models/log_reg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fetch data from DB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found MongoDB password in MongoDB.txt\n"
     ]
    },
    {
     "data": {
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       "      <th>game_date</th>\n",
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       "                                                                xBA    rhb  \\\n",
       "game_date  game_pk home  team opponent at_bat batter pitcher                 \n",
       "2016-04-03 446873  False NYM  KC       1      434158 450172   0.907  False   \n",
       "                                       2      431151 450172   0.267   True   \n",
       "                                       3      493316 450172   0.033   True   \n",
       "                                       9      446263 450172     NaN  False   \n",
       "                                       10     435522 450172     NaN  False   \n",
       "...                                                             ...    ...   \n",
       "2023-09-13 716607  True  MIN  TB       68     680777 687330   0.014   True   \n",
       "                                       73     650489 621363   0.001   True   \n",
       "                                       74     656669 621363     NaN   True   \n",
       "                                       75     571657 621363   0.251   True   \n",
       "                                       76     543877 596112     NaN   True   \n",
       "\n",
       "                                                                rhp  \\\n",
       "game_date  game_pk home  team opponent at_bat batter pitcher          \n",
       "2016-04-03 446873  False NYM  KC       1      434158 450172    True   \n",
       "                                       2      431151 450172    True   \n",
       "                                       3      493316 450172    True   \n",
       "                                       9      446263 450172    True   \n",
       "                                       10     435522 450172    True   \n",
       "...                                                             ...   \n",
       "2023-09-13 716607  True  MIN  TB       68     680777 687330    True   \n",
       "                                       73     650489 621363   False   \n",
       "                                       74     656669 621363   False   \n",
       "                                       75     571657 621363   False   \n",
       "                                       76     543877 596112    True   \n",
       "\n",
       "                                                                 events  \\\n",
       "game_date  game_pk home  team opponent at_bat batter pitcher              \n",
       "2016-04-03 446873  False NYM  KC       1      434158 450172   field_out   \n",
       "                                       2      431151 450172   field_out   \n",
       "                                       3      493316 450172   field_out   \n",
       "                                       9      446263 450172   strikeout   \n",
       "                                       10     435522 450172   strikeout   \n",
       "...                                                                 ...   \n",
       "2023-09-13 716607  True  MIN  TB       68     680777 687330   field_out   \n",
       "                                       73     650489 621363   field_out   \n",
       "                                       74     656669 621363        walk   \n",
       "                                       75     571657 621363   field_out   \n",
       "                                       76     543877 596112   strikeout   \n",
       "\n",
       "                                                              park_factor  \\\n",
       "game_date  game_pk home  team opponent at_bat batter pitcher                \n",
       "2016-04-03 446873  False NYM  KC       1      434158 450172           103   \n",
       "                                       2      431151 450172           103   \n",
       "                                       3      493316 450172           103   \n",
       "                                       9      446263 450172           103   \n",
       "                                       10     435522 450172           103   \n",
       "...                                                                   ...   \n",
       "2023-09-13 716607  True  MIN  TB       68     680777 687330            99   \n",
       "                                       73     650489 621363            99   \n",
       "                                       74     656669 621363            99   \n",
       "                                       75     571657 621363            99   \n",
       "                                       76     543877 596112            99   \n",
       "\n",
       "                                                              opp_sp bats  \\\n",
       "game_date  game_pk home  team opponent at_bat batter pitcher                \n",
       "2016-04-03 446873  False NYM  KC       1      434158 450172   450172    L   \n",
       "                                       2      431151 450172   450172    R   \n",
       "                                       3      493316 450172   450172    R   \n",
       "                                       9      446263 450172   450172    L   \n",
       "                                       10     435522 450172   450172    S   \n",
       "...                                                              ...  ...   \n",
       "2023-09-13 716607  True  MIN  TB       68     680777 687330   671737    R   \n",
       "                                       73     650489 621363   671737    S   \n",
       "                                       74     656669 621363   671737    R   \n",
       "                                       75     571657 621363   671737    R   \n",
       "                                       76     543877 596112   671737    R   \n",
       "\n",
       "                                                              hp_to_1b throws  \\\n",
       "game_date  game_pk home  team opponent at_bat batter pitcher                    \n",
       "2016-04-03 446873  False NYM  KC       1      434158 450172       4.25      R   \n",
       "                                       2      431151 450172       4.75      R   \n",
       "                                       3      493316 450172       4.42      R   \n",
       "                                       9      446263 450172       4.75      R   \n",
       "                                       10     435522 450172       4.48      R   \n",
       "...                                                                ...    ...   \n",
       "2023-09-13 716607  True  MIN  TB       68     680777 687330       4.69      R   \n",
       "                                       73     650489 621363       4.28      L   \n",
       "                                       74     656669 621363       4.43      L   \n",
       "                                       75     571657 621363       4.56      L   \n",
       "                                       76     543877 596112       4.77      R   \n",
       "\n",
       "                                                              lineup      H  \\\n",
       "game_date  game_pk home  team opponent at_bat batter pitcher                  \n",
       "2016-04-03 446873  False NYM  KC       1      434158 450172      2.0  False   \n",
       "                                       2      431151 450172      1.0  False   \n",
       "                                       3      493316 450172      6.0  False   \n",
       "                                       9      446263 450172      4.0  False   \n",
       "                                       10     435522 450172      3.0  False   \n",
       "...                                                              ...    ...   \n",
       "2023-09-13 716607  True  MIN  TB       68     680777 687330      5.0  False   \n",
       "                                       73     650489 621363      3.0  False   \n",
       "                                       74     656669 621363      8.0  False   \n",
       "                                       75     571657 621363      7.0  False   \n",
       "                                       76     543877 596112      6.0  False   \n",
       "\n",
       "                                                                BIP  \\\n",
       "game_date  game_pk home  team opponent at_bat batter pitcher          \n",
       "2016-04-03 446873  False NYM  KC       1      434158 450172    True   \n",
       "                                       2      431151 450172    True   \n",
       "                                       3      493316 450172    True   \n",
       "                                       9      446263 450172   False   \n",
       "                                       10     435522 450172   False   \n",
       "...                                                             ...   \n",
       "2023-09-13 716607  True  MIN  TB       68     680777 687330    True   \n",
       "                                       73     650489 621363    True   \n",
       "                                       74     656669 621363   False   \n",
       "                                       75     571657 621363    True   \n",
       "                                       76     543877 596112   False   \n",
       "\n",
       "                                                              statcast_tracked  \n",
       "game_date  game_pk home  team opponent at_bat batter pitcher                    \n",
       "2016-04-03 446873  False NYM  KC       1      434158 450172               True  \n",
       "                                       2      431151 450172               True  \n",
       "                                       3      493316 450172               True  \n",
       "                                       9      446263 450172               True  \n",
       "                                       10     435522 450172               True  \n",
       "...                                                                        ...  \n",
       "2023-09-13 716607  True  MIN  TB       68     680777 687330               True  \n",
       "                                       73     650489 621363               True  \n",
       "                                       74     656669 621363               True  \n",
       "                                       75     571657 621363               True  \n",
       "                                       76     543877 596112               True  \n",
       "\n",
       "[1332946 rows x 13 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from data import get_enhanced_at_bats\n",
    "from datetime import datetime\n",
    "\n",
    "enhanced_at_bats = get_enhanced_at_bats(from_date = datetime(2016, 1, 1))\n",
    "enhanced_at_bats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialize classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model import BTSBatterClassifier\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "log_reg = BTSBatterClassifier(LogisticRegressionCV(cv = 10, random_state = 57), at_bats_df = enhanced_at_bats, pkl_name = 'log_reg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batter per game and per PA aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "                   G_last_50G  HG%_last_50G  xHG%_last_50G  PA/G_last_50G  \\\n",
       "game_date  batter                                                           \n",
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       "\n",
       "                   BIP/G_last_50G  H/G_last_50G  xH/G_last_50G  \n",
       "game_date  batter                                               \n",
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       "\n",
       "[298352 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.batter_per_game_agg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "                   PA_last_200PA  BIP/PA_last_200PA  H/PA_last_200PA  \\\n",
       "game_date  batter                                                      \n",
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "log_reg.batter_per_pa_agg()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pitcher and bullpen per BF aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>152034 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "                    BF_last_200BF  K%_last_200BF  BB%_last_200BF  \\\n",
       "game_date  pitcher                                                 \n",
       "2016-04-03 112526               0       0.000000        0.000000   \n",
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       "...                           ...            ...             ...   \n",
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       "           690829              25       0.200000        0.200000   \n",
       "           690986              86       0.302326        0.093023   \n",
       "\n",
       "                    H/PA_last_200BF  xH/PA_last_200BF  \n",
       "game_date  pitcher                                     \n",
       "2016-04-03 112526          0.000000          0.000000  \n",
       "           425794          0.000000          0.000000  \n",
       "           434538          0.000000          0.000000  \n",
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       "...                             ...               ...  \n",
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       "           690829          0.200000          0.175800  \n",
       "           690986          0.220930          0.222360  \n",
       "\n",
       "[152034 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.pitcher_per_bf_agg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STL</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TB</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2023-09-13</th>\n",
       "      <th>STL</th>\n",
       "      <td>200</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.19990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TB</th>\n",
       "      <td>200</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.17187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEX</th>\n",
       "      <td>200</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.21147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>200</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.19859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WSH</th>\n",
       "      <td>200</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.23903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34341 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BF_last_200BF  K%_last_200BF  BB%_last_200BF  \\\n",
       "game_date  opponent                                                 \n",
       "2016-04-03 KC                    0          0.000           0.000   \n",
       "           NYM                   0          0.000           0.000   \n",
       "           PIT                   0          0.000           0.000   \n",
       "           STL                   0          0.000           0.000   \n",
       "           TB                    0          0.000           0.000   \n",
       "...                            ...            ...             ...   \n",
       "2023-09-13 STL                 200          0.190           0.115   \n",
       "           TB                  200          0.325           0.055   \n",
       "           TEX                 200          0.215           0.120   \n",
       "           TOR                 200          0.245           0.100   \n",
       "           WSH                 200          0.170           0.080   \n",
       "\n",
       "                     H/PA_last_200BF  xH/PA_last_200BF  \n",
       "game_date  opponent                                     \n",
       "2016-04-03 KC                  0.000           0.00000  \n",
       "           NYM                 0.000           0.00000  \n",
       "           PIT                 0.000           0.00000  \n",
       "           STL                 0.000           0.00000  \n",
       "           TB                  0.000           0.00000  \n",
       "...                              ...               ...  \n",
       "2023-09-13 STL                 0.225           0.19990  \n",
       "           TB                  0.150           0.17187  \n",
       "           TEX                 0.255           0.21147  \n",
       "           TOR                 0.220           0.19859  \n",
       "           WSH                 0.230           0.23903  \n",
       "\n",
       "[34341 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.bullpen_per_bf_agg()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial 36 features: hp_to_1b, park_factor, HG%_last_50G, xHG%_last_50G, PA/G_last_50G, BIP/G_last_50G, H/G_last_50G, xH/G_last_50G, HG%_last_50G_home_away, xHG%_last_50G_home_away, PA/G_last_50G_home_away, BIP/G_last_50G_home_away, H/G_last_50G_home_away, xH/G_last_50G_home_away, BIP/PA_last_200PA, H/PA_last_200PA, xH/PA_last_200PA, BIP/PA_last_200PA_vs_hp, H/PA_last_200PA_vs_hp, xH/PA_last_200PA_vs_hp, K%_last_200BF, BB%_last_200BF, H/PA_last_200BF, xH/PA_last_200BF, K%_last_200BF_vs_hb, BB%_last_200BF_vs_hb, H/PA_last_200BF_vs_hb, xH/PA_last_200BF_vs_hb, K%_last_200BF_bullpen, BB%_last_200BF_bullpen, H/PA_last_200BF_bullpen, xH/PA_last_200BF_bullpen, PA_last_200PA_vs_opp_sp, BIP/PA_last_200PA_vs_opp_sp, H/PA_last_200PA_vs_opp_sp, xH/PA_last_200PA_vs_opp_sp\n",
      "PCA: # of features reduced from 36 to 29\n",
      "Score on training data: 0.644\n",
      "Score on testing data: 0.649\n"
     ]
    }
   ],
   "source": [
    "log_reg.fit_model(scale_features = True, perform_pca = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulate results on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_reg.simulate_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get predictions for today"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GET https://statsapi.mlb.com/api/v1/schedule?lang=en&sportId=1&gameType=R&date=2023-09-14 (200) 0.23s\n",
      "GET https://baseballsavant.mlb.com/leaderboard/statcast-park-factors?type=venue&stat=index_Hits (200) 0.07s\n",
      "GET https://www.mlb.com/apps/beat-the-streak/game/json/units.json (200) 0.18s\n",
      "GET https://www.mlb.com/apps/beat-the-streak/game/json/squads.json (200) 0.13s\n",
      "GET https://www.mlb.com/apps/beat-the-streak/game/json/players.json (200) 0.13s\n"
     ]
    },
    {
     "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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>game_time</th>\n",
       "      <th>lineup</th>\n",
       "      <th>name</th>\n",
       "      <th>bats</th>\n",
       "      <th>opp_sp</th>\n",
       "      <th>opp_sp_name</th>\n",
       "      <th>throws</th>\n",
       "      <th>park_factor</th>\n",
       "      <th>hp_to_1b</th>\n",
       "      <th>H%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_date</th>\n",
       "      <th>game_pk</th>\n",
       "      <th>home</th>\n",
       "      <th>team</th>\n",
       "      <th>opponent</th>\n",
       "      <th>batter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">2023-09-14</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">716581</th>\n",
       "      <th>False</th>\n",
       "      <th>SF</th>\n",
       "      <th>COL</th>\n",
       "      <th>642731</th>\n",
       "      <td>2023-09-14 20:35:00</td>\n",
       "      <td>0</td>\n",
       "      <td>Thairo Estrada</td>\n",
       "      <td>R</td>\n",
       "      <td>502624</td>\n",
       "      <td>Chase Anderson</td>\n",
       "      <td>R</td>\n",
       "      <td>115</td>\n",
       "      <td>4.33</td>\n",
       "      <td>0.762462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th>COL</th>\n",
       "      <th>SF</th>\n",
       "      <th>678662</th>\n",
       "      <td>2023-09-14 20:35:00</td>\n",
       "      <td>0</td>\n",
       "      <td>Ezequiel Tovar</td>\n",
       "      <td>R</td>\n",
       "      <td>657277</td>\n",
       "      <td>Logan Webb</td>\n",
       "      <td>R</td>\n",
       "      <td>115</td>\n",
       "      <td>4.37</td>\n",
       "      <td>0.749989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <th>SF</th>\n",
       "      <th>COL</th>\n",
       "      <th>527038</th>\n",
       "      <td>2023-09-14 20:35:00</td>\n",
       "      <td>0</td>\n",
       "      <td>Wilmer Flores</td>\n",
       "      <td>R</td>\n",
       "      <td>502624</td>\n",
       "      <td>Chase Anderson</td>\n",
       "      <td>R</td>\n",
       "      <td>115</td>\n",
       "      <td>4.90</td>\n",
       "      <td>0.743015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">True</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">COL</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">SF</th>\n",
       "      <th>592178</th>\n",
       "      <td>2023-09-14 20:35:00</td>\n",
       "      <td>0</td>\n",
       "      <td>Kris Bryant</td>\n",
       "      <td>R</td>\n",
       "      <td>657277</td>\n",
       "      <td>Logan Webb</td>\n",
       "      <td>R</td>\n",
       "      <td>115</td>\n",
       "      <td>4.58</td>\n",
       "      <td>0.734717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663898</th>\n",
       "      <td>2023-09-14 20:35:00</td>\n",
       "      <td>0</td>\n",
       "      <td>Brendan Rodgers</td>\n",
       "      <td>R</td>\n",
       "      <td>657277</td>\n",
       "      <td>Logan Webb</td>\n",
       "      <td>R</td>\n",
       "      <td>115</td>\n",
       "      <td>4.64</td>\n",
       "      <td>0.727622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716585</th>\n",
       "      <th>False</th>\n",
       "      <th>MIA</th>\n",
       "      <th>MIL</th>\n",
       "      <th>607732</th>\n",
       "      <td>2023-09-14 14:05:00</td>\n",
       "      <td>0</td>\n",
       "      <td>Jacob Stallings</td>\n",
       "      <td>R</td>\n",
       "      <td>605288</td>\n",
       "      <td>Adrian Houser</td>\n",
       "      <td>R</td>\n",
       "      <td>93</td>\n",
       "      <td>5.11</td>\n",
       "      <td>0.507304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716588</th>\n",
       "      <th>False</th>\n",
       "      <th>AZ</th>\n",
       "      <th>NYM</th>\n",
       "      <th>446334</th>\n",
       "      <td>2023-09-14 16:05:00</td>\n",
       "      <td>0</td>\n",
       "      <td>Evan Longoria</td>\n",
       "      <td>R</td>\n",
       "      <td>673540</td>\n",
       "      <td>Kodai Senga</td>\n",
       "      <td>R</td>\n",
       "      <td>94</td>\n",
       "      <td>4.76</td>\n",
       "      <td>0.506369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716585</th>\n",
       "      <th>True</th>\n",
       "      <th>MIL</th>\n",
       "      <th>MIA</th>\n",
       "      <th>686894</th>\n",
       "      <td>2023-09-14 14:05:00</td>\n",
       "      <td>0</td>\n",
       "      <td>Joey Wiemer</td>\n",
       "      <td>R</td>\n",
       "      <td>691587</td>\n",
       "      <td>Eury Pérez</td>\n",
       "      <td>R</td>\n",
       "      <td>93</td>\n",
       "      <td>4.27</td>\n",
       "      <td>0.502688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716587</th>\n",
       "      <th>False</th>\n",
       "      <th>TEX</th>\n",
       "      <th>TOR</th>\n",
       "      <th>595978</th>\n",
       "      <td>2023-09-14 19:02:00</td>\n",
       "      <td>0</td>\n",
       "      <td>Austin Hedges</td>\n",
       "      <td>R</td>\n",
       "      <td>592332</td>\n",
       "      <td>Kevin Gausman</td>\n",
       "      <td>R</td>\n",
       "      <td>100</td>\n",
       "      <td>5.02</td>\n",
       "      <td>0.493180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716588</th>\n",
       "      <th>False</th>\n",
       "      <th>AZ</th>\n",
       "      <th>NYM</th>\n",
       "      <th>664874</th>\n",
       "      <td>2023-09-14 16:05:00</td>\n",
       "      <td>0</td>\n",
       "      <td>Seby Zavala</td>\n",
       "      <td>R</td>\n",
       "      <td>673540</td>\n",
       "      <td>Kodai Senga</td>\n",
       "      <td>R</td>\n",
       "      <td>94</td>\n",
       "      <td>4.62</td>\n",
       "      <td>0.474083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        game_time  lineup  \\\n",
       "game_date  game_pk home  team opponent batter                               \n",
       "2023-09-14 716581  False SF   COL      642731 2023-09-14 20:35:00       0   \n",
       "                   True  COL  SF       678662 2023-09-14 20:35:00       0   \n",
       "                   False SF   COL      527038 2023-09-14 20:35:00       0   \n",
       "                   True  COL  SF       592178 2023-09-14 20:35:00       0   \n",
       "                                       663898 2023-09-14 20:35:00       0   \n",
       "...                                                           ...     ...   \n",
       "           716585  False MIA  MIL      607732 2023-09-14 14:05:00       0   \n",
       "           716588  False AZ   NYM      446334 2023-09-14 16:05:00       0   \n",
       "           716585  True  MIL  MIA      686894 2023-09-14 14:05:00       0   \n",
       "           716587  False TEX  TOR      595978 2023-09-14 19:02:00       0   \n",
       "           716588  False AZ   NYM      664874 2023-09-14 16:05:00       0   \n",
       "\n",
       "                                                          name bats  opp_sp  \\\n",
       "game_date  game_pk home  team opponent batter                                 \n",
       "2023-09-14 716581  False SF   COL      642731   Thairo Estrada    R  502624   \n",
       "                   True  COL  SF       678662   Ezequiel Tovar    R  657277   \n",
       "                   False SF   COL      527038    Wilmer Flores    R  502624   \n",
       "                   True  COL  SF       592178      Kris Bryant    R  657277   \n",
       "                                       663898  Brendan Rodgers    R  657277   \n",
       "...                                                        ...  ...     ...   \n",
       "           716585  False MIA  MIL      607732  Jacob Stallings    R  605288   \n",
       "           716588  False AZ   NYM      446334    Evan Longoria    R  673540   \n",
       "           716585  True  MIL  MIA      686894      Joey Wiemer    R  691587   \n",
       "           716587  False TEX  TOR      595978    Austin Hedges    R  592332   \n",
       "           716588  False AZ   NYM      664874      Seby Zavala    R  673540   \n",
       "\n",
       "                                                  opp_sp_name throws  \\\n",
       "game_date  game_pk home  team opponent batter                          \n",
       "2023-09-14 716581  False SF   COL      642731  Chase Anderson      R   \n",
       "                   True  COL  SF       678662      Logan Webb      R   \n",
       "                   False SF   COL      527038  Chase Anderson      R   \n",
       "                   True  COL  SF       592178      Logan Webb      R   \n",
       "                                       663898      Logan Webb      R   \n",
       "...                                                       ...    ...   \n",
       "           716585  False MIA  MIL      607732   Adrian Houser      R   \n",
       "           716588  False AZ   NYM      446334     Kodai Senga      R   \n",
       "           716585  True  MIL  MIA      686894      Eury Pérez      R   \n",
       "           716587  False TEX  TOR      595978   Kevin Gausman      R   \n",
       "           716588  False AZ   NYM      664874     Kodai Senga      R   \n",
       "\n",
       "                                               park_factor  hp_to_1b        H%  \n",
       "game_date  game_pk home  team opponent batter                                   \n",
       "2023-09-14 716581  False SF   COL      642731          115      4.33  0.762462  \n",
       "                   True  COL  SF       678662          115      4.37  0.749989  \n",
       "                   False SF   COL      527038          115      4.90  0.743015  \n",
       "                   True  COL  SF       592178          115      4.58  0.734717  \n",
       "                                       663898          115      4.64  0.727622  \n",
       "...                                                    ...       ...       ...  \n",
       "           716585  False MIA  MIL      607732           93      5.11  0.507304  \n",
       "           716588  False AZ   NYM      446334           94      4.76  0.506369  \n",
       "           716585  True  MIL  MIA      686894           93      4.27  0.502688  \n",
       "           716587  False TEX  TOR      595978          100      5.02  0.493180  \n",
       "           716588  False AZ   NYM      664874           94      4.62  0.474083  \n",
       "\n",
       "[133 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "todays_predictions = log_reg.todays_predictions()\n",
    "todays_predictions"
   ]
  }
 ],
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