trc1+jLnMg5EpotxhZV1VYdBcfBFV8b8eXe3VfJlc9sJemhDVz5zFbe3Vd55rnOHiubD9bw8DvFLHxmKytf+JhnN5byofciIpQOcm5Qyf/RUn59axo3zk3w/MYRaHlHwXFaFpsd9tXtw2KzSFi2uKDVyauxqTZyTDl6l+K2pHkkhE5sqo3SplKHjaBE+EUwI2KGNI+EGIZDjYewqla3yjs615opa0iNTOW5/Odo623TuxyPlW3KJsQnhIXxCx1zwY+fg4+ehbQvw4qfj/7GEWjhtJnfgYo9cCrPrlMyYjI43HiYLkuXk4sTwk0c3QKncmHJD7XMsxF4d18lD79TTGWLGRWobDHz4NtF3PvPfXzpz7mkPbGFu14t4L3CKuZOCOPZz6aS98h1PHbP98A3hKn1WzB62ljapVQWQEKG3YfnVefhpXiREWP/OWJsSQ5NZua4mbLr2kVI80gInVS0V9Bl7RpxWPZAmXGZ7KvbJy/OhRii/rBsd115BFp49qOZj0p49giYrWY+PPUhSyctxdvofekTLmXXS1qu0Zxb4DMvgGEMvay67AvgF6qtuLJDekw6VtV65u+aEKOazQZbn4DwRK2xPELPbirBbOk767Eeq41391dR29bNV65M5I1vZLL3x0v5/Zcy+NwVE4gO8dN2fpy+Cg6vB2vviOtwG11N0FQ2pLyjvJo85kTNIcA7wImFCU+XlZzF4abDlLWU6V2KWxpDr3KEcC/9YdmODL/NjMvEarPKaIAQQ1RYV8iE4AlE+LlwJ5xhmDlu5pnw7NKmUr3L8TjbT23HbDU7ZmQt/6+w6WGY8RlY8we7t4oeNXyDIOMrcPg9aC6/5OFzo+ZiUAzy80mMDYf/qwU6X/0I2NmoVlWVuvZucssaeSPvJE9lH+brr+zh2ue2UdXSPeg5CrDl/iU8vGoGCyaPw8drkLd2s9ZCdwuYto/gN+RmKgu0r3auPGrrbeNg40EZWROXtDJpJQbFIKuPLsD9UkGFGCNKmkrwUryYEjbFYddMi0nD2+DN7qrdLEpY5LDrCjGaqapKUUMRmXGZepdiFwnPHr5sUzZR/lEjH1vY/wasvx+mLoPP/tWuHcdGpXl3aSuPcl+GFU9f9NAgnyCmh09nb600j8Qo12eFrT+DqBSYc/N5T3f1Wimr78TU0Hn6awdlDZ2Y6jtp77GeOc7Hy0DSuECmRQdT19ZDx4Dn+sWH2TEON/ka8A2Fg/+BqUtH9FtzGxX5gALxaXYdXlBTgE21MT9uvnPrEh4v0j+SBXELyDZl87207zkkl3Y0GaOvdoTQX0lTCUlhSfgafR12TX8vf9Ki0yT3SIghqOqsosHc4NZ5RwP1h2f/5JOf8F7Ze7KlrJ3aetvYUbmDz0//PMaRrBI68A789zuQdBV87lXwGgXBs8MVmgCzboK9/4CrH9LG2C4iIyaDfx/5N5Y+i2PGBoVwR0X/gsaj1K36CwePNFLW0ElZfceZZlFN29mriBLC/EmOCmRtegLJkYEkRQWRHBlIfJj/mZyi/syjgaNr/t5GHlhux+p1L1+YkaWNrmU9r/3a01XmQ/QMu3ewy6vJw9fo6zE/54W+Viev5pEdj7C/bj/pMel6l+NWpHkkhE5Km0qd8gnIgvgFvLD3BRrNjYzzH+fw6wsx2hTWuX/e0bnWTFnD20fe5rn857h6wtWE+IToXZLb++DEB1hslpGNrJVkwzvfgAnz4QtvaHkiY92C70Dxm7D3VVj4vYsemh6TzmuHX+Ng40HmRs91UYFCOIeqqjR29p5uCmmrh07WNfOT8sepsyVzwzt+wB4AQvy8SI4KYuGUcSRHBpIcFURSZCCJ4wLx97l0M3tNmraj2C82lVLVYiY+zJ8Hlk8/8/glzVoL+/8Pjn8I01cM97fsHlRVG1tLybL7lN3Vu0mLTsPHOIab/cJu1028Dn8vf9aXrZfm0TmkeSSEDhrNjdSZ6xyad9QvMy6TF3iB3OpcViU7cCtqIUapooYi/L38mRY+Te9S7NYfnn3r+lt5af9LPDTvIb1LcnvZpmwmBE9gduTs4V3g2Pvw7zsgNhVuexN8Ah1boKeKT4NJiyD3DzD/Wxcd4UuP1l6EF9QWSPNI6ObdfZVDasKYe/swNXSeaRKZGjo53tCJqb6Dtu4BY2ZGA3cHf0isWse2GY/y7JTLSI4KJCkykIhAnxGPGK9JS7C/WXSupCXgF6aNrnl686ipDMzNdodlN5gbONZyjNXJq51cmBgtArwDuGbCNWwq38TD8x6WlbIDSPNICB2UNmtBt47caa3fjIgZBPsEs7t6tzSPhLBDYV0hs8bNwsvgWT8SB4Znr52y1inN6NGiwdxAXk0eX5v9teG9gTN9DP/8IkROhy+9DX6y0ussC74L//wCHP4fzL7pgoeN8x9HYkgie+v28jW+5sIChdCcO/5V2WLm4XeKsdlUrkiK4PiA8bL+ZlFV69ljZnGhfiRHBXLD3HiSI4NIigpkcmQQCUEqxt+ug0lXcuutd4A75dF5+Wija4f+B5Zuz141eSYs277m0Z4abfXX/FjJOxL2y0rOItuUzceVH3PtxGv1LsdteNYrZSFGif5dkqaHO/7NntFgZH7sfHZV70JVVQnTFeIiuq3dlDSVcPus2/UuZVgkPNs+m8o3YVNtwxtZO5UHr38ewibB7e9CgHvvyKeLaSsgIlkLz5619qJvmjNiMthcvpk+W9/IsqeEGKJeq42ncw6ft+W92dLH/f8uPOuxYF8vkqMCmZ88jqTIwDMriJIiAwnwucDbpx2/ho5auOUV92oc9Zu1Fva9Bse3QooHf7hYkQ/eAVoguR1yq3MJ9g5mxrgZTi5MjCYL4hcQ4RfB+rL10jwa4JLNI0VRLgcWA/GAGTgAbFFVtdnJtQkxapU0lRAbGEuYX5hTrr8gfgHvn3yfk+0nmRQyySn3EGI0ONx0GKtq9dgQzVDfUO7LuI/HPnlMwrMvIseUw9TwqUwJH+LullX74bWbITgG7vgfBEY6p0BPZzBA5ncg+wdas23ihT/hz4jJ4O2jb3Os5ZislhMO1dljpbLFTGWzmYrTX7Vfd1HZYqauvQdVvfD5z9w050wWUWTQEMfMulthx/MwZSlMWjDy34wzJC0B/whtdM2Tm0eV+dq4rJ27XObV5JERm+Fxq4uFvrwMXqxIXMFbR96irbdNsiVPu+DfIkVRvgLcDZiAAqAU8AMWAQ8qinIA+LGqqiddUagQo0lpUykp4Y4fWevXv+X4rqpd0jwS4iI8MSz7XDdOuZG3jrwl4dkXUNlRSWF9IevS1w3txNpD8OoabUTt9v9BcKxzChwt5t4GW5/UVh9dpHnUHz5aUFsgzSNhN1VVaemyUNlipqK5i4ozjaHTX1vMtHRZzjrH26gQF+pPQpg/i6dGkRDmzyu7ys87DrQdz26dN3H4Be56Ebpb4NofDf8azmb0hhmfgQNvg8UM3v56VzR01h6oKdby1exQ1VHFqfZT3JZym5MLE6NRVnIWr5e8zvsn3uemqRceyR5LLtaCDQCuVFXVPNiTiqLMBaYC0jwSYgjMVjOmNhNLE5c67R4TgicQHxjP7urd3Jpyq9PuI4SnK6wvJCEogUh/z11RMjA8+8V9L/Lw/If1Lsmt5JhyAFiROISQ2Iaj8I8bwctPW3EUNsFJ1Y0iPoFw+Vdg5wvQZIKIpEEPiw+MJzYwlr11e7lthryhExqbTaWuvYfKlkEaQ6e/dvWePW7m720kIdyf8eH+zJ0QRkK41igaH+5PQlgAUcG+Z7a675cUGTj8Le8vpLNBax7NvBHi3TwIftZa2PuKtgHAjM/oXc3Q1RyAvl67w7Jzq3MBnLK7sRj9ZkfOZlLIJDaUbZDm0WkXbB6pqvrixU5UVXW/48sRYvQ71nwMm2pz6sojRVHIjM9kS/kWyZUQ4gJUVaWwvpArYq/Qu5QR6w/P/mfpP7lp6k2yomOAHFMOqVGpjA8eb98JTSZ45QZQbXD7Bi3LR9hn3l3wyW8h92VY+cyghyiKQnp0Onk1eZLL5+GGsmtZr9VGTWu3tmpokMZQdasZS9/ZM2VhAd4khPmTFBnIoqmRZzWGEsL9CQ/wHvKfnxFveT+YHc+DpQuueXT413CVxMUQME4bXfPE5lFlvvbVzrDs3JpcIvwimBI2xJFlIdB+Xq1OXs3v9/+ems4aYgNlBfLFxta8gK8Ba9HyjgAqgf8Cf1FV9fw1n0KISyppLgFw+pu7zLhM3jn6DocaDzEnao5T7yWEJ6rprKHeXO+xeUfnkvDs8x1vOc6R5iM8NO8h+05orYB/3KC9EbxzA0RNc26Bbmio25ifJSQeZn8W9r0K1zwMfqGDHpYRk0G2KZtT7aeYGDKCUSGhm8F2LXvw7SIOVbcyMSLwvJVDte3dZ+UNKQpEB/uSEObPZRPCWDUnTltFFOZ/ZgVRoK9zMmpGtOX9udqqYM+fIfVWiPKApr3RC2bcAEVvQm8X+AToXdHQVORDUKz2veYSVFUlrzqPebHz5OehGLaspCxe2v8S2aZsvjr7q3qXo7uLfVd+FWgBHgcqTj82HrgDeA34vFMrE2KUKm0qJcg7iIQgB71wuYD+Jbq7q3dL80iIQRTWa3lHo6V5JOHZ58s2ZWNQDCxPXH7pg9trtRVH5ha4/b8QO9v5BbqZ/+yt4OH/FNNtsQGfbmMO2P9mO/M7UPQv2PsPWHj3oIdkxGQAWu6RNI88g6pqY2XlDZ2caOziifWHztu1rMdq448fmQDwMijEhfmREObPlVMiz4yX9TeHYkP98PUaBauiP/oF2Prg6gf1rsR+s2+Cgr/BsS3aqJ0nqczXRtbsaAaZ2kzUm+tlZE2MyISQCaRGpbK+bL00j7h48yhDVdVzP3KrAHYrinLEiTUJMaqVNJUwPWK60z8FifCLICUihV3Vu/hG6jecei8hPFFhfSF+Rj+mRYye1SU3TrmRt45KeDZob3ZzTDlcEXvFpTOtOhu1jKP2GvjyfyAhfcT3H9EKHjupqkqP1UZnj5XOnj46eqx09lq1r6f/6ejpo6vHSkev9ezjzjyvPdbZY6W9x3rePcyWPn74VhEflNQRH+pHbKgfcaF+xIX6ExfqR2SQL4aBuTLxc7XRmN1/gPnfHnRHpOTQZMJ8wyioLWDt1LUO/W8ihq/PplLVYuZEYxfljZ2caNQaRScauzjR1HmmqXgxCvDJw9cSHex3Xt7QqNNk0pqkGXdCeKLe1dhv0pUQGAUH3vGs5lFXEzSVQdqX7Tr8TN5RrDSPxMhkJWfxVO5TlDaVjvlYgIs1j5oURbkFeFtVVRuAoigG4Bag2RXFCTHa9Nn6ONJ8xGWha5lxmfzf4f+jy9JFgLeHLU0WwsmK6ouYOW4m3gZvvUtxGINi4NH5Ep4NcLDxIKfaT/H1OV+/+IHmZnj1Rmg2wRf/fdGdwuw12EhP/wqeVXPizjRtunoHa+RY6Rzw+KfP950+5+wGkNV2kb3HB/DxMhDk60Wgr5FAHy+CfL0IC/BhfHiA9pivF3/bWT7oub19NooqWth0sJte69kNBC+DQkzI6YZSmNZQmhd2M9eXr6N8xxsEpN1yXoNJURTSotPYW7d3GP91xUj0Wm2cau7i5JkGUdeZJtGp5q6zcod8vAxMighg0jgtcyhxnPbvieMCufVPu6hq6T7v+vFh/sSFeuAuXsOx7RkweMHiH+hdydAYjFrTaN//QW+nFnbvCSoLtBiY/vkAACAASURBVK92hmXnVecRHxhvf96dEBewPHE5z+Y9ywbTBmkeXeS5W4GfAy8pitKM9mFCGLD19HNCiCE61X4Ks9XM9HDXfOPJjMvk7wf/zr66fVyZcKVL7imEJ+jt6+Vw02G+NONLepficKMtPHu4K3iyTdl4Gby4buJ1Fz6opx1euxnqSuAL/4Skq847RFVVui022nssn67Q6e5v8gz499OrfDp6LLxXWIX5nFUaZksf9/5rP/f+y779RrwMCoG+Xp82fHy9CPbzIjbE7/Tj2mOfHuNFoM+5jxnPPOdtNFzynpsP1lLZcv4muwlh/mx/4BpUVaWps5fq1m5qWrupbjVT3dp9+h8zxacbTH+yjuMDn1jaPvgVV+eE42UwnGkwxYb6ER/mT58tiVPtH/LhsWPMjB5PZND5O2OJ4enqtXKyqYvyhi5ONnVSPqBBVNViZmC/MdDHyKRxgaTEBbN8duyZZlFiZAAxwX5nryob4IfLUxy/a5knqTusjWcuvBtC4vSuZuhmrdWymo5s0sbYPEFlAaBAfNolD7WpNvJq8rh24rWSdyRGLMIvgisTrmRD2QbuTb8Xg3Lpn6ej1cV2WyvndK6RoijjTj/W6JqyhBid+sOyUyKct9PaQOkx6XgbvNldvVuaR0IMcKjxEBabZdTkHZ1rYHj231f83WNf6FxsBc/FGkgWq5Uc00auiF5IfauR8p4WOgas4unosdLd1c7K/XcT317EP8Y/Qe7ucDq25561Eqj99Fd7FvcoCgT6aA2bcxtHA31/6bSzGz6nGzwBPmc3iny9DC5/0/PA8ukXbQgoisK4IF/GBfkyO2HwMGxVVWnustD9yQmSdz7KH5ZYKVJSqGntpqrVzIHKVrYcqsXi5U9gEnzjX29ibb/szAqmT8fiPh2N6284XazB5IoxQWcYbt2tZsuA1UOfjpeVN3ZS195z1rHhAd5MHBdIxqRwbkofz6SIABIjtSbRuECfYf05c8quZZ7kw5+BTxAsuk/vSoZn4gIIitF2XfOU5lFFPkTPAN/gSx5a0lTy/9m777CozuyB4987hd57tWDBgqigiCaaWFIsscTEmLZJdld3U01Z0zWuyaZsqumb/NITNVWNNYkaTZEiRRAR7IUqSJU6zNzfHxcQpSPT4P08zzwjd+7ce0CYcuY951BWW0aUX5QJAhN6g1khs9idtZuEvASi/Hvv71WbYwwkSRoCzAEC67/OBjbIspxhgtgEocfJOJuBRqUx2chQe409o31GE5MTY5LzCYK1aGiWHe4dbuZIjOOC5tlHNzJnoPX0tZBlmSqdnrPnavnPloPNmvJW6fQ8tW4/uw8VKAmh6vN9fs7VrwKq1hzCoW8Bp49M4+dfdzc7hy21fKh9lQBVGk9ID/Bn4TAcbc81ru7xd7VrTPA4Na7kUTffZnf+3w5adeMqjcte3NnqCp77pw4yzg+uG3RHQkCSJDwcbeCKuyDpFa4t+45rb/rygn1kWabgXBUzNnzM5RGVTHAPI6+0itwSZRVTQ4Kp5qISObVKwtfZFn83peGy0oPJntNFFayJP924f5cafZtBW8nROaMCOFtR21hWduJsJafOnl9FVFx54dBjH2db+no6MGmwN/08Hejj6aiUmXk44upgnNLcbp1aZk2yk+DgRrjicXDwMHc0XaNSw7C5kPQZ1JwDWydzR9Q2WVZWHg2Z0aHd43PjAUSzbKHbXBF8BQ4aBzYd2ySSRy2RJOkx4GZgLRBfvzkIWCtJ0lpZll80QXyC0KNkFGcwwHUAWrXpeqxE+0fzZvKbnK06i6e9p8nOKwiWLLUglQDHALwdvM0ditE0NM9+LfE1JveZbLbm2XqDTEllLcWVtZw9V0tRRS1FlbUUnau/rjh/Ka6o5WxFbbOkwcUqavUknCxq7N3j4WhDsIcDTjZKQmdf1c8cq7Ll6ck34G7v2CQBpMFJY8D/p8XYHtuPPOcd/ju6+0sX21vBY8m6LSFg4wiRd8EfrytNbj1CGm+SJAkfZwcifEaRX53O7dF9m91dlmVKKnXklFbVr1qqVhJMpdXkllSTnlPG9hYSTA0aygSfXLcftUpCrZLQqCRUUv11/dfqxosKtQrlWgKNStXktiYXSUKtVq41Ld3eZB9Nw7+bHrvJOV7ffrjF5OjS71J4en0a55o0MJckCHC1p6+nA9eG+df3H1JWD/XxcDDaWHuhBTufA3t3GH+vuSO5NMPnQfz/4NA2GHGDuaNpW9ExqCqCwI71O4rNi6W/a398HHyMHJjQW9hr7JnWdxq/nPyFp6KfwlZta+6QzKKtZ5q/AcNlWb7gow1Jkl4DDgAieSQInZRZlMmEgAkmPWdD8ig+L57p/aeb9NyCYKlSClKI8Ln0iVqW7OLm2UNt/tItJSZVtXrOVtRQXKFTruuTQsUXJYIaLiVVOuRWyr4aEj/ujjb4utgx1N8FD0cb5eJgw4vbMiiqqG12v0A3e35/dEqLx9TpdUz+Np6r+k3hzvEXTdLT18H3f4VjP8PMV5GMkDgCUdLTKGox7HkL4v4H019qdnOEbwTv7nuX0ppSXG0vLIOTJAn3+t+N4QGtl8iVVOqIePYXWqssvCWqD3pZRm84f6kzyBjqr/WyjF5fv01uepsBvUGmpk6v3E+WqdNfvM+Fx216rKbn7CydXuaWqMDG3kN9PBwJ9rDvGaPtrd2JP+HoDrhqJdhZ+TTL4HHg7K+Urll68qgTzbJ1eh1J+UnMGWA9K24F6zArZBY/Hv2R3ad3c3W/q80djlm0lTwyAAHAyYu2+9ffJghCJxRWFVJYVWiyfkcNhnkOw9nGmdjcWJE8EgQgryKP/Mp8Rvr0zH5HTTU0z16TsZbo09l8XbuVANtCciq9eGPdQmTD3VwxxKeFpE8NRRU65bpSuW5IFrU2rlutknB3sMHDUYuHow2hfs71iSBbPBy0eDjZ4uFg05gccnfUtvtm2Eaj6vQKnpjcGEprSpnR/6LyBoMBNtwD6Rvg6v/A2HamsF2iXlvS05SLP4TNh6Qv4MonwN7tgpsjfSORkdl3Zh9XBF/R6cM3JJgC3OxbLRN8etawLoffHWRZxiDTJHFlwGCAOoOBmW/+QV5Z84llgW72/HtOmBmiFdoky7DzWXDyg7GLzB3NpVOplNK1hI+husyyk2FZCaB1AO+h7e6adjaNqroqUbImdLsovyi87b3ZdGyTSB614EFghyRJh4HT9dv6AAOB+4wdmCD0NJlFmYDpmmU3UKvUjPMbR0xODLIsi6kTQq+XWpAKQLhXz+x3BGAwyBScqyGnpIohtgvQ6tdR4f0LAbmFqIAgqZCV8gc8/r2BhwyXt3gMRxs17o42eDra4OVky2BfZyX546Rsc3ewwdOp/trRFmc7TauTmbqqKyt4thzfgouNC5cFNBkSIMuw6UFlOtKUp2GCeBljMuPvgdS1kPQ5XPbABTeN8BqBRqUhMT+xS8mjBpZcJihJEmqJJo2+zydMH5/eyyeWWZsjO+BUDMx4BWwczB1N9xg+D+LeU0rXwheYO5rWZScqU9bU7ZdnxubGIiEx1m+sCQITehO1Ss30/tNZnbGakuoS3Ozc2r9TD9PWtLVtkiQNBqKob5gNZAN7ZVnWt3Y/QRBallGk9Jkf7D64nT27X7R/NNtPbedU+Sn6ujTvLSEIvUlKQQq2aluTJ3K7S8M0q5ySqsYR6Tkl9WPTS6rJLqkiv6yaOoOMF6WMUB3jEY+zvODtzKQ+gZSpVPjV6VlSXMKj5d8w6urFeDrVrwZqkhCy01pGiUxnVvBU1VWx89ROZvSfcb63nCzDtseVxrATH4FJS40YrdCM/0joN1EpXYu+G5r0/LPT2BHmGUbimcRLOoW1lglaa9y9kizDzpXg1gci7jB3NN0naCy4BCmla5aaPKqrgbxUGPfPDu0enxvPEI8hzUphBaE7zAqZxefpn/PzyZ9ZEGqhfzNG1Gb6VpZlAxB78XZJkpxkWT5ntKgEoQfKLMokwDHALE9m0QHRAMTmxIrkkdDrpRSkMMxzmEkb13dGebWO3FIlCZRbclFyqD5ZdHHpmFYtMdi5lgkOp7nD6TiD7I8QWJWBY3U+ABtlByTZiVK1khDK1WpY4eXBMxTxV+8MGDjtgjf11mp31m6q6qrOl+jKMmxfAXHvQ/Q9MGWZWePrtcbfB2tuUkoGL+qtEukbyWcHPqOqrgp7jX2XT2GtZYLWGnevc/BHyE2Bue+Bxsbc0XQflQqGz4X4D6CqpFlpqUXISwN9LQRGtrtrVV0VKQUp3Dr0VhMEJvRGQzyGMMB1AJuPbRbJo05IRylhEwShgzKKMwj1MM9S9D7OffB39Cc2N5abhtxklhgE4WLrk7NN/ol7rb6Wg2cPcvOQm7t8jEuJu1qnb1wx1NrKofImE5YAVBL4ONvh72bHMH8Xpg7xoa+jjsGGYwRXZ+JRkoZtQQpSySloaJ/iORAGTVKW+QeM5s3ti5EvKlmtVql4092NWWsWgoMnhN0AI2+CgAhltJMV2npsK9723ozxrW+quvu/8OcbMOavcM3zVvt9Wb1BVyu/kzFvKz2Qmvw/RPhG8FHaR6QWpIoeJYJlMuhh53/AKxTCe+BrqOHzlL/NzK0wquvPjUaTnaBcd6BZdvKZZHQGHVF+vXeUumBckiQxa8AsViWtIqs8iyDnIHOHZFKtJo8kSXq4tZsAJ+OEIwg9U6WukhOlJ5jezzwNqyVJaixd0xv0qFWWUY4i9F7rk7Mv6PWRXVLFY9+nUlxZy8wR/vVjtVWN466bjte+FBlFGdQaarvcLLuluJ/4YT8AM8P9ySutbpYQarpqqKXJYZ6ONvi72dHX05HxIZ4EuNnj72ZPgKsd/m72+NjUoj2TBjnJyuVYMhQdPX8A937KJ7Jj/64ki/xHgt2FKxzzNS3/3PK0GrjlG0hZA4mfKmObvQbDyIUwYgG4BXfp52QOZbVl/J79OzeF3qQ8xv25CnY9DyNvhhmvisSROalUysqvzQ/DqVjoO77xplE+o5CQSMpPEskjwTKlfgOFmXDjZ9ATXz8FRoJrsFK6ZonJo6wEpUm5S/sf0sTnxqORNET6tr9KSRC6akb/GaxKWsWW41tYHL7Y3OGYVFsrj54HXgbqWrhNZZxwBKFnOlxyGBnZbCuPAMYHjGfdkXWkn01nhPcIs8UhCKVVOp758cAFTWIBauoM/HtjOv/emN7qfSUJNCrpfHJJJTX5WqpPNrWwvX7/Yu0O0MAnOw2slfYq29USapWqhf3rt6vPf/3pnhPN4q7S6Xn4m3089M2+ZiPpXew0SjLI1Y6RwW5KQsjVHn83OwJc7fFztbuwt1BtJeTtV5JER+uTRYWHoGEQuUsQBIyCUbc0rirCwaPdn7mfoz+5FbnNtqslDfvcfBl146dKyUL6ekj5GnashB3PQr/LleTLsNlg69zuecxpx8kd6Aw6pWQt/kP4Zbnyifrst5XkhWBeI29WJlXFvH1B8sjFxoVQj9BL7nskCEZRVwu7XlCS8kNnmzsa45AkpXQt9n2oKgZ7d3NHdKHsRGXVUQc+AIjLjWOE9wgctD2koblgkQKcAojwiWDTsU0sGrGoVw0jait5lASsl2W52bO5JEnGnW8rCD2MuSatNdWwhDc2N1YkjwSTk2WZuONFfLP3NFvSclsd9w7w/LwR6A0G6hpHW9df6+WWtxsMTW5vYXv913qDTKV0FI3BnYpKJ8oM1Y33afV++gu3G+SWYzbI8MDUQQS42hHgZk+Amx1+rvY42bbxNFtXA/n7zq8oytkHZw5Cw0wKJ1+lhCxsfn2iaBQ4+XTp578kYgkr9qygWn9+LLhWpcVB48DtW29nweAFLIlcgkvknRB5JxSfUD5tT1mjjLff/AgMnaWsSOp/ZYcm3pja1uNbCXIKYsSpZNjyLwidCdd/aJGx9ko2Dkr54O+vQdEx8AhpvCnCJ4J1R9ahM+jQqqy/95bQgyR/DiUnYearPTsJPfx62PMWZGyB0RbUL6iySFlpO/q2dnctqy0jvSi9160EEcxj1oBZrIxZSXpROsM9h5s7HJNp6xXVXcDZVm5rv+hUEIRGGUUZONs44+/ob7YYPO09CXUPJTY3lkXhi8wWh9C7nCmv5vvEbL5JOM3xwgqcbTXMjwji5/R8Csprmu0f6GbPLeOM11Lv6u+WM947ileuuKz9nVsw4cUd5JRUN9se6GbPw1e1MUlRr4Mz6U0SRcmQnw4GnXK7g6eSKAqdcX5FkUv3PV7MDJkJwKqkVeRV5OHn6MeSiCVMDp7MO/ve4cuDX7Lz9E4ei3qMa/peg+TeD654VJlMlrVXSSKl/QD7v1WSWiNuVFaS+IV1W4yXorCqkLi8OP7mMwFp4xIYMAVu/KRHNAHvUcYugj/fVFY4zPhv4+YI3whWZ6zm4NmDhHuHmzFAQWhCVwW7X4bgaGWoQE8WMBrc+iqla5aUPMpOUq470O8oMS8Rg2wQ/Y4Ek7i679W8EPcCm49tFskjAFmWMwEkSbKTZfniV8r6Fu4iCEIrMosyGeIxxOzLGqP9o1mdsfqSp9oIQlvq9AZ2ZRbwdcJpdmacQW+Qiervwf1TBjI9zB97GzVj+3lc0DsIwF6rZuk1xivtPFN5htyKXG4fdnuXj/HoNUP4Y927PMhaAqRCcmQv3mAhl19zz/md9HVKqVljoiipflpMfbLMzlV5oT7hvvOJItdgo/fkmRkyszGJ1NTSsUuZGTKTf8f8m6W7l/Jj4I88Ff0UgU6BSkzBUcrl2hfh0E+QslYZux7zNviG1fdHuhGc/Ywaf1t+PvEzBtnA9KTvlVK7m74Cja3Z4hFa4eKvTFtL/hImP9k42amhP0lSfpJIHgmWI/5DOJcHN3zc83umSdL5xtmVRR0qhzaJ7ARAAv9R7e4alxeHndqOkd5d62koCJ3hauvKpKBJbD2+lYcjH0aj6h2rnDvyXe6VJGmRLMuxAJIkzQdeANr4iFUQhAZ6g55DxYe4YfAN7e9sZOMDxvNZ+mck5yczIXCCucMRepgThRV8k3Ca7xKzOFNeg5eTLYsmhrBgTBAh3hfOWWiYTmbKaWupBakAl/TmdK76T2Zp/w9NfflXkFTIS6oPUWdXQZ6NkizKSwVdpXIHG2el3Gzc4vOJIvf+FvdGZJjnML6a8RVrMtbwVvJbzNswj3tH3cutQ289/4JIY6v0Pho2GyrOwoEflETSz08r/YVCJiurkYbMVEqUTGjrwTUMrNUxyGck3LzG5OcXOiH6HmUlW9JncNkSALzsvejr0pfE/ETuDLvTvPGZ2OZjm5utCGwpySuYWHUZ/PG6soqxX9dWqlqd4fOU6ZQZmyDiL+aORpGVAN5DwM6l3V3jcuMY7TMaG7WNCQITBJgVMosdp3YQnxvfa95XdSR5dAvwsSRJu4AAwBOYYsygBKEnOVl+kmp9tVn7HTUY7TMarUpLTG5Mr3mQE4yrWqdnW1oea/eeIvZYESoJJof6cNPYYCYP8UGrbr1HxNzRgUZNFl0spSAFrUrLUI+hXT/IjpWNiaMGakMNJHwEGnulqWrEHecTRZ4DraZPhkal4fZhtzOtzzSej3ueVxJeYdOxTTwz/hnCvC4qT3P0hKhFyqXwsJJESv0afvg72DjBsDnKiqS+lxv9+89J/4F95Sd4QHKGW7+1+MbevZ5/OPSfpKxei76nsbQw0jeS7Se3Y5ANqCTr+Ju5VJuPbb6gF1luRS4r9qwAEAkkc4t9F6qKYMoyc0diOv4jlQ83DqyzjOSRLCvNsofMaHfXwqpCjpQcEX83gklNDJqIs9aZTcc29Zr3Ve0mj2RZ3i9J0n+AL4ByYJIsy1lGj0wQeghLaJbdwEHrwCifUcTmxpo7FMHKpWWX8k3CadYnZ1NWXUdfTweWXhPK/Igg/FztzB1ei1IKUhjmOezSPpUsbe3pT4InsnpEc2Z/J3/enPImO07t4IW4F7h1y63cPORm7h99P45ax+Z38BoEU5fB5Kfg1B5lVcmBDbDvK2U6XPgCJZHkbYSSxJMxbN3+L3B15NrZnzSWQQkWbvx9sHoBpG9QythQmmb/cPgHjpQcYbB771jcvipp1QVN7AGq9dWsSlol3gSbU2UR7HkbhsyCwAhzR2M6kgRh18MfbyirSx09zRtP8XElgRfYfr+jvXl7AaU9gyCYiq3alqv7Xc2W41t4Wvd0r5jy1+5HO5IkfQQ8CISjNNHeJEnSvcYOTBB6ioNFB9GqtIS4hrS/swlE+0eTUZRBUXWRuUMRrExplY4vYk4w663fmfXWH6zde5opQ3xYvWgcvz5yJfdOHmixiSOdXkf62fRL76fi7NvydtegHpE4aiBJEtP6TmP93PUsGLyA1QdXM2f9HHae2tn6nVQqpd/QnHfgX4dg/kfgMxT+XAXvRMEHVyqrTSoKuyfIrET46ka2OjkR7jGUYB/LaN4tdMDAq8BzkNJfRVZGGEb4Km/Sk/KTzBmZSeVV5HVqu2Aif7wOtedgytPmjsT0hs9TJn5mbDR3JMpjPHSoWXZcbhzOWmeL+KBW6F087Dyoqqti3OpxXP3d1Ww+ttncIRlVR9YF7wcmy7J8XJbln4BxQC9KwwvCpcksymSg20C0FjL1p+FTmfjceDNHIlgDWZaJPXaWh77eR9R/trNswwEMBlg5Zzh7n5zGGwtHM2GAFyqVZfXwuVhmcSY1+ppLa6SprwN1C8kxrT1MXd7141owZxtnnop+ii9mfIGLrQtLfl3Ckp1L2n9za+OgrCi57Tt4+CBc8zwY6mDro/BqKKxeCAfWg6755LoOyU2FL+dxzMmdTLXM9AGzu3YcwTxUKhh/j9Ij7FQMAEFOQfg4+PSq5JGfY8tN5r3svUwcidCoLFdplB2+QEl+9za+YUq5ddoP5o5EaZatdQDv9v8f4nLjiPSL7DVNiwXLsPnYZr5I/6Lx64bS456cQGo3eSTL8huyXP+xkPJ1qSzLfzNuWILQM8iyTEZRBqEexpsg1VnDPYfjrHUmJjfG3KEIFuxMWTXv7jrC5Fd2sfCDWLYfzOfGMUFsuv9ytiyZyF/G98PVwTISoh2RUpACcGnJo99fhZITELVYmY6GpFxf96byRqMHG+k9kq9nfc1DkQ+xJ2cPczfM5auDX6E3dGD4qrMvjL8X/vkH3L1H6XOTkwzf3gGvDoaND8Kp2MYVKO06kwFfzAUbJ7aMXYhKUnFNv2su7RsUTC98Idh7QMw7gLLaLdInksT8ROSO/i5YuX96RCJd/L3KMuXVxcTkiOdos/j9FTDo4MrHzR2JeTRMXTvxO5wrMG8sWQnKlLV2VvVmn8sm61yWKFkTTK6t0uOeqtXkkSRJGyVJuk6SpGbvDiRJCpEkaaUkSX81bniCYN0Kqwopqi6yqGW0apWaKP8oYnJies0LdKFj6vQGfknP5++f7WX8izv577ZMfF3seG3BSOKfnMZzc0cQFuhq7jC7JKUgBR8Hn1Y/6W9XdhLsfkkZST/jZXgoDVaUKNc9PHHUQKvS8tewv/LDnB8Y5T2KF+Nf5Patt5NRlNHxg/gOh6ufhYfT4bYfYPC1SqPtj6+BN0fDry9A0bHW73/2KHw+B1Qa5L/8yNbcPYz1HYu3g/elf4OCadk4wJi/QsZm5f8VpXTtTNUZss71jtaahw9vRJYkPOv0SLKMv66OpUXFBOl03L39btZkrBHP06ZUfAISP4PRt4OHZbQaMIvh80A2wMEfzRdDXY0yuTQost1dG1bSR/lFGTsqQbhAbyw9biuVuwh4GHhDkqQioACwA/oBR4G3ZVneYPQIBcGKNbypCnW3nJVHoJSu7Ti1g9Plp+nj0sfc4Qhmdrywgm8STvNdYhYF5TV4O9uyeFIIC8YE09+rhQbJVii1ILXrq450VbDuH+DkqySOerlg52Dem/YeW49v5aW9L7Fw00JuH3Y7d4+8u+PNIlVqGDhVudSUw8FNSqPt3S/B7hchOBpG3qS8iTn8C+xYqTQrl1SgsYNFO0inhlPlp/hrmPgcy2pFLVJ6YsW9DzNeJtJXeaOYlJ9EsHOwmYMzrqT8JL6yhZtLy3myqPiC2+aXV/B45Cyej3uew8WHeWLcE2hV1rPS02rtekl5jLniUXNHYl4+w8BrsDJ1bayZik3y0kBf26Fm2XF5cXjYeTDQbaAJAhOE8/wc/cityG1xe0/VavJIluU84FHgUUmS+gH+QBVwSJblSpNEJwhWLrNYmbRmSWVrcL7vUWxurEge9VLVOj1b03JZG3+auONFqFUSk0O9uWlsH64M9Uar7jmjsgurCsk+l83NQ27u2gG2r4DCQ3D7erB379bYrJUkScwImcFlgZfxeuLrfHrgU345+QtPjXuKiUETO3cwW2cYdbNyKc2G/d/AvjWw6SHY/C9lH1l//lqug7z9bKk5iUalYVrfad37zQmm4+ynrOZL/hImP8kAtwG42LiQmJ/InIFzzB2d0VTVVbF8z3IC9DIPFpc0u91RhjcSNvJW8GA+OvQtJ0qP8dqVb+BmJ6YJGk1BJqSuVcpqXQLMHY15SRIMvx5++y+U57c+KMKYsjvWLFuWZeJz4xnnNw5Jsuzei0LPsyRiCSv2rLigdM1ObceSiCVmjMq4OvTuQJblE7Isx8iyvE8kjgSh4zKKMgh0CsTZxtncoVygr0tf/B39RU+FJtYnZ3PZizvp//hmLntxJ+uTs80dklGkZZfy9Pr9jP3Pdh76OoW8smqWXhPKnsen8H93jOWqYb49KnEEl9jv6OivyqqIqH/AgMndHJn1c7V1ZcWEFXx67afYqm25Z8c9LN29lMKqLk5Ucw2Eyx+Ce+Ng8S6lGbl8UV+luhoMO1ay7cQ2Lg+4HFdb6yylFOqNvwd0lZD4GSpJRYRPBElnenbT7LeT3+Zk2UlWFhTgwEVveLX2MPtN1DNf5cEqeP5MISl5Cdz8/XSO5IhBF0bz6/NKc+bLHzJ3v2L0oAAAIABJREFUJJZh+Fzzlq5lJ4CTH7gEtrnb8dLjFFQVEOUvStYE05sZMpMVE1bg7+iPhIS/oz8rJqxgZshMc4dmNKIlvSAYUWZRpkX1O2ogSRLR/tFsP7UdvUGPWqU2d0hmtT45myd+2E+VTnmTml1SxRM/7Adg7ui2X7hYg9JKHRtSslkbf5r03DJsNSpmjPBnwZhgxvX3sPhJaZcqpSAFjUrDUM9OTs6pKob19yjL96etMEZoPUakbyTfXvctH6d9zAepH/Bnzp88FPkQ8wfNRyV1IRkpSRAwGmorWrw5saaAM5USj0Q+comRC2bnNwL6XwFx/4Px9xLpG8murF0UVhX2yKlj+87s44v0z1lQVk5U/6shdIaSuCjNAtcgZXJjQx+1yL9y3bGd9I15nSXVx7ntp7v4r2MYkyY+Cf6X0PxfuFBuCqSvh0mPgmPP+53rEp+hypSzA+uU8lJTy0qAwEjluaANcXlxAIzzH2eKqAShmZkhM3t0suhiInkkCEZSqavkZNlJi31AifaPZt2RdRwsOkiYV5i5wzGrl7ZlNCaOGlTp9Dz6XSob9mXjbKfF2U7TeO3S5N8N1062GlzstDjZaVBbQDJGlmVijxXx9d5TbE3Lo6bOwPAAF56dM5zZowJxte89/TNSzqQw1GMotmrbzt1xy1KoOAMLv1Ka+wptslHb8M+R/+TafteyMnYlK2NWsvHoRpZHL2egexd7UbgGQenpZpu3untjr7HnyuArLy1owTKMvw9W3wgH1hPhHwFAYn5ij5uiV62rYtkv9+JXV8fDflfC9f+nTJIadUvLd1CpYOA0wgdOY83pGB7Y/S/uqzzAQ2tncqdrGFL03RA6s91pVEI7dj4Hdm4w4T5zR2JZhs+DXS9AWS64+JvuvJVFUHQURt/a7q7xufEEOAYQ5BRkgsAEQejQs40kSfZAH1mWM40cjyD0GIeKDyEjW+TKI6BxiW9sbmyvTB6dKa/m5wP5bEvLI7e0usV9avUGCs/VcrywgvLqOsqr66jVG9o9tqONukly6cJEk0sL2xr2c2mSiNJ0sHRsfXI2L/+USU5JFQFu9iye1J9zNXq+STjNybOVONtpWDAmmJvGBlvtpLRLoTPoSD+bzg2Db+jcHdN+gP3fwpVPQmCEcYLrofq59uOjqz9iw9ENvJLwCjduupG7ht/F4vDF2GnsOnewqcth4wNK0/J6Oq09vzg6cGXQlR1v0C1YtoHTlBV+MW8x9O/bsdfYk5Sf1LOSR7LMuz/exom6Mv7nMgrH+R8pjeM7yC94PJ8t3M7Tux/jNelXjtRks/zbv2Dr0gei/g4RfxE92briVCwc/llZXWrX+54j2zR8Hux6XildG/cP0503u75stZ1m2XqDnvi8eKb0mSL6HQmCibSbPJIk6TrgFcAG6C9J0ihgpSzLs40dnCBYs4ZJa5aaPPKy92Kw+2Bic2L5+4i/mzsck8gpqWJbWh7b0vLYe7IIWYb+Xo442Wo4V1PXbP9AN3s23n/5Bduqdfr6RJKuMaHU8O+yFraV1+gorqzlVFEl5dU6yqrrqK1rPwFlr1U3Sz65XJSQOlF4jo2puej0yijn7JIqnvkxHYDoEA8enDaI6WH+2Gl7b1nioeJDVOurO9fvqCxHadYcGAkTRVlUV0iSxNyBc5kUNIlX9r7Ch/s/5KcTP7Fs/LLGhv0d0lC+0zBtzTWImDE3U3JsLdP7TzdO8ILpqVRKo+JND6I9vZdw7/Ce1fdIlknddDeflWcy3y6QCfO/Ur7nTrLX2PPKlFW8n/o+7+57l5Nhl/NGuQGvX5bDrhdh5EIY90/wtqwhHRZLlpXHFkcfiFps7mgsj/dg8A1TStdMmjxKAOpLl9uQWZxJWW2ZKFkTBBPqyMqjFUAUsAtAluV9kiT1N2JMgtAjZBRl4Grriq+DGaZUdNB4//GszlhNVV0V9hp7c4djFCcKK9ialse2tFxSskoBGOLnzJKpSmJlsK8TG/blXNDzCJTkzdJrmr8At9OqsdOq8XbuZAlUEzV1es41JpnqGpNKLSWkymsaElN1ZJdUNd5WrWs9AeXrYsvaxeO7HF9PknJGaZYd7h3esTvIMmy4D+pqYN4HohzkEnnYefD8xOeZPXA2z8Y8y6KfFzF7wGweGfMIHnYeHTtI+ILzSSRg6+9P4GzjzGWBlxkpasEsRi5U3sjHvEPk0Im8l/IeZbVluNi4mDuyS2MwULP5IZbn/Yq3nQuPzPu2S4mjBpIkcffIuxngOoCn/niKm53cePOWLxl6cBskfwUJH8OAqRB9t3J9Cefq8Y7uhJN/wvSXwcbR3NFYpuFzlbK+0mxloIEpZCeC9xCwa/tvPz5XaSAf5SeaZQuCqXTkVbFOluXSi5YDykaKRxB6jMyiTIa4D7HopbTRAdF8lv4ZyfnJTAicYO5wuoUsyxw+c46t+/PYmpZLRl45AOFBrjx6bSjTw/zp73Xhi8SGpthNy7+WXhNqtGbZtho1tk5qPJ26noDS6Q0Mfmpriw/GZ8pquh5cD5NamIq3vTf+jh3s17D3/+DoDpjxCnh1sU+P0Ey0fzTfz/6eD1I/4JO0T/gt6zceGfMIcwbM6dRjZFVdFTtP7eTa/tdio7YxYsSCyWntYezf4LdXiIxYgIzMvjP7mBQ0ydyRdZ1BDz8+wPsnN3LUzZX3Jr+Gs233JMOu7nc1wc7B3L/zfu7Y+yz/ufw/XDXt35DwifI49tUN4DlQWYk08mawdeqW8/YIqd/Ur2Y8DZJa/GzaMmyekjxK36BMRjQ2WVaaZQ+Z0e6usXmx9Hftj4+Dj/HjEgQB6Fjy6IAkSbcAakmSBgEPAHuMG5YgWLc6Qx2HSw5zU+hN5g6lTRE+EWhUGmJzY606eSTLMmnZZWxNy2XbgTyOFVQgSTCmrzvLZg3j2jA/At3aXlk1d3SgVU1W06pVBLjZk11S1ey2gHa+194k5UwKI71HdixBUXgEfl6mfFo/tneUcpqSncaOByIeYEb/Gfw75t8s+3MZG49uZFn0Mvq59uvQMX7L+o3KukpRstZTjV0Ef65ixOHdaFQaEvMTrTd5pK+D9f/kQOZ6PgkMYO6AOVweNLFbTzHUcyhrZ61lya9LeHjXw9wz6h7+OelfSJctUd7sx70HW/4FO56FiNuVqVnu/bo1BquT+s2FfdRkPWx+GFSaC1Y4CvW8BioTEQ+sM03yqPg4VBUpZeNt0Ol1JOUnMWfAHOPHJAhCo46sZb0fGA7UAGuAMuBBYwYlCNbuZNlJavQ1FtvvqIGD1oFR3qOIzY01dyidZjDIJJ4s4rlN6Uz8769c9/Yf/O+3Y/i72vHs3DDinpjKt/+cwN8u799u4shaLb0mFPuL+hm1Vm7XG52tOkvWuayOlazp62DdYtDYwpx32h0PLHTdQPeBfDb9M5ZFL+Pg2YNc/+P1vJfyHrX62nbvu/X4VrzsvRjrO9YEkQom5+wLI27EPmUtw9wGk5RvpX2P6mrhu7vQ7f+Wp/sNxdPem6VRS41yKi97Lz6+5mNmD5jNu/veZelvS6lCD+E3wqKd8LftMGgaxL0Pb46GtbfC8d+VFR69jV6nfECgu+hDF12VshJJaNnweZAVDyXNJ192u6xE5bqdZtn7C/dTVVcl+h0Jgom1u/JIluVK4Kn6iyAIHdDQLDvUw/LfxI8PGM9byW9RVF3U8R4kZlKnNxB/vIitaXn8dCCPM+U1aNUSlw/04oEpg5g2zBcPx95TymLqcjtrk1qQCtCxZtm/v6r0WbjhE9OOJO6lVJKKBaELmNJnCi/Fv8S7+95l2/FtLB+/nEjflj9xLq8t5/es37kx9EbUnZhSJViZ6Htg31dEGtR8UZJGdV1156f0mZOuGr69Aw5t43+R8zhSlMg7k94xau8mW7Utz132HAPdBvJ64uucKjvFm1PexM/RD4LHKpeyHKWcLeETyNikNEIe908YcSNorejn21HVZZB/APL2Q16qcn3mIOhbKesuzTJtfNZk+DwluZa+ASbcZ9xzZSeA1gF8hrW5W1xeHBISY/3EBwmCYEodmba2keY9jkqBBOB/siy3PONaEHqxjKIMbFQ29He1/N7y0f7RvJX8FvG58Vzb/1pzh9NMbZ2BP48Wsm1/Hr8czKeoohY7rYorB/swfYQfk4f44GKnNXeYZmNt5XamlFKQgkbSMMyz7RehZCfB7peUN1Fh15smOAFQVk28fMXLzB4wm+din+PObXcyf9B8Hop8CFfbC8dm7zi1g1pDrShZ6+n8wiDkSiJPJvOJq4b9hfut5w1ibSV8fSsc3cnBKY/z0YlvuC7kOpOU3kmSxF1hdxHiGsJjvz/GzZtvZtXkVedXXroEwNTlMGkp7P8WYt+HH++D7c9A5F1Kqa41Js5lGcqy65NETRJFxSfO7+PgCX7hysSw5C+VsqiLuQaZLGSr4xEC/qPgwA8mSB4lKudqZ1hFfG48QzyGNHueEATBuDrS8+gY4I1SsgZwE1AODAY+BG43TmiCYL0yijIY6D4QrcrykxrDPIfhrHUmNjfWYpJH1To9uw8VsC0tj+0H8ymvrsPJVsOUIT5MD/PjilBvHGzEFCyhbamFqYR6hLa9akFXBev+AU6+MONl0wUnXGBi0ETWzVnHeynv8UX6F/x6+lceG/sY0/tPb+xXtfX4VgKdAgn36uDkPMF6jb+PUWtuRHINJjE/0TqSRzXnYM1COPEHuuveZFn2Rtzs3Hgs6jGThnFF8BV8Of1L7tt5H3dtu4sVE1Zw3YDrzu+gtYeIv8Do2+HE70oS6fdX4c83YNhcZUpbUNslQ2aj10HhoeaJoqri8/t4DFCSD6NvVxJGfiPA2e98KbLfiAt7HoHyM5m63LTfi7UZPk9JNBafBPe+xjlHXS3kpsK4xW3uVlVXRUpBCrcOvdU4cQiC0KqOvPuaIMty02ftjZIk7ZVleawkSQeMFZggWCtZlsksymRyn8nmDqVDNCoNY/3GEpMTgyzLZpsOd66mjp0ZZ9iWlsuvGQVU6fS42mu5Zrgf08P8uGygF3ZaUaoidEydoY60wjTmDZzX9o7bVyhvRm5fD/buJolNaJmD1oFHxjzCzJCZ/HvPv3ns98f48eiPTAiYwOfpn5NfmY+T1oktx7cwM2SmucMVjGnAVFw9BjPIUGsdfY+qS+GrG5UpUdd/yP/JhWQWZ7Jq8iqzrIwY6D6QNTPX8PCuh3nyjyc5WnKUByIeQCU1aXUqSdB/knIpOg7xH0LyF5D2ndJvJvpuGDYH1Gb6EKy6tJWys/reaBo7pbRp6GwlIeQXDr7DwNa57eM2NMXesVIpVXMNUhJHoll224bPVZJH6evhsiXGOUf+fqWssJ1+R8lnktEZdKLfkSCYQUeSR06SJPWRZfkUgCRJfYCGmZbtd7cUhF7mTOUZimuKCXW3/H5HDaIDotl5eidZ5VkEuwSb7LyllTp+OZjPtrRcfjtcSG2dAS8nW66PCGR6mD/jQjzQqjvS118QLnS4+DBVdVVt9zs6+qvSRDbqHzDAOpK9vcEQjyF8OeNL1mau5dW9r/Jnzp+Nt53TnWPFnhUAIoHUk6lUMP4eIvasYEN+EnWGOjQqC11tWlkEX14PeWlw4ydk+g3hg00LmdF/BlP6TDFbWO527nxw1Qc8H/88H6V9xNHSo7w48UUctY7Nd/boD9c+D5OfgH1rlMfF7/8GPz8NY/+mlLU5ehknUFlWkjgXryYqOXl+Hwcv8A9XEloNq4k8BrRb2tSq8AUiWdRZ7v0gIEKZumas5FFjs+y2J63F5cahkTRE+EQYJw5BEFrVkUfdR4A/JEk6CkhAf+AeSZIcgc+MGZwgWKPM4kwAi5+01tR4//EAxOTGGD15VFBew8/peWxLyyPm6FnqDDIBrnbcOq4P08P8iezrjlolJl0Jl6ahWXark9aqimH9PeA1GKatMFlcQseoVWpuHXorH6d9zJnKMxfcVq2vZlXSKpE86unCbyLyj/+w1lBDRlEGYV5h5o6ouYpC+HwuFGbCTV+iGzSVZZtvxcXWhSeinjB3dGjVWpZHL2eQ2yD+u/e/3LblNt6a8hZBzq3097F1VkqGxv4djmyHuPdg53Ow+2Vletu4u5WeVF2l10FBZvNEUXVJ/Q4SeA6AwAiIvON8osjJV0zAtARh1ysJxaLjSsKxu2UnKP/X7fSfis+NJ9w7HAetQ/fHIAhCmzoybW2LJEmDgIZ3wplNmmS/YbTIBMFKNUxaG+w+2MyRdFxfl774OfoRmxvLgtBL+zRufXJ2s+lf40I82JaWx9a0PBJOFGGQoa+nA3+b2J/pYf6MDHI1W7mc0DOlFKTgaedJoFMrzcS3LIWKM7DwK7ARL0AtVUFlQYvb8yryTByJYHJaeyKG3QQ5G0g89pPlJY/K8+DzOUoPmJvXwsCpfJL6AQeLDvL6la/jZudm7ggBpZH2LUNvoZ9rP/61+1/csvkWXrvyNcb4tVEapFLB4KuVS0GmshIpZa3SbLrfRGVKW+h0SPu+9fKv6lJlNVbTRFFBxoVlZ77DlXKohrIzn2Fg69R6XIJ5DZujJI/S18PlD3X/8bMSlJK1Nl4PltWWkV6UzuLwtvsiCYJgHB1d7zkICAXsgJGSJCHL8ufGC0sQrFdGUQbBzsE42VjPCyBJkoj2j2bnqZ3oDfouj8Fen5zNEz/sp0qnByC7pIqHvtmHXD+vcbCvE/dNGcT0MD+G+DmLhJFgNCkFKYz0Htny71jaD8q0oSufVD7hFiyWn6MfuRW5LW4Xej6f6PsJ/vp7Eo9u5o6oR8wdznml2fDZdUoC6bbvoN/lHC4+zHsp73FNv2uY1neauSNsZkLABFbPWM39O+9n0c+LeDr6aeYPnt/+Hb1DYdbrSmIo6XOlN9LXt4K9J9SUgUGn7Fd6GtbfDTHvKCs7m5adOXoryaEBU+oTRZdYdiaYh1sfCBqrPId2d/KosgiKjsLotptgJ+QlYJANRPlFde/5BUHokHYftSVJega4EhgGbAGmA38AInkkCC3ILMq0qpK1BtH+0aw/sp6MogyGew3v0jFe3JrRmDhqIMvgYqdh3b2XMcDbehJqgvUqri7mVPkprh90ffMby3Jg00NKT4WJFvRmVGjRkoglrNizgmp9deM2O7UdSyKM1HNDsCzOvkQ6BLKrKhdDRSEqY/Xd6Yzik0riqKoYbl8HfcZRZ6hj2Z/LcNY68+S4J80dYav6ufbjq5lf8ejuR1kRs4IjJUd4ZMwjHesnZe+u9LqJvhcyN8P3i84njhoY6iA/TWliHXlnk2lnvkb5fgQzGD4PfnoSzh5VSgy7S059Y/x2mmXH58Vjp7Zru5+hIAhG05FOtDcAU4E8WZbvAkYCph8dIQhW4FztOU6Vn7LK5FHD1IqY3JhO3a+8Wse3Cae57f/iyCurbmWfOpE4Ekymod9RsxeXsgwb7oO6Gpj3gfjU2wrMDJnJigkr8Hf0R0LC39GfFRNWiH5HvUhE6DxK1CqOx75p7lCUN8yfTFdKsv6yAfooz5ufHfiMA2cP8GT0k3jYeZg5yLa52Ljw9tS3uW3obXx58Evu3XEvZbVlHT+AWqOUL+lbmZlj0MONn8DEh2HQNJE46mmGzVGuD6zr3uNmJQISBIxuc7e43DhG+4zGRm3TvecXBKFDOpI8qpJl2QDUSZLkApwBTDeOSRCsyKHiQ4B1Nctu4GXvxWD3wcTmxLa7b22dgZ8P5HHvV0mMeW47S79L5WRRBc62Lb8ZD3Cz7+5wBaFVKQUpqCV18xV0e/8Pju6Aq58Fr4HmCU7otJkhM/n5hp9JvSOVn2/4WSSOepnIQdcBkJjxHdSZccjvmQwlcVRXDXduaix5PVZyjHf3vcu0PtO4pu815ouvEzQqDY9FPcaK8SuIz4vn1s23cqL0ROcO0lpT43aaHQtWzjUIgqPhwPruPW52glIiaefS6i6FVYUcKTlClL8oWRMEc+lI8ihBkiQ34EMgEUgCOrc0QRB6iYZm2aHuoWaOpGui/aNJOpNEVV1Vs9sMBpm4Y2d54of9jP3PdhZ/kUjMsbPcNDaY7++ewG9LJ/Ps3DDstRf2S7LXqll6jXX+PATrlFqQymD3wdhrmiQtC4/Az8tgwFRlkpAgCFYh2DkYb60LiVR3/2qHjsrbD5/WJy3v3KKUYgF6g55le5Zhr7XnqeinrK6P3/zB8/nwqg8prSnlli23sCdnT8fvPHU5aC/6YEhrr2wXerbh8yB/PxQe7p7jyfL5Ztlt2Ju3F1BeqwqCYB7tJo9kWb5HluUSWZbfB64C7qgvX2uXJEnXSpKUKUnSEUmSHm9jv/mSJMmSJLX9qCEIFi6zOBN3W3d8HHzMHUqXRPtHozPoSD6T3LgtI6+MF7dmcPlLO7npg1jWJ2dzZag3n9w5lrgnp7JyThiRfd2RJIm5owN54foRBLrZIwGBbva8cP0I5o5uZeKVIHQzvUHP/sL9F5as6etg3WLQ2MKcd8TIZ0GwIpIkEREwnkQHJ+SYt2icwGAq2Unw6Szl8eOureBzfmXxlwe/JLUglSeinsDL3gL6MXXBGL8xrJm1Bl8HX+7Zfg9fHfwKuSM/4/AFcN2b4BoMSMr1dW+en7Ym9FzDZgNS9yVzi49DVREERba5W1xuHM5aZ4Z6DO2e8wqC0GkdaZi9Q5blqQCyLJ+4eFsb91MD76AknLKAvZIk/SjLcvpF+zkDS4C4Ln0HgmBBMooyCPUItbpPHxtE+kaiUWnYfvwP9h3y4cd9OWTklaNWSUwa5MVj04cwbagvjq2UpwHMHR0okkWC2RwpOUJlXSUjfZokj35/FbIT4YZPwMXffMEJgtAlEb4R/HTyJ3IK0wk88Qf0n2iaE5+Ohy/ng70b3LER3Ps13nSi9ARvJb/F5ODJzOg/wzTxGEmgUyBfzviSx39/nBfjX+Rw8WGeGvcUWrW27TuGLxDJot7IJQD6jFeSR1c8eunHy+5Ys+y43DjG+I3p8kRgQRAuXasrjyRJspMkyQPwkiTJXZIkj/pLP6Aj7wyjgCOyLB+TZbkWWAvMaWG/Z4GXgJY77QqCldAZdBwpPmKV/Y4ASiprWZdUgI0uhLVpO/jvtkwcbNSsnDOc+Cen8sldUcwZFdhm4kgQzC2lIAWAkV71yaPsJNj9Eoy4EcJamL4mCILFi/RVViQkuXgpo+BN4cQf8PlccPRSVhw1SRzpDXqW71mOrdqWZdHLrPYDo6YctY6smryKv4/4O98f/p5FvyyiuLrY3GEJlmr4PDiTrvQCu1RZCaB1AJ9hre6SfS6brHNZjcNdBEEwj7bK1v6B0uNoSP11w2UD8HYHjh0InG7ydRYXJZ0kSYoAgmVZ3tzWgSRJWixJUoIkSQkFBQUdOLUgmN6J0hPUGmoJ9bCe/j7VOj2bUnP4+2cJjP3Pdp5al4ZUPQi1XQ4bl4zmh3su4y/j++HpZGvuUAWhQ1IKUvCw8yDIOQh0VbDuH+DkCzNeNndogiB00SD3QTjbOJMYMAwObVV6mBnT0Z3w5Q1Kc+C7tjZrAr06YzXJZ5J5LOoxvB28jRuLCakkFUsilvDCxBfYX7CfmzffzOHibuprI/Qsw+YAEqR3Q+Ps7ATwH9XmBNT43HgAovxEs2xBMKdWk0eyLK+SZbk/8C9ZlkNkWe5ffxkpy3JHkkdtkiRJBbwGPNLevrIsfyDL8hhZlsd4e/ecJ2mhZ2lolj3E3bJXHukNMr8fLuCRb1IY89x27ludTGpWCXeM78em+y/n/RsWApBdk2rmSAWh81ILUgn3DldWAmxfAYWHYO67YO9u7tAEQegilaRitM9oEqVaUNtA7LvGO1nmNli9EDwHwJ2bwdnvgptPlZ3izaQ3mRQ0ietCrjNeHGY0K2QWn1z7CTX6Gm7bchu7Tu8yd0iCpXH2hX6XX3rfo7payE1tnF7Ymri8ODzsPBjoJialCoI5tVt/IsvyW5IkTQD6Nd1fluXP27lrNhDc5Oug+m0NnIEwYFf9cl8/4EdJkmbLspzQoegFwYJkFGVgq7aln2s/c4fSjCzL7M8uZX1yDhtTcygor8HZVsP0MD/mjg4kOsQTtUpZdl9nCMNZ60xsTizX9rvWzJELQseVVJdwouwEcwbOgaO/Qtz7EPUPGDDZ3KEJgnCJIn0j+S3rN86GzcVz32qY8jQ4eHTvSdJ/hO/+Cr7D4fZ1zY5vkA0s37McrUrL8ujlPaJcrTXh3uGsmbmGJb8u4YGdD/Bg5IPcNfyuHv09C500fC5sfgTy08G39ZKzNuXvB30NBLXe70iWZeJz4xnnN078/gmCmXWkYfYXwABgH6Cv3ywD7SWP9gKDJEnqj5I0Wgjc0nCjLMulQONoCkmSdqGschKJI8EqZRZlMshtEBqV5fQEOlFYwfp92fy4L4djhRXYqFVMHuLN3FGBTB7ig522edNBjUrDWL+xxObGIsuyeKIWrEZqobJabqRLCHx3D3gNhmkrzBqTIAjdI8JHWZmQFDKBq1K+gcRPYGK7i9c7bv938MNiCIyE274DO9dmu6zNWEtifiIrJ6zE19G3+85tofwc/fj02k9Z9ucyXk98nSPFR3hmwjPYqkUpuwAMnQ1bliqrj7qaPOpAs+zjpccpqCoQ/Y4EwQJ05F3uGGCY3KG5nefJslwnSdJ9wE+AGvhYluUDkiStBBJkWf6x8+EKgmWSZZmM4gym9Zlm7lAoKK9hU2oO6/flkHK6BEmCcf09WDwphOlh/rg6tDM9BYgOiGbn6Z1klWcR7BLc7v6CYAlSClJQSSqG7/0CKs7Awq/AxsHcYQmC0A2Gew7HTm1HUs0ZrhowBeI+gPH3g8bm0g+e/BVsuBf6Xga3rAVb52a7nC4/zRtJb3BZwGXMHTj30s9pJew19rw86WUGug3knX3vcLLsJKumrMLL3qv9OwvdavOxzaxKWkVeRR5+jn4siVjCzJCZ5gvIyed86do/iZWRAAAgAElEQVTkJ6ErHzZmJSh9CS/qK9ZUXJ4ykDvKX/Q7EgRz60jyKA2lpCy3sweXZXkLsOWibctb2ffKzh5fECxFfmU+pTWl3TtpLfUb2LESSrOUJ9Wpy1sdiXuupo6fD+Sxfl8Ofx4pRG+QGervwhPThzB7VAD+rvadOnW0fzQAMbkxInkkWI3UglQG2/ngkPYDXPlkuz0UBEGwHlq1lnDvcBLzE2H8vfDlfDjwA4xceGkHTvgYNj0EIZNh4eoWE84G2cCKPStQSSpWTFjR61bkSpLEP0f+kwFuA3jqj6dYuGkhb055k2GeXVxtInTa5mObWbFnBdV6ZTh1bkUuK/asADBvAmn49bDpQcg/AH5hnb9/doKy6qiNv6m43DgCHAMIcmo9wSQIgml0JHnkBaRLkhQP1DRslGV5ttGiEgQr09gsu7uSR6nfwMYHlGlRAKWnla+hMYGk0xv47VAB6/fl8Et6HtU6A4Fu9vxjUghzRwcy2Lf5J6cd1c+lH74OvsTmxrIgtOWElSBYEr1Bz/6CVGaVlihlJ91ZziIIgkWI8I3gg9QPOBc8DifvIRDzNoTf1LUVDwCx78G2x2HQNbDgc9Datbjbd4e+Iz4vnmfGP4Ofo1+L+/QGV/W9iiCnIB749QHu2HoH8wbNY9fpXZazEqYHW5W0qjFx1KBaX82qpFXm/ZkPvU7pe3RgXeeTR1XFcPYIjLy51V30Bj178/Yytc/UXpe0FQRL1JHk0QpjByEI1i6jKAMJiUHug7rngDtWnk8cNdBVIe9YSYLLNDbsy2Zzai7FlTrcHbTcEBnEnFGBRPZxR6W69CdXSZKI9o9mV9Yu9AY9alXz3kiCYEmOlhyhoq6S8OpqWPBBmyN/BUGwTpG+kRhkA/sKU7h8/L3w4/1w4nfoP6nzB/vjdWUi49DrYP7HrZa/5ZzL4dWEV4n2j2b+oPmX9g30AEM9h7Jm5hr+svUvrMlY07jdYlbC9FB5FXmd2m4yjl7K39+BH5Qm9p1J8GQnKtdtNMvOLM6krLZMlKwJgoVQtbeDLMu7gROAtv7fe4EkI8clCFYlsyiTPi59cNQ6dsvx5NKsVrff+H4M3yVmcfkgbz66YwxxT07jubkjGNvPo1sSRw3GB4yntKa0cVWVYJk2H9vM1d9dTfhn4Vz93dVsPrbZ3CGZRepeZXT3yKj7wEuM8hWEnijcKxyNpCEpPwlGLAAHL4h5p3MHkWXY9aKSOAq7AW74tNXEkSzLPLPnGYBeWa7WGi97L3QGXbPtDSthhO7nYuPS4naLWAk3fB4UHYO81M7dLysRkCCg9RLzuNz6fkd+InkkCJag3eSRJEmLgO+A/9VvCgTWGzMoQbA2GUUZhLqHdtvx8vFscXuB7MZrC0aS8PRVvHXzaKYO9cVG0+6fcZc0TLWIyY0xyvGFS9fQAyG3IhcZufGT316XQCo8QsqRzbihos8EUa4mCD2Vg9aBoZ5Dlb5HWjuIWgSHtkHh4Y4dQJZhx79h1wsw6la4vu1Vit8f/p7Y3FgejnyYQKfAbvoueob8ivwWt5t9JUwPlHwmmbLaMlTSha/3VJKK+0bdZ6aomhh6Hag0SulaZ2Qngnco2LWcGAOlWXaIawg+Dj6XGKQgCN2hI+867wUuA8oAZFk+DIi/YEGoV15bTta5LIZ6Du2W4509V8OeuuaJKFkGByq53jcfJ1vjl+R42XsxyH0QsbmxRj+X0DVt9UDoNfR1sG4xKXY2hPuORVIZJ5kqCIJliPSNZH/hfmr0NTDmb6C2hdh327+jLMO2J5Rytci7YPbb0EZJdu65XF5JeIUovyhuDL2xG7+DnqG1FS8WsRKmB8mvyOehXx8iyDmIp8c9jb+jPxISrjauGGQDR0qOmDtEcPCAkCuV5FFHh3PL8vlm2a3Q6XUk5SeJVUeCYEE68iq7Rpbl2oYvJEnSAB18ZBCEni+zKBPgklcenTxbwbL1aSx8cTXT1XvZb+hLlsELgyyRZfDiubpbKFO5waez4PAv3RF6u6L9o0nOT6a6rrr9nQWTs9geCKb0+6uU5iZxXKNmZIB4gSkIPV2ETwQ6g479BfvByVsZIrFvDVQWtX4ngwE2Pwxx78G4u2HW6/w/e/cdV1X9P3D8ddhDpoDiFhcOUHGBmtu0LM0yy8oyzRxfEzW1XaaWlpqiZZra+LXMymyYaW5zgIqCC9wKCLKnl3nP74+rlsq44B2A7+fjwYN7zz3nc96QdM99n8/n/aaURLOqqry7/11dl7Wus+6Y8SEgOCAYO8s7C4yPaj3K9MFUU3lFeUzZMQVNoYalvZfyeIvH2TJsC5HPRfLPiH94osUTfHHii8ox27j1UEi7CPFH9ds/7SJcS4F6HUrc5VjyMTSFmpsdgIUQ5qfPu+EuRVFeB+wVRekP/Aj8btywhKg6otN0yaOKdlqLiEln4reH6b1wJ+sOXmKl8xdYWNswSX2V7vlL8cn7lu75S/nOYgiR96+Dmk3huyfg6HeG/DGKFegdSL42n/BEKXNWGbnbuRe7/Z658xsXDrs+4FiLfgC09Wxr5oCEEMbW3qs9wL/vS0H/g0INHPq8+AO0RfDbJN3r3abAwHllFvXdcHYDe6/sZUrAFOo71Tdk+NXGIJ9BzOo66+ZMGA97D6wVa34/97vccDIAVVWZvX82x1OO837392nqdmctv1c6v0KHWh14Z987nEw5aYYo/8N3EFhY67907Uax7LolJ49CE0JRUOhYu+TZSUII09InefQqkAQcA8YBfwJvGjMoIaqSqNQo3O3c8bD30PsYVVXZEZXIk5/tZ8gne9lzJpkXezTh4P0X8bkWge2gD5j6aE/qutqjAHVd7Zn3qB8PBLWD5//UdbbYMAH2LNJ/inAFdKzVESsLK1m6VgmFXw0nMy8ThVs/BCkolaMGgrEVaOCXcVCjFpE+QVgoFrTxKGebYCFEleNq50pT16a6otkAXi2hSV8I+wwK827duagQ1r8IR7+FXq9Bv1llJo6u5lxlwcEFdKjVgSd9nzTKz1BdDPIZdHMmzI7hO1jQawHHU47zzr53UI14bXIv+C7qO3479xvj246nb8O+xe5jbWHNop6LcLNzY8qOKaRoUkwc5X/Yu0GT3vovXYs9BFb24NW6xF1C40PxdffFxdbFgIEKIe6GPskje+BzVVUfV1V1GPD59W1CCHTL1nzdffXqwpJfqOWnw7EMXLKH5788yKWUa7zxYEv2vdqHVwPtcP7nPd1FcLuneaR9Xfa+2ocL8wex99U+PNL+erFOWyd4ap2u08y22bBppu7OqhE4WDvQ1rMtB65I8qgyOZp4lAlbJ1DXqS6vdX7t5p1fN1s3VFQ0hRpzh2h8W2dB8ml4ZDkR6adp6trUYN0OhRCVW4daHTiSeIRCbaFuQ9D/IPsqHF//706F+fDTKDj+E/R9B3q9WmbiSFVVZh+YTYG2gNldZ8tytXLq26Avk9tP5s8Lf7Lm+Bpzh1NlhcWHseDgAnrV78WEthNK3bemfU2W9F5Cam4q03dNL7YLnsm0Hgrpl3WzgssSdwjqtCuxYL2mUENEUoQsWROiktHnXXEbtyaL7IGtxglHiKqloKiAs+lnaeFeer2jrNwCPtt9jh4f7mD6jxEALHq8Lbtm9GZsDx+cbK3gt5dAsYDBS8u8wMXKBoauhK4v6e62/vQ8FBhnmnigdyBRqVGk5aYZZXxRPseSjjFh6wQ87D1Yff9qRrQccfPO764ndtGxVkeWRywnOz/b3KEaz7kdELoCOo9D69OTY0nHZMmaEPeQDrU6cK3w2s1l4zTpA051dO+js1zho9awqg+c+h0GzIP7puk17u/nf2d37G4mB0ymgXMDI/4E1dcLfi/wQOMHCAkPYfvl7eYOp8qJy47j5V0v09C5IfO6z9Mrgdm6ZmtmdZ3FoauHWHBwgQmiLEGLB8HSBk6sL32/wnyIjyx1ydqN5HBnb6llKERlok/yyE5V1ZufQq4/djBeSEJUHeczzlOgLcDXrfh6R1czc5m36RRd523n/T+jaOzhyBfPd+KvKffxWId62Fhd/xM8/AVc2A33zwGXevqd3MIC7p8LA96Hk7/CN4+CJt1AP9m/Ar0DUVEJSwgz+NiifE6knGDc1nG42LqwZsAaajnWuuV1RVGY3mk6qbmprD622kxRGpkmDTZMBI/m0G8WFzIukFWQhb+nv7kjE0KYSIBXAMC/S9eO/QjXkkBbAKiQGQtXj0G7pyFool5jJl1LYn7YfNp5tuMp36eMFHn1pygKs7vOpk3NNry659WbTUVE2TSFGoK3B1OkLSKkdwg1bGrofexDPg/xXKvn+D7qe345o2fdIUOzd9XNnj+xofSla1ePQ1Ee1Cu5llFofChWitXNv3UhROWgT/IoR1GUm3+5iqJ0AO6BNRFClK2kYtlnrmYx48cIun+wnVW7z9OjhSe/TerG9y8G0ruF161L3NJjYMvb0LgndBhV/iCC/gePrYGYMPjiAci8chc/0Z3aeLShhnUN9l/Zb9BxRflEpUbx4pYXcbJ24vMBn5dYFLt1zdY87PMwX5/8mrjsOBNHaQJ/zoCcRN3MOxsHIpJ0M/lk5pEQ945ajrWoW6Muh69eL7q7bTYUFbNc58Juvca7sVwtvyif2d1mY2lhacBo7z12VnaE9AnBydqJydsnm7cWTxWhqipv732b02mnmd9jPo1cGpV7jCkdphDkHcScA3NuvjeaXOuhuuRt7KGS97lZLLvk5FFYfBj+nv44WMt8BSEqE32SR8HAj4qi7FEU5R/gB+AeqMYqRNlOpZzCztKOhs4NUVWVsAupjPnyIP0X7+b3yCuM6NyAHdN78clTAfjXc71zAFWF3yeDqtVvuVpJ/IbBMz/rElGr+0Ni1N39YP9hZWFFp9qdpGi2GZ1OO83YLWNxsHZgzYA11KlR598XI9fB4ja6pRqL20DkOiYHTEZRFELCQ8wXtDEcX6+bYdBjJtTV3dOISIrA2caZRs6NzBubEMKkbtQ9UlUVMmKL36mk7bf588Kf7IzZyaR2k2js0tiAUd67vBy8WNpnKSm5KUzbOY38onxzh1SpfXHiC/66+BeTAybTo16PCo1hZWHFgp4LqOVQi6k7ppJ0LcnAUeqhxQNgaVv60rXYQ+DoVeJM+8z8TE6mnpQla0JUQqUmjxRFsQBsAF9gAjAeaKmq6mETxCZEpRedFk0zt2b8fTKRocv3MXzlfsIvpzGlXzP2vdqX2UPa0LBmKUV8j3wD57brOsC4Nbq7YHx66jqxaQvg8wFw2XDJnkDvQOKy44jJijHYmEI/59LPMXbLWGwsbFhz/xrqOf3nYitynS75mBEDqLrvv0+m9rndPNvqWTZd2MSxpGNmi92gMq/AH1N1NRLue/nm5sikSPw9/fUqWC+EqD461OpAam4qFzIvlLzcW49l4MmaZOaFzcPf05+RrUYaOMp7W2uP1sztNpfwxHDmHJgjHdhK8E/cPyw5vIQBjQYwps2YuxrLxdaFkD4hZBdkM2XnFNMn7eycoWk/3dI1rbb4feIO6ZaslfC+fSjhEFpVS5faXYwYqBCiIkpNHqmqqgU+UVW1QFXV49e/zFjGX4jKQ5NfSGTiSc7EODP+m3BSc/KZM6Q1+17ty5R+zXF3tCl9gMwrsPkNaNgNOr1gmKC8/WHM3+DoAf83BE79YZBhA+voul3I7CPTOp9xnjGbx2ChWLBmwJo7C7hum61rWf9fBRrYNpsxfmOoaVeTBYcWVP0LdlWFXyfp2nAP/exmd5as/CzOpZ+TJWtC3INu1EI5fPUw9H0brG9rBGxtr9teClVVee/Ae2gKNMzpOkeWqxnBwMYDGec/jg1nN/D1ya/NHU6lcynzEjN3z6SZWzNmd51tkBshzd2a817394hMiuS90PdMfw3QeihkXYHYYmplatIg5WypxbLDEsKws7STWoZCVEJ6dVtTFOUxRW7rCgFA+rV8lm07Q7eFP5OnzcFRacDHT7Vn+8s9GRnUCHsbPS4+VVU3i6IoHwYv0xW/NhS3hjB6C9RqA+tGwsG7b5fb2LkxXg5eUvfIhC5lXuKFzS+gorJmwJri6x+UuFQjBsfCfCa1n8SRxCNsvVzFG2QeXA3ntukKyns0vbn5WNIxVFRJHglxD2ro3JCadjV1RbP9h8PDS8GlPqDovj+8VLe9FJsvbWbr5a1MbDcRH1cf0wR+D5rYbiL9GvRj0eFF7IndY+5wKo2cghyCtwdjqVgS0jvEoPV9+jfsz1i/saw/s54fon8w2Lh6aTEQrOzgRDGFu+OuF7kvo1h2e6/22FiWcRNWCGFy+nxiHQf8COQripKpKEqWoiiZRo5LiEonJvUas347QdC87Sz6+zQNvHWdzZYMfZCH/OtgZVmOBFDkOjj9F/R9C2o2MXywjjXhud+gaX/YOA22v1d654syKIpCkHcQYQlhFGmLDBioKE5MZgyjN4+mUFvImvvX4ONSzIeaogLd9PCSfNiEofu+oKmNO4sPLqi69SaSz8KWt3QdXG6boReRHIGCgp+Hn5mCE0KYi6IoBNQK+Lfjmv9wmHocZqXrvpeROErNTeX9A+/TpmYbnmv9nAkivndZKBa81/09mrk2Y+bumZxPP2/ukMxOq2p5fc/rXMy8yIKeC25dkm4gk9pPome9nnwQ9gGHEkopYG1otk7QrH/xS9fiDgMK1Cm+i1qyJpmz6Wfp4i1L1oSojMr8tKuqqpOqqhaqqlqrqup8/Xkpn1iEqF6Ox2Uw+fsj9Fq4k28OXOIBv9r8NeU+BrTXoqDQ3K15+QbMugqbZkK9ztBlvHGCBrBxhCe/g/bPwO4P4beXoKiwwsMF1gkkIy+DqDTDFeMWd4rNimX0ltHkFeWx6v5VNHVreudOceHwWW/IzQDltplu1vbQ6zXoNhnL7CSmX44iJiee7z/vBjvmQXzEXSUSTaqoEH55EaxsYcgnd9RHiEiKoIlrk3K1MxZCVB8danXgSs4V4rPjy33s+6Hvk1WQxexus7GysDJCdOK/HKwdWNZnGTaWNkzaPon03HRzh2RWKyNWsj1mOy93fJlA70CjnMNCsWDeffOo51SPl3e9XKG/kwprPRSyE+DybTPWYw+BZ4sSb34dTDgIIMkjISqpMpNHis4ziqK8df15fUVRpPy9qNZUVWX36SSeWR3KQ8v+Ydupq4zu1ojdM3vz0fB2+NZ2Jio1iobODcs3zVhVdTOBCjS6D8PGrq9gaQWDP9Z1pzryNfzwNORfq9BQNy5uDlyRukfGEp8dzwtbXuBawTVW9V9FC/cWt+6Qn6Ork7W6L+QkwRPfwNAVdy7V6PWqrgj7pDC6vbCPbg71WGmdR/qeBbCyByzxgz9nwvmdxbe3riz2LNLdpXxoMTh73/KSVtUSmRQpS9aEuId1qKWrm3I4sXx9XP6+9DebL25mQtsJNHNrZozQRDG8a3gT0juEhJwEpu+aToG2Er//GNH2y9tZHrGcwU0G80zLZ4x6LicbJ5b2WUp+UT7BO4LJLcw16vluajYArOxvXbqmqrpi2aXUOwqND8XJ2omW7i1NEKQQorz0WWezHAgCnrr+PBv4xGgRCWFGBUVafj0ax4NL/+HZz8OIvprFKwN92fdaX94Y1Io6rv8W5IxOi8bX3bd8JzixHqL+gN6vg2c5ZyxVlKJAnzdg0EdwZgv832DISSn3MB72HjR1bSpFs40kISeB0ZtHk5mXyWf3f0bLmrddOJ3bAcuDYP/H0H4k/C8UWj5c9lKNmk14ud9SciwUVvZ9SZdMrNUGwr/SFVVf0AR+Hqu7wMvLMt0PXJa4cNj1Afg9Dm0evePli5kXycrPkuSREPewZq7NqGFdQ1c0W09puWnMPTCXlu4teb7N80aMThSnnVc73gl6h9CEUD4I+8Dc4ZjcufRzvLbnNVrXbM1bgW+ZpFNoY5fGzL9vPlGpUczaP8s0BbRta0Dz++Hkr3Cj3EHaRbiWUmbyqGPtjlK8XohKSp95ul1UVQ1QFOUIgKqqaYqiSAUzUa3k5BXyw8EY1vxzgbh0DU29avDhY/4MaV8HW6s738Ay8jKIy47j8eaPl+MkyfDnDN0676BJBoxeT53GQA0v+GkMfD4AnvlZV1y7HILqBPFD1A/kFuZiZ2VnpEDvPYnXEhmzeQzpeel81v8zWtds/e+L11J1NX+OfgPuTeC5P6DxfeUav5lbM4Y2Hcras7/y5CMbaBgwUjeL6dwOiP4TojfBsXVgaQONe4Lvg9DiQXCqbeCfVE8FGvhlHNSoBQ8uKHaXiMQIAEkeCXEPs7SwpL1X+3/rHulhXtg8XZK+/2dYW1gbMTpRkiFNh3Au/RxfnPiCpq5NedL3SXOHZBKZ+ZlM3j4ZOys7lvReYtLrqJ71ezKp/SSWHVlGS/eWpqnz1fpRXfLo0j7ddUvc9SRvCcWy47LjiM2O5ZlWxp2NJYSoOH1mHhUoimIJqACKongC2tIPEaJqSMrKY+HmaLrO387sP05S19We1c92ZMuUHgzvVL/YxBHA6bTTAOWbefTndN3MjkeW32w1bnItH4Znf4WcRFjTHxKOlevwQO9A8rX5HEk8YqQA7z3JmmTGbB5DsiaZT/t9ip/n9eLPqgrH18MnnSHie+g+DSbsLXfi6IZJ7SdhY2nD4sOLdRtsHKHlQ7p/j9PPwKg/ofOLkHJG1wlwUQtY1Ve3dCwxyrR1krbOguTTutjs3YrdJSIpAicbp+K70Akh7hkBtQI4n3Ge1NzUMvfdfnk7my5s4kX/F+9cFixMKjggmB71ejA/bD6h8aHmDsfoirRFzNw9kys5V1jcazG1HU1/c2as31j6N+zPR4c/Yt+VfcY/YbP7wdrh36VrcYd1S9m8Whe7e1h8GABdaku9IyEqK32SR0uBXwAvRVHeA/4B3jdqVEIY0IYjcXSbv53Gr26k2/ztbDgSx7mkbF5bH0m3D7bzyc6zBPnUZP3ErqwbH0S/VrWwsCh9GnFUqq5otN4Xnyd/07159pwJXmZex90wCEZvBgsr+OJBOL9L70M71uqIlWIlS9cMJEWTwpjNY7h67Sqf9vuUdl7tdC9kxMHap+Cn58G5Dry4E/q9oyuGXUEe9h6M8RvDtsvb7uy6YmkFjbrBgPdg8lGYsB/6vAlqEWybDcu7wLIOsOVNuLT/3ynoxnBuB4SugM7joEnvEneLTI7E38MfC6UcXQ6FENXOjbpHR66WflMjIy+DOQfm0MKtBS/4vVDqvsL4LC0s+eC+D2js0phpO6dxOfOyuUMyqmVHlrE3bi+vdX6NgFrFdxozNkVRmNttLj4uPszYNYOYzBjjntDGAZoP1M0+KirUFcuu067EG6ihCaG427nTxNUIXYiFEAahT7e1b4GZwDwgHnhEVdUfjR2YEIaw4Ugcr60/Rly6BhWIS9cwbd1R+i7axc/hcTwWUI9t03qyYmQHAhoUP8OhOFGpUXjYe+Bh71H2ztdSdUWya/tDtykV/2EMyasljPkbXOrBN4/B8Z/1OszB2gF/T39JHhlAWm4aY/8ey5XsK3zS9xPdxaRWCwfXwCdddEmU/nPghe3g7W+Qc45sNZJaDrVYcGgBWrWECaSKArVaQY8ZuqTVtFMwaBG4NYIDK+CLgbCwOfz6P4j6U7fEzFA0abBhIng01xX8LkF2fjZn087KkjUhBK1rtsbW0rbMotkfhH1Aem46c7rNwdpSlqtVBjVsarC0z1IsFAsmbZ9EZn6muUMyir8u/MWa42sY1nwYw1sML/sAI3KwdmBp76UATN4xmWsFFWuiorfWQ+FaMpzfoev2WkK9I1VVCY0PpUvtLiapAyWEqJgSk0eKotgpijJFUZSPgZ7ASlVVP1ZV9ZTpwhPi7izYHI2m4NZZEloVnGyt2PtKH+Y96oePZ/nbfEenRus/62jTK7oPxY8sh8p0wepSF57/E+p1gp9Gw/7leh0WWCeQUymn7vk2u3cjIy+DsVvGcjnzMsv6LqNT7U6QfAa+HKRLNNYNgIn7oNtkgy5xtLeyJzggmJMpJ9l4fqN+BznXgU4vwMj1MPMcDPscfHrByd9h7Qj4oDGsfRqOfFuhQuy3+HOGbknl0JW6O5YlOJZ8DBVVkkdCCGwsbfDz8Cu17tGumF38fv53RvuNvrMZgTCr+k71+ajXR8RkxjBz10wKtYXmDsmgolOjeWvvW7T3as/rnV83dzgA1Heuz4KeCzifcZ43975p3ALazfqDTQ3YPheK8kpMHl3IuECyJpku3rJkTYjKrLSZR18BHYFjwAPAQpNEJIQBXUkvflZEdl4hnk62FRqzoKiAcxnn8HXTo97RjULE902H2n4VOp9R2bvByF+g5WDY/JpuWZK29JJmQd5BqKiEJlT/GgXGcCNxdCHjAkt7LyXQqwPsXgCfdoXEEzDkE11dKncfo5x/kM8gWtVsxdIjS8vfstfOBdo8BsPWwIyzun877Z+BK0fg14mwsKluKeS+jyH1fPnGPr4ejv0IPWbqkmeliEyKBKCNZ5vynUMIUS0F1ArgVOopcgpy7ngtMz+T2ftn09S1KeP8x5khOlGWTrU78UbgG+y9spePDn9k7nAMJi03jcnbJ+Ns68xHvT6qVDPeutbpyrQO0/j70t+sOrbKeCeytgevVhB/VPd88+sQue6O3W7MaO/s3dl4sQgh7lppyaNWqqo+o6rqSmAY0MNEMQlhMM72xc/aqONa8dox5zLOUagtLLtYtiYNfp+ia4t+38sVPp/RWdvB419Cp7Gwb5muy1Vhfom7t/Fog6O1oyxdq4Cs/CzG/z2es+lnWdx7MV2xhZU9dXfkWjwI/zuoS8YYccq2hWLB9I7TSchJ4OuTX1d8ICsbaNIHBi2EqSd0S9zumw65GbDlDVjaHpYHwbY5uiKZpd3ZzLyiK9Jdt4NefysRSRE0cWmCs41zxeMXQlQbHWp1QKtqb3Zh/K8FBxeQkpvC3A+ODgYAACAASURBVG5zsbGUZsGV1bDmw3i65dN8ffJr1p9Zb+5w7lqhtpDpu6aTrEkmpHeIfmUOTOzZVs8yyGcQHx/5mF0x+te/LJfIdf8mjgCy4uH3yXckkMISwqhboy71neobJw4hhEGUljwquPFAVdXqNYdUVHuqqvLhX1FkaAq5vfa1vbUlMwZUvMuK3sWyN78BOUm6mSRWlfyC1cJS1xK9z1u6mVLfDdd1hiuGlYUVnWp34sAVSR6VR3Z+NuO3jicqLYqPur9Pj+N/wep+oEmFJ7+D4V+BUy2TxNKpdif61O/D6mOrSdYk3/2AigJ12kOfN3Qd4YIjYMA8cKgJ/yyGVX3go1bwxzQ4u/Xf5GTkOljcBj5qqUs6tRpa5jI9VVWJTI6krZcsWRNC6LTzbIelYnlH3aM9sXvYcHYDz7d5ntYexXd4EpXH9I7TCfIOYs6BORy+WnoNq8pu0aFFhCWE8XbQ27TxqJyzZBVFYVbQLHzdfXl1z6uczyjnjGF9bJsNRbfdkCzQ6LZfV6Qt4mDCQTrXlllHQlR2pSWP2iqKknn9Kwvwv/FYUZTqWdFOVAuFRVpe+TmS5TvPMaJzAxYOa0tdV3sUoK6rPfMe9eOR9nUrPH5UahT2VvY0cGpQ8k5n/oaj30L3KbrOElWBokCP6TBkOVzYrau/k51Y7K6B3oHEZscSk2XkTh3VxLWCa0zcNpETySdY2PxZev06Aw58Ah1Gwf9CwXeQyWOa2mEq+UX5LD+qX62rcnFrBEETYdQfuuVtj6yAeh0gYq2uQPuHPrCqr67odsaNf0Mq7Hyv2Ons/3Up8xIZeRn4eximiLgQoupzsHbA1933loRDVn4W7+5/Fx8XH8a3HW/G6IS+rCysWNBzAfVq1GPqjqnEZceZO6QK+fXsr3xz6hueafkMQ5oOMXc4pbKzsiOkdwg2ljYEbw8mK7/4G4cVlhFb5vaotCgy8zNlyZoQVUCJySNVVS1VVXW+/uWkqqrVfx7LWgFRKeUWFDH+m3DWHYplct9mvD+0DY92qMfeV/twYf4g9r7a564SR6BLHjV3a46lhWUJQWTA78Hg6Qs9X7mrc5lF+6dhxFpdAec1/SHl3B27BNUJApCla3q4kTiKTIrkA7tm9N00Cyxt4PlN8NBiXR0hM2jk0ognfJ/g5zM/czbtrPFO5OAO7UbAE9/AzPPw1DpoMxSuhJd5N7I4EUm6ZSlSLFsI8V8danXgWNIx8q//f2XRoUUkaZKY020OtpYVq3EoTM/F1oVlfZZRqBby0vaXiq1jVZkdSzrG7P2z6Vy7M9M6TjN3OHrxruHNop6LiM2K5dU9r5bcjbUiXOqVuT0sPgyALrWlWLYQlV1pM4+EqFIyrhUwck0o26KuMmdIa6b1b27wdp+qqhKdGl16vaMtb+nWdA9ZDlZV9IK1+f3w3B+6pWtr+utq1vxHY+fGeDl4ydK1MmgKNUze/hJHroYzL+0aA6K26+oCjd8LDbuaOzzG+4/H0dqRhYdN1A/B2g6aD4DBy0qugVTSXcrrIpIiqGFdAx9X4xQUF0JUTQG1AsjX5nMi5QT74vbx85mfea7Vc/h7yizFqqaRSyMW9lzI+fTzhk9mGFGyJpkpO6fgYe/Bwp4LsbaoPAWyy9Kxdkde6fwKu2N38/GRjw03cN+3dUWz/8vaXrf9utCEUHxcfPB08DTceYUQRiHJI1EtJGTkMnzlfiJiMvh4RAAjgxoZ5Txx2XFkF2SXXO/o3A4I/wqCJumW6VRl9TrA6C26FqtfPqRbinedoigEegcSmhBaZS7qTC2vKI/gLeMISwhjbmISD9jXhRd3Qd+3dEmUSsDVzpVx/uPYG7eXfXH7THtyPe5GFicyKRI/Dz8sFHn7EkL8K0WTAsCzm55lwrYJeNh5MLHdRDNHJSqqa52uzOg0g50xO1l2ZJm5wylTQVEB03ZOIzMvk5A+IbjZuZk7pHJ7osUTPNrsUVYdW8WWi1sMM6j/cHh4KbjUBxTd94eX6raj+72FXw2ni7fMOhKiKpCrb1HlnU3M5rFP9xGXruHL5zsxyN/baOeKTo0GwNetmJlHeVnw22So2RR6v260GEzKoymM+Vv3M333BBz97uZLgd6BZORl3CwgLv6VX5BL8C+PcSAxnNmpWTzc/U14YSvUrnxFM0f4jqBejXosPLyQIm2R6U6sx93I2+UU5HAm/YwUyxZC3GLj+Y0sOLjg5nOtqiUzP5Ntl7eZMSpxt57yfYphzYex+thq/jj/h7nDKdW8sHkcSTzCnG5zyu7GW0kpisIbXd7A39OfN/e+yem004YZ2H84TD0Os9J1368njgCOJR9DU6iRJWtCVBGSPBJV2tGYdB5fsY+8wiLWvhhI16bGbYUalRaFhWJBU7emd764dZau+O+Q5Xd+KK7KnGrBqI3Q+D7YMAH2LAJVJdA7EID9V/abOcDKpeDqcaZ90529OZd4R/HikVE7Ieh/uo52lZCNpQ1TO0zlTNoZNpzdYLoTl3E3sjjHk4+jVbVS70gIcYuQ8BByi3Jv2ZavzSckPMRMEQlDUBSF1zu/TsdaHXln7ztEJkWaO6RirYtex4+nf2R0m9EMbDzQ3OHcFRtLGxb3WkwN6xpM3j6ZjLwMo54vND4UBYWOtTsa9TxCCMMoMXl0o6taMV/SbU1UCrtOJ/HUqgM42Vnz0/iutKlr/MLDUalRNHJuhL3VbcmhC3vg4GoInAANquHdEztneOpH8HtcV9B400w87dxp6tpUimbfUJhPwc75TF8/lF0WebxVdyCPPbtN13mskuvfsD/tvdrz8dGPuVZwzXQnLuVuZHFufHDw8/AzRXRCiCoiISehXNtF1WFtac1HvT7C08GT4B3Ble6/afjVcOaFzaNb3W5Mbj/Z3OEYhJeDF4t7LybxWiLTd02nUFtotHOFJoTSsmZLXGzN0zxECFE+pXVbc/pPtzXn2zqvSbc1YVYbjsQx5suDNKrpyE8Tgmjk4WiS80anRt9Z7yg/B36bBG6Noc9bJonDLKxsYOhnunpOYZ/BT88TWKsT4VfDyS3MLfv46iz2EIWf9eCVk6vY7mDHa21fYni/BWDggu3GoigK0ztOJ1mTzOfHPzd3OCWKSIqgsUtjucgUQtyitmPtcm0XVYubnRsf99Hd3Ji8fTKaQo25QwJ0yclpO6dRx7EOH9z3QcldeKugtp5teSvwLQ7EH2DJ4SVGOYemUENEUoQsWROiCtF72ZqiKF6KojS48WXMoIQozZp/LjDlh6N0bOTG2nGBeDmZpvhwem468TnxtHRveesL2+ZA2kUY8jHYOJgkFrOxsIAB78H978HJXwk69Tf52nyOJh01d2TmkZcNm16lcHU/XrfK4m9HB2Z2mslT7V40d2Tl5u/pzwONHuCrE19Vuju7oOt0GJkUKUvWhBB3CA4Ixs7y1msBO0s7ggOCzRSRMLSmbk35sMeHRKVG8eY/b6KW1LHTRPKK8piyYwqaQg1L+yytljc1hjYbygjfEXx18iuj1Jw6cvUIhdpCOnt3NvjYQgjjKDN5pCjKYEVRzgAXgF3ARWCTkeMS4g6qqjJ/UxRz/jjJA21q8+XznXG2M10b1Og0XbHsW2YeXT4AoSug01ho1N1ksZhd10nw2Bo6xB7HSoX9F/4u+5jq5uxWWB5EUeinvOnbmU22FkzrMI2RrUaaO7IKC+4QjFbVVsrONjFZMaTlpUnbbSHEHQb5DGJW11l4O3qjoODt6M2srrMY5DPI3KEJA+pZvydTO0xly6UtrIhcYbY4VFVl9v7ZnEg5wbz75tHEtYnZYjG2GZ1m0LFWR2btm8WJlBMGHTs0IRQrxYoArwCDjiuEMB59Zh7NAQKB06qqNgb6AlLkRJhUYZGWmT9FsmLXOZ7u0oCPnwrAztq004NvdBVr4XY9eVSggV//B671od8sk8ZSKfgNw/HpH/HPL+DAqR8g8R7pupaTAuvHwTePobW25e2gJ9iYF09wQDDPt3ne3NHdlbo16vJ0q6f57dxvnEw5ae5wbhGRFAEgM4+EEMUa5DOILcO2EPlcJFuGbZHEUTU1qvUoBjcZzPKjyw3XTr6cvj31Lb+d+42JbSfSp0Efs8RgKtYW1izqtQh3O3em7JhCiibFYGOHxYfh7+mPg3U1n7UvRDWiT/KoQFXVFMBCURQLVVV3AFISX5iMJr+IcV8f5sfDsUzp14y5j7TB0sL0tWSiU6Pxsveipn1N3YYd70HKWRi8DGxrmDyeSsGnF4GtnuSUJaR/MVA3E6u6UlWI/BE+6QTHf0bbYybvthvIbwn7mdhuIi/4vWDuCA1irN9Y3GzdWHhoodmXBfxXRFIEjtaONHGpvnd4hRBClE5RFN4Oepu2nm154583OJVyyqTnPxB/gIWHFtKnfh/GtR1n0nObi7udO0t6LyE9N51pO6dRoC246zEz8zM5mXqSLt5S70iIqkSf5FG6oig1gD3At4qihAA5xg1LCJ30a/mMXBPK9uhE5jzShin9mqOYqQhxVFrUv0vWYg/B/k+gwyjw6WWWeCqLoOaPoCoKYU6u8H9D4K/XYHEbmOWq+x65ztwh3r30GPhuOKx/Adwao764i7n2Raw/9yvj/Mcxoe0Ec0doME42TkxsN5GDCQfZGbPT3OHcFJkUSRuPNtWqIKkQQojys7W0ZUnvJbjaufLS9pdI1iSb5LyxWbHM2DWDRs6NeP++97FQ9C4dW+W1qtmKWV1nEZ4YzgdhH9z1eIcSDqFVtXSuLfWOhKhK9Pm/3hBAA0wB/gLOAQ8bMyghAOIzNAxfuZ/I2Aw+eSqAkYENzRZLXlEeF9Iv4OvuCwW5sGEiONWB/nPMFlNl0dqjNY7WjhzwexBqeMOB5ZARA6i6779PrroJJK0WQj+D5YFwcS8MnI86ejPvX9zAj6d/ZEybMfyv3f/MHaXBDWs+jMYujfno8EcGucN4t64VXON02mlZsiaEEAIAD3sPlvVZRmZ+JsE7gskryjPq+a4VXCN4RzBFahEhfUJwtDZNl9/KZJDPIEa1HsUP0T/w8+mf72qs0PhQ7CztpI6hEFVMmckjVVVzAA9gIJACrL2+jE0IozmbmM1jy/dxJT2XL0d34kE/b7PGcy79HIVqoW7m0a4PIDkaBoeAnbNZ46oMrC2s6VSrE/sTj4A2/84dCjTw16twejNc2A2xh+HqSUi9AFlXITcTigpNH/jtItfdOmPqnyXw+QDYNAPqd4GJ+1G7jOfDw4tYG72WUa1HERwQbLaZcMZkZWHFyx1e5mLmRdZFmz/xdyLlBEVqkSSPhBBC3OTr7sv73d8nMimSd/e9a7Sl1qqq8va+tzmTdoYPe3xIQ2fz3cw0tykBU+hapytzQ+dyNLHinXbDEsIIqBWAjaWNAaMTQhibVVk7KIoyHFgA7AQUYJmiKDNUVf3JyLGJe9SRy2mM/vIglhYWrH0xkDZ1zd/+NDpV12nNt0ALe0Og3TPQtJ+Zo6o8AusEsjN2JzHXrlK/uB2upeiWfZXGwhqsHcDGAaztdY+t7W977HDb49v2s3Es+TVrB7C0geKSPZHrdDOkCjS65xkxsPUdsHaEoZ+B/3BU4KPDH/HNqW94puUzTOswrVomjm7oUa8HXWp3YUXECh5u8jDONuZLlN4olu3vIXcohRBC/Ktfw35MajeJj49+TFO3poxuM9rg51hzfA2bL25mSsAUute9hzrrFsPSwpIPe3zIk388ydSdU/nhoR/wcvAq1xjJmmTOpp/lIZ+HjBSlEMJYykweAW8AnVRVTQRQFMUT2ApI8kgY3M7oRCZ8E46nky1fj+lMw5qVY1rwqdRTOFg5UP/v2VDDCwa8Z+6QKpUg7yAAQt29qZ8Yc+cONWrDk99BwTVdguaW78Vt0/z7OP8aaNJv3VaggUJN+QNVLItPSl09AcVNebd3gbZPoKoqS48s5csTX/JkiyeZ2WlmtU4cga4o6fRO0xn++3BWRa7i5Y4vmy2WiKQIGjk3wtXO1WwxCCGEqJxe9H+Rc+nnWHJ4CT4uPvSq38tgY++J3cPS8KUMbDTQKImpqsjF1oWlfZby9J9PM3XHVD4f+Dm2lrZ6Hx8WHwZAoHegsUIUQhiJPskjixuJo+tS0K9WkhDl8suRWGb8GEmL2k58+XxnPJ30fyMytujUaFpYOmCRGA4jfgB7+RD7X41dGuNl78UBVw+GpSX/O4MHdMmZ++dAvQ6GPalWC4W5ZSeg8nOKT0r9N3lVUq2EzHgAlkcsZ/Wx1Tze/HFe7/J6tU8c3eDr7svgJoP59tS3PNHiCeo51TN5DKqqEpkUec/f7RVCCFE8RVGY3W02l7Mu88ruV/jmwW9o5tbsrse9mHGRV3a/QnO35rzb9d175r1fH83cmvF+9/eZunMqcw/MZXbX2Xr/fsISwnCydtLVERVCVCn6JI/+UhRlM/D99edPAH8aLyRxL1q95zxzN56ia5OarBzZASc7a3OHdJNW1RKdcpKH01LA/wloMdDcIVU6iqIQWCeQ3bG70T4UgsX2OZARCy71oO/b4F/GkrWKsLDQLXGzcbj7sRa3uV7k+zYu9VgRsYIVESt4tNmjvBn45j138fhS+5fYcmkLS8KXsLDnQpOfPzY7ltTcVKl3JIQQokR2VnaE9A5hxMYRvLT9Jb4b9B3udu4VHi87P5vgHcFYWlgS0icEB2sDXGtUM/0a9mOc/zhWRq6kpXtLnmr5lF7HHYg/QMfaHaV7qhBVkD4Fs2cAKwH/61+fqar6irEDE/cGVVWZt+kUczee4kG/2nzxfKdKlTgCiMu4SE5RLr7YwMD55g6n0gr0DiQ9L52oen4w9TjMStd9N0biyND6vq2bIfVf1vasbt2HT45+wuAmg3kn6J17qi3vDbUcazGq9Sg2X9x8V8UxK+pGvSNJHgkhhChNLcdahPQOIVmTzLSd0ygoqli3UK2q5bV/XuNS5iUW9VxE3Rp1DRxp9TGx3UR61evFhwc/5GDCwTL3j82KJS47ji7eXUwQnRDC0PT6JKSq6npVVadd//rF2EGJe0NBkZbpP0ayctd5nglswLIRAdhaVb67EFH7PgLANzAYHCp+F6u6u7F2/UD8ATNHUgH+w+HhpeBSH1DApT5fdh5BSPwOBvkMYnbX2fdk4uiGUa1H4WnvyYJDC4zWzaYkkUmR2FvZ08S1iUnPK4QQourx8/RjdtfZHL56mPdC36vQe9aKiBXsjNnJjE4z6Ozd2QhRVh8WigXz7ptHA+cGvLzzZa5kXyl1/xsJpi61JXkkRFVU4qchRVHGKIoy4z/PYxVFyVQUJUtRlPGmCU9UV5r8IsZ9fZifw2OZ2q85c4a0wdKiEi4HunqSqDN/YAk0af+8uaOp1DwdPGnq2pQDV6pg8gjYWMOR++vXwb9xA7rVdmHRla080OgB5nabe89PrXawduCl9i8RmRTJ5oubTXruiKQI/Dz8sLLQZ5W1EEKIe92DPg8y1m8sP5/5me+ivivXsdsubePTiE8Z3GQwT/nqtwzrXlfDpgYhvUMo0BYwZccUNKU0NDkQf4CadjXlhpAQVVRpt9LHA5//53mSqqrOgCcwwqhRiWot/Vo+T68+wM7oRN4b2obgfs0qZx2ZokL4dSLR9g40dm6EnZWduSOq9AK9AwlPDCevpALUldTG8xuZtW8W8TnxqKhk5mdioVjQvW53SVpcN7jJYJq7NWdJ+BKT/ffVFGo4nXpalqwJIYQol0ntJ9Gnfh8+PPgh++L26XXM2bSzvP7P67Sp2Ya3g96unNemlVRjl8Z80OMDolKjeGffO8XO+FJVlbCEMDp7d5bfrRBVVGnJI0VV1ZT/PP8RQFXVXMC++EOEKF18hobHV+zneFwmy58O4OkuDc0dUsn2L4MrR4iq4UYLj9bmjqZKCPQOJK8ojyOJR8wdSqnyivI4l36OnTE7+b8T/8fs/bPJLcq9ZR+tquXjox+bKcLKx9LCkukdpxOXHcd3p8p3J7eiTqacpFAtxN/T3yTnE0IIUT3cWE7V1LUp03dN50LGhVL3z8jLIHhHMPZW9izuvbhcreeFTo96PXip/UtsurCJL098ecfrFzIukKxJliVrQlRhpd1Sv6UXuaqq7wMoimIBeBgzKFE9nU3M4tk1YWTlFvLV6M4ENalp7pBKlnQadswjzfcBruadwNdN2onqo2PtjlgpVhy4cuBmDSRzyc7PJiYrhpisGC5nXSY2K5bLWZeJyYrhas5VVMqug5CQk2CCSKuOoDpB9KjXg1WRq3ik6SO42bkZ9Xw3imVL8kgIIUR5OVg7sKzPspsd2L598FtcbF3u2K9IW8Qru1/hSs4VPh/wObUda5sh2urhBb8XOJV6iiXhS2ju1pxudbvdfO1GTUwpli1E1VVa8miLoihzVVV987bts4EtRoxJVEPhl9MY/eVBrCwsWDsukNZ17nzzrjS0RfDrRLBxIKrTc/DPTHxrSvJIH47Wjvh7+pukaLaqqqTlpemSQ5m3JodismJIzU29ZX93O3caODWgc+3O1HOqRwOnBtR3qk8DpwYM/2M48Tnxd5xDLiDv9HKHl3n0t0f5NOJTXu/yulHPFZEYQQOnBnfVblkIIcS9q06NOizpvYTRm0czfdd0Pu336R3L0UOOhLD3yl7eDnqb9l7tzRRp9aAoCnO7zeVi5kVm7J7B2kFraeDcAICwhDDq1qhLPad6Zo5SCFFRpSWPZgCrFUU5C0Rc39YWOAS8YOzARPWxIyqRCd8eppazHV+P7kKDmg7mDql0Bz6F2IPw6CqiNVcBaOHWwsxBVR2B3oF8GvEpGXkZxd7hKw+tqiXxWuLNhNCNRNGNx9kF2Tf3VVCo7Vib+k716V2/ty4x5KxLENV3qo+jtWOJ5wkOCGbWvlm3LF2zs7QjOCD4ruKvjnxcfRjWfBjrotfxpO+T+Lj4GOU8qqoSmRxp9hlsQgghqrb2Xu15J+gd3tr7FgsOLuC1Lq/dfG3ThU18cfwLhjcfzuPNHzdjlNWHg7UDIb1DGLFxBME7gvnmwW+ws7QjLCGMfg36mTs8IcRdKDF5pKpqDjBCURQf4EbBl5Oqqp4zSWSiWlgfHsuMnyJp6e3EF6M64+lUydeQp5yD7XOg+QPg9zhR/7xGLYdaRl+eU51oVS0qKt3Xdsfb0ZvggGAG+Qwqcf8CbQEJ2Qk3Zw3dnD2UGUNsduwtxZmtFCvqOunuWrXzandz9lB95/rUrVG3wjUKbsQXEh5CQk4CtR1rlxn3vWxC2wn8cf4PFh9azLK+y4xyjis5V0jWJEuxbCGEEHftkaaPcDbtLF+d/IprBdcITQglIScBFZWGTg15tfOr5g6xWqnvVJ8FPRYwfut4Rv81mkRNIln5WeyI2cHG8xvl+kqIKqrMNkKqqp4HzpsgFlHNrNp9nvf+PEXXJjVZObIDTnbW5g6pdFot/DoJrGzhocWgKESnRuPrLkvW9LXx/MZbiiTG58Qza98s8ovy8fPwuzU5dP3rSvYVitSim8fYWdpRz6keDZ0b0r1udxo4N7i5zKy2Y22jdT8b5DNILmb0VNO+Ji/4vUBIeAhh8brOKYYWkaib8CrJIyGEEIYwtcNU9l/Zz4ZzG27ZnnAtgS2Xtsg1gIEF1QnigUYPsPHCxpvb0vPSmbVvFoD8voWogqQHtTA4rVZl/l9RfLb7PIP8vPnoibbYWlmaO6yyHVwFl/fBkOXg7E1uYS4XMi7Qp0Efc0dWZYSEh9zRtSy3KJe39719yzYnayfqO9endc3WDGw08ObSsgbODfC095QWrlXAyFYjWRe9joWHFrL2obVYKKU17yy/yORI7K3saebWzKDjCiGEuDdZWliSkZ9xx/a8ojxCwkMkmWEE4Ynhd2zLLcqV37cQVZQkj4RBFRRpeeXnSNaHx/FsUEPeebg1lhZVIBGQegG2zoKm/aHdUwCcSz9HkVokM4/KobTuZPPvm39zmZmLrYskiKo4W0tbpgRM4ZU9r/D7ud8Z0nSIQcePSIygdc3WRptpJoQQ4t6TeC2x2O3SXdU4Svq9yu9biKqpxFvFiqK4l/ZlyiBF1XAtv5AX/+8Q68PjeLl/c94dXEUSR1ot/PYSKJbw8BK4ntSISo0CwNdNkkf6Kqk7mbejN4N8BuHn6YernaskjqqJBxo/gJ+HH0uPLEVTqDHYuLmFuUSlRsmSNSGEEAZV0nWKdFc1Dvl9C1G9lLbO4DC6zmqHgSTgNHDm+uPDxg9NVCVpOfk8vTqUXaeTeH+oHy/1bVZ1EgSHv4CLe2DAXHD5t33oqdRTOFo7UteprhmDq1qCA4Kxs7S7ZZt0Lau+FEVhRqcZJF5L5KsTXxls3FOppyhUC/H39DfYmEIIIYRcp5iW/L6FqF5K67bWGEBRlFXAL6qq/nn9+QPAI6YJT1QFV9I1PPt5GJdTr7H86QAGtvE2d0j6S78Mf78NPr0g4LlbXopOjaaFWwuD13KpzqRr2b2nvVd7+jfsz+fHP+exZo/h6eB512NKsWwhhBDGINcppiW/byGqF0VV1dJ3UJRjqqr6lbXNVDp27KgeOnTIHKcWxThzNYtnPw8jO7eQVc91JNCnprlD0p+qwtdDISYMJu4Ht4Y3X9KqWgK/C2Ro06G81uU1MwYpROUXkxnD4F8HM7jJYN7t+u5djzd1x1SiUqPY9NgmA0QnhBBCCCGE0IeiKIdVVe1Y3Gv6TKm4oijKm4qiNLr+9QZwxbAhiqro8KU0hq3YT6FW5YdxQVUrcQRw5Gs4vwP6v3tL4gggJisGTaFGimULoYf6zvUZ4TuCX878QnRq9F2NpaoqEUkRsmRNCCGEEEKISkSfNjYjgHeAX64/3319m7jHbDgSx4LN0VxJ1+DuaEOGJp96bg58PaYL9d0dzB1e+WTEweY3oNF90HHMHS/fKJbdwr2FqSMTokoa5z+O3879xqJDi1jZf2WFa54l5CSQpEmSJWtCCCGENa+DXAAAIABJREFUEEJUImXOPFJVNVVV1WBVVdtf/wpWVTXVFMGJymPDkTheW3+MuHQNKpCSk0+RCmPua1z1EkeqCn9MAW0hDF4KFnf+GUSnRmOlWNHEtYkZAhSi6nGxdWG8/3j2x+/nn7h/KjxORNL1ekdekjwSQgghhBCisihx5pGiKL8DJRZEUlV1sFEiEpXSgs3RaAqKbtmmqrBi53lGBjYyT1AVFbEWzmyBgfPB3afYXaJSo2js2hhbS1sTBydE1fVEiyf4Pup7Fh1aRFCdIKws9JncequIpAjsLO1o7tbcCBEKIYQQQgghKqK0K/uFJotCVHpX0jXl2l5pZSXAX69A/UDoPK7E3aJTo+ni3cWEgQlR9VlbWjOtwzSm7JzC+jPrGd5ieLnHiEyKpFXNVlhbWBshQiGEEEIIIURFlJg8UlV1143HiqLYAw1UVb27SqiiynK2tyJDU3jH9jqu9maIpoJUFf6YBoV5MOSTYperAaRoUkjUJEq9IyEqoE+DPgR4BfDJ0U94sPGD1LCpofex+UX5nEo9xTOtnjFihEIIIYQQQojyKrPmkaIoDwNHgb+uP2+nKMpvxg5MVB5fH7hEhqYQi9vq39pbWzJjQBVIsESug8Vt4F1XiN4Ivg+BR9MSd7/RLaqle0tTRShEtaEoCjM7zSQ1N5U1x9eU69iTKScp0BbQ1kPqHQkhhBBCCFGZlJk8AmYBnYF0AFVVjwKNjRiTqETWhl3mrQ3H6dfSiw8f86euqz0KUNfVnnmP+vFI+7rmDrF0kevg98mQEfPvtuiNuu0liEqTTmtC3I3WHq15yOchvj75NfHZ8XofJ8WyhRBCCCGEqJz0qWZaoKpqxm1tl0sspC2qj58Ox/LaL8fo2dyTT54OwNbKkmEd65s7rPLZNhsKbqvLVKDRbfcvvh5LVGoU3o7euNi6mCBAIaqn4IBg/r70NyFHQph/33y9jolMiqRujbp42HsYOTohhBBCCCFEeegz8+iEoihPAZaKojRTFGUZsM/IcQkz+/VoHDN+iqBbEw9WjuyArZWluUOqmIzY8m1Ht2xNZh0JcXdqO9bm2VbPsvH8Ro4nH9frmIikCPw9/I0cmRBCCCGEEKK89EkevQS0BvKA74FMYIoxgxLmtTEynqk/HKVLY3dWPdsRO+sqmDhSVQhbRYmT5FzqFbtZU6jhYuZFfN19jRebEPeIMX5jcLdzZ8HBBahq6RNWE3ISuHrtqixZE0IIIYQQohIqM3mkquo1VVXfUFW1k6qqHa8/zjVFcML0/jqewOS1R+jQ0I01z3XC3qYKJo7yc2D9i/DndKjlB1a3dYSztoe+bxd76Nm0s2hVLb5ukjwS4m45Wjvyv3b/IzwxnG2Xt5W6b2RSJABtPSV5JIQQQgghRGVTYvJIUZQl17//rijKb7d/mS5EYSpbT17lpe/DaVvPhS+e74yjrT4lsSqZ5DOwqi8c+xH6vAnjdsPgpeBSH1B03x9eWnK9IymWLYRBPdrsUZq4NGHx4cUUFBWUuF9EUgS2lra0cJO/PSGEEEIIISqb0rIDX1//vtAUgQjz2hmdyMRvw2nl7cyXoztToyomjk5sgF//B1a2MPIXaNJbt91/eInJottFpUThZO1E3RqVvIucEFWElYUVL3d8mYnbJrI2ei0jW40sdr+IpAha1WyFtaW1iSMUQgghhBBClKW0DEESgKqqu0wUizCTPWeSePHrwzSrVYP/G90FZ7sq9uGtqAC2zoL9H0O9TvD4lyXWNCpLVFoULdxbcFt3QSHEXehetztd63RlRcQKBjcZfEcnw/yifE6lnGKE7wgzRSiEEEIIIYQoTWk1jzbceKAoys8miEWYwb5zybzw1SF8PBz5ZkwXXByqWOIoMx6+eliXOOo8Dkb9WeHEUZG2iDNpZ6RYthAGpigKL3d8meyCbFZGrrzj9ajUKPK1+VIsWwghhBBCiEqqtOTRf6de+Bg7EGF6YRdSGfPlIRq4O/DtC11wc7Qxd0jlc2EPrLwP4iPgsTXw4IdgVfGf4XLWZTSFGql3JIQRNHdrztCmQ/k+6nsuZ16+5bWIpAhAimULIYQQQghRWZWWPFJLeCyqgcOX0nj+izC8Xe34dmwXatawNXdI+lNV+GcJ/N9gsHOFsdvBb9hdDxudGg0gM4+EMJJJ7SdhbWHN4sOLb9kemRRJbcfaeDl4mSkyIYQQQgghRGlKSx61VRQlU1GULMD/+uNMRVGyFEXJNFWAwvCOxqQz6vMwvJzt+H5sIF5OduYOSX+adFj7NGx9B1oNgRd3gFdLgwwdlRqFlYUVTVyaGGQ8IcStPOw9GN1mNFsvb+Xw1cM3t0ckRcisIyGEEEIIISqxEpNHqqpaqqrqrKqqk6qqVtcf33jubMogheEcj8vg2TWhuDna8N3YLtRyrkKJo4Rj8FkvOLMZBs6HYV+ArZPBho9Ki6KJSxPp9iSEET3X+jm8HLxYeHAhWlVL4rVE4nPiJXkkhBBCCCFEJVbazCNRzZy8kskza0JxsrPmu7Fd8HaxN3dI+jv6HazuB4W5MGojBE4AA3dEi06NlnpHQhiZvZU9wQHBHE85zqYLm4hMigTA39PfzJEJIYQQQgghSiLJo3vE6atZPLMmFHtrS74fG0g9Nwdzh6Sfglz4PRg2TIB6nWDcbmgQaPDTJGuSSdYkS70jIUzgIZ//b+/O46wsC/6Pfy6GYd9xAVlEZEfZRKAkMzG0MMU0RaTssTIri7LsZ+rjbpbpk2g8lVu2sKi4Sz3mUpopCOEAAjOAyCq4DTsMDDPX749zVJA5wOjMuc/MfN6vF6+Zc537vs93Bs/ryPd1Xdd9Kr3b9OammTdxxYtXAPCT53/C9GXTE04mSZIkqSKWR3XA0re3MPaumdSvF5j8rWF0bltDiqP1y+HekfCf+2D4JfDVR6FZ9WyoW1hcCLhZtpQN9UI9hncYzsadG9m2axsA67au45qXrrFAkiRJknKQ5VEt98a7Wxl71wwAJn9rGEcc1DThRAdo8d/h95+F4uUwZgqcdDXk1a+2l3u/PHLZmpQdTy57cq+xkrISJsyZkEAaSZIkSftSreVRCOGUEEJRCGFpCOGyCp6/KIQwP4RQEEJ4MYTQpzrz1DUr39vG2LtmUFYemfytoXQ7pFnSkfavvAyeuxEmfwVadoJv/xN6fbHaX7aouIgOzTrQooF7wUvZsG7rukqNS5IkSUpOtZVHIYQ8YCLwBaAPcG4F5dDkGOPRMcYBwM3A/1RXnrpmVfE2zr1rBttLy/jLN4fS49CquytZtdn6HvzlTHjhZhgwDr75NLTpmpWXLiwupGdrZx1J2dKuabtKjUuSJElKTnXOPBoCLI0xLosx7gSmAqfvfkCMcdNuD5sCsRrz1BlvbtjO2LtnsLmklL98Yyi929eA2TSrZ8Pvj4cVL8Fpd8DoiZCfnbvBbSvdxopNK9zvSMqi8YPG0yiv0R5jjfIaMX7Q+IQSSZIkScqk+jaRgQ7Aqt0erwaGfvSgEML3gEuABsCJFV0ohHAhcCFA586dqzxobbJuYwlj75rBhq2l/OWbQzmqQ8ukI+1bjDDrbvi/n0GL9vCNv8NhA7IaYcmGJUSi+x1JWTSq6ygAJsyZwLqt62jXtB3jB43/YFySJElS7qjO8uiAxBgnAhNDCGOBK4HzKzjmTuBOgMGDBzs7KYO3N5cw9u4ZvLN5B3/6xlD6d2qVdKR927kVnhgP8x+E7ifDl38PjVtnPUZRcRHgndakbBvVdZRlkSRJklQDVGd5tAbotNvjjumxTKYCv63GPLXau1t2cN5dM1m3sYQ/XjCEYw7PfglTKe8shge+Cu8uhhP/G4ZfAvWSufnfouJFtGjQgvZN2yfy+pIkSZIk5bLqLI9mAd1DCEeQKo3GAGN3PyCE0D3GuCT9cBSwBFXa+q07GXf3TFat38Yfvj6EY7u0STrSvi14BB67GOo3hHEPw5GfSzROUXERvdr0IoSQaA5JkiRJknJRtZVHMcZdIYSLgaeAPODeGOOCEMJ1wOwY4+PAxSGEk4BSYD0VLFnTvm3cVsq4e2byxrtbuef8Y/nUkW2TjpRZWSk8fTXMmAgdj4Wv/BFadkg00q7yXSxev5ize56daA5JkiRJknJVte55FGP8K/DXj4xdtdv33lbnE9hUUspX753Jkre2cOfXjmF494OSjpTZpjfhwf+CVTNgyLdh5A1Qv0HSqVi5aSU7yna435EkSZIkSRkkvmG2Pp7NJaWcf+8rLFq7id+NO4YTeh6SdKTM3ngBpl0AO7fBmffA0WclnegDhcWFAPRs7Z3WJEmSJEmqiOVRDbR1xy4uuG8W81dvZOJ5gxjR+9CkI1UsRvj3bfDsddC2G5z/JBySWzN8CtcXkl8vn64tuyYdRZIkSZKknGR5VMNs31nGN/44izkrN3D7mIGc3Ldd0pEqtn0DPPpdKJoOfc+A0+6Ahs2TTrWXouIiurXqRn5eftJRJEmSJEnKSZZHNUhJaRnf/NMsXnmjmF+fM4BR/XL01vJr58EDX4ONq+CUX8DQiyAH72QWY6SwuJDjOx6fdBRJkiRJknKW5VENUVJaxoV//g8vvf4et5zVn9MHJHuXsoxenQTTL4HGreHrf4XOQ5NOlNE729+huKTYzbIlSZIkSdoHy6MaYOeucr47aQ4vLH6HX555NGce0zHpSHsrLYG//RTm/BG6fAbO+gM0OzjpVPv0/mbZlkeSJEmSJGVmeZTjSsvKuXjyHJ4rfJsbRh/FOcd2TjrS3tYvTy1TWzsXhl8Cn7sC8nL/P62i4iIAerTukXASSZIkSZJyV+7/C78O21VWzg+nFvD3hW9x7Wl9GTfs8KQj7W3x3+Hhb6XurHbuVOj5haQTHbDC4kI6NutI8wa5t5G3JEmSJEm5wvIoR5WVRy55YC7T56/lylG9Of/TXZKOBPMegGevg42roWUHaNc/dTe1dkfD2X+CNjXrdvdF64tcsiZJkiRJ0n5YHuWgsvLIpdPm8vjcN/l/p/Tim5/JgVJm3gPwxA+gdHvq8cbVqT+HHwfjHoL8xsnmq6StpVtZuWklp3Y9NekokiRJkiTltHpJB9Ceyssjlz88n4fnrOHHn+/Bd044MulIKc9e92FxtLsNK2tccQSwZP0SItGZR5IkSZIk7YflUQ6JMfLfj73G/bNX8YMTu/H9Ed2TjvShjasrN57jvNOaJEmSJEkHxvIoR8QYufaJhUyauZLvnHAkP/p8jt0BrGXHyo3nuMLiQlo2bMmhTQ5NOookSZIkSTnN8igHxBi5cfoi7ntpOd8cfgQ/PbknIYSkY+1pxFVMb9aMkR0Po1+XTozseBjTW7SCEVclnexjKSwupFebXrn3e5YkSZIkKcdYHiUsxsgv/6+Iu198g69/ugtXjOqdk4XG9AZwTdtWrM2vTwyBtfn1ueagtkxv1jTpaJW2q3wXS9YvoVdrl6xJkiRJkrQ/3m0tAY++uoZfPVXEmxu206xhfTbv2MV5Qztz9Zf65GRxBDDhP7dRUm/PrrEkljJhzgRGdR2VUKqPZ/nG5ews30nPNj2TjiJJkiRJUs5z5lGWPfrqGn728HzWbNhOBDbv2EVevcDgzq1ztjgCWFe6ucLxtVvXMm3xNLaVbstyoo+vcL2bZUuSJEmSdKAsj7LsV08Vsb20bI+xsvLILU8vTijRAXiniLZluyp8qn69+lz78rWMeHAEP5/5c17f8HqWw1VeUXERDeo1oEvLLklHkSRJkiQp57lsLcve3LC9UuO5oOzVSTSKe483ymvE1Z+6mg7NO3B/0f1MWzyNKYVTOObQYzin5zmc1Pkk8vPysx94PwqLC+nWuhv59XIvmyRJkiRJucaZR1l2WKvGlRpPXHkZ05ZMY3V+fc7peQ7tm7YnEGjftD3XfPoaTj3yVAYeMpBffOYXPPOVZ/jRMT9i3dZ1/PSFn3LStJO4fc7tvLnlzaR/ig/EGCkqLnLJmiRJkiRJB8iZR1l26ck9+dnD8/dYutY4P49LT87NzZvfXfQYExoHhrboyhVDr+DKYVdmPLZNozZccNQFfL3v13npzZe4v+h+7nntHu6efzfHdzyes3uezXGHHUdevbws/gR7emvbW6zfsZ6erXPz9y1JkiRJUq6xPMqy0QM7AHxwt7XDWjXm0pN7fjCea3716gRK6gWu/OyvDnhD73qhHsM7DGd4h+Gs3bKWaUum8dDih3h+9fN0aNaBs3qcxRndzqBt47bVnH5vRcVFAPRu2zvrry1JkiRJUk1keZSA0QM75GxZtLuXlz/NX8uK+U7TbnRp0+NjXaN9s/Z8f+D3uajfRTy76lkeKHqACXMmMLFgIp8//POM6TmGgYcMzNqd5gqLU3da69H64/08kiRJkiTVNZZHqtCOsh3c+PJ1dC4t5RvDfvaJr5efl88pXU7hlC6nsGzDMh5Y/ACPL32cv73xN7q16sY5Pc/h1K6n0qxBsypIn1nR+iI6N+9M0/ym1fo6kiRJkiTVFm6YrQrdO/9eVuzcwBW7mtOw45AqvXbXVl25bMhlPPOVZ7j209fSIK8BN868kRMfPJHrXr7ug6Vl1aGwuJCebdzvSJIkSZKkA+XMI+1lxaYV3DX/Tr6wZSufHnghVNOSsib5Tfhy9y/z5e5f5rV3X2Nq4VQef/1xHlz8IP0P7s85Pc9hZJeRNMxrWCWvt2XnFlZtXsXobqOr5HqSJEmSJNUFzjzSHmKM3DDjBhrFwE/Xb4Kjz87K6x510FHcMPwGnv3Ks1w6+FI27tjI5S9ezkkPnsSts29l1aZVn/g1Fq9fDECvNr0+8bUkSZIkSaorLI+0h7+98TdmrJ3BDzaXcNARJ0LzQ7P6+i0btuRrfb/G46Mf566Rd3Fsu2P588I/88VHvsi3n/42z618jl3luz7Wtd/fLLtna5etSZIkSZJ0oFy2pg9s2rmJm2fdzFHNOvOVN16EE36eWJYQAsPaD2NY+2G8ve1tHlryENMWT2P8P8ZzaJNDOavHWZzZ/UwObnLwAV+zsLiQ1g1bc0iTQ6oxuSRJkiRJtYszj/SB2+fczvod6/nvXc3Ia9QKen4h6UgAHNLkEL7T/zs8deZT3Pa52+jasisTCyYyctpILvnnJcxcO5MY436vU1hcSK82vQjVtIeTJEmSJEm1kTOPBMD8d+bzQNEDnNf9LPo8+xsY9DWoXzUbVVeV+vXqM6LzCEZ0HsGKTSt4sOhBHn39UZ5e8TRdWnTh7J5nc9qRp9GyYcu9zi0tL2XphqWM6z0ugeSSJEmSJNVczjwSu8p3cd2M6zi48cF8L+9gKNsBA8YmHWufDm9xOD859ic8c9Yz3Dj8Rlo0bMHNs27mpAdP4qp/X8WCdxd8cOz0ZdMZOW0kpeWlPLL0EaYvm55gckmSJEmSahZnHokphVMoLC7k1s/eSrNnboaDe8NhA5OOdUAa1W/EaUeexmlHnkZhcSH3F93P9GXTeWTpI/Rt25debXoxfdl0SspKANiwYwPXvHQNAKO6jkowuSRJkiRJNYMzj+q4dVvX8ZtXf8NnOnyGzzftAqtnpWYd1cB9gXq16cXVn7qaZ7/yLJcPvZySXSU8tOShD4qj95WUlTBhzoSEUkqSJEmSVLNYHtVxN8+6mbJYxuVDLyfMnQIhD/qdnXSsT6R5g+ac2+tcHjn9kYzHrNu6LouJJEmSJEmquSyP6rAXVr/A0yue5qL+F9GxaXuYOxW6jYDm7ZKOViVCCLRv2r7C59o1rR0/oyRJkiRJ1c3yqI7avms7P5/5c7q27Mr5fc6HZf+EzW/m/EbZlTV+0Hga5TXaY6xRXiPGDxqfUCJJkiRJkmoWN8yuo+6cdydrtqzhDyf/gfy8fCiYDI1aQY8vJB2tSr2/KfaEORNYt3Ud7Zq2Y/yg8W6WLUmSJEnSAbI8qoOWrl/Kfa/dx+lHns7gdoNh+wYofBIGjoP8Rvu/QA0zqusoyyJJkiRJkj4ml63VMTFGrp9xPU0bNOWSwZekBhc8ArtKat2SNUmSJEmS9MlZHtUxj73+GHPensMlx1xCm0ZtUoMFk+HgXnDYoGTDSZIkSZKknGN5VIdsKNnArbNvZeAhAxndbXRq8N0lsPqV1KyjEJINKEmSJEmSco7lUR3y6zm/ZsvOLVw57ErqhfRf/dwpEOpBv3OSDSdJkiRJknKS5VEdMeetOTy85GG+2ver9GjdIzVYXgZzp0K3k6B5u2QDSpIkSZKknGR5VAeUlpdy/Yzrad+0PRf1u+jDJ954Hjatgf7nJhdOkiRJkiTltPpJB1D1+/PCP7N0w1LuOPEOmuQ3+fCJgsnQqCX0/GJy4SRJkiRJUk5z5lEtt2bLGn5b8FtO7HQiJ3Q64cMnSjbCoifgqLMgv1Fi+SRJkiRJUm6zPKrFYozcNPMmQghcNuSyPZ9c8AjsKoEB5yUTTpIkSZIk1QiWR7XYc6ue4/nVz/O9Ad+jfbP2ez5ZMBkO6gkdBiUTTpIkSZIk1QiWR7XUttJt3DTzJnq07sHY3mP3fPLdpbBqJgwYCyEkE1CSJEmSJNUIbphdS00smMhb297ils/eQn69/D2fnDsFQj3od04y4SRJkiRJUo3hzKNaqLC4kEmLJnFWj7MYcMiAPZ8sL0uVR0eOgBbtK76AJEmSJElSmuVRLVMey7n+5etp2bAlPxz0w70PeOMF2LQGBpyb/XCSJEmSJKnGsTyqZaYtnsa8d+fxk8E/oWXDlnsfUDAZGraEnqOyH06SJEmSJNU4lke1yLvb3+W2ObdxbLtjObXrqXsfULIRFj0BR58J+Y2yH1CSJEmSJNU4lke1yK2zb2X7ru1cOexKQkV3UVvwKOzaDgPOy344SZIkSZJUI1ke1RIz187kyWVPcsFRF9C1ZdeKDyqYDAf1gA7HZDecJEmSJEmqsSyPaoGdZTu5YcYNdGreiW8d/a2KD3rvdVg1AwaMhYpmJUmSJEmSJFWgftIB9Mnd+9q9LN+0nN+d9Dsa1c+wl1HBZAj1oN852Q0nSZIkSZJqNGce1XArN63krnl3cUqXUziuw3EVH1ReDnOnwpEnQovDshtQkiRJkiTVaJZHNViMkRtn3kiDvAZceuylmQ9c/gJsWp1asiZJkiRJklQJlkc12FPLn+KlN1/i+wO/zyFNDsl8YMFkaNgSeo7KXjhJkiRJklQrWB7VUJt3buaXs35Jn7Z9OKfnPvYxKtkECx+Ho74M+Rn2Q5IkSZIkScrADbNrqDtevYPikmJ+M+I35NXLy3zgwkdh13YYcF72wkmSJEmSpFrDmUc10IJ3FzC1cCpjeo6hb9u++z64YDK07Q4dB2cnnCRJkiRJqlUsj2qYsvIyrn35Wg5qfBAXD7x43we/9zqsfDm1UXYI2QkoSZIkSZJqFcujGmZq0VQWFS/ip0N+SvMGzfd98NwpEOpB/zHZCSdJkiRJkmody6Ma5O1tb3PHq3dw3GHHcfLhJ+/74PJymDsVun4OWhyWnYCSJEmSJKnWsTyqQW6edTOlZaVcMfQKwv6WoS3/F2xclVqyJkmSJEmS9DFZHtUQL655kaeWP8WF/S6kU4tO+z+hYDI0bAm9RlV/OEmSJEmSVGtZHtUAJbtKuGHGDXRp0YX/Ouq/DuCETbDwMTjqDMhvXP0BJUmSJElSrVU/6QDavzvn3cmaLWu4Z+Q9NMhrsP8TFj4Gu7bDgPOqP5wkSZIkSarVnHmU45ZtWMYfFvyBL3X9EkPaDzmwkwomQ9tu0PHY6g0nSZIkSZJqPcujHBZj5PoZ19O4fmN+PPjHB3bSe6/DypdSG2Xvb1NtSZIkSZKk/bA8ymFPLHuC2W/N5kfH/Ii2jdse2ElzpwIB+o2p1mySJEmSJKlusDzKURt3bOSWWbfQ7+B+nNn9zAM7qbwc5k6BIz8HLTtUb0BJkiRJklQnWB7lqF//59ds2rmJq4ZdRb1wgH9NK16EjavcKFuSJEmSJFUZy6McVPB2AQ8teYhxvcfRs03PSpw4GRq2gF6jqi+cJEmSJEmqUyyPckxpeSnXzbiOQ5scyncHfPfAT9yxGRY+Bn3PgPzG1RdQkiRJkiTVKfWTDqA9TVo4iSXrl3Db526jSX6TAz9x4WNQus0la5IkSZIkqUo58yiHrN2ylv+d+7+c0PEETux0YuVOLpgMbY6ETkOqJ5wkSZIkSaqTLI9yyE2v3ATAz4b+jBDCgZ9YvAxW/BsGjIXKnCdJkiRJkrQflkc54rmVz/GPVf/gO/2/w2HNDqvcyXOnAgH6j6mWbJIkSZIkqe6yPMoB20q3cdMrN9GtVTfG9RlXuZPLy6FgCnQ9AVp2rI54kiRJkiSpDrM8ygG/m/s71m1dx1Wfuor8evmVO3nFv2HjSjfKliRJkiRJ1cLyKGGL1y/mTwv/xJndz2TgIQMrf4GCydCwBfQaVfXhJEmSJElSnWd5lKDyWM71L19PiwYt+OGgH1b+Aju2wMLHoO8Z0KBJ1QeUJEmSJEl1XrWWRyGEU0IIRSGEpSGEyyp4/pIQwsIQwrwQwrMhhMOrM0+umL5sOiOnjaT/n/pT8E4BIzqPoFWjVpW/0MLHoHRr6i5rkiRJkiRJ1aDayqMQQh4wEfgC0Ac4N4TQ5yOHvQoMjjH2A6YBN1dXnlwxfdl0rnnpGtZuXfvB2JPLnmT6sumVv1jBZGjTFToNrcKEkiRJkiRJH6rOmUdDgKUxxmUxxp3AVOD03Q+IMf4jxrgt/XAGUOtvFzZhzgRKykr2GCspK2HCnAmVu1DxG7DixdSsoxCqMKEkSZIkSdKHqrM86gCs2u3x6vRYJt8A/lbREyGEC0MIs0MIs995550qjJh967auq9R4RnOnAgH6jfnkoSRJkiRJkjLIiQ2zQwjjgMHAryp6PsZ4Z4xxcIxx8MEHH5zdcFWsXdN2lRqvUHk5zJ0MXT8LrTpVUTJJkiRJkqS9VWd5tAbYvdnomB7bQwj5J8KDAAAMZElEQVThJOAK4LQY445qzJMTxg8aT6O8RnuMNcprxPhB4w/8Iiv+DRtWwoDzqjidJEmSJEnSnupX47VnAd1DCEeQKo3GAHvcFiyEMBD4PXBKjPHtasySM0Z1HQWk9j5at3Ud7Zq2Y/yg8R+MH5C5U6BBc+h1ajWllCRJkiRJSqm28ijGuCuEcDHwFJAH3BtjXBBCuA6YHWN8nNQytWbAgyG16fPKGONp1ZUpV4zqOqpyZdHudmyBBY/C0WdCgyZVG0ySJEmSJOkjqnPmETHGvwJ//cjYVbt9f1J1vn6ttOhxKN0K/cfu/1hJkiRJkqRPKCc2zFYlFEyG1kdA52FJJ5EkSZIkSXWA5VFNsn45LP9XaqPs1DI/SZIkSZKkamV5VJPMnQoE6D8m6SSSJEmSJKmOsDyqKcrLU0vWjjgeWnVKOo0kSZIkSaojLI9qipUvwYYVqSVrkiRJkiRJWWJ5VFMUTIEGzaH3qUknkSRJkiRJdYjlUU2wYwsseAT6joYGTZNOI0mSJEmS6hDLo5pg0RNQutUla5IkSZIkKessj2qCgknQ+gjoPCzpJJIkSZIkqY6xPMp161fA8n/BgLEQQtJpJEmSJElSHWN5lOvmTk197T8m2RySJEmSJKlOsjzKZeXlqSVrRxwPrTonnUaSJEmSJNVBlke5bOXLsGGFG2VLkiRJkqTEWB7lsoLJ0KAZ9P5S0kkkSZIkSVIdZXmUq3ZuhYWPQt/R0KBp0mkkSZIkSVIdZXmUqxY9ATu3uGRNkiRJkiQlyvIoVxVMgtZdoPOnkk4iSZIkSZLqMMujXLRhJbzxAvQfCyEknUaSJEmSJNVhlke5aO7U1Nf+Y5LNIUmSJEmS6jzLo1wTY2rJWpfPQOvDk04jSZIkSZLqOMujXLPyZVi/3I2yJUmSJElSTrA8yjUFk6BBM+hzWtJJJEmSJEmSLI9yys6tsOAx6DMaGjRNOo0kSZIkSZLlUU5Z9CTs3AwDxiadRJIkSZIkCbA8yi0Fk6B1F+j8qaSTSJIkSZIkAZZHuWPDSnjjBeh/LtTzr0WSJEmSJOUGW4pcMfd+IEL/MUknkSRJkiRJ+oDlUS6IMbVkrctnUsvWJEmSJEmScoTlUS5YOQPWv+FG2ZIkSZIkKedYHuWCgkmQ3xR6n5Z0EkmSJEmSpD1YHiVt5zZY8Cj0HQ0NmyWdRpIkSZIkaQ+WR0krfBJ2bnbJmiRJkiRJykmWR0krmAStDofOn046iSRJkiRJ0l4sj5K0YRUsez4166iefxWSJEmSJCn32Fgkad5UIEL/MUknkSRJkiRJqpDlUVJihILJcPhwaN0l6TSSJEmSJEkVsjxKwrwH4JYeULwM3l6YeixJkiRJkpSD6icdoM6Z9wA88QMo3Z56vL049Rig39nJ5ZIkSZIkSaqAM4+y7dnrPiyO3le6PTUuSZIkSZKUYyyPsm3j6sqNS5IkSZIkJcjyKNtadqzcuCRJkiRJUoIsj7JtxFWQ33jPsfzGqXFJkiRJkqQcY3mUbf3Ohi/dDi07ASH19Uu3u1m2JEmSJEnKSd5tLQn9zrYskiRJkiRJNYIzjyRJkiRJkpSR5ZEkSZIkSZIysjySJEmSJElSRpZHkiRJkiRJysjySJIkSZIkSRlZHkmSJEmSJCkjyyNJkiRJkiRlZHkkSZIkSZKkjCyPJEmSJEmSlJHlkSRJkiRJkjKyPJIkSZIkSVJGlkeSJEmSJEnKyPJIkiRJkiRJGVkeSZIkSZIkKSPLI0mSJEmSJGVkeSRJkiRJkqSMLI8kSZIkSZKUUYgxJp2hUkII7wArks5RRQ4C3k06hKQ9+L6UcpPvTSk3+d6Uco/vS31ch8cYD67oiRpXHtUmIYTZMcbBSeeQ9CHfl1Ju8r0p5Sbfm1Lu8X2p6uCyNUmSJEmSJGVkeSRJkiRJkqSMLI+SdWfSASTtxfellJt8b0q5yfemlHt8X6rKueeRJEmSJEmSMnLmkSRJkiRJkjKyPEpACOGUEEJRCGFpCOGypPNISgkhLA8hzA8hFIQQZiedR6qrQgj3hhDeDiG8tttYmxDC0yGEJemvrZPMKNU1Gd6X14QQ1qQ/NwtCCF9MMqNUF4UQOoUQ/hFCWBhCWBBCGJ8e93NTVcryKMtCCHnAROALQB/g3BBCn2RTSdrN52KMA7y9qZSo+4BTPjJ2GfBsjLE78Gz6saTsuY+935cAv05/bg6IMf41y5kkwS7gxzHGPsAw4Hvpf1/6uakqZXmUfUOApTHGZTHGncBU4PSEM0mSlDNijC8AxR8ZPh34Y/r7PwKjsxpKquMyvC8lJSzGuDbGOCf9/WZgEdABPzdVxSyPsq8DsGq3x6vTY5KSF4G/hxD+E0K4MOkwkvZwaIxxbfr7dcChSYaR9IGLQwjz0svaXBYjJSiE0AUYCMzEz01VMcsjSfrQ8BjjIFLLSr8XQjg+6UCS9hZTt4r1drFS8n4LHAkMANYCtyYbR6q7QgjNgIeAH8YYN+3+nJ+bqgqWR9m3Bui02+OO6TFJCYsxrkl/fRt4hNQyU0m54a0QQnuA9Ne3E84j1XkxxrdijGUxxnLgLvzclBIRQsgnVRxNijE+nB72c1NVyvIo+2YB3UMIR4QQGgBjgMcTziTVeSGEpiGE5u9/D4wEXtv3WZKy6HHg/PT35wOPJZhFEh/8g/R9Z+DnppR1IYQA3AMsijH+z25P+bmpKhVSM9iUTenbmN4G5AH3xhhvTDiSVOeFELqSmm0EUB+Y7HtTSkYIYQpwAnAQ8BZwNfAo8ADQGVgBnB1jdPNeKUsyvC9PILVkLQLLgW/vtseKpCwIIQwH/gXMB8rTw5eT2vfIz01VGcsjSZIkSZIkZeSyNUmSJEmSJGVkeSRJkiRJkqSMLI8kSZIkSZKUkeWRJEmSJEmSMrI8kiRJkiRJUkaWR5IkqcYKIbQNIRSk/6wLIazZ7fFLSeerDiGEf4YQBmd4bloIoes+zr0lhHBi9aWTJEm1Uf2kA0iSJH1cMcb3gAEAIYRrgC0xxlsSDZWQEEJfIC/GuGwfh90B3AU8l51UkiSpNnDmkSRJqpVCCFvSX08IITwfQngshLAshPCLEMJ5IYRXQgjzQwhHpo87OITwUAhhVvrPcRVc8+vp6/wzhLAkhHD1bs89GkL4TwhhQQjhwvRYXgjhvhDCa+nX+lF6/AchhIUhhHkhhKnpsaYhhHvTuV4NIZyeHm8cQpgaQlgUQngEaJzhRz4PeGxfrxtjXAG0DSG0q5rfsiRJqguceSRJkuqC/kBvoBhYBtwdYxwSQhgPfB/4ITAB+HWM8cUQQmfgqfQ5HzUEOArYBswKIUyPMc4GLogxFocQGqfHHwK6AB1ijEcBhBBapa9xGXBEjHHHbmNXAM/FGC9Ij70SQngG+DawLcbYO4TQD5iT4Wc8DpiS/n5Ahtclff5xwEMH8ouTJEly5pEkSaoLZsUY18YYdwCvA39Pj88nVfAAnAT8JoRQADwOtAghNKvgWk/HGN+LMW4HHgaGp8d/EEKYC8wAOgHdSRVVXUMId4QQTgE2pY+dB0wKIYwDdqXHRgKXpV//n0AjoDNwPPAXgBjjvPS5FWkPvJP+PtPrArwNHJbhGpIkSXtx5pEkSaoLduz2ffluj8v58P+H6gHDYowl+7lW/OjjEMIJpMqnT8UYt4UQ/gk0ijGuDyH0B04GLgLOBi4ARpEqhb4EXBFCOBoIwJkxxqLdLx5CONCfcTupwol9vC7pY7Yf6EUlSZKceSRJkpTyd1JL2AAIIQzIcNznQwht0svTRgP/BloC69PFUS9gWPoaBwH1YowPAVcCg0II9YBOMcZ/AP8vfW4zUsvkvh/SbVEIYWD69V4AxqbHjgL6Zci1COiW6XV3O64H8NqB/UokSZKceSRJkvS+HwATQwjzSP0/0gukZu181Cuk9gvqCPwlxjg7hDAfuCiEsAgoIrV0DaAD8Id0YQTwMyAP+EsIoSWp2Ua3xxg3hBCuB24D5qWPfwM4Ffht+hqLSBVE/8mQfzpwAvBMhtclhJBPqmCaXanfjCRJqtNCjB+deS1JkqSKhBC+DgyOMV6cdJaPSs+E+gdwXIyxLMMxZwCDYoz/ndVwkiSpRnPZmiRJUi2Q3sD7alKzjjKpD9yanUSSJKm2cOaRJEmSJEmSMnLmkSRJkiRJkjKyPJIkSZIkSVJGlkeSJEmSJEnKyPJIkiRJkiRJGVkeSZIkSZIkKSPLI0mSJEmSJGX0/wGGleRzpg2kjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the field goal percentages of average shots, shots taken in the last two minutes,\n",
    "# and shots taken in the last two minutes of close games, based on seconds into the shot clock when the shot was taken\n",
    "plt.plot(field_goal_percentages, marker = 'o')\n",
    "plt.plot(field_goal_percentages_last_two, marker = 'o')\n",
    "plt.plot(field_goal_percentages_last_two_close, marker = 'o')\n",
    "\n",
    "# Label lines\n",
    "plt.legend(['Field Goal Percentage General', 'Last Two Minutes', 'Last Two Minutes in Close Games'])\n",
    "plt.ylabel('Field Goal Percentage (x100)')\n",
    "plt.xlabel('Time passed (s)')\n",
    "plt.title('Field Goal Percentage VS Time passed')\n",
    "\n",
    "#Change Dimensions\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "fig_size[0] = 20\n",
    "fig_size[1] = 9\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the points per shot of average shots, shots taken in the last two minutes,\n",
    "# and shots taken in the last two minutes of close games, based on seconds into the shot clock when the shot was taken\n",
    "plt.plot(points_per_shots, marker = 'o')\n",
    "plt.plot(points_per_shots_last_two, marker = 'o')\n",
    "plt.plot(points_per_shots_last_two_close, marker = 'o')\n",
    "\n",
    "# Label lines\n",
    "plt.legend(['General', 'Last Two Minutes', 'Last Two Minutes in Close Games'])\n",
    "plt.ylabel('Points per shot')\n",
    "plt.xlabel('Time passed (s)')\n",
    "plt.title('Points per shot VS Time passed')\n",
    "\n",
    "#Change Dimensions\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "fig_size[0] = 20\n",
    "fig_size[1] = 9\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the shot distances of average shots, shots taken in the last two minutes,\n",
    "# and shots taken in the last two minutes of close games, based on seconds into the shot clock when the shot was taken\n",
    "plt.plot(shot_dist, marker = 'o')\n",
    "plt.plot(shot_dist_last_two, marker = 'o')\n",
    "plt.plot(shot_dist_last_two_close, marker = 'o')\n",
    "\n",
    "# Label lines\n",
    "plt.legend(['General', 'Last Two Minutes', 'Last Two Minutes in a Close Game'])\n",
    "plt.ylabel('Distance (ft)')\n",
    "plt.xlabel('Time passed (s)')\n",
    "plt.title('Distance VS Time passed')\n",
    "\n",
    "#Change Dimensions\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "fig_size[0] = 20\n",
    "fig_size[1] = 9\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first graph showing the distribution of shot attempts stands out to us immediately since it appears to have a Gaussian distribution. Although it is not perfectly normally distributed around the mean, it still has the form of a Gaussian distribution with the outlier here being the 24 second shot. We believe this outlier to be unique because in order to have a chance to make it within the last second without a shot clock violation, players must shoot the ball immediately. This often leads to what is known as a “clutch” shot.\n",
    "\n",
    "Looking at the second graph which shows the “Field Goal Percentage VS Time passed,” we can see that there is a clear relationship between the time that has passed as the team has held the ball and the percentage of shots that are made at that time difference. According to our data, the teams who attempt to shoot earlier are the ones who make more shots. This completely contradicts our hypothesis, which says that teams who take their time in shooting the ball will make more shots. \n",
    "\n",
    "To make this correlation more clear, here is a best fit line plot of the same data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "0.009665 x + 0.3399\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the field goal percentage vs time passed by using poly1d, a\n",
    "# best fit line function.\n",
    "x = np.arange(23)\n",
    "y = np.poly1d(np.polyfit(x, field_goal_percentages, 1))\n",
    "plt.plot(np.unique(x), y(np.unique(x)))\n",
    "# Label graph\n",
    "plt.ylabel('Field Goal Percentage (x100)')\n",
    "plt.xlabel('Time passed (s)')\n",
    "plt.title('Field Goal Percentage VS Time passed (Best Fit)')\n",
    "# Set the y limit to be the same as the \"field goal percentage \n",
    "# vs time passed\" graph\n",
    "plt.ylim(bottom=0)\n",
    "plt.ylim(top = 0.7)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our equation for the best fit line is y = -0.009738 x + 0.5633. The lower the value of x, which is the time passed in the shot clock, the higher chance a team has of making a field goal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the third line graph, “Distance vs Time Passed,” we can begin to provide statistical evidence to some of our previously made theories. Our main theory thus far is that field goal percentage is higher early in the shot clock primarily because of fast breaks and offensive rebounding. This graph shows that shots taken in the first 5 seconds are much closer to the basket than shots taken in the last 5 seconds, which is consistent with the claim that these early shots are layups and dunks from fastbreaks and offensive putbacks. \n",
    "\n",
    "Shots in the clutch are on average 18 feet away from the basket, while all shots in general are an average of 13 feet away. Contrary to our hypothesis, this difference in distance shows that plays are not being successfully executed with more time, but that plays are actually falling apart and devolving into long distance isolation shots and other contested hail mary type shots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "0.009665 x + 0.3399\n",
      " \n",
      "-0.2104 x + 16.19\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the shot attempt distance vs time passed by using poly1d, a\n",
    "# best fit line function.\n",
    "x = np.arange(23)\n",
    "z = np.poly1d(np.polyfit(x, shot_dist, 1))\n",
    "plt.plot(np.unique(x), z(np.unique(x)))\n",
    "# Label graph\n",
    "plt.ylabel('Distance (ft)')\n",
    "plt.xlabel('Time passed (s)')\n",
    "plt.title('Shot Attempt Distance VS Time passed (Best Fit)')\n",
    "print(y)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several possible reasons as to why our data contradicts our hypothesis:\n",
    "\n",
    "1) Fastbreaks\n",
    "\n",
    "2) Offensive Rebounds\n",
    "\n",
    "3) Free-throw Offensive Rebounds\n",
    "\n",
    "During a fastbreak, the defending team will get a defensive rebound or turnover and will run down the court as fast as possible to attempt a shot (which is usually a layup or a dunk). The team who initiates the fast break has a good chance of scoring the basketball because they outrun the other team. This is a consequence of their haste in grabbing the ball off of the turnover or rebound and surprising the other team. Since the player who makes a shot attempt during a fastbreak does not usually have any defenders impeding him, he will most likely make the shot. Thus, because the fastbreak happens relatively quickly and the team who initiates it makes a shot attempt earlier in the shot clock, the fastbreak offers a good explanation as to why more shots are made earlier.\n",
    "\n",
    "Before we delve into the offensive rebounds possibility, we must first explain the new rules that the NBA has introduced into the 2018-2019 NBA season. Starting this season, if a team picks up an offensive rebound then the shot clock will reset to 14 seconds for that team. The data we currently have is from the 2014-2015 NBA season where the shot clock always resets to 24 seconds, so it is not affected by this new ruling. The new ruling would definitely affect our data, and it would be interesting as a future topic of discussion to see how it would skew the field goal percentage and shot clock graph.\n",
    "\n",
    "Offensive rebounds offer a good explanation as to why teams have a higher chance of making a shot earlier in the shot clock. When a team misses their own shot and grabs the offensive rebound, the ball often lands closer to the paint zone which is right next to the basket. Since centers are taller than most players who play the other positions, they utilize their height to grab the offensive rebound and make the basket (which happens earlier in the shot clock). In fact, it doesn’t have to be a center who grabs the offensive rebound and makes a shot attempt. The ball could be pushed behind the paint a bit and a player who plays a different position utilizes the offensive rebound to run up to the basket and make a layup early in the shot clock.\n",
    "\n",
    "Similarly, free-throw offensive rebounds can explain why the data shows that early shot attempts are made more often. They are more rare than offensive rebounds in general since the defending team has two players that are closer to the basket. Regardless, they do affect the outcome of our data because players who get a free-throw offensive rebound will often utilize the surprise of the other plays to post a shot attempt.\n",
    "\n",
    "The previously seen best fit line regarding “Field Goal Percentage vs Time Passed” can be seen with a negative slope showing a lower efficiency in scoring the basketball as the shot clock winds down. The best fit line of “Distance vs Time Passed” adds context to this lower percentage. As you can see from the positive slope, shots are being taken much further from the basket as the shot clock counts down. If you allow yourself to imagine these two in combination, you can begin to picture a play where the ball is passed around and dribbled while players look for an open shot that they never find. As the clock nears its final seconds, the ball is either dribbled or passed out to the perimeter for a desperate long range attempt, resulting in a lower field goal percentage for these kinds of shots. \n",
    "\n",
    "It seems counter intuitive that shots in the clutch are being taken further away than the average shot throughout the game. There are two great explanations for this result. At the end of a close game, players are locked in and are playing better defense, forcing shots to be taken from far away. Additionally, the team that is trailing in score might actually be preferring to take long range shots in an attempt to make a three and close the scoring gap faster, since they have limited time to catch up to their competition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With respect to the bar graph depicting the average shot frequencies based on the time of the shot clock, there are many elements that can support our claim regarding the attempt of players to make “buzzer beater” or “clutch” shots. In the first two minutes of games, the frequency of shots is most numerous after 10 to 15 seconds have passed, in addition to there being some frequency of shots fired before 10 seconds have passed, although to a lesser degree. After 15 seconds have passed, we see a drastic decrease in the frequency of shots attempted; however, when only one second remains on the shot clock, there is an abnormally drastic increase in the frequency of shots attempted. This can be presumed to be the “clutch” shot many players are looking for in order to secure the game. Disregarding the effectiveness of the shot, players are frequently looking for a last second opportunity to make a shot. Now, with respect to the bar graph depicting the average shot frequency based on the first two minutes of games, we can see an almost similar pattern. With only minor differences, it appears players tend to shoot more in the beginning overall and less towards the end of the play. The data runs mostly the same. These “clutch” shots are considerably looked for and attempted. With respect to the last two minutes of games, the graph similarly displays the data, with the only difference being that there is a smaller overall frequency of shots being taken. This makes sense as players can become fatigued; however, the strategy seemingly does not change. The “clutch” shot is still present to the same proportion. With respect to the last two minutes of close games, we can see, once again, similar patterns, although with less overall frequency. In the last two minutes of close game, shots are being taken more towards the end as opposed to the beginning of the play with the outlier being the last second “clutch” shots. Considering this strategy can be seen in the data, one can assume that changes in whether the game is close or not affect the frequency and degree to which shots are being taken. In close games, the frequency of shots is far less, perhaps because there is a need to be accurate in order to win or perhaps due to fatigue. These data all depict shot frequency,  but how accurate are these players given the same conditions?\n",
    "\n",
    "Looking at the first line graph, “Field Goal Percentage VS Time Passed,” we can see there is disparity pertaining to the field goal percentage within the first 5 seconds of both close games and not close games, as well as within the last five seconds. The field goal percentage in the first five seconds is greater than the field goal percentage in the last five seconds. In close games, it appears the number drastically drops and rises, suggesting there is an element in close games not necessarily present in not close games. Perhaps this element, which can be especially seen when twenty seconds have passed, implies players are very drastically looking for shots to take in order to win the game. These “clutch” shots make the difference in that they can be the difference between winning and losing a close game. The field goal percentage, the general number of shots made versus attempted, decreases as the shot clock time increases or passes. This suggests the efficiency of shots generally decreases as the time progresses, with the exception of cases where it rises and falls very quickly. Generally, in not close games the efficiency of shots worsens, but in close games the field goal percentage can spike drastically to become back to being efficient, presumably because there is a need to do so in order to win the game. These late “hail mary” shots can be effective when taking into consideration how close the game is. The closer the game is, it appears, the more effective these “clutch” shots are, as opposed to in not close games where these shots follow a similar path, but to a lesser degree. However, it still appears as though the teams who tend to take their shots earlier in the game are more accurate and collect more overall points. Overall, the frequency and accuracy of shots worsen as time progresses. These “clutch” plays  only make the difference in close games. Shots such as Harden’s stepback, a product of his footwork capability, can prove to be effective some of the time and with respect to how much time is left on the shot clock. Perhaps, when it is necessary for an athlete to be accurate in order to win a close game, there is an underlying element, such as their mentality or drive to win, for example, that allows them this opportunity to become seemingly opportunistic and effective. \n",
    "\n",
    "Looking at the second line graph “Points per shot VS Time passed,” we can see a similar occurrence when considering the field goal percentage of shots made. Again, we see the teams who tend to shoot earlier appear to make more points overall as opposed to those who shoot later on in the play. There is greater efficiency in attempting to shoot and in being accurate, it appears, when players attempt to score within the first five seconds of a play. As the shot clock drops, the overall amount of shots considerably drops as well, with the exception of some occurrences where they spike back towards being efficient only momentarily. These moments can be seen when the shot clock time passes by about ten, fifteen, and twenty seconds. Additionally, we can see that this drastic increase in points made is more relevant among close games. That is, it appears the closer the games are, in terms of overall points made, the higher the frequency and accuracy of shots. However, one must consider that this accuracy and frequency is not to the same degree as within the five seconds of the play. It appears these “clutch” shots can be effective to a certain extent, as seen by having a higher overall frequency and accuracy, but not to the same degree as shooting the ball earlier.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An ideal dataset provides a connection to the question or topic at hand, for the purpose of furthering our understanding of that topic. The data provided by these sets contain a variety of information, some of which may be available for use in the public and some which are more private. While undergoing the process of answering our research question, we had to consider the possibility that any private data we collect may be leaked and biases could potentially happen. During the process, these must be avoided in order to ensure the validity and credibility of our findings. Ethical considerations, with respect to the dataset and those researching, help guide the data as to ensure its validity and morality among its participants.\n",
    "\n",
    "With respect to the dataset we will be using data from NBA.com/Stats, a public website providing substantial information on player turnovers, points, etc. The data by nature is open to the general public for use; therefore, we do not have any high concerns for privacy or consent. With respect to informed consent, players already affirmatively opt-in and have a clear understanding that data will be collected by the NBA. With respect to privacy, the association themselves released the dataset for public consumption. There is no need to mask the names of teams and/or games played due to having an open to the public dataset.\n",
    "\n",
    "With respect to the processes and actions taken by the researchers themselves while manipulating the data, there is always a possibility of human bias.  As avid watchers of the NBA, we are very familiar with teams and athletes and probably have beliefs and/or ideas regarding them. By having preconceived notions of NBA players, it is possible to perhaps skew the data to compensate in favor of or against certain players. Even a subtle advantage or disadvantage would be enough to compromise the data and our findings. The dataset we are working with is very numerical and allows the advantage of making it extremely difficult to alter anything. We believe a double blind approach, where the player’s data would be anonymous to even us via the changing of name to random ones, is valuable for the reasons provided and more, but not necessary in this case.Our question requires numbers and statistics as research, specifically those of NBA players. Due to the objective nature of these questions, we believe it is not necessary to add this step.\n",
    "\n",
    "In addition, other types of biases can happen. In order to avoid the bias of omitting confounding variables, we make sure to include the dataset in its entirety. Through peer review, we are all able to view the data provided and can account for and ensure the credibility of our research. Confirmation bias, in which one seeks out data that will intentionally correlate to what one predicts will happen, again can be mitigated via peer review. Advantageously, our dataset is numerically driven, making it very difficult to alter or omit in such a way as to benefit anything inorganic. \n",
    "It is important to remember that our data attempts to create an understanding of what form of play is best with respect to shot clock time. If a team manages to have an overall higher field goal percentage, that does not necessarily correlate to winning the game. Our data should not be misinterpreted to imply the greater the accuracy the greater the chance of winning because the amount of possessions is just as important. Those who view the data may assume our findings show that slower styles of play lead to more wins; however, this is false. To guard against misinterpretation of our data, we have emphasized our findings with graphs to visually represent the data. \n",
    "\n",
    "Ethical consideration is of great importance when researching because it can skew the researcher’s findings by either inflating or minimizing the data to one’s preferences. It is important to consider the morality of how one goes about collecting and manipulating data. We believe we have taken the necessary steps to ensure the validity and morality of our research\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, our hypothesis was quite wrong. Our expectation was that teams who took their time with running plays would be leaders in field goal percentage, but it turns out pace might be a more important factor than field goal percentage. The higher pace ends up leading to more early shots, and these early shots end up being the most efficient shots as well. \n",
    "\n",
    "FIeld goal percentage has a strong negative correlation with time passed in the shot clock, and it is almost always better to try and get in position for an early shot attempt. In conjunction with our collected data and analysis, it would be safe to assume that the team with the fastest pace should be winning the most games. This is because they are maximizing the amount of possessions they have, as well as increasing the amount of early shots that they can take. \n",
    "\n",
    "In contrast, if you take our data to the extreme it would suggest that a team that takes shots later in the shot clock would have the slowest pace, the worst field goal percentage, and the least amount of field goal attempts. \n",
    "\n",
    "Referring to 2019, there are two teams in the NBA that have very strict models; the Spurs and the Warriors. These two teams are interesting to compare because of how different they are in playstyle, yet similar in success. Their models are as follows: The Warriors have a fast pace (11th in the league) and typically shoot early in the shot clock and from long distance (3pt range); The Spurs have a slow pace (23rd in the league) and prefer to run successful plays, meaning they typically don’t take a shot till the end of the shot clock, and they try to be as close to the basketball hoop as possible. The Warriors match the success metrics of our data better than the Spurs do, so it is no wonder why they have had better playoff and regular season success.\n"
   ]
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