SluggingDS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "350a1f76",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('seaborn') # pretty matplotlib plots\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "import scipy.stats as stats\n",
    "from sklearn import datasets, linear_model, metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "702ab809",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a44aa8bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9465891",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get slugging percentages from Batting data\n",
    "slugpct = pd.read_csv(\"https://raw.githubusercontent.com/chadwickbureau/baseballdatabank/master/core/Batting.csv\",sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e7c03f7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['playerID', 'yearID', 'stint', 'teamID', 'lgID', 'G', 'AB', 'R', 'H',\n",
       "       '2B', '3B', 'HR', 'RBI', 'SB', 'CS', 'BB', 'SO', 'IBB', 'HBP', 'SH',\n",
       "       'SF', 'GIDP'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slugpct.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea0d1844",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110495 entries, 0 to 110494\n",
      "Data columns (total 22 columns):\n",
      " #   Column    Non-Null Count   Dtype  \n",
      "---  ------    --------------   -----  \n",
      " 0   playerID  110495 non-null  object \n",
      " 1   yearID    110495 non-null  int64  \n",
      " 2   stint     110495 non-null  int64  \n",
      " 3   teamID    110495 non-null  object \n",
      " 4   lgID      109758 non-null  object \n",
      " 5   G         110495 non-null  int64  \n",
      " 6   AB        110495 non-null  int64  \n",
      " 7   R         110495 non-null  int64  \n",
      " 8   H         110495 non-null  int64  \n",
      " 9   2B        110495 non-null  int64  \n",
      " 10  3B        110495 non-null  int64  \n",
      " 11  HR        110495 non-null  int64  \n",
      " 12  RBI       109739 non-null  float64\n",
      " 13  SB        108127 non-null  float64\n",
      " 14  CS        86954 non-null   float64\n",
      " 15  BB        110495 non-null  int64  \n",
      " 16  SO        108395 non-null  float64\n",
      " 17  IBB       73845 non-null   float64\n",
      " 18  HBP       107679 non-null  float64\n",
      " 19  SH        104427 non-null  float64\n",
      " 20  SF        74392 non-null   float64\n",
      " 21  GIDP      85054 non-null   float64\n",
      "dtypes: float64(9), int64(10), object(3)\n",
      "memory usage: 18.5+ MB\n"
     ]
    }
   ],
   "source": [
    "slugpct.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec72db80",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearID</th>\n",
       "      <th>stint</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>RBI</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>SO</th>\n",
       "      <th>IBB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SH</th>\n",
       "      <th>SF</th>\n",
       "      <th>GIDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>110495.000000</td>\n",
       "      <td>110495.000000</td>\n",
       "      <td>110495.000000</td>\n",
       "      <td>110495.000000</td>\n",
       "      <td>110495.000000</td>\n",
       "      <td>110495.000000</td>\n",
       "      <td>110495.000000</td>\n",
       "      <td>110495.000000</td>\n",
       "      <td>110495.000000</td>\n",
       "      <td>109739.000000</td>\n",
       "      <td>108127.000000</td>\n",
       "      <td>86954.000000</td>\n",
       "      <td>110495.000000</td>\n",
       "      <td>108395.000000</td>\n",
       "      <td>73845.000000</td>\n",
       "      <td>107679.000000</td>\n",
       "      <td>104427.000000</td>\n",
       "      <td>74392.00000</td>\n",
       "      <td>85054.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1968.051948</td>\n",
       "      <td>1.079624</td>\n",
       "      <td>50.605258</td>\n",
       "      <td>138.557084</td>\n",
       "      <td>18.397312</td>\n",
       "      <td>36.184117</td>\n",
       "      <td>6.177429</td>\n",
       "      <td>1.233893</td>\n",
       "      <td>2.859939</td>\n",
       "      <td>16.720664</td>\n",
       "      <td>2.893190</td>\n",
       "      <td>1.162431</td>\n",
       "      <td>12.787556</td>\n",
       "      <td>20.625038</td>\n",
       "      <td>1.040368</td>\n",
       "      <td>1.060810</td>\n",
       "      <td>2.168529</td>\n",
       "      <td>1.01480</td>\n",
       "      <td>2.874586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39.992877</td>\n",
       "      <td>0.288199</td>\n",
       "      <td>46.831714</td>\n",
       "      <td>183.317500</td>\n",
       "      <td>27.990668</td>\n",
       "      <td>52.066687</td>\n",
       "      <td>9.613576</td>\n",
       "      <td>2.580559</td>\n",
       "      <td>6.386896</td>\n",
       "      <td>26.187532</td>\n",
       "      <td>7.564655</td>\n",
       "      <td>2.662906</td>\n",
       "      <td>20.555314</td>\n",
       "      <td>28.724308</td>\n",
       "      <td>2.688349</td>\n",
       "      <td>2.299942</td>\n",
       "      <td>4.129955</td>\n",
       "      <td>1.92328</td>\n",
       "      <td>4.658673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1871.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1938.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1977.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2002.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2021.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>716.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>19.00000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              yearID          stint              G             AB  \\\n",
       "count  110495.000000  110495.000000  110495.000000  110495.000000   \n",
       "mean     1968.051948       1.079624      50.605258     138.557084   \n",
       "std        39.992877       0.288199      46.831714     183.317500   \n",
       "min      1871.000000       1.000000       1.000000       0.000000   \n",
       "25%      1938.000000       1.000000      12.000000       3.000000   \n",
       "50%      1977.000000       1.000000      34.000000      45.000000   \n",
       "75%      2002.000000       1.000000      78.000000     222.000000   \n",
       "max      2021.000000       5.000000     165.000000     716.000000   \n",
       "\n",
       "                   R              H             2B             3B  \\\n",
       "count  110495.000000  110495.000000  110495.000000  110495.000000   \n",
       "mean       18.397312      36.184117       6.177429       1.233893   \n",
       "std        27.990668      52.066687       9.613576       2.580559   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         4.000000       8.000000       1.000000       0.000000   \n",
       "75%        26.000000      55.000000       9.000000       1.000000   \n",
       "max       198.000000     262.000000      67.000000      36.000000   \n",
       "\n",
       "                  HR            RBI             SB            CS  \\\n",
       "count  110495.000000  109739.000000  108127.000000  86954.000000   \n",
       "mean        2.859939      16.720664       2.893190      1.162431   \n",
       "std         6.386896      26.187532       7.564655      2.662906   \n",
       "min         0.000000       0.000000       0.000000      0.000000   \n",
       "25%         0.000000       0.000000       0.000000      0.000000   \n",
       "50%         0.000000       3.000000       0.000000      0.000000   \n",
       "75%         2.000000      24.000000       2.000000      1.000000   \n",
       "max        73.000000     191.000000     138.000000     42.000000   \n",
       "\n",
       "                  BB             SO           IBB            HBP  \\\n",
       "count  110495.000000  108395.000000  73845.000000  107679.000000   \n",
       "mean       12.787556      20.625038      1.040368       1.060810   \n",
       "std        20.555314      28.724308      2.688349       2.299942   \n",
       "min         0.000000       0.000000      0.000000       0.000000   \n",
       "25%         0.000000       1.000000      0.000000       0.000000   \n",
       "50%         2.000000       9.000000      0.000000       0.000000   \n",
       "75%        18.000000      29.000000      1.000000       1.000000   \n",
       "max       232.000000     223.000000    120.000000      51.000000   \n",
       "\n",
       "                  SH           SF          GIDP  \n",
       "count  104427.000000  74392.00000  85054.000000  \n",
       "mean        2.168529      1.01480      2.874586  \n",
       "std         4.129955      1.92328      4.658673  \n",
       "min         0.000000      0.00000      0.000000  \n",
       "25%         0.000000      0.00000      0.000000  \n",
       "50%         0.000000      0.00000      0.000000  \n",
       "75%         3.000000      1.00000      4.000000  \n",
       "max        67.000000     19.00000     36.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slugpct.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ffc7d5dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110495, 22)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slugpct.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3eef5abe",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>yearID</th>\n",
       "      <th>stint</th>\n",
       "      <th>teamID</th>\n",
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       "      <th>SB</th>\n",
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       "      <th>HBP</th>\n",
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       "      <th>GIDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>110490</th>\n",
       "      <td>zimmejo02</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>MIL</td>\n",
       "      <td>NL</td>\n",
       "      <td>2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>110491</th>\n",
       "      <td>zimmeky01</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>KCA</td>\n",
       "      <td>AL</td>\n",
       "      <td>52</td>\n",
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       "    <tr>\n",
       "      <th>110492</th>\n",
       "      <td>zimmery01</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>WAS</td>\n",
       "      <td>NL</td>\n",
       "      <td>110</td>\n",
       "      <td>255</td>\n",
       "      <td>27</td>\n",
       "      <td>62</td>\n",
       "      <td>16</td>\n",
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       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110493</th>\n",
       "      <td>zuberty01</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>KCA</td>\n",
       "      <td>AL</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
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       "      <td>zuninmi01</td>\n",
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       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         playerID  yearID  stint teamID lgID    G   AB   R   H  2B  ...   RBI  \\\n",
       "110490  zimmejo02    2021      1    MIL   NL    2    1   0   0   0  ...   0.0   \n",
       "110491  zimmeky01    2021      1    KCA   AL   52    0   0   0   0  ...   0.0   \n",
       "110492  zimmery01    2021      1    WAS   NL  110  255  27  62  16  ...  46.0   \n",
       "110493  zuberty01    2021      1    KCA   AL   31    1   0   0   0  ...   0.0   \n",
       "110494  zuninmi01    2021      1    TBA   AL  109  333  64  72  11  ...  62.0   \n",
       "\n",
       "         SB   CS  BB     SO  IBB  HBP   SH   SF  GIDP  \n",
       "110490  0.0  0.0   0    1.0  0.0  0.0  0.0  0.0   0.0  \n",
       "110491  0.0  0.0   0    0.0  0.0  0.0  0.0  0.0   0.0  \n",
       "110492  0.0  0.0  16   77.0  0.0  0.0  0.0  2.0   9.0  \n",
       "110493  0.0  0.0   0    1.0  0.0  0.0  0.0  0.0   0.0  \n",
       "110494  0.0  0.0  34  132.0  0.0  7.0  0.0  1.0   7.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slugpct.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "68df92db",
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    "scrolled": false
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    {
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      ],
      "text/plain": [
       "        playerID  yearID  stint  teamID   lgID      G     AB      R      H  \\\n",
       "0          False   False  False   False   True  False  False  False  False   \n",
       "1          False   False  False   False   True  False  False  False  False   \n",
       "2          False   False  False   False   True  False  False  False  False   \n",
       "3          False   False  False   False   True  False  False  False  False   \n",
       "4          False   False  False   False   True  False  False  False  False   \n",
       "...          ...     ...    ...     ...    ...    ...    ...    ...    ...   \n",
       "110490     False   False  False   False  False  False  False  False  False   \n",
       "110491     False   False  False   False  False  False  False  False  False   \n",
       "110492     False   False  False   False  False  False  False  False  False   \n",
       "110493     False   False  False   False  False  False  False  False  False   \n",
       "110494     False   False  False   False  False  False  False  False  False   \n",
       "\n",
       "           2B  ...    RBI     SB     CS     BB     SO    IBB    HBP     SH  \\\n",
       "0       False  ...  False  False  False  False  False   True   True   True   \n",
       "1       False  ...  False  False  False  False  False   True   True   True   \n",
       "2       False  ...  False  False  False  False  False   True   True   True   \n",
       "3       False  ...  False  False  False  False  False   True   True   True   \n",
       "4       False  ...  False  False  False  False  False   True   True   True   \n",
       "...       ...  ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "110490  False  ...  False  False  False  False  False  False  False  False   \n",
       "110491  False  ...  False  False  False  False  False  False  False  False   \n",
       "110492  False  ...  False  False  False  False  False  False  False  False   \n",
       "110493  False  ...  False  False  False  False  False  False  False  False   \n",
       "110494  False  ...  False  False  False  False  False  False  False  False   \n",
       "\n",
       "           SF   GIDP  \n",
       "0        True  False  \n",
       "1        True  False  \n",
       "2        True  False  \n",
       "3        True  False  \n",
       "4        True  False  \n",
       "...       ...    ...  \n",
       "110490  False  False  \n",
       "110491  False  False  \n",
       "110492  False  False  \n",
       "110493  False  False  \n",
       "110494  False  False  \n",
       "\n",
       "[110495 rows x 22 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slugpct.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5ffe4a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "slugpct = slugpct.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "45f4ad51",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Change types to all ints for regression modeling\n",
    "slugpct[\"RBI\"] = slugpct[\"RBI\"].astype(int)\n",
    "slugpct[\"SB\"] = slugpct[\"SB\"].astype(int)\n",
    "slugpct[\"CS\"] = slugpct[\"CS\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a10c5150",
   "metadata": {},
   "outputs": [
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       "      <td>0.886871</td>\n",
       "      <td>0.924404</td>\n",
       "      <td>0.875768</td>\n",
       "      <td>0.646179</td>\n",
       "      <td>0.672984</td>\n",
       "      <td>0.868204</td>\n",
       "      <td>0.547138</td>\n",
       "      <td>0.553139</td>\n",
       "      <td>0.842087</td>\n",
       "      <td>0.787410</td>\n",
       "      <td>0.480654</td>\n",
       "      <td>0.595973</td>\n",
       "      <td>0.463177</td>\n",
       "      <td>0.588913</td>\n",
       "      <td>0.684314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AB</th>\n",
       "      <td>-0.111691</td>\n",
       "      <td>-0.099032</td>\n",
       "      <td>0.942596</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.953444</td>\n",
       "      <td>0.987703</td>\n",
       "      <td>0.932339</td>\n",
       "      <td>0.711248</td>\n",
       "      <td>0.702428</td>\n",
       "      <td>0.922266</td>\n",
       "      <td>0.601608</td>\n",
       "      <td>0.575850</td>\n",
       "      <td>0.872595</td>\n",
       "      <td>0.804607</td>\n",
       "      <td>0.479665</td>\n",
       "      <td>0.626991</td>\n",
       "      <td>0.477476</td>\n",
       "      <td>0.596552</td>\n",
       "      <td>0.697742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>-0.117776</td>\n",
       "      <td>-0.093063</td>\n",
       "      <td>0.886871</td>\n",
       "      <td>0.953444</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967960</td>\n",
       "      <td>0.921795</td>\n",
       "      <td>0.740833</td>\n",
       "      <td>0.736156</td>\n",
       "      <td>0.925483</td>\n",
       "      <td>0.653566</td>\n",
       "      <td>0.561106</td>\n",
       "      <td>0.894141</td>\n",
       "      <td>0.752114</td>\n",
       "      <td>0.461942</td>\n",
       "      <td>0.633036</td>\n",
       "      <td>0.415730</td>\n",
       "      <td>0.552644</td>\n",
       "      <td>0.620616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>-0.109087</td>\n",
       "      <td>-0.094596</td>\n",
       "      <td>0.924404</td>\n",
       "      <td>0.987703</td>\n",
       "      <td>0.967960</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.947331</td>\n",
       "      <td>0.735261</td>\n",
       "      <td>0.708201</td>\n",
       "      <td>0.936426</td>\n",
       "      <td>0.611091</td>\n",
       "      <td>0.577220</td>\n",
       "      <td>0.868961</td>\n",
       "      <td>0.762984</td>\n",
       "      <td>0.479592</td>\n",
       "      <td>0.621424</td>\n",
       "      <td>0.463164</td>\n",
       "      <td>0.583239</td>\n",
       "      <td>0.679985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2B</th>\n",
       "      <td>-0.033436</td>\n",
       "      <td>-0.090734</td>\n",
       "      <td>0.875768</td>\n",
       "      <td>0.932339</td>\n",
       "      <td>0.921795</td>\n",
       "      <td>0.947331</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.652081</td>\n",
       "      <td>0.730861</td>\n",
       "      <td>0.917643</td>\n",
       "      <td>0.524955</td>\n",
       "      <td>0.539904</td>\n",
       "      <td>0.837343</td>\n",
       "      <td>0.769629</td>\n",
       "      <td>0.491071</td>\n",
       "      <td>0.604670</td>\n",
       "      <td>0.381489</td>\n",
       "      <td>0.605320</td>\n",
       "      <td>0.685138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3B</th>\n",
       "      <td>-0.271024</td>\n",
       "      <td>-0.065868</td>\n",
       "      <td>0.646179</td>\n",
       "      <td>0.711248</td>\n",
       "      <td>0.740833</td>\n",
       "      <td>0.735261</td>\n",
       "      <td>0.652081</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.344628</td>\n",
       "      <td>0.657161</td>\n",
       "      <td>0.610340</td>\n",
       "      <td>0.423974</td>\n",
       "      <td>0.593775</td>\n",
       "      <td>0.420319</td>\n",
       "      <td>0.166032</td>\n",
       "      <td>0.429008</td>\n",
       "      <td>0.470635</td>\n",
       "      <td>0.209881</td>\n",
       "      <td>0.262115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HR</th>\n",
       "      <td>0.139208</td>\n",
       "      <td>-0.066901</td>\n",
       "      <td>0.672984</td>\n",
       "      <td>0.702428</td>\n",
       "      <td>0.736156</td>\n",
       "      <td>0.708201</td>\n",
       "      <td>0.730861</td>\n",
       "      <td>0.344628</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.839324</td>\n",
       "      <td>0.267738</td>\n",
       "      <td>0.376207</td>\n",
       "      <td>0.735221</td>\n",
       "      <td>0.796674</td>\n",
       "      <td>0.613057</td>\n",
       "      <td>0.508221</td>\n",
       "      <td>0.064490</td>\n",
       "      <td>0.651891</td>\n",
       "      <td>0.678223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RBI</th>\n",
       "      <td>-0.066333</td>\n",
       "      <td>-0.089031</td>\n",
       "      <td>0.868204</td>\n",
       "      <td>0.922266</td>\n",
       "      <td>0.925483</td>\n",
       "      <td>0.936426</td>\n",
       "      <td>0.917643</td>\n",
       "      <td>0.657161</td>\n",
       "      <td>0.839324</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.505040</td>\n",
       "      <td>0.482203</td>\n",
       "      <td>0.859916</td>\n",
       "      <td>0.781229</td>\n",
       "      <td>0.529901</td>\n",
       "      <td>0.607950</td>\n",
       "      <td>0.349682</td>\n",
       "      <td>0.622892</td>\n",
       "      <td>0.682771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SB</th>\n",
       "      <td>-0.151329</td>\n",
       "      <td>-0.056676</td>\n",
       "      <td>0.547138</td>\n",
       "      <td>0.601608</td>\n",
       "      <td>0.653566</td>\n",
       "      <td>0.611091</td>\n",
       "      <td>0.524955</td>\n",
       "      <td>0.610340</td>\n",
       "      <td>0.267738</td>\n",
       "      <td>0.505040</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.544206</td>\n",
       "      <td>0.539346</td>\n",
       "      <td>0.409508</td>\n",
       "      <td>0.178128</td>\n",
       "      <td>0.443049</td>\n",
       "      <td>0.370460</td>\n",
       "      <td>0.241281</td>\n",
       "      <td>0.216266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CS</th>\n",
       "      <td>0.060558</td>\n",
       "      <td>-0.056846</td>\n",
       "      <td>0.553139</td>\n",
       "      <td>0.575850</td>\n",
       "      <td>0.561106</td>\n",
       "      <td>0.577220</td>\n",
       "      <td>0.539904</td>\n",
       "      <td>0.423974</td>\n",
       "      <td>0.376207</td>\n",
       "      <td>0.482203</td>\n",
       "      <td>0.544206</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.523886</td>\n",
       "      <td>0.497095</td>\n",
       "      <td>0.313098</td>\n",
       "      <td>0.335880</td>\n",
       "      <td>0.298950</td>\n",
       "      <td>0.412381</td>\n",
       "      <td>0.413103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BB</th>\n",
       "      <td>-0.046381</td>\n",
       "      <td>-0.085250</td>\n",
       "      <td>0.842087</td>\n",
       "      <td>0.872595</td>\n",
       "      <td>0.894141</td>\n",
       "      <td>0.868961</td>\n",
       "      <td>0.837343</td>\n",
       "      <td>0.593775</td>\n",
       "      <td>0.735221</td>\n",
       "      <td>0.859916</td>\n",
       "      <td>0.539346</td>\n",
       "      <td>0.523886</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.761479</td>\n",
       "      <td>0.550540</td>\n",
       "      <td>0.581557</td>\n",
       "      <td>0.364929</td>\n",
       "      <td>0.565501</td>\n",
       "      <td>0.633314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO</th>\n",
       "      <td>0.138111</td>\n",
       "      <td>-0.090093</td>\n",
       "      <td>0.787410</td>\n",
       "      <td>0.804607</td>\n",
       "      <td>0.752114</td>\n",
       "      <td>0.762984</td>\n",
       "      <td>0.769629</td>\n",
       "      <td>0.420319</td>\n",
       "      <td>0.796674</td>\n",
       "      <td>0.781229</td>\n",
       "      <td>0.409508</td>\n",
       "      <td>0.497095</td>\n",
       "      <td>0.761479</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.534418</td>\n",
       "      <td>0.569730</td>\n",
       "      <td>0.239412</td>\n",
       "      <td>0.664014</td>\n",
       "      <td>0.702463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBB</th>\n",
       "      <td>0.154893</td>\n",
       "      <td>-0.054059</td>\n",
       "      <td>0.480654</td>\n",
       "      <td>0.479665</td>\n",
       "      <td>0.461942</td>\n",
       "      <td>0.479592</td>\n",
       "      <td>0.491071</td>\n",
       "      <td>0.166032</td>\n",
       "      <td>0.613057</td>\n",
       "      <td>0.529901</td>\n",
       "      <td>0.178128</td>\n",
       "      <td>0.313098</td>\n",
       "      <td>0.550540</td>\n",
       "      <td>0.534418</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.313283</td>\n",
       "      <td>-0.009000</td>\n",
       "      <td>0.594251</td>\n",
       "      <td>0.555168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HBP</th>\n",
       "      <td>-0.016978</td>\n",
       "      <td>-0.061881</td>\n",
       "      <td>0.595973</td>\n",
       "      <td>0.626991</td>\n",
       "      <td>0.633036</td>\n",
       "      <td>0.621424</td>\n",
       "      <td>0.604670</td>\n",
       "      <td>0.429008</td>\n",
       "      <td>0.508221</td>\n",
       "      <td>0.607950</td>\n",
       "      <td>0.443049</td>\n",
       "      <td>0.335880</td>\n",
       "      <td>0.581557</td>\n",
       "      <td>0.569730</td>\n",
       "      <td>0.313283</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.268908</td>\n",
       "      <td>0.403734</td>\n",
       "      <td>0.423867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SH</th>\n",
       "      <td>-0.223690</td>\n",
       "      <td>-0.065961</td>\n",
       "      <td>0.463177</td>\n",
       "      <td>0.477476</td>\n",
       "      <td>0.415730</td>\n",
       "      <td>0.463164</td>\n",
       "      <td>0.381489</td>\n",
       "      <td>0.470635</td>\n",
       "      <td>0.064490</td>\n",
       "      <td>0.349682</td>\n",
       "      <td>0.370460</td>\n",
       "      <td>0.298950</td>\n",
       "      <td>0.364929</td>\n",
       "      <td>0.239412</td>\n",
       "      <td>-0.009000</td>\n",
       "      <td>0.268908</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.040377</td>\n",
       "      <td>0.104938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>0.233472</td>\n",
       "      <td>-0.059765</td>\n",
       "      <td>0.588913</td>\n",
       "      <td>0.596552</td>\n",
       "      <td>0.552644</td>\n",
       "      <td>0.583239</td>\n",
       "      <td>0.605320</td>\n",
       "      <td>0.209881</td>\n",
       "      <td>0.651891</td>\n",
       "      <td>0.622892</td>\n",
       "      <td>0.241281</td>\n",
       "      <td>0.412381</td>\n",
       "      <td>0.565501</td>\n",
       "      <td>0.664014</td>\n",
       "      <td>0.594251</td>\n",
       "      <td>0.403734</td>\n",
       "      <td>0.040377</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.693176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GIDP</th>\n",
       "      <td>0.202922</td>\n",
       "      <td>-0.067632</td>\n",
       "      <td>0.684314</td>\n",
       "      <td>0.697742</td>\n",
       "      <td>0.620616</td>\n",
       "      <td>0.679985</td>\n",
       "      <td>0.685138</td>\n",
       "      <td>0.262115</td>\n",
       "      <td>0.678223</td>\n",
       "      <td>0.682771</td>\n",
       "      <td>0.216266</td>\n",
       "      <td>0.413103</td>\n",
       "      <td>0.633314</td>\n",
       "      <td>0.702463</td>\n",
       "      <td>0.555168</td>\n",
       "      <td>0.423867</td>\n",
       "      <td>0.104938</td>\n",
       "      <td>0.693176</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          yearID     stint         G        AB         R         H        2B  \\\n",
       "yearID  1.000000  0.008484 -0.004129 -0.111691 -0.117776 -0.109087 -0.033436   \n",
       "stint   0.008484  1.000000 -0.124525 -0.099032 -0.093063 -0.094596 -0.090734   \n",
       "G      -0.004129 -0.124525  1.000000  0.942596  0.886871  0.924404  0.875768   \n",
       "AB     -0.111691 -0.099032  0.942596  1.000000  0.953444  0.987703  0.932339   \n",
       "R      -0.117776 -0.093063  0.886871  0.953444  1.000000  0.967960  0.921795   \n",
       "H      -0.109087 -0.094596  0.924404  0.987703  0.967960  1.000000  0.947331   \n",
       "2B     -0.033436 -0.090734  0.875768  0.932339  0.921795  0.947331  1.000000   \n",
       "3B     -0.271024 -0.065868  0.646179  0.711248  0.740833  0.735261  0.652081   \n",
       "HR      0.139208 -0.066901  0.672984  0.702428  0.736156  0.708201  0.730861   \n",
       "RBI    -0.066333 -0.089031  0.868204  0.922266  0.925483  0.936426  0.917643   \n",
       "SB     -0.151329 -0.056676  0.547138  0.601608  0.653566  0.611091  0.524955   \n",
       "CS      0.060558 -0.056846  0.553139  0.575850  0.561106  0.577220  0.539904   \n",
       "BB     -0.046381 -0.085250  0.842087  0.872595  0.894141  0.868961  0.837343   \n",
       "SO      0.138111 -0.090093  0.787410  0.804607  0.752114  0.762984  0.769629   \n",
       "IBB     0.154893 -0.054059  0.480654  0.479665  0.461942  0.479592  0.491071   \n",
       "HBP    -0.016978 -0.061881  0.595973  0.626991  0.633036  0.621424  0.604670   \n",
       "SH     -0.223690 -0.065961  0.463177  0.477476  0.415730  0.463164  0.381489   \n",
       "SF      0.233472 -0.059765  0.588913  0.596552  0.552644  0.583239  0.605320   \n",
       "GIDP    0.202922 -0.067632  0.684314  0.697742  0.620616  0.679985  0.685138   \n",
       "\n",
       "              3B        HR       RBI        SB        CS        BB        SO  \\\n",
       "yearID -0.271024  0.139208 -0.066333 -0.151329  0.060558 -0.046381  0.138111   \n",
       "stint  -0.065868 -0.066901 -0.089031 -0.056676 -0.056846 -0.085250 -0.090093   \n",
       "G       0.646179  0.672984  0.868204  0.547138  0.553139  0.842087  0.787410   \n",
       "AB      0.711248  0.702428  0.922266  0.601608  0.575850  0.872595  0.804607   \n",
       "R       0.740833  0.736156  0.925483  0.653566  0.561106  0.894141  0.752114   \n",
       "H       0.735261  0.708201  0.936426  0.611091  0.577220  0.868961  0.762984   \n",
       "2B      0.652081  0.730861  0.917643  0.524955  0.539904  0.837343  0.769629   \n",
       "3B      1.000000  0.344628  0.657161  0.610340  0.423974  0.593775  0.420319   \n",
       "HR      0.344628  1.000000  0.839324  0.267738  0.376207  0.735221  0.796674   \n",
       "RBI     0.657161  0.839324  1.000000  0.505040  0.482203  0.859916  0.781229   \n",
       "SB      0.610340  0.267738  0.505040  1.000000  0.544206  0.539346  0.409508   \n",
       "CS      0.423974  0.376207  0.482203  0.544206  1.000000  0.523886  0.497095   \n",
       "BB      0.593775  0.735221  0.859916  0.539346  0.523886  1.000000  0.761479   \n",
       "SO      0.420319  0.796674  0.781229  0.409508  0.497095  0.761479  1.000000   \n",
       "IBB     0.166032  0.613057  0.529901  0.178128  0.313098  0.550540  0.534418   \n",
       "HBP     0.429008  0.508221  0.607950  0.443049  0.335880  0.581557  0.569730   \n",
       "SH      0.470635  0.064490  0.349682  0.370460  0.298950  0.364929  0.239412   \n",
       "SF      0.209881  0.651891  0.622892  0.241281  0.412381  0.565501  0.664014   \n",
       "GIDP    0.262115  0.678223  0.682771  0.216266  0.413103  0.633314  0.702463   \n",
       "\n",
       "             IBB       HBP        SH        SF      GIDP  \n",
       "yearID  0.154893 -0.016978 -0.223690  0.233472  0.202922  \n",
       "stint  -0.054059 -0.061881 -0.065961 -0.059765 -0.067632  \n",
       "G       0.480654  0.595973  0.463177  0.588913  0.684314  \n",
       "AB      0.479665  0.626991  0.477476  0.596552  0.697742  \n",
       "R       0.461942  0.633036  0.415730  0.552644  0.620616  \n",
       "H       0.479592  0.621424  0.463164  0.583239  0.679985  \n",
       "2B      0.491071  0.604670  0.381489  0.605320  0.685138  \n",
       "3B      0.166032  0.429008  0.470635  0.209881  0.262115  \n",
       "HR      0.613057  0.508221  0.064490  0.651891  0.678223  \n",
       "RBI     0.529901  0.607950  0.349682  0.622892  0.682771  \n",
       "SB      0.178128  0.443049  0.370460  0.241281  0.216266  \n",
       "CS      0.313098  0.335880  0.298950  0.412381  0.413103  \n",
       "BB      0.550540  0.581557  0.364929  0.565501  0.633314  \n",
       "SO      0.534418  0.569730  0.239412  0.664014  0.702463  \n",
       "IBB     1.000000  0.313283 -0.009000  0.594251  0.555168  \n",
       "HBP     0.313283  1.000000  0.268908  0.403734  0.423867  \n",
       "SH     -0.009000  0.268908  1.000000  0.040377  0.104938  \n",
       "SF      0.594251  0.403734  0.040377  1.000000  0.693176  \n",
       "GIDP    0.555168  0.423867  0.104938  0.693176  1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slugpct.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "73770189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(slugpct.corr())\n",
    "plt.title(\"Heatmap of Slugging Data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f9ad435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          0\n",
       "1          0\n",
       "2          0\n",
       "3          2\n",
       "4          0\n",
       "          ..\n",
       "110490     0\n",
       "110491     0\n",
       "110492    14\n",
       "110493     0\n",
       "110494    33\n",
       "Name: HR, Length: 110495, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slugpct[\"HR\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c4eeb9af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(slugpct[\"yearID\"], slugpct[\"HR\"])\n",
    "plt.title(\"Total Home Runs Hit By Year\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Home Runs Hit\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ff49c36e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(slugpct[\"yearID\"], slugpct[\"IBB\"])\n",
    "plt.title(\"Total Intentional Walks by Year\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Intentional Walks Thrown\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "75644959",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Regression Plot\n",
    "sns.regplot(slugpct[\"AB\"], slugpct[\"H\"])\n",
    "plt.title(\"Regression Between At-Bats & Hits\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "48e27270",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110495 entries, 0 to 110494\n",
      "Data columns (total 22 columns):\n",
      " #   Column    Non-Null Count   Dtype  \n",
      "---  ------    --------------   -----  \n",
      " 0   playerID  110495 non-null  object \n",
      " 1   yearID    110495 non-null  int64  \n",
      " 2   stint     110495 non-null  int64  \n",
      " 3   teamID    110495 non-null  object \n",
      " 4   lgID      110495 non-null  object \n",
      " 5   G         110495 non-null  int64  \n",
      " 6   AB        110495 non-null  int64  \n",
      " 7   R         110495 non-null  int64  \n",
      " 8   H         110495 non-null  int64  \n",
      " 9   2B        110495 non-null  int64  \n",
      " 10  3B        110495 non-null  int64  \n",
      " 11  HR        110495 non-null  int64  \n",
      " 12  RBI       110495 non-null  int64  \n",
      " 13  SB        110495 non-null  int64  \n",
      " 14  CS        110495 non-null  int64  \n",
      " 15  BB        110495 non-null  int64  \n",
      " 16  SO        110495 non-null  float64\n",
      " 17  IBB       110495 non-null  float64\n",
      " 18  HBP       110495 non-null  float64\n",
      " 19  SH        110495 non-null  float64\n",
      " 20  SF        110495 non-null  float64\n",
      " 21  GIDP      110495 non-null  float64\n",
      "dtypes: float64(6), int64(13), object(3)\n",
      "memory usage: 18.5+ MB\n"
     ]
    }
   ],
   "source": [
    " slugpct.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "202bf212",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change types to all ints for regression modeling, part 2\n",
    "slugpct[\"SO\"] = slugpct[\"SO\"].astype(int)\n",
    "slugpct[\"IBB\"] = slugpct[\"IBB\"].astype(int)\n",
    "slugpct[\"HBP\"] = slugpct[\"HBP\"].astype(int)\n",
    "slugpct[\"SH\"] = slugpct[\"SH\"].astype(int)\n",
    "slugpct[\"SF\"] = slugpct[\"SF\"].astype(int)\n",
    "slugpct[\"GIDP\"] = slugpct[\"GIDP\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ead8175b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110495 entries, 0 to 110494\n",
      "Data columns (total 22 columns):\n",
      " #   Column    Non-Null Count   Dtype \n",
      "---  ------    --------------   ----- \n",
      " 0   playerID  110495 non-null  object\n",
      " 1   yearID    110495 non-null  int64 \n",
      " 2   stint     110495 non-null  int64 \n",
      " 3   teamID    110495 non-null  object\n",
      " 4   lgID      110495 non-null  object\n",
      " 5   G         110495 non-null  int64 \n",
      " 6   AB        110495 non-null  int64 \n",
      " 7   R         110495 non-null  int64 \n",
      " 8   H         110495 non-null  int64 \n",
      " 9   2B        110495 non-null  int64 \n",
      " 10  3B        110495 non-null  int64 \n",
      " 11  HR        110495 non-null  int64 \n",
      " 12  RBI       110495 non-null  int64 \n",
      " 13  SB        110495 non-null  int64 \n",
      " 14  CS        110495 non-null  int64 \n",
      " 15  BB        110495 non-null  int64 \n",
      " 16  SO        110495 non-null  int64 \n",
      " 17  IBB       110495 non-null  int64 \n",
      " 18  HBP       110495 non-null  int64 \n",
      " 19  SH        110495 non-null  int64 \n",
      " 20  SF        110495 non-null  int64 \n",
      " 21  GIDP      110495 non-null  int64 \n",
      "dtypes: int64(19), object(3)\n",
      "memory usage: 18.5+ MB\n"
     ]
    }
   ],
   "source": [
    "slugpct.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c7b9c707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['playerID', 'yearID', 'stint', 'teamID', 'lgID', 'G', 'AB', 'R', 'H',\n",
       "       '2B', '3B', 'HR', 'RBI', 'SB', 'CS', 'BB', 'SO', 'IBB', 'HBP', 'SH',\n",
       "       'SF', 'GIDP'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slugpct.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4ee71981",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find connections on multiple regression between at bats (y) and everything else (x)\n",
    "x = slugpct.drop([\"playerID\",\"stint\",\"teamID\",\"lgID\",\"AB\"], axis=1)\n",
    "y = slugpct[\"AB\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bcda990c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['yearID', 'G', 'R', 'H', '2B', '3B', 'HR', 'RBI', 'SB', 'CS', 'BB',\n",
       "       'SO', 'IBB', 'HBP', 'SH', 'SF', 'GIDP'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "180d5df2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "223bbada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15.35554255, 113.3439254 , 141.60712609, ..., 266.13384233,\n",
       "         3.1854613 , 317.59656543])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_h = regr.predict(x)\n",
    "pred_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "19564db7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split train and test datasets in 80-20 form: 80 training, 20 testing\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6e587f23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (88396, 17)\n",
      "X_test: (22099, 17)\n",
      "Y_train: (88396,)\n",
      "Y_test: (22099,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train:\",x_train.shape)\n",
    "print(\"X_test:\",x_test.shape)\n",
    "print(\"Y_train:\",y_train.shape)\n",
    "print(\"Y_test:\",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4a7c680b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_slug = LinearRegression()\n",
    "lr_slug.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "15c9d7b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([169.551311  ,  39.09589109,   4.74391468, ..., 135.89102023,\n",
       "         4.48151496, 141.90278321])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr_slug.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "271797fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of model is:  98.85670485819468\n"
     ]
    }
   ],
   "source": [
    "# Get accuracy of model\n",
    "slg_acc = r2_score(y_test, y_pred) * 100\n",
    "print(\"Accuracy of model is: \", slg_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "41561ce4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results\n",
    "plt.scatter(y_test, y_pred)\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.title(\"Actual vs. Predicted\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7a05d8f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Regression plot - Seaborn\n",
    "sns.regplot(y_test, y_pred, None, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42675658",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd7565c5",
   "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": "3.8.3"
  },
  "vscode": {
   "interpreter": {
    "hash": "510211a358bee591c079ad379252538b7a3e7674a2d1c83fcb989ad163683766"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}