scratch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c66df723-2171-44d6-989d-3654361fb760",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import seaborn as sns\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8ebaf2f-420f-4ff9-b7c5-1f5b27bdf261",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c694cb5a-26e4-4bbc-bf48-9bdb16a14048",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0309553e-5c4d-4c68-bc8a-40fe4d15c7a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lines = pd.read_csv('nba_betting_money_line.csv')\n",
    "df_games = pd.read_csv('nba_games_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "72ffbac4-bea4-4178-b629-d0513fb2fc0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_id</th>\n",
       "      <th>player_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>team_id</th>\n",
       "      <th>team_abbreviation</th>\n",
       "      <th>team_name</th>\n",
       "      <th>game_id</th>\n",
       "      <th>game_date</th>\n",
       "      <th>matchup</th>\n",
       "      <th>wl</th>\n",
       "      <th>min</th>\n",
       "      <th>fgm</th>\n",
       "      <th>fga</th>\n",
       "      <th>fg_pct</th>\n",
       "      <th>fg3m</th>\n",
       "      <th>fg3a</th>\n",
       "      <th>fg3_pct</th>\n",
       "      <th>ftm</th>\n",
       "      <th>fta</th>\n",
       "      <th>ft_pct</th>\n",
       "      <th>oreb</th>\n",
       "      <th>dreb</th>\n",
       "      <th>reb</th>\n",
       "      <th>ast</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>tov</th>\n",
       "      <th>pf</th>\n",
       "      <th>pts</th>\n",
       "      <th>plus_minus</th>\n",
       "      <th>season_type</th>\n",
       "      <th>season_year</th>\n",
       "      <th>season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1223192</th>\n",
       "      <td>22017</td>\n",
       "      <td>202347</td>\n",
       "      <td>Quincy Pondexter</td>\n",
       "      <td>1610612741</td>\n",
       "      <td>CHI</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>21700759</td>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>CHI @ POR</td>\n",
       "      <td>L</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246919</th>\n",
       "      <td>22017</td>\n",
       "      <td>203468</td>\n",
       "      <td>CJ McCollum</td>\n",
       "      <td>1610612757</td>\n",
       "      <td>POR</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>21700547</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>POR @ CHI</td>\n",
       "      <td>W</td>\n",
       "      <td>45</td>\n",
       "      <td>11</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.367</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.455</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.833</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246920</th>\n",
       "      <td>22017</td>\n",
       "      <td>203200</td>\n",
       "      <td>Justin Holiday</td>\n",
       "      <td>1610612741</td>\n",
       "      <td>CHI</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>21700547</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>CHI vs. POR</td>\n",
       "      <td>L</td>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246921</th>\n",
       "      <td>22017</td>\n",
       "      <td>1628374</td>\n",
       "      <td>Lauri Markkanen</td>\n",
       "      <td>1610612741</td>\n",
       "      <td>CHI</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>21700547</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>CHI vs. POR</td>\n",
       "      <td>L</td>\n",
       "      <td>34</td>\n",
       "      <td>7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.583</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246922</th>\n",
       "      <td>22017</td>\n",
       "      <td>202329</td>\n",
       "      <td>Al-Farouq Aminu</td>\n",
       "      <td>1610612757</td>\n",
       "      <td>POR</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>21700547</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>POR @ CHI</td>\n",
       "      <td>W</td>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.529</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.714</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         season_id  player_id       player_name     team_id team_abbreviation  \\\n",
       "1223192      22017     202347  Quincy Pondexter  1610612741               CHI   \n",
       "1246919      22017     203468       CJ McCollum  1610612757               POR   \n",
       "1246920      22017     203200    Justin Holiday  1610612741               CHI   \n",
       "1246921      22017    1628374   Lauri Markkanen  1610612741               CHI   \n",
       "1246922      22017     202329   Al-Farouq Aminu  1610612757               POR   \n",
       "\n",
       "                      team_name   game_id   game_date      matchup wl  min  \\\n",
       "1223192           Chicago Bulls  21700759  2018-01-31    CHI @ POR  L    6   \n",
       "1246919  Portland Trail Blazers  21700547  2018-01-01    POR @ CHI  W   45   \n",
       "1246920           Chicago Bulls  21700547  2018-01-01  CHI vs. POR  L   41   \n",
       "1246921           Chicago Bulls  21700547  2018-01-01  CHI vs. POR  L   34   \n",
       "1246922  Portland Trail Blazers  21700547  2018-01-01    POR @ CHI  W   41   \n",
       "\n",
       "         fgm   fga  fg_pct  fg3m  fg3a  fg3_pct  ftm  fta  ft_pct  oreb  dreb  \\\n",
       "1223192    0   0.0     NaN   0.0   0.0      NaN  0.0  0.0     NaN   0.0   1.0   \n",
       "1246919   11  30.0   0.367   5.0  11.0    0.455  5.0  6.0   0.833   1.0   5.0   \n",
       "1246920    4  10.0   0.400   1.0   4.0    0.250  5.0  5.0   1.000   0.0   7.0   \n",
       "1246921    7  12.0   0.583   4.0   6.0    0.667  1.0  2.0   0.500   0.0   8.0   \n",
       "1246922    9  17.0   0.529   5.0   7.0    0.714  1.0  1.0   1.000   1.0   6.0   \n",
       "\n",
       "         reb  ast  stl  blk  tov   pf  pts  plus_minus     season_type  \\\n",
       "1223192  1.0  2.0  1.0  0.0  0.0  0.0    0         4.0  Regular Season   \n",
       "1246919  6.0  8.0  0.0  0.0  2.0  2.0   32        17.0  Regular Season   \n",
       "1246920  7.0  4.0  0.0  1.0  0.0  3.0   14       -12.0  Regular Season   \n",
       "1246921  8.0  1.0  0.0  1.0  0.0  2.0   19       -14.0  Regular Season   \n",
       "1246922  7.0  1.0  1.0  0.0  1.0  4.0   24        13.0  Regular Season   \n",
       "\n",
       "         season_year   season  \n",
       "1223192         2017  2017-18  \n",
       "1246919         2017  2017-18  \n",
       "1246920         2017  2017-18  \n",
       "1246921         2017  2017-18  \n",
       "1246922         2017  2017-18  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game_stats = pd.read_csv('nba_players_game_stats.csv')\n",
    "df_game_stats[df_game_stats['game_date'] >= '2018-01-01'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c563c51-083d-416c-a433-535c0802f160",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th>book_name</th>\n",
       "      <th>book_id</th>\n",
       "      <th>team_id</th>\n",
       "      <th>a_team_id</th>\n",
       "      <th>price1</th>\n",
       "      <th>price2</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41100314</td>\n",
       "      <td>Pinnacle Sports</td>\n",
       "      <td>238</td>\n",
       "      <td>1610612759</td>\n",
       "      <td>1610612760</td>\n",
       "      <td>165.0</td>\n",
       "      <td>-183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41100314</td>\n",
       "      <td>5Dimes</td>\n",
       "      <td>19</td>\n",
       "      <td>1610612759</td>\n",
       "      <td>1610612760</td>\n",
       "      <td>165.0</td>\n",
       "      <td>-175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41100314</td>\n",
       "      <td>Bookmaker</td>\n",
       "      <td>93</td>\n",
       "      <td>1610612759</td>\n",
       "      <td>1610612760</td>\n",
       "      <td>160.0</td>\n",
       "      <td>-190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41100314</td>\n",
       "      <td>BetOnline</td>\n",
       "      <td>1096</td>\n",
       "      <td>1610612759</td>\n",
       "      <td>1610612760</td>\n",
       "      <td>165.0</td>\n",
       "      <td>-190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41100314</td>\n",
       "      <td>Bovada</td>\n",
       "      <td>999996</td>\n",
       "      <td>1610612759</td>\n",
       "      <td>1610612760</td>\n",
       "      <td>155.0</td>\n",
       "      <td>-175.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    game_id        book_name  book_id     team_id   a_team_id  price1  price2\n",
       "0  41100314  Pinnacle Sports      238  1610612759  1610612760   165.0  -183.0\n",
       "1  41100314           5Dimes       19  1610612759  1610612760   165.0  -175.0\n",
       "2  41100314        Bookmaker       93  1610612759  1610612760   160.0  -190.0\n",
       "3  41100314        BetOnline     1096  1610612759  1610612760   165.0  -190.0\n",
       "4  41100314           Bovada   999996  1610612759  1610612760   155.0  -175.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lines.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "53ba57e5-cf70-4997-a3c2-dcc77c12cfa0",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th>game_date</th>\n",
       "      <th>matchup</th>\n",
       "      <th>team_id</th>\n",
       "      <th>is_home</th>\n",
       "      <th>wl</th>\n",
       "      <th>w</th>\n",
       "      <th>l</th>\n",
       "      <th>w_pct</th>\n",
       "      <th>min</th>\n",
       "      <th>fgm</th>\n",
       "      <th>fga</th>\n",
       "      <th>fg_pct</th>\n",
       "      <th>fg3m</th>\n",
       "      <th>fg3a</th>\n",
       "      <th>fg3_pct</th>\n",
       "      <th>ftm</th>\n",
       "      <th>fta</th>\n",
       "      <th>ft_pct</th>\n",
       "      <th>oreb</th>\n",
       "      <th>dreb</th>\n",
       "      <th>reb</th>\n",
       "      <th>ast</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>tov</th>\n",
       "      <th>pf</th>\n",
       "      <th>pts</th>\n",
       "      <th>a_team_id</th>\n",
       "      <th>season_year</th>\n",
       "      <th>season_type</th>\n",
       "      <th>season</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20800741</td>\n",
       "      <td>2009-02-06</td>\n",
       "      <td>SAC vs. UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>f</td>\n",
       "      <td>W</td>\n",
       "      <td>29.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.569</td>\n",
       "      <td>240</td>\n",
       "      <td>39.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.527</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.308</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>44</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>111</td>\n",
       "      <td>1610612758</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20800701</td>\n",
       "      <td>2009-01-31</td>\n",
       "      <td>POR vs. UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>f</td>\n",
       "      <td>L</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.542</td>\n",
       "      <td>240</td>\n",
       "      <td>37.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.544</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>108</td>\n",
       "      <td>1610612757</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20800584</td>\n",
       "      <td>2009-01-16</td>\n",
       "      <td>MEM vs. UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>f</td>\n",
       "      <td>W</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.600</td>\n",
       "      <td>240</td>\n",
       "      <td>37.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.521</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.368</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>33</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>101</td>\n",
       "      <td>1610612763</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20800558</td>\n",
       "      <td>2009-01-12</td>\n",
       "      <td>IND @ UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>t</td>\n",
       "      <td>W</td>\n",
       "      <td>23.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.605</td>\n",
       "      <td>240</td>\n",
       "      <td>39.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.459</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.444</td>\n",
       "      <td>34.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>40</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1610612754</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20800440</td>\n",
       "      <td>2008-12-27</td>\n",
       "      <td>HOU vs. UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>f</td>\n",
       "      <td>L</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.563</td>\n",
       "      <td>290</td>\n",
       "      <td>49.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.490</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.385</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>115</td>\n",
       "      <td>1610612745</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    game_id  game_date      matchup     team_id is_home wl     w     l  w_pct  \\\n",
       "0  20800741 2009-02-06  SAC vs. UTA  1610612762       f  W  29.0  22.0  0.569   \n",
       "1  20800701 2009-01-31  POR vs. UTA  1610612762       f  L  26.0  22.0  0.542   \n",
       "2  20800584 2009-01-16  MEM vs. UTA  1610612762       f  W  24.0  16.0  0.600   \n",
       "3  20800558 2009-01-12    IND @ UTA  1610612762       t  W  23.0  15.0  0.605   \n",
       "4  20800440 2008-12-27  HOU vs. UTA  1610612762       f  L  18.0  14.0  0.563   \n",
       "\n",
       "   min   fgm    fga  fg_pct  fg3m  fg3a  fg3_pct   ftm   fta  ft_pct  oreb  \\\n",
       "0  240  39.0   74.0   0.527   4.0  13.0    0.308  29.0  37.0     1.0   8.0   \n",
       "1  240  37.0   68.0   0.544   5.0  20.0    0.250  29.0  37.0     1.0   7.0   \n",
       "2  240  37.0   71.0   0.521   7.0  19.0    0.368  20.0  27.0     1.0   7.0   \n",
       "3  240  39.0   85.0   0.459   8.0  18.0    0.444  34.0  37.0     1.0   9.0   \n",
       "4  290  49.0  100.0   0.490   5.0  13.0    0.385  12.0  18.0     1.0  11.0   \n",
       "\n",
       "   dreb  reb   ast   stl  blk   tov    pf  pts   a_team_id  season_year  \\\n",
       "0  36.0   44  19.0   5.0  4.0  18.0  26.0  111  1610612758         2008   \n",
       "1  20.0   27  17.0   6.0  0.0  15.0  22.0  108  1610612757         2008   \n",
       "2  26.0   33  23.0   9.0  3.0  15.0  22.0  101  1610612763         2008   \n",
       "3  31.0   40  24.0  10.0  6.0   8.0  20.0  120  1610612754         2008   \n",
       "4  29.0   40  35.0  13.0  7.0   9.0  27.0  115  1610612745         2008   \n",
       "\n",
       "      season_type   season  \n",
       "0  Regular Season  2008-09  \n",
       "1  Regular Season  2008-09  \n",
       "2  Regular Season  2008-09  \n",
       "3  Regular Season  2008-09  \n",
       "4  Regular Season  2008-09  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_games.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47c17a81-4a03-4caa-8f51-438cf0db5b06",
   "metadata": {},
   "source": [
    "# Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90781669-09d3-4ecd-a201-7e0617ea071a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the median moneyline by game\n",
    "\n",
    "df_lines_agg = df_lines.groupby(['game_id','team_id','a_team_id']).agg({'price1':'median','price2':'median'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2882956c-610f-4103-ba4a-8f7a55e7fa27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# string to date\n",
    "\n",
    "df_games['game_date'] =  pd.to_datetime(df_games['game_date'], format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e25b0a42-3af6-4fa8-920d-b0e07ef4d08c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine moneylines with games, keep only games with moneyline\n",
    "\n",
    "df_gl = df_games.merge(df_lines_agg, how='inner', on=['game_id','team_id','a_team_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a76f7870-255e-45d1-a000-669577ee4572",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate whether a game is an upset\n",
    "# with moneylines, negative indicates the favorite. -200 means the payout is $100 for every $200 wagered.\n",
    "# positive indicates the underdog. +200 means the payout is $200 for every $100 wagered.\n",
    "# we define an upset as a game where the winner had a positive moneyline, and the loser had a negative moneyline\n",
    "\n",
    "df_gl['is_upset'] = df_gl.apply(lambda row: (row['price1'] > 0 and row['price2'] < 0 and row['wl'] == 'W') or \n",
    "                                            (row['price1'] < 0 and row['price2'] > 0 and row['wl'] == 'L'), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "404f61f5-78b7-4a35-a0ab-17ac8c342b78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th>game_date</th>\n",
       "      <th>matchup</th>\n",
       "      <th>team_id</th>\n",
       "      <th>is_home</th>\n",
       "      <th>wl</th>\n",
       "      <th>w</th>\n",
       "      <th>l</th>\n",
       "      <th>w_pct</th>\n",
       "      <th>min</th>\n",
       "      <th>fgm</th>\n",
       "      <th>fga</th>\n",
       "      <th>fg_pct</th>\n",
       "      <th>fg3m</th>\n",
       "      <th>fg3a</th>\n",
       "      <th>fg3_pct</th>\n",
       "      <th>ftm</th>\n",
       "      <th>fta</th>\n",
       "      <th>ft_pct</th>\n",
       "      <th>oreb</th>\n",
       "      <th>dreb</th>\n",
       "      <th>reb</th>\n",
       "      <th>ast</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>tov</th>\n",
       "      <th>pf</th>\n",
       "      <th>pts</th>\n",
       "      <th>a_team_id</th>\n",
       "      <th>season_year</th>\n",
       "      <th>season_type</th>\n",
       "      <th>season</th>\n",
       "      <th>price1</th>\n",
       "      <th>price2</th>\n",
       "      <th>is_upset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20800741</td>\n",
       "      <td>2009-02-06</td>\n",
       "      <td>SAC vs. UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>f</td>\n",
       "      <td>W</td>\n",
       "      <td>29.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.569</td>\n",
       "      <td>240</td>\n",
       "      <td>39.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.527</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.308</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>44</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>111</td>\n",
       "      <td>1610612758</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20800701</td>\n",
       "      <td>2009-01-31</td>\n",
       "      <td>POR vs. UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>f</td>\n",
       "      <td>L</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.542</td>\n",
       "      <td>240</td>\n",
       "      <td>37.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.544</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>108</td>\n",
       "      <td>1610612757</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>380.0</td>\n",
       "      <td>-499.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20800584</td>\n",
       "      <td>2009-01-16</td>\n",
       "      <td>MEM vs. UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>f</td>\n",
       "      <td>W</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.600</td>\n",
       "      <td>240</td>\n",
       "      <td>37.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.521</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.368</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>33</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>101</td>\n",
       "      <td>1610612763</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>-182.5</td>\n",
       "      <td>160.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20800440</td>\n",
       "      <td>2008-12-27</td>\n",
       "      <td>HOU vs. UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>f</td>\n",
       "      <td>L</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.563</td>\n",
       "      <td>290</td>\n",
       "      <td>49.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.490</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.385</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>115</td>\n",
       "      <td>1610612745</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>402.5</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20800395</td>\n",
       "      <td>2008-12-20</td>\n",
       "      <td>CHI vs. UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>f</td>\n",
       "      <td>L</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.586</td>\n",
       "      <td>240</td>\n",
       "      <td>38.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.481</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.182</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>33</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>98</td>\n",
       "      <td>1610612741</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>-135.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    game_id  game_date      matchup     team_id is_home wl     w     l  w_pct  \\\n",
       "0  20800741 2009-02-06  SAC vs. UTA  1610612762       f  W  29.0  22.0  0.569   \n",
       "1  20800701 2009-01-31  POR vs. UTA  1610612762       f  L  26.0  22.0  0.542   \n",
       "2  20800584 2009-01-16  MEM vs. UTA  1610612762       f  W  24.0  16.0  0.600   \n",
       "3  20800440 2008-12-27  HOU vs. UTA  1610612762       f  L  18.0  14.0  0.563   \n",
       "4  20800395 2008-12-20  CHI vs. UTA  1610612762       f  L  17.0  12.0  0.586   \n",
       "\n",
       "   min   fgm    fga  fg_pct  fg3m  fg3a  fg3_pct   ftm   fta  ft_pct  oreb  \\\n",
       "0  240  39.0   74.0   0.527   4.0  13.0    0.308  29.0  37.0     1.0   8.0   \n",
       "1  240  37.0   68.0   0.544   5.0  20.0    0.250  29.0  37.0     1.0   7.0   \n",
       "2  240  37.0   71.0   0.521   7.0  19.0    0.368  20.0  27.0     1.0   7.0   \n",
       "3  290  49.0  100.0   0.490   5.0  13.0    0.385  12.0  18.0     1.0  11.0   \n",
       "4  240  38.0   79.0   0.481   2.0  11.0    0.182  20.0  22.0     1.0  12.0   \n",
       "\n",
       "   dreb  reb   ast   stl  blk   tov    pf  pts   a_team_id  season_year  \\\n",
       "0  36.0   44  19.0   5.0  4.0  18.0  26.0  111  1610612758         2008   \n",
       "1  20.0   27  17.0   6.0  0.0  15.0  22.0  108  1610612757         2008   \n",
       "2  26.0   33  23.0   9.0  3.0  15.0  22.0  101  1610612763         2008   \n",
       "3  29.0   40  35.0  13.0  7.0   9.0  27.0  115  1610612745         2008   \n",
       "4  21.0   33  21.0   9.0  5.0  20.0  19.0   98  1610612741         2008   \n",
       "\n",
       "      season_type   season  price1  price2  is_upset  \n",
       "0  Regular Season  2008-09  -150.0   131.0     False  \n",
       "1  Regular Season  2008-09   380.0  -499.5     False  \n",
       "2  Regular Season  2008-09  -182.5   160.0     False  \n",
       "3  Regular Season  2008-09   402.5  -500.0     False  \n",
       "4  Regular Season  2008-09  -135.0   120.0      True  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0c41aa5f-5b77-41ab-aa5e-9726b60795c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert moneyline to decimal and fractional odds\n",
    "# calculate a categorical size_of_underdog column\n",
    "\n",
    "def moneyline_to_decimal(moneyline):\n",
    "    \"\"\" convert moneyline to decimal odds \"\"\"\n",
    "    if moneyline > 0:\n",
    "        return moneyline / 100 + 1\n",
    "    elif moneyline < 0:\n",
    "        return -100 / moneyline + 1\n",
    "    elif moneyline == 0:\n",
    "        return 2\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def moneyline_to_fractional(moneyline):\n",
    "    \"\"\" convert moneyline to fractional odds, returned as tuple (numerator, denominator) \"\"\"\n",
    "    if moneyline > 0:\n",
    "        return (moneyline / 100, 1)\n",
    "    elif moneyline < 0:\n",
    "        return (1, -moneyline / 100)\n",
    "    elif moneyline == 0:\n",
    "        return (1, 1)\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def size_of_underdog(row):\n",
    "    \"\"\" apply to dataframe row. Create approximate buckets for size of underdog \"\"\"\n",
    "   \n",
    "    # get fractional odds for the underdog\n",
    "    if row['price1'] > 0 and row['price2'] < 0:\n",
    "        fractional = moneyline_to_fractional(row['price1'])\n",
    "    elif row['price1'] < 0 and row['price2'] > 0:\n",
    "        fractional = moneyline_to_fractional(row['price2'])\n",
    "    else:\n",
    "        return \"No Underdog\"\n",
    "\n",
    "    # calculate size of underdog\n",
    "    numerator = fractional[0]\n",
    "    denominator = fractional[1]\n",
    "    rounded_n = int(round(numerator, 0))\n",
    "    rounded_d = int(round(denominator, 0))\n",
    "    if rounded_n >= 10:\n",
    "        return \"GTE 10 to 1\"\n",
    "    elif rounded_n <= 1:\n",
    "        return \"1 to 1\"\n",
    "    else:\n",
    "        return f\"{rounded_n} to {rounded_d}\"\n",
    "\n",
    "df_gl['decimal_odds1'] = df_gl['price1'].apply(moneyline_to_decimal)\n",
    "df_gl['decimal_odds2'] = df_gl['price2'].apply(moneyline_to_decimal)\n",
    "df_gl['fractional_odds1'] = df_gl['price1'].apply(moneyline_to_fractional)\n",
    "df_gl['fractional_odds2'] = df_gl['price2'].apply(moneyline_to_fractional)\n",
    "df_gl['size_of_underdog'] = df_gl.apply(size_of_underdog, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b7c05398-e0c8-47e8-b5c8-15a6be61d6fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th>game_date</th>\n",
       "      <th>matchup</th>\n",
       "      <th>team_id</th>\n",
       "      <th>is_home</th>\n",
       "      <th>wl</th>\n",
       "      <th>w</th>\n",
       "      <th>l</th>\n",
       "      <th>w_pct</th>\n",
       "      <th>min</th>\n",
       "      <th>fgm</th>\n",
       "      <th>fga</th>\n",
       "      <th>fg_pct</th>\n",
       "      <th>fg3m</th>\n",
       "      <th>fg3a</th>\n",
       "      <th>fg3_pct</th>\n",
       "      <th>ftm</th>\n",
       "      <th>fta</th>\n",
       "      <th>ft_pct</th>\n",
       "      <th>oreb</th>\n",
       "      <th>dreb</th>\n",
       "      <th>reb</th>\n",
       "      <th>ast</th>\n",
       "      <th>stl</th>\n",
       "      <th>blk</th>\n",
       "      <th>tov</th>\n",
       "      <th>pf</th>\n",
       "      <th>pts</th>\n",
       "      <th>a_team_id</th>\n",
       "      <th>season_year</th>\n",
       "      <th>season_type</th>\n",
       "      <th>season</th>\n",
       "      <th>price1</th>\n",
       "      <th>price2</th>\n",
       "      <th>is_upset</th>\n",
       "      <th>decimal_odds1</th>\n",
       "      <th>decimal_odds2</th>\n",
       "      <th>fractional_odds1</th>\n",
       "      <th>fractional_odds2</th>\n",
       "      <th>size_of_underdog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20800741</td>\n",
       "      <td>2009-02-06</td>\n",
       "      <td>SAC vs. UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>f</td>\n",
       "      <td>W</td>\n",
       "      <td>29.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.569</td>\n",
       "      <td>240</td>\n",
       "      <td>39.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.527</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.308</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>44</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>111</td>\n",
       "      <td>1610612758</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>2.310000</td>\n",
       "      <td>(1, 1.5)</td>\n",
       "      <td>(1.31, 1)</td>\n",
       "      <td>1 to 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20800701</td>\n",
       "      <td>2009-01-31</td>\n",
       "      <td>POR vs. UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>f</td>\n",
       "      <td>L</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.542</td>\n",
       "      <td>240</td>\n",
       "      <td>37.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.544</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>108</td>\n",
       "      <td>1610612757</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>380.0</td>\n",
       "      <td>-499.5</td>\n",
       "      <td>False</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>1.200200</td>\n",
       "      <td>(3.8, 1)</td>\n",
       "      <td>(1, 4.995)</td>\n",
       "      <td>4 to 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20800584</td>\n",
       "      <td>2009-01-16</td>\n",
       "      <td>MEM vs. UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>f</td>\n",
       "      <td>W</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.600</td>\n",
       "      <td>240</td>\n",
       "      <td>37.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.521</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.368</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>33</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>101</td>\n",
       "      <td>1610612763</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>-182.5</td>\n",
       "      <td>160.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.547945</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>(1, 1.825)</td>\n",
       "      <td>(1.6, 1)</td>\n",
       "      <td>2 to 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20800440</td>\n",
       "      <td>2008-12-27</td>\n",
       "      <td>HOU vs. UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>f</td>\n",
       "      <td>L</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.563</td>\n",
       "      <td>290</td>\n",
       "      <td>49.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.490</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.385</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>115</td>\n",
       "      <td>1610612745</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>402.5</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>False</td>\n",
       "      <td>5.025000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>(4.025, 1)</td>\n",
       "      <td>(1, 5.0)</td>\n",
       "      <td>4 to 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20800395</td>\n",
       "      <td>2008-12-20</td>\n",
       "      <td>CHI vs. UTA</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>f</td>\n",
       "      <td>L</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.586</td>\n",
       "      <td>240</td>\n",
       "      <td>38.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.481</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.182</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>33</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>98</td>\n",
       "      <td>1610612741</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>-135.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>True</td>\n",
       "      <td>1.740741</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>(1, 1.35)</td>\n",
       "      <td>(1.2, 1)</td>\n",
       "      <td>1 to 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20801103</td>\n",
       "      <td>2009-03-30</td>\n",
       "      <td>GSW vs. MEM</td>\n",
       "      <td>1610612763</td>\n",
       "      <td>f</td>\n",
       "      <td>W</td>\n",
       "      <td>19.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.260</td>\n",
       "      <td>240</td>\n",
       "      <td>40.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.506</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.278</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>46</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>114</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>187.5</td>\n",
       "      <td>-225.0</td>\n",
       "      <td>True</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>1.444444</td>\n",
       "      <td>(1.875, 1)</td>\n",
       "      <td>(1, 2.25)</td>\n",
       "      <td>2 to 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20801090</td>\n",
       "      <td>2009-03-28</td>\n",
       "      <td>POR vs. MEM</td>\n",
       "      <td>1610612763</td>\n",
       "      <td>f</td>\n",
       "      <td>L</td>\n",
       "      <td>18.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>240</td>\n",
       "      <td>28.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>66</td>\n",
       "      <td>1610612757</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>800.0</td>\n",
       "      <td>-1150.0</td>\n",
       "      <td>False</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.086957</td>\n",
       "      <td>(8.0, 1)</td>\n",
       "      <td>(1, 11.5)</td>\n",
       "      <td>8 to 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20800796</td>\n",
       "      <td>2009-02-17</td>\n",
       "      <td>UTA vs. MEM</td>\n",
       "      <td>1610612763</td>\n",
       "      <td>f</td>\n",
       "      <td>L</td>\n",
       "      <td>15.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.283</td>\n",
       "      <td>240</td>\n",
       "      <td>35.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.467</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>99</td>\n",
       "      <td>1610612762</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>568.0</td>\n",
       "      <td>-700.0</td>\n",
       "      <td>False</td>\n",
       "      <td>6.680000</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>(5.68, 1)</td>\n",
       "      <td>(1, 7.0)</td>\n",
       "      <td>6 to 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20800634</td>\n",
       "      <td>2009-01-23</td>\n",
       "      <td>NYK vs. MEM</td>\n",
       "      <td>1610612763</td>\n",
       "      <td>f</td>\n",
       "      <td>L</td>\n",
       "      <td>11.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.262</td>\n",
       "      <td>240</td>\n",
       "      <td>30.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.390</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.438</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>40</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>88</td>\n",
       "      <td>1610612752</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>200.0</td>\n",
       "      <td>-240.0</td>\n",
       "      <td>False</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>(2.0, 1)</td>\n",
       "      <td>(1, 2.4)</td>\n",
       "      <td>2 to 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20800263</td>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>ATL vs. MEM</td>\n",
       "      <td>1610612763</td>\n",
       "      <td>f</td>\n",
       "      <td>L</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.222</td>\n",
       "      <td>240</td>\n",
       "      <td>35.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.455</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.364</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>95</td>\n",
       "      <td>1610612737</td>\n",
       "      <td>2008</td>\n",
       "      <td>Regular Season</td>\n",
       "      <td>2008-09</td>\n",
       "      <td>377.5</td>\n",
       "      <td>-475.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4.775000</td>\n",
       "      <td>1.210526</td>\n",
       "      <td>(3.775, 1)</td>\n",
       "      <td>(1, 4.75)</td>\n",
       "      <td>4 to 1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    game_id  game_date      matchup     team_id is_home wl     w     l  w_pct  \\\n",
       "0  20800741 2009-02-06  SAC vs. UTA  1610612762       f  W  29.0  22.0  0.569   \n",
       "1  20800701 2009-01-31  POR vs. UTA  1610612762       f  L  26.0  22.0  0.542   \n",
       "2  20800584 2009-01-16  MEM vs. UTA  1610612762       f  W  24.0  16.0  0.600   \n",
       "3  20800440 2008-12-27  HOU vs. UTA  1610612762       f  L  18.0  14.0  0.563   \n",
       "4  20800395 2008-12-20  CHI vs. UTA  1610612762       f  L  17.0  12.0  0.586   \n",
       "5  20801103 2009-03-30  GSW vs. MEM  1610612763       f  W  19.0  54.0  0.260   \n",
       "6  20801090 2009-03-28  POR vs. MEM  1610612763       f  L  18.0  54.0  0.250   \n",
       "7  20800796 2009-02-17  UTA vs. MEM  1610612763       f  L  15.0  38.0  0.283   \n",
       "8  20800634 2009-01-23  NYK vs. MEM  1610612763       f  L  11.0  31.0  0.262   \n",
       "9  20800263 2008-12-03  ATL vs. MEM  1610612763       f  L   4.0  14.0  0.222   \n",
       "\n",
       "   min   fgm    fga  fg_pct  fg3m  fg3a  fg3_pct   ftm   fta  ft_pct  oreb  \\\n",
       "0  240  39.0   74.0   0.527   4.0  13.0    0.308  29.0  37.0     1.0   8.0   \n",
       "1  240  37.0   68.0   0.544   5.0  20.0    0.250  29.0  37.0     1.0   7.0   \n",
       "2  240  37.0   71.0   0.521   7.0  19.0    0.368  20.0  27.0     1.0   7.0   \n",
       "3  290  49.0  100.0   0.490   5.0  13.0    0.385  12.0  18.0     1.0  11.0   \n",
       "4  240  38.0   79.0   0.481   2.0  11.0    0.182  20.0  22.0     1.0  12.0   \n",
       "5  240  40.0   79.0   0.506   5.0  18.0    0.278  29.0  40.0     1.0  12.0   \n",
       "6  240  28.0   68.0   0.412   0.0   8.0    0.000  10.0  15.0     1.0   8.0   \n",
       "7  240  35.0   75.0   0.467   3.0  12.0    0.250  26.0  32.0     1.0  17.0   \n",
       "8  240  30.0   77.0   0.390   7.0  16.0    0.438  21.0  29.0     1.0  15.0   \n",
       "9  240  35.0   77.0   0.455   4.0  11.0    0.364  21.0  24.0     1.0  10.0   \n",
       "\n",
       "   dreb  reb   ast   stl  blk   tov    pf  pts   a_team_id  season_year  \\\n",
       "0  36.0   44  19.0   5.0  4.0  18.0  26.0  111  1610612758         2008   \n",
       "1  20.0   27  17.0   6.0  0.0  15.0  22.0  108  1610612757         2008   \n",
       "2  26.0   33  23.0   9.0  3.0  15.0  22.0  101  1610612763         2008   \n",
       "3  29.0   40  35.0  13.0  7.0   9.0  27.0  115  1610612745         2008   \n",
       "4  21.0   33  21.0   9.0  5.0  20.0  19.0   98  1610612741         2008   \n",
       "5  34.0   46  22.0  11.0  8.0  19.0  20.0  114  1610612744         2008   \n",
       "6  20.0   28  12.0   6.0  4.0  13.0  19.0   66  1610612757         2008   \n",
       "7  24.0   41  15.0   4.0  5.0  18.0  25.0   99  1610612762         2008   \n",
       "8  25.0   40  13.0   7.0  1.0  17.0  25.0   88  1610612752         2008   \n",
       "9  20.0   30  18.0   7.0  0.0   9.0  25.0   95  1610612737         2008   \n",
       "\n",
       "      season_type   season  price1  price2  is_upset  decimal_odds1  \\\n",
       "0  Regular Season  2008-09  -150.0   131.0     False       1.666667   \n",
       "1  Regular Season  2008-09   380.0  -499.5     False       4.800000   \n",
       "2  Regular Season  2008-09  -182.5   160.0     False       1.547945   \n",
       "3  Regular Season  2008-09   402.5  -500.0     False       5.025000   \n",
       "4  Regular Season  2008-09  -135.0   120.0      True       1.740741   \n",
       "5  Regular Season  2008-09   187.5  -225.0      True       2.875000   \n",
       "6  Regular Season  2008-09   800.0 -1150.0     False       9.000000   \n",
       "7  Regular Season  2008-09   568.0  -700.0     False       6.680000   \n",
       "8  Regular Season  2008-09   200.0  -240.0     False       3.000000   \n",
       "9  Regular Season  2008-09   377.5  -475.0     False       4.775000   \n",
       "\n",
       "   decimal_odds2 fractional_odds1 fractional_odds2 size_of_underdog  \n",
       "0       2.310000         (1, 1.5)        (1.31, 1)           1 to 1  \n",
       "1       1.200200         (3.8, 1)       (1, 4.995)           4 to 1  \n",
       "2       2.600000       (1, 1.825)         (1.6, 1)           2 to 1  \n",
       "3       1.200000       (4.025, 1)         (1, 5.0)           4 to 1  \n",
       "4       2.200000        (1, 1.35)         (1.2, 1)           1 to 1  \n",
       "5       1.444444       (1.875, 1)        (1, 2.25)           2 to 1  \n",
       "6       1.086957         (8.0, 1)        (1, 11.5)           8 to 1  \n",
       "7       1.142857        (5.68, 1)         (1, 7.0)           6 to 1  \n",
       "8       1.416667         (2.0, 1)         (1, 2.4)           2 to 1  \n",
       "9       1.210526       (3.775, 1)        (1, 4.75)           4 to 1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gl.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f27744c-7220-444e-8ab6-c1b39475955b",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a1e098b6-8430-4e31-8015-4f927f8ceb37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# upset rates by season and season type\n",
    "\n",
    "df_upset_rates = df_gl.groupby(['season_year','season_type']).agg(upset_count = ('is_upset','sum'),\n",
    "                                                                  games_count = ('game_id','count')).reset_index()\n",
    "df_upset_rates['upset_rate'] = df_upset_rates['upset_count'] / df_upset_rates['games_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b113a797-6846-404b-b3cb-4fa2a4e58ae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x188226ed0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C1dVVszwwMLBExygJe3t7zJ07F19//TXef//9Um2b91fdjRs30LdvX6hUKk0jzmHDhhW6zZYtW7BixYoi95lXuxEVFVXo61FRUVo1IHliYmKKXJb3f2hkZIQlS5ZgyZIlePLkiaaWZPDgwQgJCdH8Xy5YsKDI8ue1JyirvFANqBu/KpVKbNq0CQcOHNC0bfnll1/g5uaGffv2ab2fsrKySnwca2trtGzZEl9++WWhr+d9WQLAm2++iTfffBPp6ek4d+4cFi1ahEGDBuHu3bta77vCFHXd5XK5Vm3B22+/ja+//hpbtmxBZmYmFAoFZsyYUeLzyU8ikeC9997D0qVLcevWLQDQ/N+tXr1aq9dXfnnvm7y2JV999ZXW6/Hx8ZraPODlnxsWFhaQSqWIjo4ucKy8Rqkvfj5Q9cCaEapw5ubmGD58OGbNmoXExETNOBFF/YWSd8vll19+0Vp+5coVBAcHaxrFlVb//v2RmZlZbA+MvC+i/H89CoKAjRs3lumYRfnoo49gaWmJjz/+uFTb5YUiGxsbAMDx48fx6NEjzJo1C6dPny7waNasGXbs2FFoQ+Q8DRs2hKurK/bv31+gcXJcXBxOnz6NXr16Fdju33//1eqRolQqsW/fPtSvX79AiATUX0yTJk3C6NGjcefOHWRkZKBx48Zo2LAhbty4UWgtnKenp6Z7aXn9RbtixQpYWFjgs88+g0qlAqD+f5fL5VpBJCYmpkBvmrxyFFaGQYMG4datW6hfv36h55E/jOQxMjJC//79sXDhQmRnZ+P27dsvLf/Bgwe1amvS0tJw+PBhdOnSBTo6Oprl9vb2GDFiBNasWYN169Zh8ODBcHFxeen+C/uiB9Rf9qmpqZrz6NSpE8zNzREUFFTk/51cLgegvr4v1sgcOXKkyAAMFP65YWRkBC8vLxw8eFDr/0ClUuGXX36Bk5OTVsNkqj5YM0IVYvDgwZrxHerUqYPw8HCsWrUKrq6uaNiwIQCgRYsWAIAffvgBEydOhK6uLho3bozGjRvjrbfewurVqyGVStG/f39NbxpnZ2e89957ZSrT6NGjsXXrVsyYMQN37tyBj48PVCoVLl26BHd3d/zvf/9D7969IZfLMXr0aHz44YfIzMzE2rVryzxAVlFMTU2xcOHCYs8lIiICFy9eBKC+V+7v749ly5bB1dVVU4uwefNmyGQyfPLJJ4V+2U2fPh1z5szBkSNHMGTIkCKP9e2332LkyJHo2bMnpk2bBjs7O9y7dw9ff/015HJ5obeUrK2t0aNHD3z66aea3jQhISFa3Xu9vLwwaNAgtGzZEhYWFggODsbOnTvh7e2tqR1bv349+vfvj759+2LSpElwdHREYmIigoODcf36dezfvx+AerwQANiwYQNMTEygr68PNze3AtXwL2NhYYEFCxbgww8/xO7duzFu3DgMGjQIBw8exMyZMzF8+HBERkbi888/h729fYHRWlu0aIEzZ87g8OHDsLe3h4mJCRo3boylS5fixIkT6NixI+bMmYPGjRsjMzMTYWFhOHr0KNatWwcnJydMmzYNBgYG6NSpE+zt7RETE4Nly5bBzMys2F4ueXR0dNC7d2/MmzcPKpUKy5cvR2pqKpYsWVJg3XfffRdeXl4AoOnN8zJvvfUWkpOT8cYbb6B58+bQ0dFBSEgIVq5cCalUqunNZWxsjNWrV2PixIlITEzE8OHDYWNjg7i4ONy4cQNxcXFYu3YtAHVQ27ZtG5o0aYKWLVvi2rVr+OabbwqE1pJ8bixbtgy9e/eGj48P5s+fD7lcjjVr1mjG4KkKowVTGYjafJaqpLxW6FeuXCn09YEDB760N813330ndOzYUbC2thbkcrng4uIiTJkyRQgLC9PabsGCBYKDg4MglUq1ekoolUph+fLlQqNGjQRdXV3B2tpaGDdunBAZGam1fbdu3YRmzZoVWs4Xe9MIgiA8e/ZM+Oyzz4SGDRsKcrlcsLKyEnr06CH4+flp1jl8+LDQqlUrQV9fX3B0dBQ++OAD4e+//y7Qk6MsvWnyy8rKEtzc3ErUm0ZfX19o1KiRMHfuXE0vgLi4OEEulxfbWyUpKUkwMDAo0OOhMCdPnhT69OkjmJubCzKZTLC3txfGjRsn3Lt3r8C6eWVes2aNUL9+fUFXV1do0qSJsGvXLq31Pv74Y8HT01OwsLAQ9PT0hHr16gnvvfeeEB8fr7XejRs3hJEjRwo2NjaCrq6uYGdnJ/To0UNYt26d1nqrVq0S3NzcBB0dHQGAsHXr1iLPp7j38bNnzwQXFxehYcOGmt5JX3/9tVC3bl1BT09PcHd3FzZu3CgsWrSoQE+iwMBAoVOnToKhoaEAQOs9FhcXJ8yZM0dwc3MTdHV1BUtLS8HDw0NYuHCh8PTpU0EQBGH79u2Cj4+PYGtrK8jlcsHBwUEYOXKk8N9//xV5LoLw/H2xfPlyYcmSJYKTk5Mgl8uFNm3aCMePHy9yu7p16wru7u7F7ju/48ePC5MnTxaaNm0qmJmZad4Lw4YNE/z9/Qusf/bsWWHgwIGCpaWloKurKzg6OgoDBw7U6p2VlJQkTJkyRbCxsREMDQ2Fzp07C76+vgV+R0v6ueHr6yv06NFDMDIyEgwMDIQOHToIhw8f1lqnqP//vN5jJe2VRZVDIggcTo+ISkcikWDWrFn46aefxC4KFeO///5Dq1at8PPPP2saBBNVRbxNQ0RUwzx48ADh4eH45JNPYG9vX+pJLIkqGxuwEhHVMJ9//jl69+6Np0+fYv/+/cX2XiOqCnibhoiIiERVppqRNWvWaCa+8vDwgK+vb5HrnjlzRjOhVP7HywZiIiIiotqh1GFk3759mDt3LhYuXIiAgAB06dIF/fv3f+nQ1nfu3EF0dLTmkddNi4iIiGq3Ut+m8fLyQtu2bTX9xwH1CIdDhw7FsmXLCqx/5swZ+Pj4ICkpSWukPSIiIiKglL1psrOzce3atQKjRvbp0wd+fn7FbtumTRtkZmaiadOm+L//+z/4+PgUuW5WVpbWMMwqlQqJiYmwsrLigDZERETVhCAISEtLe+m8YKUKI/Hx8VAqlQXmqbC1tS10vgRAPSTxhg0b4OHhgaysLOzcuRM9e/bEmTNn0LVr10K3WbZsWaGjCRIREVH1ExkZWeg0EXnKNM7Ii7UTgiAUWWORN7x3Hm9vb0RGRuLbb78tMowsWLAA8+bN0zxPSUmBi4sLIiMjYWpqWpYiExERUSVLTU2Fs7OzZo6popQqjFhbW0NHR6dALUhsbGyhs3oWpUOHDgUmQctPT0+v0GmuTU1NGUaIiIiqmZc1sShVbxq5XA4PDw+cOHFCa3ne5FAlFRAQAHt7+9IcmoiIiGqoUt+mmTdvHsaPHw9PT094e3tjw4YNiIiIwIwZMwCob7FERUVhx44dAIBVq1ahbt26aNasGbKzs/HLL7/gt99+w2+//Va+Z0JERETVUqnDyKhRo5CQkIClS5ciOjoazZs3x9GjR+Hq6goAiI6O1hpzJDs7G/Pnz0dUVBQMDAzQrFkzHDlyBAMGDCi/syAiIqJqq1oMB5+amgozMzOkpKSwzQgRURUiCAIUCgWUSqXYRSER6OjoQCaTFdkmpKTf35y1l4iIyiQ7OxvR0dHIyMgQuygkIkNDQ9jb20Mul5d5HwwjRERUaiqVCqGhodDR0YGDgwPkcjkHpaxlBEFAdnY24uLiEBoaioYNGxY7sFlxGEaIiKjUsrOzoVKp4OzsDENDQ7GLQyIxMDCArq4uwsPDkZ2dDX19/TLtp2wRhoiICCjzX8JUc5THe4DvIiIiIhIVwwgRERGJimGEiIgo15kzZyCRSJCcnFypx42JiUHv3r1hZGQEc3PzIpfVVAwjRERUq0yaNAkSiQQSiQS6urqoV68e5s+fj/T0dNHKtHLlSkRHRyMwMBB3794tcllNxd40RERU6/Tr1w9bt25FTk4OfH19MXXqVKSnp2PUqFGilOfBgwfw8PBAw4YNi11WU7FmhIiIXpkgCMjIVojyKMtA4np6erCzs4OzszPGjBmDsWPH4tChQwXWS0hIwOjRo+Hk5ARDQ0O0aNECe/bs0by+Y8cOWFlZISsrS2u7N954AxMmTNA8X7t2LerXrw+5XI7GjRtj586dmtfq1q2L3377DTt27IBEIsGkSZMKXQYAixcvhouLC/T09ODg4IA5c+aU+tyrItaMEBHRK3uWo0TTz46LcuygpX1hKH+1rzMDAwPk5OQUWJ6ZmQkPDw989NFHMDU1xZEjRzB+/HjUq1cPXl5eGDFiBObMmYM///wTI0aMAADEx8fjr7/+wrFjxwAAv//+O959912sWrUKvXr1wl9//YU333wTTk5O8PHxwZUrVzBhwgSYmprihx9+gIGBAbKzswssO3DgAFauXIm9e/eiWbNmiImJwY0bN17pvKsKhhEiIqrVLl++jN27d6Nnz54FXnN0dMT8+fM1z9955x0cO3YM+/fvh5eXFwwMDDBmzBhs3bpVE0Z27doFJycndO/eHQDw7bffYtKkSZg5cyYAYN68ebh48SK+/fZb+Pj4oE6dOtDT04OBgQHs7Ow0x3pxWUREBOzs7NCrVy/o6urCxcUF7du3r6jLUqkYRoiI6JUZ6OogaGlf0Y5dWn/99ReMjY2hUCiQk5ODIUOGYPXq1QgKCtJaT6lU4uuvv8a+ffsQFRWFrKwsZGVlwcjISLPOtGnT0K5dO0RFRcHR0RFbt27VNJIFgODgYLz11lta++3UqRN++OGHUpV5xIgRWLVqFerVq4d+/fphwIABGDx4MGSy6v9VXv3PgIiIRCeRSF75Vkll8vHxwdq1a6GrqwsHBwfo6uoCQIEw8t1332HlypVYtWoVWrRoASMjI8ydOxfZ2dmaddq0aYNWrVphx44d6Nu3L27evInDhw9r7efFeXsEQSj1XD7Ozs64c+cOTpw4gZMnT2LmzJn45ptvcPbsWU35qys2YCUiolrHyMgIDRo0gKura7Ff5L6+vhgyZAjGjRuHVq1aoV69erh3716B9aZOnYqtW7diy5Yt6NWrF5ydnTWvubu74/z581rr+/n5wd3dvdTlNjAwwGuvvYYff/wRZ86cgb+/P27evFnq/VQ11SfGEhERVbIGDRrgt99+g5+fHywsLPD9998jJiamQJAYO3Ys5s+fj40bN2LHjh1ar33wwQcYOXIk2rZti549e+Lw4cM4ePAgTp48WaqybNu2DUqlEl5eXjA0NMTOnTthYGAAV1fXVz5PsbFmhIiIqAiffvop2rZti759+6J79+6ws7PD0KFDC6xnamqKN954A8bGxgVeHzp0KH744Qd88803aNasGdavX4+tW7dqGriWlLm5OTZu3IhOnTqhZcuW+Pfff3H48GFYWVmV/QSrCIlQlg7alSw1NRVmZmZISUmBqamp2MUhIqr1MjMzERoaCjc3tzJPG1/T9O7dG+7u7vjxxx/FLkqlKu69UNLvb96mISIiegWJiYn4559/cOrUKfz0009iF6daYhghIiJ6BW3btkVSUhKWL1+Oxo0bi12caolhhIiI6BWEhYWJXYRqjw1YiYiISFQMI0RERCQqhhEiIiISFcMIERERiYphhIiIiETFMEJERESiYhghIiIiUTGMEBFRrTFp0iRIJBJIJBLIZDK4uLjg7bffRlJSUoUf++HDhxg9ejQcHBygr68PJycnDBkyBHfv3q3wY1d1HPSMiIhqlX79+mHr1q1QKBQICgrC5MmTkZycjD179lTYMbOzs9G7d280adIEBw8ehL29PR49eoSjR48iJSWlwo5bXbBmhIiIXp0gANnp4jxKOd+rnp4e7Ozs4OTkhD59+mDUqFH4559/tNbZunUr3N3doa+vjyZNmmDNmjVar/v5+aF169bQ19eHp6cnDh06BIlEgsDAwEKPGRQUhIcPH2LNmjXo0KEDXF1d0alTJ3z55Zdo166dZr2oqCiMGjUKFhYWsLKywpAhQ7RGeL1y5Qp69+4Na2trmJmZoVu3brh+/brWsRYvXgwXFxfo6enBwcEBc+bM0byWlJSECRMmwMLCAoaGhujfvz/u3buneX3btm0wNzfH8ePH4e7uDmNjY/Tr1w/R0dGlusalxZoRIiJ6dTkZwFcO4hz7k8eA3KhMmz58+BDHjh2Drq6uZtnGjRuxaNEi/PTTT2jTpg0CAgIwbdo0GBkZYeLEiUhLS8PgwYMxYMAA7N69G+Hh4Zg7d26xx6lTpw6kUikOHDiAuXPnQkdHp8A6GRkZ8PHxQZcuXXDu3DnIZDJ88cUX6NevH/777z/I5XKkpaVh4sSJmpmBv/vuOwwYMAD37t2DiYkJDhw4gJUrV2Lv3r1o1qwZYmJicOPGDc0xJk2ahHv37uHPP/+EqakpPvroIwwYMABBQUGaa5CRkYFvv/0WO3fuhFQqxbhx4zB//nzs2rWrTNe4JBhGiIioVvnrr79gbGwMpVKJzMxMAMD333+vef3zzz/Hd999h2HDhgEA3NzcEBQUhPXr12PixInYtWsXJBIJNm7cCH19fTRt2hRRUVGYNm1akcd0dHTEjz/+iA8//BBLliyBp6cnfHx8MHbsWNSrVw8AsHfvXkilUmzatAkSiQSAuobG3NwcZ86cQZ8+fdCjRw+t/a5fvx4WFhY4e/YsBg0ahIiICNjZ2aFXr17Q1dWFi4sL2rdvDwCaEHLhwgV07NgRALBr1y44Ozvj0KFDGDFiBAAgJycH69atQ/369QEAs2fPxtKlS1/5uheHYYSIiF6drqG6hkKsY5eCj48P1q5di4yMDGzatAl3797FO++8AwCIi4tDZGQkpkyZohUuFAoFzMzMAAB37txBy5Ytoa+vr3k97wu/OLNmzcKECRNw+vRpXLp0Cfv378dXX32FP//8E71798a1a9dw//59mJiYaG2XmZmJBw8eAABiY2Px2Wef4dSpU3jy5AmUSiUyMjIQEREBABgxYgRWrVqFevXqoV+/fhgwYAAGDx4MmUyG4OBgyGQyeHl5afZtZWWFxo0bIzg4WLPM0NBQE0QAwN7eHrGxsSW+vmXBMEJERK9OIinzrZLKZmRkhAYNGgAAfvzxR/j4+GDJkiX4/PPPoVKpAKhv1eT/0gagubUiCIKm5iKPUMJ2KyYmJnjttdfw2muv4YsvvkDfvn3xxRdfoHfv3lCpVPDw8Cj0dkidOnUAqG+zxMXFYdWqVXB1dYWenh68vb2RnZ0NAHB2dsadO3dw4sQJnDx5EjNnzsQ333yDs2fPFlnGF88n/y0rAJBIJCU+v7JiGCEiolpt0aJF6N+/P95++204ODjA0dERDx8+xNixYwtdv0mTJti1axeysrKgp6cHALh69WqpjyuRSNCkSRP4+fkBANq2bYt9+/bBxsYGpqamhW7j6+uLNWvWYMCAAQCAyMhIxMfHa61jYGCgCTyzZs1CkyZNcPPmTTRt2hQKhQKXLl3S3KZJSEjA3bt34e7uXurylyf2piEiolqte/fuaNasGb766isA6t4oy5Ytww8//IC7d+/i5s2b2Lp1q6ZdyZgxY6BSqfDWW28hODgYx48fx7fffgsABWpM8gQGBmLIkCE4cOAAgoKCcP/+fWzevBlbtmzBkCFDAABjx46FtbU1hgwZAl9fX4SGhuLs2bN499138ejRIwBAgwYNsHPnTgQHB+PSpUsYO3YsDAwMNMfZtm0bNm/ejFu3buHhw4fYuXMnDAwM4OrqioYNG2LIkCGYNm0azp8/jxs3bmDcuHFwdHTUlEEsDCNERFTrzZs3Dxs3bkRkZCSmTp2KTZs2Ydu2bWjRogW6deuGbdu2wc3NDQBgamqKw4cPIzAwEK1bt8bChQvx2WefAYBWO5L8nJycULduXSxZsgReXl5o27YtfvjhByxZsgQLFy4EoG6rce7cObi4uGDYsGFwd3fH5MmT8ezZM01NyZYtW5CUlIQ2bdpg/PjxmDNnDmxsbDTHMTc3x8aNG9GpUye0bNkS//77Lw4fPgwrKysA6gaxHh4eGDRoELy9vSEIAo4ePVrg1kxlkwgVfSOoHKSmpsLMzAwpKSlFVl0REVHlyczMRGhoKNzc3Ir8Aq5Ndu3ahTfffBMpKSlaNRW1QXHvhZJ+f7PNCBERUSnt2LED9erVg6OjI27cuIGPPvoII0eOrHVBpLwwjBAREZVSTEwMPvvsM8TExMDe3h4jRozAl19+KXaxqi2GESIiolL68MMP8eGHH4pdjBqDDViJiIhIVAwjRERUZtWgDwRVsPJ4DzCMEBFRqeWfVI1qt7z3wKt0D2abESIiKjUdHR2Ym5tr5iwxNDQscsAvqpkEQUBGRgZiY2Nhbm5e6EzEJcUwQkREZWJnZwcAFT6JGlVt5ubmmvdCWTGMEBFRmUgkEtjb28PGxgY5OTliF4dEoKur+0o1InkYRoiI6JXo6OiUyxcS1V5swEpERESiYhghIiIiUTGMEBERkagYRoiIiEhUDCNEREQkKoYRIiIiEhXDCBEREYmKYYSIiIhExTBCREREomIYISIiIlExjBAREZGoGEaIiIhIVAwjREREJCqGESKiSuZ3Px5z9wbg7pM0sYtCVCWUKYysWbMGbm5u0NfXh4eHB3x9fUu03YULFyCTydC6deuyHJaIqNrLVqjw3q+BOBT4GG+s8cPpO7FiF4lIdKUOI/v27cPcuXOxcOFCBAQEoEuXLujfvz8iIiKK3S4lJQUTJkxAz549y1xYIqLq7vCNx3iSmgUASMtSYMq2K9hyPhSCIIhcMiLxlDqMfP/995gyZQqmTp0Kd3d3rFq1Cs7Ozli7dm2x202fPh1jxoyBt7d3mQtLRFSdCYKAjb4PAQDzejfCSE8nqARg6V9BWHjoFnKUKpFLSCSOUoWR7OxsXLt2DX369NFa3qdPH/j5+RW53datW/HgwQMsWrSoRMfJyspCamqq1oOIqLo7dy8eITFpMJLrYGLHulj+RkssHOAOiQTYfSkCE7dcRnJGttjFJKp0pQoj8fHxUCqVsLW11Vpua2uLmJiYQre5d+8ePv74Y+zatQsymaxEx1m2bBnMzMw0D2dn59IUk4ioStp4Tl0rMqqdC8wMdCGRSDCtaz1sHO8JI7kO/B4k4PU1fngY91TkkhJVrjI1YJVIJFrPBUEosAwAlEolxowZgyVLlqBRo0Yl3v+CBQuQkpKieURGRpalmEREVcbtxyk4fz8eOlIJJneuq/Var6a2OPB2RziaGyA0Ph1Df76AC/fjxSkokQhKFUasra2ho6NToBYkNja2QG0JAKSlpeHq1auYPXs2ZDIZZDIZli5dihs3bkAmk+HUqVOFHkdPTw+mpqZaDyKi6myTbygAYGALezhZGBZ43d3eFIdmdUJbF3OkZiowYctl7LoUXtnFJBJFqcKIXC6Hh4cHTpw4obX8xIkT6NixY4H1TU1NcfPmTQQGBmoeM2bMQOPGjREYGAgvL69XKz0RUTXwOPkZDt94DACY1qVekevVMdHD7mkdMLS1A5QqAQt/v4XFf96Ggg1bqYYrWSOOfObNm4fx48fD09MT3t7e2LBhAyIiIjBjxgwA6lssUVFR2LFjB6RSKZo3b661vY2NDfT19QssJyKqqbZeCIVCJcC7nhVaOJkVu66+rg5WjmqNhrYm+Ob4HWzzC0NofDpWj2kDU33dSioxUeUqdRgZNWoUEhISsHTpUkRHR6N58+Y4evQoXF1dAQDR0dEvHXOEiKi2SM3MwZ7L6nZvb3UtulYkP4lEglk+DVDP2gjv/RqIs3fjMGyNH7ZMbAcXq4K3eIiqO4lQDUbaSU1NhZmZGVJSUth+hIiqlQ3nHuCroyFoaGOMf97rWmhj/+LcikrBlO1X8CQ1CxaGulg3zgNe9awqqLRE5auk39+cm4aIqIJkK1TYcj4MADCta71SBxEAaO5ohj9nd0ZLJzMkZeRg3OZL+PUqexhSzcIwQkRUQf767zFiUjNRx0QPQ1o7lHk/tqb62PeWNwa2sEeOUsCHB/7DsqPBUKqqfMU2UYkwjBARVQBBELAhd5CzSR3rQk+m80r7M5DrYPXoNpjTsyEAYP25h5i+8xrSsxSvXFYisTGMEBFVgPP31UO/G8p1MM7LtVz2KZVKMK93I/zwv9aQy6Q4GfwEb6z1Q1Tys3LZP5FYGEaIiCrABs3Q784wMyzfLrlDWjti31sdYG2sh5CYNAz56TyuRySV6zGIKhPDCBFROQt6nArfe/GQSoDJndwq5BhtXCzwx+xOcLc3RfzTbPxvw0X8ERhVIcciqmgMI0RE5WyTr7pWZEALezhbVty4II7mBjgwwxu93G2RrVDh3b2B+O6fO1CxYStVMwwjRETlKDrlGf7MHfq9pIOcvQojPRk2jPfA9G7qY60+dR+z91zHs2xlhR+bqLwwjBARlaNtF8KgUAnoUM8SLZ3MK+WYUqkEC/q745vhLaGrI8HRmzEYud4fMSmZlXJ8olfFMEJEVE7SMnOw+5J6OozKqBV50QhPZ+ya2gEWhrq4GZWCIT+fx81HKZVeDqLSYhghIioney9HIi1LgQY2xujeyEaUMrR3s8QfszqjoY0xnqRmYcR6Pxy9GS1KWYhKimGEiKgc5ChV2HIhFAAwrYsbpNLSD/1eXlysDPHbzI7o1qgOMnNUmLnrOlb/ew/VYCoyqqUYRoiIysGR/6IRnZIJa2M9DG3jKHZxYKqvi80TPfFmp7oAgO9O3MXcfYHIzGHDVqp6GEaIiF5R/qHf3+z06kO/lxeZjhSLBjfDl683h0wqwR+BjzF640XEpWWJXTQiLQwjRESv6ML9BARFp8JAVwdjvVzELk4BY71csWNye5jqyxAQkYwhP51HcHSq2MUi0mAYISJ6RRt8nw/9bm4oF7k0hevYwBqHZnWCm7URHqdk4o21fjgR9ETsYhEBYBghInolwdGpOHc3DlIJMKVzxQz9Xl7q1THGoZmd0LG+FTKylXhr51WsP/uADVtJdAwjRESvYJOvugdN/woe+r28mBnqYvvk9hjj5QJBAJb9HYIPD/yHbIVK7KJRLcYwQkRURjEpmfjzhnpyure6VP4gZ2WlqyPFl0ObY9HgppBKgP3XHmHcpktITM8Wu2hUSzGMEBGV0Va/UOQoBbR3s0QrZ3Oxi1MqEokEb3Zyw5ZJ7WCiJ8PlsEQM/fkC7j1JE7toVAsxjBARlUFaZg52X8wd+r0a1Yq8qHtjGxyc2RHOlgaISMzAsDV+OHMnVuxiUS3DMEJEVAb7rqiHfq9fxwg9mogz9Ht5aWhrgj9mdUb7upZIy1Jg8rYr2HYhlA1bqdIwjBARlVKOUoWtF8IAANO61BN16PfyYmkkx86p7THcwwkqAVh8OAj/d+gWlCoGEqp4DCNERKV09GY0opKfwdpYXiWGfi8vejIdfDO8JRb0bwKJBNh1KQJbc+fbIapIDCNERKWQf+j3id51oa9bNYZ+Ly8SiQTTu9XHkteaAQB+On0fKc9yRC4V1XQMI0REpeD/IAG3H6uHfh/XwVXs4lSYMe1d0MDGGMkZOVh39oHYxaEajmGEiKgU8oZ+H+npBAujqjn0e3mQ6UjxUb8mAIAt50MRnfJM5BJRTcYwQkRUQndi0nDmjnro98lVfOj38tDL3Qbt6logS6HCyhN3xS4O1WAMI0REJbQxt1akX3M7uFoZiVyaiieRSPBxf3cAwIFrj3CXA6JRBWEYISIqgSepmfgjUD30+7RqPMhZaXm4WqBfMzuoBGD53yFiF4dqKIYRIqIS2OYXhhylgHZ1LdDGxULs4lSqD/o1ho5Ugn9DYnHpYYLYxaEaiGGEiOglnmYpsOtiOADgra71RS5N5atfxxj/a+cMQD3LL0dmpfLGMEJE9BL7rkQiNVOBetZG6FnNh34vq3d7NoSBrg4CI5Nx7FaM2MWhGoZhhIioGAqlClvOq0chnVpDhn4vCxtTfUzrou5BtOL4HeQoVSKXiGoShhEiomIcvRWDqORnsDKSY1jbmjP0e1m81a0+rIzkCI1Px94rkWIXh2oQhhEioiKoh35Xjz46sWPNG/q9tIz1ZJjTsyEA4IeT95CepRC5RFRTMIwQERXB/2ECbkWlQl9XWqOHfi+N0e1d4GpliPinWZpxV4heFcMIEVERNuZOiDfCwxmWNXjo99KQy6T4oG9jAMCGcw8Rl5YlcomoJmAYISIqxN0naTh9Jw4SCTClFgz9XhoDW9ijlZMZMrKV+PHfe2IXh2oAhhEiokJsyr0F0bepHepa1/yh30sj/zDxey5HIDQ+XeQSUXXHMEJURShVAlIycsQuBgGITc3EoYDHAIC3utWeod9Lw7u+FXwa14FCJeCb4xwmnl4NwwiRyNIyc7D5fCi6fXMabT7/B2fuxIpdpFpvm18YspUqeLpaoG0tG/q9ND7q3wQSCXD0ZgwCIpLELg5VYwwjRCKJSn6GL48EoeOyU/j8ryA8SnoGlQCsOfNA7KLVaulZCvySO/T7tK6sFSlOEztTDGvjBAD4msPE0ytgGCGqZIGRyZi9+zq6rjiNjb6hSMtSoH4dIywc4A4dqQSXQxMRHJ0qdjFrrV+vqod+d7M2Qi93W7GLU+XN69MIcpkUl0ITcZq1elRGMrELQFQbKFUCTgTFYJNvKK6GP6/O7tTAClM710O3RnUglUoQGJmMIzejscM/DMuGtRSxxLWTQqnCZs3Q727QqaVDv5eGo7kB3uxYF+vPPcTyv++gWyMbXjcqtdodRvKqFCX8xaGK8TRLgf1XI7HlQigiE58BAHR1JHitlSOmdHZDUwdTrfUneLviyM1o/B4QhY/7ucPMUFeMYtdaf9+KwaOkZ7A0kuONtk5iF6famNm9AfZeicSdJ2n47fojjPR0FrtIVM3U7ts0Nw8AW/sDD049DyZE5eBx8jMsOxoM72X/YsnhIEQmPoO5oS5m+zTAhY964LuRrQoEEQBo72aJJnYmyMxR4dernPujMqmHfld3553g7Vrrh34vDTNDXczyqQ8AWHniLjJzlCKXiKqb2h1G/H4EIvyBna8Dm3oBd/9hKKFX8t+jZMzZE4AuK05j/bmHSMuddv6Loc3h/3FPzO/bGDam+kVuL5FIMLFjXQDAzovhUKr4fqwsl0ITcTMqBXoyKcZz6PdSm+BdF47mBohOycTWC2FiF4eqmdodRsbsA7zeBmT6QNRVYPcIYEN3IOQoQwmVmFIl4PjtGIxc54/XfrqAP288hlIlwLueFTZP9MTJed0wroMrDOQl+0t7SGsHmOrLEJGYwW6+lShv6PfhHk6wMtYTuTTVj76uDub1bgQAWHPmPpLSs0UuEVUntTuMmDoA/b8G3v0P6PgOoGsIRAcCe0cD67oAQX8CKpXYpaQqKj1Lge1+Yejx3RlM33kNl8MSIZNKMKyNI/56pzP2vNUBPd1tIS1lYz5DuQyj2qnvuW/3D6+IotML7sem4d+QWEgkwNQu7M5bVkPbOKKJnQnSMhX4+fR9sYtD1UjtDiN5TGyBPl8Ac28Cnd8D5MbAk5vAr+OBdZ2AW78BKt4DJbXolGf4+u8QeC/7F4v+vI3whAyYGehiZvf6OP9RD3w/qjWaO5q90jHGdXCFRAKcuxuHh3FPy6nkVJSN59Q9aPo0tYUbh34vMx2pBB/3bwIA2OEfjsjEDJFLRNUFw0h+RtZAr8XqUNL1Q0DPFIgNAg5MBtZ0AP77FVAqxC4lieTmoxTM3RuALstPY93ZB0jNVKCulSE+H9IM/gt64MN+TWBnVnR7kNJwtTKCT2MbAOoPdao4sWmZ+D0gCgDwFgc5e2XdGtVBx/pWyFaqsPLEXbGLQ9UEw0hhDC2BHgvVoaT7J4C+GRB/Fzg4Dfi5PRC4G1ByDpHaQKUScCLoCUat98fgn87jUOBjKFQCvNwssXGCJ0693x3jvevCUF7+veTzGrL+du0RnmYxBFeUHX7hyFaq0NbFHB6ulmIXp9pTT6Knrh35PTAKQY85gB+9HMNIcQzMge4fAXNvAT0+BQwsgMQHwKG3gdUewLXtgIKNtGqijGwFdvqr24NM23EVl0LV7UGGtnbA4dmdsW+6N3o3LX17kNLo0sAabtZGSMtS4PfrjyrsOLVZRrYCO3OHfn+ra32RS1NztHQyx6CW9hAE4OtjnESPHSJejmGkJPRNga7z1TUlvZYAhtZAcjhweA6wui1wZTOgyBK7lFQOYlIysfxYCLyXncKnf9xGWEIGTPVlmNGtPnw/8sGq/7VBC6dXaw9SUlKpBBO81V1Mt/uHc96PCvDrlUikPMtBXStD9G7Kod/L0wd9G0NXR4Jzd+Nw4X682MWpfIIA3DsBbOwJfGED7BwGBOwCniWLXbIqSSJUg0+41NRUmJmZISUlBaamBQeKqnTZ6cC1bcCFH4CnT9TLTByAznOBthMAXQMxS0dlcCsqBZvPh+LwDfVtGABwtTLE5E5uGO7hBCM9cQYrTs3MQYev/kVGthK7p3qhYwNrUcpREymUKvh8dwaRic/w+dDmHFukAiz+8za2+YWhuaMp/pzVuUJrEqsMQQDu/QOc+Rp4fL3g6zpyoEEvoPkbQKN+gJ5x5ZexEpX0+5th5FXkPAOu7wDOrwLSHquXGdsCnd4FPN4E5IaiFo+Kp1IJOBUSi03nH+Liw0TN8vZ1LTGlixt6udtWiTk2/u/QTfxyMQJ9mtpiwwRPsYtTYxz5Lxqzdl+HhaEu/D7uWeJxYKjkEp5mods3Z/A0S4Ef/tcaQ1o7il2kiiMIwN3jwNmvgccB6mUyA6D9VKDZ68D9f9U9M+Py3baSGQCN+qqDScPeNfIPWYaRyqTIAgJ+Ac6vBFJyh/A2qqMeu8RzSo1PvtXNs2wlDlx/hK3nQ/EwPh2AukviwBb2mNLZDa2czcUt4AvuPUlD75XnIJUA5z70gZMFQ+6rEgQBQ3++gBuPUjCnZ0PNYF1U/lb/ew/fnbgLZ0sDnJzXDXqyGhb6BAG4e0xdExIdqF6mawi0mwp0nAMY19Fe/0mQOpTcPggkPny+XG4MNBmoDib1fACZvNJOoSIxjIhBkQ3c2AP4fqduUwIABpZAx9lAu2nqtickmiepmdjhH4ZdlyKQnKHuDWWiL8OY9i6Y2LEuHMyr7l8lYzZehN+DBMzoVl/TU4HK7tLDBIzacBF6MikufNwD1hxxtcJkZCvQ/ZsziE3LwmeDmmJyZzexi1Q+BAG487e6JiT6hnqZrpG6JsT7nYIhpLDtowPVweTW70Bqvkbq+uaA+2B1MKnbBdCpvnPaMoyISZkD3NwPnPvmefLVNwc6zAS8pqt76VCJCIKAbKUK6VlKpGcp8DRLgYxsBZ7mf56lQHq2UvPvvNfSsxXqn1nq156kZmragzhbGmByJzeM8HSGsUjtQUrj+O0YTN95DRaGuvBf0JOTuL2iqduv4GRwLMZ4ueCr11uIXZwab/elCHzy+01YGOri7Ic+MNWvxrNRCwJw56i6JiTmP/UyXSOg/TR1bbhRGdp1qVTAoyvq2pLbvz9viwioO0w0Gwo0Gwa4eAPS6tXvhGGkKlAq1G+uc9+oxykB1AOpec0AOrytHs+khhEEAVkKlVYIUIcHhSZQ5IWEokJDRvbzf6dnKTQBojx4ulpgahc39G5qVyXag5SUQqlCt2/OICr5Gb4Z3hIjOEV7md2PfYpe35+FRAKcnNcN9evwNmpFUyhV6LvqHB7EpWO2TwPM79tY7CKVniAAIUeAs8ufhxC5sTqEeL8DGFmVz3FUSiD8AnDrIBD0B/DseXs2mDio2580HwY4egCSqv8ZxjBSlaiUQNAh4Ow3QFywepnmTTy7bEm6CtnuF4bN50ORnJGNjGxluYaH/PR1pTCSy2Ckl/uQ68BITwZjPRmM9HRgKM/7twzGuc/zXjfU04G1kR5crKpve4u1Zx5g+bEQNHc0xeHZnSGpBh9EVdGCg/9hz+VI9G5qi41sEFxp8mr39HWlOPuBD2yLmb26SlGpgDt5IeSmepncGGj/Vu7ndzmFkMIoc4CHZ9W3ckL+ArLyDSBn7qK+jdNsGGDXosoGE4aRqkilAkIOq0PJk9w3ta4h0G5KbkMnG3HLV0oqlYBlfwdjo29ooa8b6OrkBgcdGMnzhQY9GYzl+ULDCwGisEBhJNeBTKd6VU+Wt8T0bHRY9i+yFSr89nZHeLhaiF2kaicuLQudlp9CtkKF/TO80a5uzaudrKoEQcDwdf64Fp6E0e2dsWxYS7GLVDyVSh0Azq54/nktN1bfaveeXfk12zmZwIN/1TUmd44COfnm/bFqqA4mzYcBdapWrVOFhpE1a9bgm2++QXR0NJo1a4ZVq1ahS5cuha57/vx5fPTRRwgJCUFGRgZcXV0xffp0vPfee+V+MtWGpuHT8uetr2X6gOdkdSgxtRe1eCWRo1ThwwP/aeb0+KBvY/RtZqdVS1GdboNUF/P338CBa4/wWisH/Di6jdjFqXa+++cOVp+6jzYu5jj4dkfWLlWyq2GJGL7OH1IJ8M97XdHAxkTsIhWk+aNxBfDklnqZ3CQ3hMyqGrfXs9PV3YhvHwTu/gMo8w26adv8+a0cS/HnWqqwMLJv3z6MHz8ea9asQadOnbB+/Xps2rQJQUFBcHFxKbB+QEAAQkJC0LJlSxgZGeH8+fOYPn06Vq5cibfeeqtcT6bayRuh7+xyIOqqepmOnnrgtM5zATMnUYtXlPQsBd7edR3n7sZBRyrBijda4g2PqlnWmuZWVAoGrT4PmVQCv497wKa6VHVXARnZCnT8+hSSM3Kwdmxb9G9R9UN/TTRtx1WcCHpS9W6TqVRA8J/qEBJ7W71MbgJ0mKHufFAVQkhhMlPVNSW3DqprTlT55rFyaKsOJc1eF+37pMLCiJeXF9q2bYu1a9dqlrm7u2Po0KFYtmxZifYxbNgwGBkZYefOnSVav8aGkTyCADw8DZxZDkReVC+T6gJtxgGd3wMsqs7IkAlPszB52xXceJQCA10drBnXVjO7LFWOYWsu4HpEMt7r1Qjv9mpYOQfNTAFiQ9SzWKfHAVYN1H+BWdarNt0Od/iH4bM/bsPF0hCn53dnzZ1I7semoc/Kc1AJwIEZ3vAU+1aZSgUE/5EbQoLUy6prR4OMRCD4sLrGJPQcIKiev+birW5f0mxopTYJKOn3d6k+RbKzs3Ht2jV8/PHHWsv79OkDPz+/Eu0jICAAfn5++OKLL4pcJysrC1lZz6udUlNr+KyPEglQv4d6oJswX/UvRZgvcG0rELATaDECcPIEzOsCFnUBc2dAVvnjIkQmZmDClssIjU+HhaEutkxqhzYubLdQ2SZ2rIvrEYHYdSkcb3evD7msHNvSZKUBcXeA2GD1Iy5YHULyRhh+kUxffY/atjlg2yz30bzKNcpWqgRsym3bNLWLG4OIiBrYmGBUO2fsuRyJr44G4zexbpepVLkdC1Y871igZ6oOIB3eVk+MWt0YWgIeE9WPp7Hq3ji3DgIRfkCEv/px7CP12CXNhwHur1WZsFWqMBIfHw+lUglbW+0JpWxtbRETE1Pstk5OToiLi4NCocDixYsxderUItddtmwZlixZUpqi1QwSCeDWVf0I91P/kjw8rR5I7cae/CsCpg7qYGJRFzB3zf137k9j23JvWX37cQombb2CuLQsOJobYMeU9uwSKZL+ze3xuXEwYtOycPx2DAa3cij9TrLT1aEjLiQ3dOT+zBtBuDCmjkCdJur3V8I99UiSOenqAZ/yBn3KY2yrHU5smqpDiwghGlD35IhIzICFoS5GeIjULVqZo76+SeFAUpi6y6ahtfpamdiqfxrVAXSq8RgcJTS3VyP8HhCF6xHJOH77Cfo1t6u8gxfWu1HPLDeEzKieIaQwxjbqHpvtpwEpUepzvvUbEHUNCD2rfhx5X/2HcLNhQJMBgH7lTAJamDLVr76YYgVBeGmy9fX1xdOnT3Hx4kV8/PHHaNCgAUaPHl3ougsWLMC8efM0z1NTU+HsXMvGVXDtCEw4BEReBm4fApJCn3+I5aQDqVHqR/iFgtvKDNTdvvLCSl5IyQsupRye3u9BPKbvuIa0LAWa2Jlg++T21adbXg0kl0kxxssFP/57D9v9wooPIzmZ6jFu8tdyxAWr30so4g6tsR1g0wSo4w7Y5D7qNC74QaVSAclhwJPbuY9b6p+JoepBm54+AR6cer6+VKZu9Z8/pNg2UwfrCvzLWBAErD+nHnxwfAfXipuDRqVSn3NyuPr65v1MClP/OzVKu9q8KIZW6mCiedho/9vETv1T37zKdud8GVtTfUztXA8/nb6PFcdD0MvdpuJ7y6mU6gHFzn3zfH4YPTPAe6b6lkxNHozSzFHd+NZ7lvr38/bv6hqTJzfVk/rd+0fdXrH/csDzTVGKWKowYm1tDR0dnQK1ILGxsQVqS17k5qYeArhFixZ48uQJFi9eXGQY0dPTg54eh2cGADi3Vz/yCAKQHp/7QRemHVKSwtVDCiueAfF31I/CGFoXHlIs6qr/+s3XBuDIf9F4b18gspUqeLlZYsMET5gZ1Py/3Kq6sV4uWHP6Pq6GJ+FWVAqa2+oDCffz3V7JrelICi36C9Cojrqmw8Y992du7UVJq22lUnWbEct66qGr82Q9VR9fE1Jyg0pmsjoIxQUDtw48X1/f/IXbPM3UZZIblfXyaLkanoQbkcmQy6QY71331Xb2LLlgyMgLHskRgCKz+O1l+rm/a67q38OMBOBpjLpK/WksICjVyzISnrdfKIqOPF9YscsXWvKFFxNbwMgG0K16fzxM71YPuy9H4GFcOn69+ghjvAp2gCgXeSHk7Irnn4n6ZkCHWbVzRGxLN6DLPPUj7q66fcmt39R/tNQRb6qJUoURuVwODw8PnDhxAq+//rpm+YkTJzBkyJAS70cQBK02IVQKEol6zgPjOup2JC/SVAWH5QspYc/Dy7MkICNe/cjrwZOfVKZudW1RF3ezrXArTBe9BRs4NXDHeyPaQ1+/ejRWrLGUOUDCA9jGBeNHu9NAXDBsdiwAsh+pv8gKY2CRGzTyBw/3imvXoWesfm/mf38KApD6WLsG5clt9QdgZjIQfl790JCoPzTz16DYNlO3myrlcNgbcmtF3mjriDomL/kjJydTHSqSXwgbef/OTCl+e4kUMHXKDfquue28XJ8HkOJuoapU6ls3T58AaXkB5Um+n/kemSmAMlv9u17crbU8+mYv1LbYFgwvJnbqubQqabhxE31dvNOjAZYcDsLKk3cxtI0DDOXl+PmiUqr/+j+34vkI2Ppm6jFCvKaLekuiyqjTCOj+MdDtI/Xvo01T0YpS5q6969atg7e3NzZs2ICNGzfi9u3bcHV1xYIFCxAVFYUdO3YAAH7++We4uLigSRN14jp//jzmzp2Ld955p9hGrPnV+N40lSkzpfCQkhSm/hBWZhe/vZ5pvg/Xus8fpo7q9gA6uupAI9UFpDoFn1fTauVKp1SoazXy13LEhQDx9wBVTuHb6Jmpb6/YuOfeYsm91WJsU3WvuyJL/UXxYkjJPzdHfrpGgG3T3NqTvJqUpkXe538Qpx76XRDUQ783sDZQhyKtWylhz/+dFv3yMhvVeR4u8n7m1S6aOVVOm4+cTCA9tzYlLeaF0BKbr7blyct/p/OT6OSGk9yAYmQDGFqoQ4qBhbrWTOvfFq807X2WQole359FZOIzvN+7Ed7pWQ69w1RK9V/6Z1eo2zYB6to379mA11sMIZWswgc9W7FiBaKjo9G8eXOsXLkSXbt2BQBMmjQJYWFhOHPmDABg9erVWL9+PUJDQyGTyVC/fn1MmzYN06dPh7SECZxhpJKoVFCkRGHjn6dw/04QXKSx6OvwDI31kiBJClN/wL0qqex5ONHJ92+p7IXnLwSZ4l4rdt38+5Wp/0KHoP1TUBVcBiG3SUVhy4vbJv9PlH6bvIal8Xe1BzLKT24M1GkCoU4TbLqjh3MpddCne3eM792h6oaO0noapx7rIf+tntjgoq+JqZP2bR4zJyA1CsfOX0Lio3toZZyCZoZJQHJk0WEuj9y4YMjICx7mLqVucyUqQVDXPGlCS/5alhdqXDISynYMmYF2ONH6t2Xhzw0sAJkcAPBHYBTe3RsIYz0Zzn7QHVZlnUFZqVCHkHMr1LcsAfVxvGerh27nrOmi4HDwVGrPspV4Z891nAyOhVQCfPV6C/yvfb77uDnP1LUneTUp+WtY0qLVtxBUCvWHff6Bd6hsdA3VbTjy13LYuKu/aHNDx74rEfjot5twsjDA2Q98anaXVaVCPQt2/hqUJ7eBlIjS7UcqU4eKQms36qq/OGtKqCsNZY56DJmnT4C03ICSHqtuJ5ORqL7F+yz3Z97zom4NloTcBDCwgGBogcB4CR5l6sPO1h7tmjZ4HlwKhBhz9R8fWuVWqNsgnV0BJD5QLzOwUM+g2/4tQK8KjvJaizCMUKkkZ2Rj8rYruB6RDD2ZFKtHt0GfZq/Q3U4Q1NWlecFEmfOS54pSrJvvuSYAKfK99uLzfNtCkvtFk/tTIn1hGQqu89Jt8v9EMdtKi98vJOpbXdYN1e06zF1fev8+M0eJDsv+RXJGDjZO8ETvpsU3JK+RMlPU3Yzz16SkPkaUyhKXkk2QbeKCUb07QZJXy2HqUPALjUpPENQTt2kFleTc5y+ElvzPM1NQZE+uktA3065xSXyofgDq5R3fUXdnZQipEipk0DOqmR4nP8OELZdxP/YpTPVl2DKp3auPiiiRqG+PVJPROasrfV0djPJ0xvpzD7HDP6x2hhF9M8DVW/3I9SxbiUFf/4uknBz83K8tJC059Hu5k0jU117fDIBbybdTKdWB5IWgsut0IOLjY9DGSoWuzrIXQk0SkJ2m3j4zJbftW+jzfRpYAp3mAO2mMoRUU/ymqOXuPknDhM2XEZOaCXszfWyf3B6NbPnLXJ2M6+CKDb4P4XsvHvdjn6KBTTVq01BBDlyLRFJGDpwtDSp3QC16OamOukbjhS7kbeoMwMDVvhCeAH8M74RWzuba2ylzcsPLCzUuEh111/Lq1JaHCqjdc7LXclfDEjF8rR9iUjPRwMYYv73dkUGkGnK2NETPJuoakZ3+YeIWpgpQqgRsOp879HvnejW7HU0N0tTBFK+3dgQAfP13CAq0INDRVffyqdNYXQvWZIB6/q7WoxlEagCGkVrqRNATjN10CamZCrR1MceBGd5wMC97Fz0S16SOdQEAB649QlrmS3qL1HAngmIQnpABMwNdjPDkbNLVybw+jSDXkcL/YQLO3o0TuzhUiRhGaqG9lyMwfedVZClU6NnEBrumdoC5oVzsYtEr6NTACvXqGCE9W4mD16PELo5onmUrseKYepTNcR1cyncQLapwThaGmNjRFYC6dkSpqvL9K6icMIzUIoIg4Md/7+HjgzehEoCRnk5YP96j4ubqoEojkUgwMXeo8+3+YVDV0g/xZX8H42F8OmxN9fBWl/piF4fKYJZPA5joyxASk4ZDAbU3WNc2DCO1hFIl4LM/buP7E+phkWf7NMDyN1pW/ORUVGne8HCCsZ4MD+PSceFBvNjFqXRn78Zhh384AODbEa1gZsg5lKojc0M5ZnZvAAD4/sRdZOa8wlgmVG3wm6gWyMxRYvbu69h5MRwSCbDktWaY37fxS2dapurFWE+GN9qqGwBu9wsXuTSVKzkjGx/svwEAmOjtii4N64hcInoVb3aqC3szfUQlP8MONsquFRhGarjUzBxM3HIZf9+KgVxHip9Gt8XE3MaOVPPkzUr7b8gTRCZmiFuYSiIIAhYeuoXYtCzUq2OEj/u7i10kekX6ujp4r3cjAMDPpx8gJaN2N8quDRhGarAnqZkYuc4fl0ITYawnw7bJ7TCQgz/VaA1sjNGloTUEAfjlYu2oHfnzxmMc+S8aOlIJVo5szTZQNcQbbZ3Q2NYEKc9ysObMfbGLQxWMYaSGehD3FMPW+CEkJg11TPSwb3oHdKxfQVPGU5UyIbd2ZO+VSDzLrtn32x8nP8Onh24BAOb0aFhwoCyqtnSkEnzUvzEAYKtfGKKSn4lcIqpIDCM1UEBEEoav9UNU8jO4WRvh4Nsd0cyB02bXFj2a2MDJwgApz3Lw542a2xtBpRLwwYEbSM1UoJWzOWb5sPdMTePT2AZebpbIVqjw/T93xS4OVSCGkRrm9J1YjNl4CUkZOWjpZIYDM7zhbGkodrGoEulIJRjfQT1Ww3a/8IIjWdYQ2/3DcOF+AvR1pVg5shV7htVAEokECwao2wAdDHiEkJhUkUtEFaVW//b+dOoehvx8AV8dDcbJoCfVvpHUb9ceYdr2q3iWo0TXRnWwZ1oHWBnriV0sEsGods7Qk0kRFJ2Kq+FJYhen3N2PTcPXf4cAABYOcEe9OhwOvKZq7WyOgS3sIQjA8tz/c6p5avXwhH4PEnAjMhk3IpOx4dxDSCRAY1sTeLlZor2bFdq5WcDGRF/sYr6UIAjYcO4hluX+or7exhHL32gJuaxWZ81azdxQjqGtHbHvaiS2+4Wh3avOwlyFZCtUmLsvEFkKFbo2qoNxubVAVHN90Lcxjt+Owek7cfB/kADv+lZiF4nKmUSoBnW4qampMDMzQ0pKCkxNTcttv1HJz3A5NAGXQxNxKTQRD+PSC6zjZm2E9nUt0d5N/XCyMKhS43OoVAK+PBqMzbkTg73VtR4+7tcEUk4OVuvdfpyCgT+eh0wqwYWPe8DWtOoH65L47p87WH3qPswNdXF8btcac15UvM/+uIUd/uFo5WSGQ7M6VanPYSpaSb+/a3UYeVFcWhauhCVqwklITCpevDoOZvq5wcQK7d0sUb+OkWi/FNkKFebvv4E/bzwGoK6unta1nihloappxDo/XAlLwpyeDTEvd9yG6ux6buNslQD8PKYtu6rXInFpWej+zWmkZyvx05g2GNTSQewiUQkwjJSDlIwcXA1PxOXcgHLzUQoUL8z5YWUk19SatHezRBM700qZsvxplgJv/3INvvfiIZNK8O2IVhjaxrHCj0vVy+Ebj/HOngBYG+vB7+Me1frWXUa2AgN+8EVYQgZeb+OIlaNai10kqmSrTt7FqpP34GpliBPvdavW7+faoqTf37W6zcjLmBnqoqe7LXq62wJQfxgGRCTjUmgiLocmICAiGQnp2fj7Vgz+vhUDADDRl6Fdvts6zR3Myv0XJv5pFt7cegU3o1JgKNfB2nEe6NaIw19TQf2a28HGRA+xaVn4+1Y0hrSuvoH1yyPBCEvIgL2ZPha/1kzs4pAIpnWph18uRiA8IQN7LkdwNOkahDUjryBLocTNRym54SQR18KT8DRLobWOga4O2riYa8JJG2eLVxohMjwhHRO2XEZ4QgYsjeTYOqkdB3qiYv1w8h5WnryLti7mODizk9jFKZPTIbF4c9sVAMDuqV7o2IAD+NVWOy+G49NDt2BlJMeZD7rDRJ8TIlZlvE0jAoVSheDotNzbOuqGsUkvdBfW1ZGgpdPzcOLpalHiX6ZbUSmYtPUy4p9mw9nSADsme8HN2qgiToVqkNi0THT6+hRylAIOz+6MFk7VawC8xPRs9F11DnFpWZjcyQ2fDW4qdpFIRDlKFfquPIeH8emY06MB5vVpLHaRqBgMI1WASiXgQdxTTc3JpdAEPEnN0lpHKgGaOpiifV0rTUCxNJIX2NeF+/GYvvManmYp4G5viu2T21WLbsdUNby7NwB/BD7GcA8nfDuildjFKTFBEDBz13X8fSsGDW2McfidztDX5dwztd3fN6Px9q7rMNDVwdkPu/OzsApjGKmCBEFAZOIzXMqtNbkclojwhIIzqza0MdYEEy83K1wJS8S8XwORoxTgXc8K6yd4wJRVk1QK18KT8MZaP8hlUlxc0LPQwFsVHbz+CPN+vQGZVIJDszqhuWP1qtWhiiEIAoat9UNARDLGerngy9dbiF0kKgLDSDURk5KJy2GJuJJbe3LnSVqR6w5sYY/vR7WCnox/GVLpCIKA1366gJtRKfiwX2PM7N5A7CK9VFTyM/RbeQ5pWQp80LcxZvlU/TJT5bkcmoiR6/2hI5Vg+5vt0bkh2xFVRQwj1VRSerZmrJPLYYm4FZUClQBM9HbFZ4ObVUq3YaqZ9l+NxAcH/oOjuQHOftC9Ss/lolIJGLPpIi4+TERbF3P8Ot27SpeXxDF951Ucv/0EEgkwq3sDzO3VkO+TKoZhpIZIy8zBk9QsNLDh3Bv0ajJzlPBe9i+SMnKwbpwH+jW3E7tIRdrk+xBfHAmGoVwHR+d0QV021KZCPMtWYulft7HnciQAwNPVAj+MbgNHcwORS0Z5Svr9zQhZxZno6zKIULnQ19XB/9q7AAB2+IeJW5hi3IlJw4rjdwAA/zewKYMIFclAroNlw1pi9eg2MNGT4Wp4EvqvOodjt6LFLhqVEsMIUS0y1ssFUol6ksh7xbRPEkveJHjZChV6NLHB6PbOYheJqoHBrRxw9N0uaOVsjtRMBWb8ch3/d+gmMnOUYheNSohhhKgWcbIwRO+m6hGFt1fB2pFVJ+8iODoVFoa6+PqNFpwMjUrM2dIQB2Z4Y3o39fxcv1yMwNCfL+B+bNUL3VQQwwhRLTPRuy4A4OD1KKRm5hS/ciW6GpaIdWcfAACWDWvBsSOo1HR1pFjQ3x3bJ7eHtbEcITFpGLT6PPZdiUA1aB5ZqzGMENUy3vWt0NDGGBnZShy4+kjs4gBQT/w479cbUAnAG22d0K85Z+OlsuvWqA6OvtsFXRpaIzNHhY9+u4l39gRUqfBN2hhGiGoZiUSCCbkTjO28GA6VSvy/GL/4KwgRiRlwNDfAotc43Du9OhsTfWx/sz0+6tcEMqkEf/0XjYE/+iIwMlnsolEhGEaIaqFhbRxhoidDaHw6zt2LE7UsJ4OeYO+VSEgkwHcjW3F0YSo3UqkEb3evj19neMPJwgCRic8wfK0f1p99UCVCOD3HMEJUCxnpyTDc0wkAsMM/XLRyJDzNwscH/wOgnh6+Qz0r0cpCNVdbFwscmdMFA1vYQ6ESsOzvEEzadgVxaVkv35gqBcMIUS01voMrAOD0nViEJ6RX+vEFQcCCgzcR/zQbjW1NMK93o0ovA9UeZga6+GlMGywb1gJ6MinO3Y1D/x98cf5evNhFIzCMENVa9eoYo1ujOhAEYKcItSP7rz3CP0FPoKsjwcpRrTkbL1U4iUSC0e1dcPidzmhka4z4p1kYv+USlh8LQY5SJXbxajWGEaJabGJHde3Ir1cjkZGtqLTjRiZmYOnhIADA+30ao6lD7ZrmgcTVyNYEf8zqjDFeLhAEYO2ZBxi53h+RiQVnUafKwTBCVIt1b2QDF0tDpGYqcCjgcaUcU6kS8P6vN/A0S4H2dS0xrUu9SjkuUX4Gch189XoLrBnbFib6MgREJGPAj7448h+HkhcDwwhRLSaVSjDBW107ssM/rFIGhtrk+xCXwxJhJNfBdyNbcSZqEtWAFvY4OqcL2rqYIy1TgVm7r+OT3zmUfGVjGCGq5UZ4OMNAVwchMWm4HJpYoccKepyKb/9RT4K3aHAzOFsaVujxiErC2dIQ+6Z7Y2b3+pBIgN2XIvDaT+dxtwrO31RTMYwQ1XJmhroY2sYRQMXOV5OlUGLer4HIUQro3dQWI3K7FhNVBbo6UnzYrwl2TvZCHRM93H3yFINXn8fuSxxKvjIwjBCRpiHr8dtPEJ3yrEKO8f0/dxESkwZrYzmWDeMkeFQ1dW5ojb/f7YKujeogS6HCJ7/fxOzdAUh5xqHkKxLDCBGhiZ0pvNwsoVQJ2HUxotz3f/FhAjb4PgQALBvWEtbGeuV+DKLyYm2sh22T2uGTAeqh5I/cVA8lfz0iSeyi1VgMI0QEAJiYO1/NnssRyFKUX+O9tMwcvP/rDQgCMMrTGb2b2pbbvokqilQqwVtd6+PA2x3hYmmIR0nPMGKdP9ae4VDyFYFhhIgAAL2b2sLOVB8J6dk4erP8ujcuORyEqORncLY0wKeDOQkeVS+tnc3x15zOGNTSHkqVgOXHQjBx62XEpmWKXbQahWGEiACoG/CN6+ACANjmVz4jsh67FYMD1x5BIgG+H9kaxnqyctkvUWUy1dfF6tFtsPyNFtDXlcL3XjwG/OCLs3fFnWSyJmEYISKN/7V3gVxHihuRya881XpsWiY++f0mAGBGt/poV9eyHEpIJA6JRIJR7Vzw1zud0cTOBPFPszFxy2UsOxqMbAWHkn9VDCNEpGFtrIdBLe0BADv8wsq8H0EQsOC3m0hMz4a7vSne68VJ8KhmaGBjgkOzOmkmmlx/7iFGrPdHRAKHkn8VDCNEpGVCbkPWv/6LRvzTsk2xvvdKJP4NiYVcR4pVo1pDLuNHDdUc+ro6+Hxoc6wb5wFTfRluRCZj4I++OHyjcqZUqIn4CUFEWlo7m6OVkxmylSrsuxJZ6u3DE9Lx+V/qSfA+6NsYje1MyruIRFVCv+Z2OPpuF3i6WiAtS4F39gTg49/+w7NsDiVfWgwjRFRAXjffXy6GQ1GKqdWVKgHzfr2BjGwlOtSzxJTObhVUQqKqwcnCEHvf6oB3ejSARKKuFRz803mExKSKXbRqhWGEiAoY2NIeVkZyRKdk4kTQkxJvt+7sA1wLT4KJngzfjmgFKSfBo1pApiPF+30aY9cUL9iY6OF+7FO89tMF7LwYzqHkS4hhhIgK0JPpYHT7vG6+YSXa5lZUClaeuAsAWPxaMzhZcBI8ql06NlAPJe/TuA6yFSp8eugW3v7lOlIyOJT8y7DTPxEVaoyXC9aefYBLoYkIiUlFEzvTItfNzFHivX2BUKgE9Gtmh2FtHSuxpERVh5WxHjZPbIctF0Kx/FgIjt2OwbWIJHi4WMDV2hBuVkZwtTJCXWtD2Jros/YwF8MIERXKwdwAfZra4u9bMdjhH46vXm9R5LrfHL+De7FPYW2sh684CR7VclKpBFO71EN7N0u8sycA4QkZOHY7psB6+rpSuFoawdXKEG7WuSHFyhB1rY1gZ1q7gopEqAY3tFJTU2FmZoaUlBSYmhb91xkRla+LDxPwvw0XYaCrg4sLesLMULfAOn734zFm0yUAwJZJnujRhHPPEOV5lq3ExYcJCEtIR1h8OsISMhCekI7IpGdQFjPHjVwmhaulOpjUtTKEq5VRbmAxhL2ZAXSqSVAp6fc3a0aIqEhebpZobGuCO0/SsP9aJKZ2qaf1esqzHMzffwOA+rYOgwiRNgO5Dnya2BRYnqNUISrpWYGQEpaQgcjEDGQrVLgX+xT3Yp8W2FYuk8LF0lATUvICS10rIziYV5+gkh/DCBEVSSKRYGLHuvjk95vYeTEckzu5aVUdL/nzNh6nZKKulSEWDnAXsaRE1YuujlQdIqyNgMbarymUKjxOzkRoQro6oMRnqENLQromqNyPfYr7hQQVXR0JnC2126bUtTLKDSr6kOlUzX4rDCNEVKyhbRzw9d/BCE/IwNm7cZq/8o7ejMbBgChIJcD3o1rDiJPgEZULmY4ULlaGcLEyBFBH6zWlSsDj5NwalYQMhMWna2pUIhIykK1U4WFcOh7GpRfYr66OBM4WhnB94bZPXSsjOFkYiBpU+OlBRMUylMsw0tMZm86HYptfGHya2CA29fkkeLN8GqCti4XIpSSqHXSk6poPZ0tDdGmo/ZpSJSA65RnCEzIQmi+khMWnIzy3RuVhfDoexqcD0J5xWCaVYNmwFhjh6Vx5J5P/+KIclYiqlXEdXLH5QijO3o3Dw7inWPpXEJIzctDc0RRzejZ8+Q6IqMLpSCVwsjCEk4UhOjWw1npNpRIQk5qp1T5FHVjUt4CyFCpYm+iJVHKGESIqgbrWRujeqA5O34nD1B1X8TAuHXoyKVaObA3dKnoPmoiek0olcDA3gIO5ATo20H5NpRLwJC0TZgYFe8tVljJ9iqxZswZubm7Q19eHh4cHfH19i1z34MGD6N27N+rUqQNTU1N4e3vj+PHjZS4wEYkjb76avHvRH/Vrgoa2nASPqLqTSiWwNzOAoVy8+olSh5F9+/Zh7ty5WLhwIQICAtClSxf0798fERERha5/7tw59O7dG0ePHsW1a9fg4+ODwYMHIyAg4JULT0SVp2vDOnCzNgIAdGpghUm54YSI6FWVetAzLy8vtG3bFmvXrtUsc3d3x9ChQ7Fs2bIS7aNZs2YYNWoUPvvss0Jfz8rKQlZWluZ5amoqnJ2dOegZkcguPkzAb9ce4YO+jWFjqi92cYioiivpoGelqhnJzs7GtWvX0KdPH63lffr0gZ+fX4n2oVKpkJaWBktLyyLXWbZsGczMzDQPZ2dxWvcSkbYO9azwzYhWDCJEVK5KFUbi4+OhVCpha6s9yqKtrS1iYgqOu1+Y7777Dunp6Rg5cmSR6yxYsAApKSmaR2RkZGmKSURERNVImVqrvDgJliAIJZoYa8+ePVi8eDH++OMP2NgUHB43j56eHvT0xOtiRERERJWnVGHE2toaOjo6BWpBYmNjC9SWvGjfvn2YMmUK9u/fj169epW+pERERFQjleo2jVwuh4eHB06cOKG1/MSJE+jYsWOR2+3ZsweTJk3C7t27MXDgwLKVlIiIiGqkUt+mmTdvHsaPHw9PT094e3tjw4YNiIiIwIwZMwCo23tERUVhx44dANRBZMKECfjhhx/QoUMHTa2KgYEBzMzMyvFUiIiIqDoqdRgZNWoUEhISsHTpUkRHR6N58+Y4evQoXF1dAQDR0dFaY46sX78eCoUCs2bNwqxZszTLJ06ciG3btr36GRAREVG1VupxRsRQ0n7KREREVHVUyDgjREREROWNYYSIiIhExTBCREREomIYISIiIlExjBAREZGoGEaIiIhIVAwjREREJCqGESIiIhIVwwgRERGJimGEiIiIRMUwQkRERKJiGCEiIiJRMYwQERGRqBhGiIiISFQMI0RERCQqhhEiIiISFcMIERERiYphhIiIiETFMEJERESiYhghIiIiUTGMEBERkagYRoiIiEhUDCNEREQkKoYRIiIiEhXDCBEREYmKYYSIiIhExTBCREREomIYISIiIlExjBAREZGoGEaIiIhIVAwjREREJCqGESIiIhIVwwgRERGJimGEiIiIRMUwQkRERKJiGCEiIiJRMYwQERGRqBhGiIiISFQMI0RERCQqhhEiIiISFcMIERERiYphhIiIiETFMEJERESiYhghIiIiUTGMEBERkagYRoiIiEhUDCNEREQkKoYRIiIiEhXDCBEREYmKYYSIiIhExTBCREREomIYISIiIlExjBAREZGoGEaIiIhIVAwjREREJCqGESIiIhIVwwgRERGJimGEiIiIRMUwQkRERKJiGCEiIiJRMYwQERGRqBhGiIiISFQMI0RERCSqMoWRNWvWwM3NDfr6+vDw8ICvr2+R60ZHR2PMmDFo3LgxpFIp5s6dW9ayEhERUQ1U6jCyb98+zJ07FwsXLkRAQAC6dOmC/v37IyIiotD1s7KyUKdOHSxcuBCtWrV65QITERFRzSIRBEEozQZeXl5o27Yt1q5dq1nm7u6OoUOHYtmyZcVu2717d7Ru3RqrVq0qdr2srCxkZWVpnqempsLZ2RkpKSkwNTUtTXGJiIhIJKmpqTAzM3vp93epakays7Nx7do19OnTR2t5nz594OfnV7aSFmLZsmUwMzPTPJydnctt30RERFS1lCqMxMfHQ6lUwtbWVmu5ra0tYmJiyq1QCxYsQEpKiuYRGRlZbvsmIiKiqkVWlo0kEonWc0EQCix7FXp6etDT0yu3/REREVHVVaqaEWtra+jo6BSoBYmNjS1QW0JERERUEqUKI3K5HB4eHjhx4oTW8hMnTqBjx47lWjAiIiKqHUp9m2bevHkYP348PD094e3tjQ0bNiAiIgIzZswAoG7vERUVhR07dmi2CQwMBAA8ffoUcXFxCAwMhFwuR9OmTcvnLIiIiKjaKnUYGTVqFBISErB06VJER0ejefPmOHr0KFxdXQGoBzl7ccyRNm3aaP597do17N69G66urggLC3u10hMREVG1V+pxRsRQ0n7KREREVHVUyDgjREREROWNYYSIiIhExTBCREREomIYISIiIlExjBAREZGoGEaIiIhIVAwjREREJCqGESIiIhIVwwgRERGJimGEiIiIRMUwQkRERKJiGCEiIiJRMYwQERGRqBhGiIiISFQMI0RERCQqhhEiIiISFcMIERERiYphhIiIiETFMEJERESiYhghIiIiUTGMEBERkagYRoiIiEhUDCNEREQkKoYRIiIiEhXDCBEREYmKYYSIiIhExTBCREREomIYISIiIlExjBAREZGoGEaIiIhIVAwjREREJCqGESIiIhIVwwgRERGJimGEiIiIRMUwQkRERKJiGCEiIiJRMYwQERGRqBhGiIiISFQMI0RERCQqhhEiIiISFcMIERERiYphhIiIiETFMEJERESiYhghIiIiUTGMEBERkagYRoiIiEhUDCNEREQkKoYRIiIiEhXDCBEREYmKYYSIiIhExTBCREREomIYISIiIlExjBAREZGoGEaIiIhIVAwjREREJCqGESIiIhIVwwgRERGJimGEiIiIRMUwQkRERKJiGCEiIiJRMYwQERGRqBhGiIiISFRlCiNr1qyBm5sb9PX14eHhAV9f32LXP3v2LDw8PKCvr4969eph3bp1ZSosERER1TylDiP79u3D3LlzsXDhQgQEBKBLly7o378/IiIiCl0/NDQUAwYMQJcuXRAQEIBPPvkEc+bMwW+//fbKhSciIqLqTyIIglCaDby8vNC2bVusXbtWs8zd3R1Dhw7FsmXLCqz/0Ucf4c8//0RwcLBm2YwZM3Djxg34+/uX6JipqakwMzNDSkoKTE1NS1NcIiIiEklJv79lpdlpdnY2rl27ho8//lhreZ8+feDn51foNv7+/ujTp4/Wsr59+2Lz5s3IycmBrq5ugW2ysrKQlZWleZ6SkgJAfVJERERUPeR9b7+s3qNUYSQ+Ph5KpRK2trZay21tbRETE1PoNjExMYWur1AoEB8fD3t7+wLbLFu2DEuWLCmw3NnZuTTFJSIioiogLS0NZmZmRb5eqjCSRyKRaD0XBKHAspetX9jyPAsWLMC8efM0z1UqFRITE2FlZVXscUorNTUVzs7OiIyM5O2fV8DrWD54HcsHr2P54HUsH7X9OgqCgLS0NDg4OBS7XqnCiLW1NXR0dArUgsTGxhao/chjZ2dX6PoymQxWVlaFbqOnpwc9PT2tZebm5qUpaqmYmprWyjdJeeN1LB+8juWD17F88DqWj9p8HYurEclTqt40crkcHh4eOHHihNbyEydOoGPHjoVu4+3tXWD9f/75B56enoW2FyEiIqLapdRde+fNm4dNmzZhy5YtCA4OxnvvvYeIiAjMmDEDgPoWy4QJEzTrz5gxA+Hh4Zg3bx6Cg4OxZcsWbN68GfPnzy+/syAiIqJqq9RtRkaNGoWEhAQsXboU0dHRaN68OY4ePQpXV1cAQHR0tNaYI25ubjh69Cjee+89/Pzzz3BwcMCPP/6IN954o/zOooz09PSwaNGiAreEqHR4HcsHr2P54HUsH7yO5YPXsWRKPc4IERERUXni3DREREQkKoYRIiIiEhXDCBEREYmKYYSIiIhExTBCREREoqr2YWTZsmVo164dTExMYGNjg6FDh+LOnTta6wiCgMWLF8PBwQEGBgbo3r07bt++rbVOVlYW3nnnHVhbW8PIyAivvfYaHj16VOB4R44cgZeXFwwMDGBtbY1hw4ZV6PlVlsq8jnfv3sWQIUNgbW0NU1NTdOrUCadPn67wc6wM5XUdN2zYgO7du8PU1BQSiQTJyckFjpWUlITx48fDzMwMZmZmGD9+fKHrVUeVdR3DwsIwZcoUuLm5wcDAAPXr18eiRYuQnZ1d0adYKSrz/ZgnKysLrVu3hkQiQWBgYAWcVeWr7OtYU79nilPtw8jZs2cxa9YsXLx4ESdOnIBCoUCfPn2Qnp6uWWfFihX4/vvv8dNPP+HKlSuws7ND7969kZaWplln7ty5+P3337F3716cP38eT58+xaBBg6BUKjXr/Pbbbxg/fjzefPNN3LhxAxcuXMCYMWMq9XwrSmVex4EDB0KhUODUqVO4du0aWrdujUGDBhU52WJ1Ul7XMSMjA/369cMnn3xS5LHGjBmDwMBAHDt2DMeOHUNgYCDGjx9foedXWSrrOoaEhEClUmH9+vW4ffs2Vq5ciXXr1hV73auTynw/5vnwww9fOg9JdVOZ17Emf88US6hhYmNjBQDC2bNnBUEQBJVKJdjZ2Qlff/21Zp3MzEzBzMxMWLdunSAIgpCcnCzo6uoKe/fu1awTFRUlSKVS4dixY4IgCEJOTo7g6OgobNq0qRLPRjwVdR3j4uIEAMK5c+c066SmpgoAhJMnT1bGqVWqslzH/E6fPi0AEJKSkrSWBwUFCQCEixcvapb5+/sLAISQkJCKORkRVdR1LMyKFSsENze3cit7VVLR1/Ho0aNCkyZNhNu3bwsAhICAgIo4DdFV1HWsbd8z+VX7mpEXpaSkAAAsLS0BAKGhoYiJiUGfPn006+jp6aFbt27w8/MDAFy7dg05OTla6zg4OKB58+aada5fv46oqChIpVK0adMG9vb26N+/f4FquJqioq6jlZUV3N3dsWPHDqSnp0OhUGD9+vWwtbWFh4dHZZ1epSnLdSwJf39/mJmZwcvLS7OsQ4cOMDMzK9V+qouKuo5FHSvvODVNRV7HJ0+eYNq0adi5cycMDQ3Lr9BVUEVdx9r2PZNfjQojgiBg3rx56Ny5M5o3bw4Amqr/F2cVtrW11bwWExMDuVwOCwuLItd5+PAhAGDx4sX4v//7P/z111+wsLBAt27dkJiYWKHnVdkq8jpKJBKcOHECAQEBMDExgb6+PlauXIljx45V6MzMYijrdSyJmJgY2NjYFFhuY2NTI2535VeR1/FFDx48wOrVqzVzbdUkFXkdBUHApEmTMGPGDHh6epZfoaugiryOtel75kU1KozMnj0b//33H/bs2VPgNYlEovVcEIQCy16Ufx2VSgUAWLhwId544w14eHhg69atkEgk2L9/fzmdQdVQkddREATMnDkTNjY28PX1xeXLlzFkyBAMGjQI0dHR5XcSVUB5X8eX7aOs+6nqKvo65nn8+DH69euHESNGYOrUqWXaR1VWkddx9erVSE1NxYIFC165nFVdRV7H2vQ986IaE0beeecd/Pnnnzh9+jScnJw0y+3s7ACgQDqNjY3VpFg7OztkZ2cjKSmpyHXs7e0BAE2bNtW8rqenh3r16mlNDFjdVfR1PHXqFP766y/s3bsXnTp1Qtu2bbFmzRoYGBhg+/btFXlqlepVrmNJ2NnZ4cmTJwWWx8XFlWo/VV1FX8c8jx8/ho+PD7y9vbFhw4ZXK3QVVNHX8dSpU7h48SL09PQgk8nQoEEDAICnpycmTpxYDmdQNVT0dawt3zOFqfZhRBAEzJ49GwcPHsSpU6fg5uam9bqbmxvs7Oxw4sQJzbLs7GycPXsWHTt2BAB4eHhAV1dXa53o6GjcunVLax09PT2t7lw5OTkICwvTzFhcnVXWdczIyAAASKXabz2pVKr5q6A6K4/rWBLe3t5ISUnB5cuXNcsuXbqElJSUUu2nqqqs6wgAUVFR6N69O9q2bYutW7cWeG9WZ5V1HX/88UfcuHEDgYGBCAwMxNGjRwEA+/btw5dfflk+JyOiyrqONf17pliV2Fi2Qrz99tuCmZmZcObMGSE6OlrzyMjI0Kzz9ddfC2ZmZsLBgweFmzdvCqNHjxbs7e2F1NRUzTozZswQnJychJMnTwrXr18XevToIbRq1UpQKBSadd59913B0dFROH78uBASEiJMmTJFsLGxERITEyv1nCtCZV3HuLg4wcrKShg2bJgQGBgo3LlzR5g/f76gq6srBAYGVvp5l7fyuo7R0dFCQECAsHHjRk3vo4CAACEhIUGzTr9+/YSWLVsK/v7+gr+/v9CiRQth0KBBlXq+FaWyrmNUVJTQoEEDoUePHsKjR4+0jlUTVOb7Mb/Q0NAa1ZumMq9jTf6eKU61DyMACn1s3bpVs45KpRIWLVok2NnZCXp6ekLXrl2Fmzdvau3n2bNnwuzZswVLS0vBwMBAGDRokBAREaG1TnZ2tvD+++8LNjY2gomJidCrVy/h1q1blXGaFa4yr+OVK1eEPn36CJaWloKJiYnQoUMH4ejRo5VxmhWuvK7jokWLXrqfhIQEYezYsYKJiYlgYmIijB07tkRdV6uDyrqOW7duLfJYNUFlvh/zq2lhpDKvY03+nimORBAE4dXqVoiIiIjKrubcHCUiIqJqiWGEiIiIRMUwQkRERKJiGCEiIiJRMYwQERGRqBhGiIiISFQMI0RERCQqhhEiIiISFcMIERERiYphhIiIiETFMEJERESi+n/liN8WzjTOmwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "ax.plot(df_upset_rates[df_upset_rates['season_type']=='Playoffs']['season_year'],\n",
    "        df_upset_rates[df_upset_rates['season_type']=='Playoffs']['upset_rate'],\n",
    "        label='Playoffs')\n",
    "ax.plot(df_upset_rates[df_upset_rates['season_type']=='Playoffs']['season_year'],\n",
    "        df_upset_rates[df_upset_rates['season_type']=='Regular Season']['upset_rate'],\n",
    "        label='Reg Season')\n",
    "ax.set_ylim(0,0.5)\n",
    "ax.set_title('Historical NBA Upset Rates by Season')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ad897d01-47a2-434b-b204-d3e37d5a6d3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# upset rates by size of underdog\n",
    "\n",
    "df_upset_rates2 = df_gl.groupby(['size_of_underdog']).agg(upset_count = ('is_upset','sum'),\n",
    "                                                                  games_count = ('game_id','count')).reset_index()\n",
    "df_upset_rates2['upset_rate'] = df_upset_rates2['upset_count'] / df_upset_rates2['games_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "84affd62-b2a2-4b19-835e-6ea4cdb418c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_of_underdog</th>\n",
       "      <th>upset_count</th>\n",
       "      <th>games_count</th>\n",
       "      <th>upset_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 to 1</td>\n",
       "      <td>1760</td>\n",
       "      <td>3927</td>\n",
       "      <td>0.448179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2 to 1</td>\n",
       "      <td>1711</td>\n",
       "      <td>5091</td>\n",
       "      <td>0.336083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3 to 1</td>\n",
       "      <td>469</td>\n",
       "      <td>1984</td>\n",
       "      <td>0.236391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4 to 1</td>\n",
       "      <td>248</td>\n",
       "      <td>1259</td>\n",
       "      <td>0.196982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5 to 1</td>\n",
       "      <td>81</td>\n",
       "      <td>549</td>\n",
       "      <td>0.147541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6 to 1</td>\n",
       "      <td>60</td>\n",
       "      <td>457</td>\n",
       "      <td>0.131291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7 to 1</td>\n",
       "      <td>26</td>\n",
       "      <td>191</td>\n",
       "      <td>0.136126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8 to 1</td>\n",
       "      <td>20</td>\n",
       "      <td>244</td>\n",
       "      <td>0.081967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9 to 1</td>\n",
       "      <td>13</td>\n",
       "      <td>124</td>\n",
       "      <td>0.104839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GTE 10 to 1</td>\n",
       "      <td>41</td>\n",
       "      <td>655</td>\n",
       "      <td>0.062595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>No Underdog</td>\n",
       "      <td>0</td>\n",
       "      <td>425</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   size_of_underdog  upset_count  games_count  upset_rate\n",
       "0            1 to 1         1760         3927    0.448179\n",
       "1            2 to 1         1711         5091    0.336083\n",
       "2            3 to 1          469         1984    0.236391\n",
       "3            4 to 1          248         1259    0.196982\n",
       "4            5 to 1           81          549    0.147541\n",
       "5            6 to 1           60          457    0.131291\n",
       "6            7 to 1           26          191    0.136126\n",
       "7            8 to 1           20          244    0.081967\n",
       "8            9 to 1           13          124    0.104839\n",
       "9       GTE 10 to 1           41          655    0.062595\n",
       "10      No Underdog            0          425    0.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_upset_rates2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0a758cf1-80c2-4a62-a5d8-8e642cfb9b4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "ax.bar(df_upset_rates2[df_upset_rates2['size_of_underdog']!='No Underdog']['size_of_underdog'],\n",
    "       df_upset_rates2[df_upset_rates2['size_of_underdog']!='No Underdog']['upset_rate'])\n",
    "# ax.set_ylim(0,0.5)\n",
    "ax.set_title('Historical NBA Upset Rates (2006-2017) by Size of Underdog')\n",
    "ax.tick_params(axis='x', labelrotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1278d42b-08f3-45a9-899f-94822a8b071e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}