elo-testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def win_probability(df, away_elo, home_elo):\n",
    "    away_team_wp = 1/(1 + 10**(((home_elo+100)-away_elo)/400))\n",
    "    home_team_wp = 1/(1 + 10**((away_elo-(home_elo+100))/400))\n",
    "\n",
    "    df['away_win_prob'] = away_team_wp\n",
    "    df['home_win_prob'] = home_team_wp\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_score</th>\n",
       "      <th>home_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>result</th>\n",
       "      <th>away_elo_i</th>\n",
       "      <th>away_elo_n</th>\n",
       "      <th>home_elo_i</th>\n",
       "      <th>home_elo_n</th>\n",
       "      <th>elo_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>131410290001</td>\n",
       "      <td>1314</td>\n",
       "      <td>2013-10-29</td>\n",
       "      <td>ORL</td>\n",
       "      <td>87</td>\n",
       "      <td>IND</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>1313.231700</td>\n",
       "      <td>1311.533869</td>\n",
       "      <td>1583.572100</td>\n",
       "      <td>1585.269931</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>131410290002</td>\n",
       "      <td>1314</td>\n",
       "      <td>2013-10-29</td>\n",
       "      <td>CHI</td>\n",
       "      <td>95</td>\n",
       "      <td>MIA</td>\n",
       "      <td>107</td>\n",
       "      <td>1</td>\n",
       "      <td>1519.912400</td>\n",
       "      <td>1516.608783</td>\n",
       "      <td>1691.889600</td>\n",
       "      <td>1695.193217</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>131410290003</td>\n",
       "      <td>1314</td>\n",
       "      <td>2013-10-29</td>\n",
       "      <td>LAC</td>\n",
       "      <td>103</td>\n",
       "      <td>LAL</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>1586.588400</td>\n",
       "      <td>1575.414216</td>\n",
       "      <td>1511.020000</td>\n",
       "      <td>1522.194184</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>131410300004</td>\n",
       "      <td>1314</td>\n",
       "      <td>2013-10-30</td>\n",
       "      <td>BRK</td>\n",
       "      <td>94</td>\n",
       "      <td>CLE</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1548.436500</td>\n",
       "      <td>1540.172399</td>\n",
       "      <td>1369.282300</td>\n",
       "      <td>1377.546401</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>131410300005</td>\n",
       "      <td>1314</td>\n",
       "      <td>2013-10-30</td>\n",
       "      <td>BOS</td>\n",
       "      <td>87</td>\n",
       "      <td>TOR</td>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "      <td>1484.961500</td>\n",
       "      <td>1479.784941</td>\n",
       "      <td>1483.983500</td>\n",
       "      <td>1489.160059</td>\n",
       "      <td>1</td>\n",
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       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1419.106244</td>\n",
       "      <td>1415.566964</td>\n",
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       "      <td>1571.500976</td>\n",
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       "    <tr>\n",
       "      <th>11975</th>\n",
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       "      <td>2223</td>\n",
       "      <td>2023-04-09</td>\n",
       "      <td>NOP</td>\n",
       "      <td>108</td>\n",
       "      <td>MIN</td>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>1543.862541</td>\n",
       "      <td>1538.995985</td>\n",
       "      <td>1535.490950</td>\n",
       "      <td>1540.357507</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>11976</th>\n",
       "      <td>222304091228</td>\n",
       "      <td>2223</td>\n",
       "      <td>2023-04-09</td>\n",
       "      <td>MEM</td>\n",
       "      <td>100</td>\n",
       "      <td>OKC</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1613.827418</td>\n",
       "      <td>1599.928440</td>\n",
       "      <td>1505.092965</td>\n",
       "      <td>1518.991944</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>11977</th>\n",
       "      <td>222304091229</td>\n",
       "      <td>2223</td>\n",
       "      <td>2023-04-09</td>\n",
       "      <td>LAC</td>\n",
       "      <td>119</td>\n",
       "      <td>PHO</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>1537.722013</td>\n",
       "      <td>1548.985300</td>\n",
       "      <td>1588.174579</td>\n",
       "      <td>1576.911292</td>\n",
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       "    <tr>\n",
       "      <th>11978</th>\n",
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       "      <td>2223</td>\n",
       "      <td>2023-04-09</td>\n",
       "      <td>GSW</td>\n",
       "      <td>157</td>\n",
       "      <td>POR</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1581.483746</td>\n",
       "      <td>1599.615968</td>\n",
       "      <td>1328.023658</td>\n",
       "      <td>1309.891436</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11979 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            game_id  season        date away_team  away_score home_team  \\\n",
       "0      131410290001    1314  2013-10-29       ORL          87       IND   \n",
       "1      131410290002    1314  2013-10-29       CHI          95       MIA   \n",
       "2      131410290003    1314  2013-10-29       LAC         103       LAL   \n",
       "3      131410300004    1314  2013-10-30       BRK          94       CLE   \n",
       "4      131410300005    1314  2013-10-30       BOS          87       TOR   \n",
       "...             ...     ...         ...       ...         ...       ...   \n",
       "11974  222304091226    2223  2023-04-09       UTA         117       LAL   \n",
       "11975  222304091227    2223  2023-04-09       NOP         108       MIN   \n",
       "11976  222304091228    2223  2023-04-09       MEM         100       OKC   \n",
       "11977  222304091229    2223  2023-04-09       LAC         119       PHO   \n",
       "11978  222304091230    2223  2023-04-09       GSW         157       POR   \n",
       "\n",
       "       home_score  result   away_elo_i   away_elo_n   home_elo_i   home_elo_n  \\\n",
       "0              97       1  1313.231700  1311.533869  1583.572100  1585.269931   \n",
       "1             107       1  1519.912400  1516.608783  1691.889600  1695.193217   \n",
       "2             116       1  1586.588400  1575.414216  1511.020000  1522.194184   \n",
       "3              98       1  1548.436500  1540.172399  1369.282300  1377.546401   \n",
       "4              93       1  1484.961500  1479.784941  1483.983500  1489.160059   \n",
       "...           ...     ...          ...          ...          ...          ...   \n",
       "11974         128       1  1419.106244  1415.566964  1567.961696  1571.500976   \n",
       "11975         113       1  1543.862541  1538.995985  1535.490950  1540.357507   \n",
       "11976         115       1  1613.827418  1599.928440  1505.092965  1518.991944   \n",
       "11977         114       0  1537.722013  1548.985300  1588.174579  1576.911292   \n",
       "11978         101       0  1581.483746  1599.615968  1328.023658  1309.891436   \n",
       "\n",
       "       elo_pred  \n",
       "0             1  \n",
       "1             1  \n",
       "2             1  \n",
       "3             0  \n",
       "4             1  \n",
       "...         ...  \n",
       "11974         1  \n",
       "11975         1  \n",
       "11976         0  \n",
       "11977         1  \n",
       "11978         0  \n",
       "\n",
       "[11979 rows x 13 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elo_df = pd.read_csv('https://raw.githubusercontent.com/luke-lite/NBA-Prediction-Modeling/main/data/game_info_elo.csv', index_col=0)\n",
    "elo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>game_id</th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>away_team</th>\n",
       "      <th>away_score</th>\n",
       "      <th>home_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>result</th>\n",
       "      <th>away_elo_i</th>\n",
       "      <th>away_elo_n</th>\n",
       "      <th>home_elo_i</th>\n",
       "      <th>home_elo_n</th>\n",
       "      <th>elo_pred</th>\n",
       "      <th>away_win_prob</th>\n",
       "      <th>home_win_prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>131410290001</td>\n",
       "      <td>1314</td>\n",
       "      <td>2013-10-29</td>\n",
       "      <td>ORL</td>\n",
       "      <td>87</td>\n",
       "      <td>IND</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>1313.231700</td>\n",
       "      <td>1311.533869</td>\n",
       "      <td>1583.572100</td>\n",
       "      <td>1585.269931</td>\n",
       "      <td>1</td>\n",
       "      <td>0.106039</td>\n",
       "      <td>0.893961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>131410290002</td>\n",
       "      <td>1314</td>\n",
       "      <td>2013-10-29</td>\n",
       "      <td>CHI</td>\n",
       "      <td>95</td>\n",
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       "      <td>107</td>\n",
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       "      <td>1516.608783</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>131410290003</td>\n",
       "      <td>1314</td>\n",
       "      <td>2013-10-29</td>\n",
       "      <td>LAC</td>\n",
       "      <td>103</td>\n",
       "      <td>LAL</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>1586.588400</td>\n",
       "      <td>1575.414216</td>\n",
       "      <td>1511.020000</td>\n",
       "      <td>1522.194184</td>\n",
       "      <td>1</td>\n",
       "      <td>0.464898</td>\n",
       "      <td>0.535102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>131410300004</td>\n",
       "      <td>1314</td>\n",
       "      <td>2013-10-30</td>\n",
       "      <td>BRK</td>\n",
       "      <td>94</td>\n",
       "      <td>CLE</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1548.436500</td>\n",
       "      <td>1540.172399</td>\n",
       "      <td>1369.282300</td>\n",
       "      <td>1377.546401</td>\n",
       "      <td>0</td>\n",
       "      <td>0.611981</td>\n",
       "      <td>0.388019</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>131410300005</td>\n",
       "      <td>1314</td>\n",
       "      <td>2013-10-30</td>\n",
       "      <td>BOS</td>\n",
       "      <td>87</td>\n",
       "      <td>TOR</td>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "      <td>1484.961500</td>\n",
       "      <td>1479.784941</td>\n",
       "      <td>1483.983500</td>\n",
       "      <td>1489.160059</td>\n",
       "      <td>1</td>\n",
       "      <td>0.361233</td>\n",
       "      <td>0.638767</td>\n",
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       "      <td>222304091226</td>\n",
       "      <td>2223</td>\n",
       "      <td>2023-04-09</td>\n",
       "      <td>UTA</td>\n",
       "      <td>117</td>\n",
       "      <td>LAL</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>1419.106244</td>\n",
       "      <td>1415.566964</td>\n",
       "      <td>1567.961696</td>\n",
       "      <td>1571.500976</td>\n",
       "      <td>1</td>\n",
       "      <td>0.192705</td>\n",
       "      <td>0.807295</td>\n",
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       "    <tr>\n",
       "      <th>11975</th>\n",
       "      <td>222304091227</td>\n",
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       "      <td>2023-04-09</td>\n",
       "      <td>NOP</td>\n",
       "      <td>108</td>\n",
       "      <td>MIN</td>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>1543.862541</td>\n",
       "      <td>1538.995985</td>\n",
       "      <td>1535.490950</td>\n",
       "      <td>1540.357507</td>\n",
       "      <td>1</td>\n",
       "      <td>0.371111</td>\n",
       "      <td>0.628889</td>\n",
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       "    <tr>\n",
       "      <th>11976</th>\n",
       "      <td>222304091228</td>\n",
       "      <td>2223</td>\n",
       "      <td>2023-04-09</td>\n",
       "      <td>MEM</td>\n",
       "      <td>100</td>\n",
       "      <td>OKC</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1613.827418</td>\n",
       "      <td>1599.928440</td>\n",
       "      <td>1505.092965</td>\n",
       "      <td>1518.991944</td>\n",
       "      <td>0</td>\n",
       "      <td>0.512567</td>\n",
       "      <td>0.487433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11977</th>\n",
       "      <td>222304091229</td>\n",
       "      <td>2223</td>\n",
       "      <td>2023-04-09</td>\n",
       "      <td>LAC</td>\n",
       "      <td>119</td>\n",
       "      <td>PHO</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>1537.722013</td>\n",
       "      <td>1548.985300</td>\n",
       "      <td>1588.174579</td>\n",
       "      <td>1576.911292</td>\n",
       "      <td>1</td>\n",
       "      <td>0.296072</td>\n",
       "      <td>0.703928</td>\n",
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       "    <tr>\n",
       "      <th>11978</th>\n",
       "      <td>222304091230</td>\n",
       "      <td>2223</td>\n",
       "      <td>2023-04-09</td>\n",
       "      <td>GSW</td>\n",
       "      <td>157</td>\n",
       "      <td>POR</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1581.483746</td>\n",
       "      <td>1599.615968</td>\n",
       "      <td>1328.023658</td>\n",
       "      <td>1309.891436</td>\n",
       "      <td>0</td>\n",
       "      <td>0.707524</td>\n",
       "      <td>0.292476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11979 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            game_id  season        date away_team  away_score home_team  \\\n",
       "0      131410290001    1314  2013-10-29       ORL          87       IND   \n",
       "1      131410290002    1314  2013-10-29       CHI          95       MIA   \n",
       "2      131410290003    1314  2013-10-29       LAC         103       LAL   \n",
       "3      131410300004    1314  2013-10-30       BRK          94       CLE   \n",
       "4      131410300005    1314  2013-10-30       BOS          87       TOR   \n",
       "...             ...     ...         ...       ...         ...       ...   \n",
       "11974  222304091226    2223  2023-04-09       UTA         117       LAL   \n",
       "11975  222304091227    2223  2023-04-09       NOP         108       MIN   \n",
       "11976  222304091228    2223  2023-04-09       MEM         100       OKC   \n",
       "11977  222304091229    2223  2023-04-09       LAC         119       PHO   \n",
       "11978  222304091230    2223  2023-04-09       GSW         157       POR   \n",
       "\n",
       "       home_score  result   away_elo_i   away_elo_n   home_elo_i   home_elo_n  \\\n",
       "0              97       1  1313.231700  1311.533869  1583.572100  1585.269931   \n",
       "1             107       1  1519.912400  1516.608783  1691.889600  1695.193217   \n",
       "2             116       1  1586.588400  1575.414216  1511.020000  1522.194184   \n",
       "3              98       1  1548.436500  1540.172399  1369.282300  1377.546401   \n",
       "4              93       1  1484.961500  1479.784941  1483.983500  1489.160059   \n",
       "...           ...     ...          ...          ...          ...          ...   \n",
       "11974         128       1  1419.106244  1415.566964  1567.961696  1571.500976   \n",
       "11975         113       1  1543.862541  1538.995985  1535.490950  1540.357507   \n",
       "11976         115       1  1613.827418  1599.928440  1505.092965  1518.991944   \n",
       "11977         114       0  1537.722013  1548.985300  1588.174579  1576.911292   \n",
       "11978         101       0  1581.483746  1599.615968  1328.023658  1309.891436   \n",
       "\n",
       "       elo_pred  away_win_prob  home_win_prob  \n",
       "0             1       0.106039       0.893961  \n",
       "1             1       0.172841       0.827159  \n",
       "2             1       0.464898       0.535102  \n",
       "3             0       0.611981       0.388019  \n",
       "4             1       0.361233       0.638767  \n",
       "...         ...            ...            ...  \n",
       "11974         1       0.192705       0.807295  \n",
       "11975         1       0.371111       0.628889  \n",
       "11976         0       0.512567       0.487433  \n",
       "11977         1       0.296072       0.703928  \n",
       "11978         0       0.707524       0.292476  \n",
       "\n",
       "[11979 rows x 15 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "win_probability(elo_df, elo_df['away_elo_i'], elo_df['home_elo_i'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 11979 entries, 0 to 11978\n",
      "Data columns (total 15 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   game_id        11979 non-null  int64  \n",
      " 1   season         11979 non-null  int64  \n",
      " 2   date           11979 non-null  object \n",
      " 3   away_team      11979 non-null  object \n",
      " 4   away_score     11979 non-null  int64  \n",
      " 5   home_team      11979 non-null  object \n",
      " 6   home_score     11979 non-null  int64  \n",
      " 7   result         11979 non-null  int64  \n",
      " 8   away_elo_i     11979 non-null  float64\n",
      " 9   away_elo_n     11979 non-null  float64\n",
      " 10  home_elo_i     11979 non-null  float64\n",
      " 11  home_elo_n     11979 non-null  float64\n",
      " 12  elo_pred       11979 non-null  int64  \n",
      " 13  away_win_prob  11979 non-null  float64\n",
      " 14  home_win_prob  11979 non-null  float64\n",
      "dtypes: float64(6), int64(6), object(3)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "elo_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15915/3449563420.py:12: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  bin_accuracy = elo_df.groupby('win_prob_bin').apply(lambda x: (x['elo_pred'] == x['result']).mean() * 100)\n",
      "/tmp/ipykernel_15915/3449563420.py:12: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  bin_accuracy = elo_df.groupby('win_prob_bin').apply(lambda x: (x['elo_pred'] == x['result']).mean() * 100)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adjust the home Elo by adding 100\n",
    "elo_df['home_elo_adjusted'] = elo_df['home_elo_i'] + 100\n",
    "\n",
    "# Determine the win probability of the favored team\n",
    "elo_df['favored_win_prob'] = np.where(elo_df['away_win_prob'] > elo_df['home_win_prob'],\n",
    "                                      elo_df['away_win_prob'], elo_df['home_win_prob'])\n",
    "\n",
    "# Bin the favored win probabilities into 10 distinct bins\n",
    "elo_df['win_prob_bin'] = pd.cut(elo_df['favored_win_prob'], bins=np.linspace(0.5, 1, 11))\n",
    "\n",
    "# Calculate the percentage of correct predictions in each bin\n",
    "bin_accuracy = elo_df.groupby('win_prob_bin').apply(lambda x: (x['elo_pred'] == x['result']).mean() * 100)\n",
    "\n",
    "# Plotting the data\n",
    "plt.figure(figsize=(10, 6))\n",
    "bin_accuracy.plot(kind='bar', color='skyblue')\n",
    "plt.xlabel('Favored Team Win Probability Bin')\n",
    "plt.ylabel('Percentage of Correct Elo Predictions')\n",
    "plt.title('Percentage of Correct Elo Predictions by Favored Team Win Probability Bin')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the total count in each bin\n",
    "bin_count = elo_df['win_prob_bin'].value_counts(sort=False)\n",
    "\n",
    "# Plotting the data\n",
    "plt.figure(figsize=(10, 6))\n",
    "bin_count.plot(kind='bar', color='lightcoral')\n",
    "plt.xlabel('Favored Team Win Probability Bin')\n",
    "plt.ylabel('Total Count')\n",
    "plt.title('Total Count of Entries by Favored Team Win Probability Bin')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  }
 ],
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