baseball_VIF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "904352a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "# 한글 폰트를 사용하기 위한 코드\n",
    "import platform\n",
    "if platform.system() == 'Windows':\n",
    "    plt.rc('font', family='Malgun Gothic')\n",
    "elif platform.system() == 'Linux':\n",
    "    plt.rc('font', family='NanumGothic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28880543",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "년도         0\n",
      "날짜         0\n",
      "승패         0\n",
      "상대         0\n",
      "AVG_K      0\n",
      "OPS_K      0\n",
      "LI_K       0\n",
      "WPA_K      0\n",
      "HR/AB_K    0\n",
      "SO/AB_K    0\n",
      "ERA_K      0\n",
      "WHIP_K     0\n",
      "LI_K.1     0\n",
      "WPA_K.1    0\n",
      "SO/IP_K    0\n",
      "AVG_R      0\n",
      "OPS_R      0\n",
      "LI_R       0\n",
      "WPA_R      0\n",
      "HR/AB_R    0\n",
      "SO/AB_R    0\n",
      "ERA_R      0\n",
      "WHIP_R     0\n",
      "LI_R.1     0\n",
      "WPA_R.1    0\n",
      "SO/IP_R    0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>년도</th>\n",
       "      <th>날짜</th>\n",
       "      <th>승패</th>\n",
       "      <th>상대</th>\n",
       "      <th>AVG_K</th>\n",
       "      <th>OPS_K</th>\n",
       "      <th>LI_K</th>\n",
       "      <th>WPA_K</th>\n",
       "      <th>HR/AB_K</th>\n",
       "      <th>SO/AB_K</th>\n",
       "      <th>...</th>\n",
       "      <th>OPS_R</th>\n",
       "      <th>LI_R</th>\n",
       "      <th>WPA_R</th>\n",
       "      <th>HR/AB_R</th>\n",
       "      <th>SO/AB_R</th>\n",
       "      <th>ERA_R</th>\n",
       "      <th>WHIP_R</th>\n",
       "      <th>LI_R.1</th>\n",
       "      <th>WPA_R.1</th>\n",
       "      <th>SO/IP_R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>3월 24일</td>\n",
       "      <td>0</td>\n",
       "      <td>kt</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.779</td>\n",
       "      <td>1.75</td>\n",
       "      <td>-0.225</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.883</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.243</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>3월 25일</td>\n",
       "      <td>1</td>\n",
       "      <td>kt</td>\n",
       "      <td>0.410</td>\n",
       "      <td>1.224</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.205</td>\n",
       "      <td>...</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.23</td>\n",
       "      <td>-0.105</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.242</td>\n",
       "      <td>15.75</td>\n",
       "      <td>2.38</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.395</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>3월 27일</td>\n",
       "      <td>1</td>\n",
       "      <td>삼성</td>\n",
       "      <td>0.389</td>\n",
       "      <td>1.494</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.278</td>\n",
       "      <td>...</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.32</td>\n",
       "      <td>-0.218</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.156</td>\n",
       "      <td>19.13</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.282</td>\n",
       "      <td>1.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>3월 28일</td>\n",
       "      <td>0</td>\n",
       "      <td>삼성</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.89</td>\n",
       "      <td>-0.386</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.156</td>\n",
       "      <td>...</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.386</td>\n",
       "      <td>0.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>3월 29일</td>\n",
       "      <td>1</td>\n",
       "      <td>삼성</td>\n",
       "      <td>0.394</td>\n",
       "      <td>1.080</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-0.353</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.321</td>\n",
       "      <td>7.88</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.147</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>2021</td>\n",
       "      <td>10월 24일</td>\n",
       "      <td>0</td>\n",
       "      <td>NC</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.411</td>\n",
       "      <td>1.28</td>\n",
       "      <td>-0.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.698</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.323</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.361</td>\n",
       "      <td>1.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>2021</td>\n",
       "      <td>10월 27일</td>\n",
       "      <td>1</td>\n",
       "      <td>롯데</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.452</td>\n",
       "      <td>1.20</td>\n",
       "      <td>-0.441</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.161</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-0.059</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>2021</td>\n",
       "      <td>10월 28일</td>\n",
       "      <td>1</td>\n",
       "      <td>롯데</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.219</td>\n",
       "      <td>...</td>\n",
       "      <td>0.754</td>\n",
       "      <td>1.38</td>\n",
       "      <td>-0.278</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.171</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.222</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>2021</td>\n",
       "      <td>10월 29일</td>\n",
       "      <td>0</td>\n",
       "      <td>두산</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.81</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.314</td>\n",
       "      <td>...</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.098</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.040</td>\n",
       "      <td>1.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>2021</td>\n",
       "      <td>10월 30일</td>\n",
       "      <td>0</td>\n",
       "      <td>키움</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.58</td>\n",
       "      <td>-0.235</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.226</td>\n",
       "      <td>...</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.143</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>554 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       년도       날짜  승패  상대  AVG_K  OPS_K  LI_K  WPA_K  HR/AB_K  SO/AB_K  ...  \\\n",
       "0    2018   3월 24일   0  kt  0.333  0.779  1.75 -0.225    0.000    0.222  ...   \n",
       "1    2018   3월 25일   1  kt  0.410  1.224  0.24  0.395    0.103    0.205  ...   \n",
       "2    2018   3월 27일   1  삼성  0.389  1.494  0.30  0.282    0.167    0.278  ...   \n",
       "3    2018   3월 28일   0  삼성  0.156  0.479  0.89 -0.386    0.000    0.156  ...   \n",
       "4    2018   3월 29일   1  삼성  0.394  1.080  0.61  0.147    0.061    0.212  ...   \n",
       "..    ...      ...  ..  ..    ...    ...   ...    ...      ...      ...  ...   \n",
       "549  2021  10월 24일   0  NC  0.194  0.411  1.28 -0.361    0.000    0.333  ...   \n",
       "550  2021  10월 27일   1  롯데  0.286  0.718  1.26  0.059    0.000    0.200  ...   \n",
       "551  2021  10월 28일   1  롯데  0.188  0.559  0.96  0.222    0.031    0.219  ...   \n",
       "552  2021  10월 29일   0  두산  0.286  0.833  0.81 -0.040    0.029    0.314  ...   \n",
       "553  2021  10월 30일   0  키움  0.161  0.419  0.58 -0.235    0.032    0.226  ...   \n",
       "\n",
       "     OPS_R  LI_R  WPA_R  HR/AB_R  SO/AB_R  ERA_R  WHIP_R  LI_R.1  WPA_R.1  \\\n",
       "0    0.883  1.01  0.275    0.081    0.243   4.00    1.56     1.7    0.225   \n",
       "1    0.545  0.23 -0.105    0.030    0.242  15.75    2.38     0.2   -0.395   \n",
       "2    0.454  0.32 -0.218    0.000    0.156  19.13    2.88     0.3   -0.282   \n",
       "3    0.826  0.60  0.114    0.026    0.179   0.00    0.78     0.9    0.386   \n",
       "4    0.372  0.84 -0.353    0.000    0.321   7.88    2.13     0.6   -0.147   \n",
       "..     ...   ...    ...      ...      ...    ...     ...     ...      ...   \n",
       "549  0.698  1.20  0.139    0.032    0.323   1.00    0.89     1.3    0.361   \n",
       "550  0.452  1.20 -0.441    0.032    0.161   3.00    1.56     1.3   -0.059   \n",
       "551  0.754  1.38 -0.278    0.057    0.171   5.00    1.11     1.0   -0.222   \n",
       "552  0.892  0.42  0.460    0.049    0.098   5.00    1.89     0.8    0.040   \n",
       "553  0.886  0.51  0.265    0.029    0.143   1.00    0.56     0.6    0.235   \n",
       "\n",
       "     SO/IP_R  \n",
       "0      0.889  \n",
       "1      1.000  \n",
       "2      1.250  \n",
       "3      0.556  \n",
       "4      0.875  \n",
       "..       ...  \n",
       "549    1.333  \n",
       "550    0.778  \n",
       "551    0.778  \n",
       "552    1.222  \n",
       "553    0.778  \n",
       "\n",
       "[554 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data 가져오기\n",
    "df = pd.read_csv('./t_data.csv')\n",
    "# 결측값 확인\n",
    "print(df.isnull().sum())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e3a1b062",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>승패</th>\n",
       "      <th>AVG_K</th>\n",
       "      <th>OPS_K</th>\n",
       "      <th>HR/AB_K</th>\n",
       "      <th>SO/AB_K</th>\n",
       "      <th>ERA_K</th>\n",
       "      <th>WHIP_K</th>\n",
       "      <th>SO/IP_K</th>\n",
       "      <th>AVG_R</th>\n",
       "      <th>OPS_R</th>\n",
       "      <th>HR/AB_R</th>\n",
       "      <th>SO/AB_R</th>\n",
       "      <th>ERA_R</th>\n",
       "      <th>WHIP_R</th>\n",
       "      <th>SO/IP_R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.222</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.297</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.243</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.410</td>\n",
       "      <td>1.224</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.205</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.242</td>\n",
       "      <td>15.75</td>\n",
       "      <td>2.38</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.389</td>\n",
       "      <td>1.494</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.156</td>\n",
       "      <td>19.13</td>\n",
       "      <td>2.88</td>\n",
       "      <td>1.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.156</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.394</td>\n",
       "      <td>1.080</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.321</td>\n",
       "      <td>7.88</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>0</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.333</td>\n",
       "      <td>3.38</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.250</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.323</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>1</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.161</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>1</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.219</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.171</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.314</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.11</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.098</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>0</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.226</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.143</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>554 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     승패  AVG_K  OPS_K  HR/AB_K  SO/AB_K  ERA_K  WHIP_K  SO/IP_K  AVG_R  OPS_R  \\\n",
       "0     0  0.333  0.779    0.000    0.222   5.00    1.33    1.000  0.297  0.883   \n",
       "1     1  0.410  1.224    0.103    0.205   1.00    0.78    0.889  0.212  0.545   \n",
       "2     1  0.389  1.494    0.167    0.278   0.00    0.89    0.556  0.188  0.454   \n",
       "3     0  0.156  0.479    0.000    0.156   6.00    1.78    0.778  0.359  0.826   \n",
       "4     1  0.394  1.080    0.061    0.212   0.00    1.00    1.000  0.107  0.372   \n",
       "..   ..    ...    ...      ...      ...    ...     ...      ...    ...    ...   \n",
       "549   0  0.194  0.411    0.000    0.333   3.38    1.38    1.250  0.258  0.698   \n",
       "550   1  0.286  0.718    0.000    0.200   2.00    0.56    0.556  0.161  0.452   \n",
       "551   1  0.188  0.559    0.031    0.219   3.00    1.33    0.667  0.257  0.754   \n",
       "552   0  0.286  0.833    0.029    0.314   8.00    2.11    0.444  0.317  0.892   \n",
       "553   0  0.161  0.419    0.032    0.226   6.00    2.00    0.556  0.343  0.886   \n",
       "\n",
       "     HR/AB_R  SO/AB_R  ERA_R  WHIP_R  SO/IP_R  \n",
       "0      0.081    0.243   4.00    1.56    0.889  \n",
       "1      0.030    0.242  15.75    2.38    1.000  \n",
       "2      0.000    0.156  19.13    2.88    1.250  \n",
       "3      0.026    0.179   0.00    0.78    0.556  \n",
       "4      0.000    0.321   7.88    2.13    0.875  \n",
       "..       ...      ...    ...     ...      ...  \n",
       "549    0.032    0.323   1.00    0.89    1.333  \n",
       "550    0.032    0.161   3.00    1.56    0.778  \n",
       "551    0.057    0.171   5.00    1.11    0.778  \n",
       "552    0.049    0.098   5.00    1.89    1.222  \n",
       "553    0.029    0.143   1.00    0.56    0.778  \n",
       "\n",
       "[554 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 필요없는 특징 제거\n",
    "df_1 = df.drop(['년도', '날짜', '상대', 'WPA_K', 'WPA_K.1', 'LI_K', 'LI_K.1', 'LI_R', 'LI_R.1', 'WPA_R', 'WPA_R.1'], axis=1)\n",
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e1149204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=200)\n",
    "plt.title(\"승패\")\n",
    "plt.xlabel('승패')\n",
    "plt.ylabel('수')\n",
    "plt.hist(df_1['승패'], bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5464ced3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>승패</th>\n",
       "      <th>AVG_K</th>\n",
       "      <th>OPS_K</th>\n",
       "      <th>HR/AB_K</th>\n",
       "      <th>SO/AB_K</th>\n",
       "      <th>ERA_K</th>\n",
       "      <th>WHIP_K</th>\n",
       "      <th>SO/IP_K</th>\n",
       "      <th>AVG_R</th>\n",
       "      <th>OPS_R</th>\n",
       "      <th>HR/AB_R</th>\n",
       "      <th>SO/AB_R</th>\n",
       "      <th>ERA_R</th>\n",
       "      <th>WHIP_R</th>\n",
       "      <th>SO/IP_R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.628842</td>\n",
       "      <td>0.460377</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.428884</td>\n",
       "      <td>0.233863</td>\n",
       "      <td>0.373737</td>\n",
       "      <td>0.533293</td>\n",
       "      <td>0.561576</td>\n",
       "      <td>0.585232</td>\n",
       "      <td>0.124615</td>\n",
       "      <td>0.412548</td>\n",
       "      <td>0.209096</td>\n",
       "      <td>0.421233</td>\n",
       "      <td>0.474680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.810875</td>\n",
       "      <td>0.796226</td>\n",
       "      <td>0.616766</td>\n",
       "      <td>0.391685</td>\n",
       "      <td>0.046773</td>\n",
       "      <td>0.188552</td>\n",
       "      <td>0.466707</td>\n",
       "      <td>0.352217</td>\n",
       "      <td>0.319717</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>0.410646</td>\n",
       "      <td>0.823314</td>\n",
       "      <td>0.702055</td>\n",
       "      <td>0.542404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.761229</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.551422</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.225589</td>\n",
       "      <td>0.266947</td>\n",
       "      <td>0.293103</td>\n",
       "      <td>0.248233</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.247148</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.873288</td>\n",
       "      <td>0.694936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.210402</td>\n",
       "      <td>0.233962</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.284464</td>\n",
       "      <td>0.280636</td>\n",
       "      <td>0.525253</td>\n",
       "      <td>0.400120</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.540456</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.290875</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154110</td>\n",
       "      <td>0.271507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.773050</td>\n",
       "      <td>0.687547</td>\n",
       "      <td>0.365269</td>\n",
       "      <td>0.407002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.262626</td>\n",
       "      <td>0.533293</td>\n",
       "      <td>0.093596</td>\n",
       "      <td>0.183818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.560837</td>\n",
       "      <td>0.411918</td>\n",
       "      <td>0.616438</td>\n",
       "      <td>0.466138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300236</td>\n",
       "      <td>0.182642</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.671772</td>\n",
       "      <td>0.158092</td>\n",
       "      <td>0.390572</td>\n",
       "      <td>0.683263</td>\n",
       "      <td>0.465517</td>\n",
       "      <td>0.439906</td>\n",
       "      <td>0.049231</td>\n",
       "      <td>0.564639</td>\n",
       "      <td>0.052274</td>\n",
       "      <td>0.191781</td>\n",
       "      <td>0.745577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.517730</td>\n",
       "      <td>0.414340</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.380744</td>\n",
       "      <td>0.093545</td>\n",
       "      <td>0.114478</td>\n",
       "      <td>0.266947</td>\n",
       "      <td>0.226601</td>\n",
       "      <td>0.246661</td>\n",
       "      <td>0.049231</td>\n",
       "      <td>0.256654</td>\n",
       "      <td>0.156822</td>\n",
       "      <td>0.421233</td>\n",
       "      <td>0.406955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.286052</td>\n",
       "      <td>0.294340</td>\n",
       "      <td>0.185629</td>\n",
       "      <td>0.422319</td>\n",
       "      <td>0.140318</td>\n",
       "      <td>0.373737</td>\n",
       "      <td>0.333533</td>\n",
       "      <td>0.463054</td>\n",
       "      <td>0.483896</td>\n",
       "      <td>0.087692</td>\n",
       "      <td>0.275665</td>\n",
       "      <td>0.261370</td>\n",
       "      <td>0.267123</td>\n",
       "      <td>0.406955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.517730</td>\n",
       "      <td>0.501132</td>\n",
       "      <td>0.173653</td>\n",
       "      <td>0.630197</td>\n",
       "      <td>0.374181</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.199760</td>\n",
       "      <td>0.610837</td>\n",
       "      <td>0.592302</td>\n",
       "      <td>0.075385</td>\n",
       "      <td>0.136882</td>\n",
       "      <td>0.261370</td>\n",
       "      <td>0.534247</td>\n",
       "      <td>0.677852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.188679</td>\n",
       "      <td>0.191617</td>\n",
       "      <td>0.437637</td>\n",
       "      <td>0.280636</td>\n",
       "      <td>0.599327</td>\n",
       "      <td>0.266947</td>\n",
       "      <td>0.674877</td>\n",
       "      <td>0.587588</td>\n",
       "      <td>0.044615</td>\n",
       "      <td>0.222433</td>\n",
       "      <td>0.052274</td>\n",
       "      <td>0.078767</td>\n",
       "      <td>0.406955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>554 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      승패     AVG_K     OPS_K   HR/AB_K   SO/AB_K     ERA_K    WHIP_K  \\\n",
       "0    0.0  0.628842  0.460377  0.000000  0.428884  0.233863  0.373737   \n",
       "1    1.0  0.810875  0.796226  0.616766  0.391685  0.046773  0.188552   \n",
       "2    1.0  0.761229  1.000000  1.000000  0.551422  0.000000  0.225589   \n",
       "3    0.0  0.210402  0.233962  0.000000  0.284464  0.280636  0.525253   \n",
       "4    1.0  0.773050  0.687547  0.365269  0.407002  0.000000  0.262626   \n",
       "..   ...       ...       ...       ...       ...       ...       ...   \n",
       "549  0.0  0.300236  0.182642  0.000000  0.671772  0.158092  0.390572   \n",
       "550  1.0  0.517730  0.414340  0.000000  0.380744  0.093545  0.114478   \n",
       "551  1.0  0.286052  0.294340  0.185629  0.422319  0.140318  0.373737   \n",
       "552  0.0  0.517730  0.501132  0.173653  0.630197  0.374181  0.636364   \n",
       "553  0.0  0.222222  0.188679  0.191617  0.437637  0.280636  0.599327   \n",
       "\n",
       "      SO/IP_K     AVG_R     OPS_R   HR/AB_R   SO/AB_R     ERA_R    WHIP_R  \\\n",
       "0    0.533293  0.561576  0.585232  0.124615  0.412548  0.209096  0.421233   \n",
       "1    0.466707  0.352217  0.319717  0.046154  0.410646  0.823314  0.702055   \n",
       "2    0.266947  0.293103  0.248233  0.000000  0.247148  1.000000  0.873288   \n",
       "3    0.400120  0.714286  0.540456  0.040000  0.290875  0.000000  0.154110   \n",
       "4    0.533293  0.093596  0.183818  0.000000  0.560837  0.411918  0.616438   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "549  0.683263  0.465517  0.439906  0.049231  0.564639  0.052274  0.191781   \n",
       "550  0.266947  0.226601  0.246661  0.049231  0.256654  0.156822  0.421233   \n",
       "551  0.333533  0.463054  0.483896  0.087692  0.275665  0.261370  0.267123   \n",
       "552  0.199760  0.610837  0.592302  0.075385  0.136882  0.261370  0.534247   \n",
       "553  0.266947  0.674877  0.587588  0.044615  0.222433  0.052274  0.078767   \n",
       "\n",
       "      SO/IP_R  \n",
       "0    0.474680  \n",
       "1    0.542404  \n",
       "2    0.694936  \n",
       "3    0.271507  \n",
       "4    0.466138  \n",
       "..        ...  \n",
       "549  0.745577  \n",
       "550  0.406955  \n",
       "551  0.406955  \n",
       "552  0.677852  \n",
       "553  0.406955  \n",
       "\n",
       "[554 rows x 15 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 편차를 줄이기 위해 정규화 과정 진행\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler()\n",
    "\n",
    "# 정규화 과정 진행\n",
    "df_2 = sc.fit_transform(df_1)\n",
    "\n",
    "# 데이터 \n",
    "df_sc = pd.DataFrame(df_2, columns = df_1.columns)\n",
    "df_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "445e04cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>승패</td>        <th>  R-squared (uncentered):</th>      <td>   0.789</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.783</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   144.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 06 Dec 2022</td> <th>  Prob (F-statistic):</th>          <td>6.64e-172</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:10:08</td>     <th>  Log-Likelihood:    </th>          <td> -149.02</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   554</td>      <th>  AIC:               </th>          <td>   326.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   540</td>      <th>  BIC:               </th>          <td>   386.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    14</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AVG_K</th>   <td>   -0.2548</td> <td>    0.264</td> <td>   -0.965</td> <td> 0.335</td> <td>   -0.774</td> <td>    0.264</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OPS_K</th>   <td>    1.0005</td> <td>    0.438</td> <td>    2.285</td> <td> 0.023</td> <td>    0.141</td> <td>    1.860</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HR/AB_K</th> <td>   -0.2938</td> <td>    0.174</td> <td>   -1.686</td> <td> 0.092</td> <td>   -0.636</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SO/AB_K</th> <td>    0.4001</td> <td>    0.476</td> <td>    0.841</td> <td> 0.401</td> <td>   -0.535</td> <td>    1.335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ERA_K</th>   <td>   -1.1524</td> <td>    0.153</td> <td>   -7.541</td> <td> 0.000</td> <td>   -1.453</td> <td>   -0.852</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WHIP_K</th>  <td>    0.0223</td> <td>    0.178</td> <td>    0.125</td> <td> 0.900</td> <td>   -0.328</td> <td>    0.372</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SO/IP_K</th> <td>   -1.4213</td> <td>    0.368</td> <td>   -3.864</td> <td> 0.000</td> <td>   -2.144</td> <td>   -0.699</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AVG_R</th>   <td>    0.2746</td> <td>    0.195</td> <td>    1.410</td> <td> 0.159</td> <td>   -0.108</td> <td>    0.657</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OPS_R</th>   <td>   -0.1425</td> <td>    0.255</td> <td>   -0.558</td> <td> 0.577</td> <td>   -0.644</td> <td>    0.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HR/AB_R</th> <td>   -0.2079</td> <td>    0.305</td> <td>   -0.681</td> <td> 0.496</td> <td>   -0.807</td> <td>    0.391</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SO/AB_R</th> <td>    2.1087</td> <td>    0.437</td> <td>    4.821</td> <td> 0.000</td> <td>    1.249</td> <td>    2.968</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ERA_R</th>   <td>    0.9090</td> <td>    0.151</td> <td>    6.028</td> <td> 0.000</td> <td>    0.613</td> <td>    1.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WHIP_R</th>  <td>    0.0949</td> <td>    0.215</td> <td>    0.441</td> <td> 0.660</td> <td>   -0.328</td> <td>    0.518</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SO/IP_R</th> <td>   -0.3321</td> <td>    0.453</td> <td>   -0.734</td> <td> 0.463</td> <td>   -1.221</td> <td>    0.557</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>24.543</td> <th>  Durbin-Watson:     </th> <td>   2.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  10.707</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.029</td> <th>  Prob(JB):          </th> <td> 0.00473</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.321</td> <th>  Cond. No.          </th> <td>    74.7</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                     승패   R-squared (uncentered):                   0.789\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.783\n",
       "Method:                 Least Squares   F-statistic:                              144.0\n",
       "Date:                Tue, 06 Dec 2022   Prob (F-statistic):                   6.64e-172\n",
       "Time:                        10:10:08   Log-Likelihood:                         -149.02\n",
       "No. Observations:                 554   AIC:                                      326.0\n",
       "Df Residuals:                     540   BIC:                                      386.5\n",
       "Df Model:                          14                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "AVG_K         -0.2548      0.264     -0.965      0.335      -0.774       0.264\n",
       "OPS_K          1.0005      0.438      2.285      0.023       0.141       1.860\n",
       "HR/AB_K       -0.2938      0.174     -1.686      0.092      -0.636       0.049\n",
       "SO/AB_K        0.4001      0.476      0.841      0.401      -0.535       1.335\n",
       "ERA_K         -1.1524      0.153     -7.541      0.000      -1.453      -0.852\n",
       "WHIP_K         0.0223      0.178      0.125      0.900      -0.328       0.372\n",
       "SO/IP_K       -1.4213      0.368     -3.864      0.000      -2.144      -0.699\n",
       "AVG_R          0.2746      0.195      1.410      0.159      -0.108       0.657\n",
       "OPS_R         -0.1425      0.255     -0.558      0.577      -0.644       0.359\n",
       "HR/AB_R       -0.2079      0.305     -0.681      0.496      -0.807       0.391\n",
       "SO/AB_R        2.1087      0.437      4.821      0.000       1.249       2.968\n",
       "ERA_R          0.9090      0.151      6.028      0.000       0.613       1.205\n",
       "WHIP_R         0.0949      0.215      0.441      0.660      -0.328       0.518\n",
       "SO/IP_R       -0.3321      0.453     -0.734      0.463      -1.221       0.557\n",
       "==============================================================================\n",
       "Omnibus:                       24.543   Durbin-Watson:                   2.027\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               10.707\n",
       "Skew:                           0.029   Prob(JB):                      0.00473\n",
       "Kurtosis:                       2.321   Cond. No.                         74.7\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# r- square\n",
    "df_test = df_sc.drop(['승패'], axis=1)\n",
    "lm = sm.OLS(df_sc['승패'], df_test)\n",
    "results = lm.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "67b7e748",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1번째 VIF 측정\n",
      "Max VIF feature & value : OPS_K, 212.4602117021559\n",
      "2번째 VIF 측정\n",
      "Max VIF feature & value : HR/AB_K, 175.1352849146165\n",
      "3번째 VIF 측정\n",
      "Max VIF feature & value : ERA_K, 139.68683823575674\n",
      "4번째 VIF 측정\n",
      "Max VIF feature & value : WHIP_K, 91.47837642042252\n",
      "5번째 VIF 측정\n",
      "Max VIF feature & value : SO/AB_K, 35.37218570150683\n",
      "6번째 VIF 측정\n",
      "Max VIF feature & value : AVG_R, 31.190494251759223\n",
      "7번째 VIF 측정\n",
      "Max VIF feature & value : AVG_K, 14.018788770160459\n",
      "8번째 VIF 측정\n",
      "Max VIF feature & value : HR/AB_K, 11.472960626722791\n",
      "9번째 VIF 측정\n",
      "Max VIF feature & value : ERA_K, 5.45852783033772\n",
      "\n",
      "\n",
      "승패의 vif는 4.2입니다.\n",
      "HR/AB_K의 vif는 2.35입니다.\n",
      "ERA_K의 vif는 3.93입니다.\n",
      "HR/AB_R의 vif는 1.71입니다.\n",
      "SO/AB_R의 vif는 4.46입니다.\n",
      "ERA_R의 vif는 5.46입니다.\n",
      "SO/IP_R의 vif는 4.52입니다.\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>승패</th>\n",
       "      <th>HR/AB_K</th>\n",
       "      <th>ERA_K</th>\n",
       "      <th>HR/AB_R</th>\n",
       "      <th>SO/AB_R</th>\n",
       "      <th>ERA_R</th>\n",
       "      <th>SO/IP_R</th>\n",
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       "      <td>0.209096</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.280636</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.290875</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.271507</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.365269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.560837</td>\n",
       "      <td>0.411918</td>\n",
       "      <td>0.466138</td>\n",
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       "      <th>...</th>\n",
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       "      <th>549</th>\n",
       "      <td>0.0</td>\n",
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       "      <th>550</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.093545</td>\n",
       "      <td>0.049231</td>\n",
       "      <td>0.256654</td>\n",
       "      <td>0.156822</td>\n",
       "      <td>0.406955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.185629</td>\n",
       "      <td>0.140318</td>\n",
       "      <td>0.087692</td>\n",
       "      <td>0.275665</td>\n",
       "      <td>0.261370</td>\n",
       "      <td>0.406955</td>\n",
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       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.173653</td>\n",
       "      <td>0.374181</td>\n",
       "      <td>0.075385</td>\n",
       "      <td>0.136882</td>\n",
       "      <td>0.261370</td>\n",
       "      <td>0.677852</td>\n",
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       "    <tr>\n",
       "      <th>553</th>\n",
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       "      <td>0.052274</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>554 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      승패   HR/AB_K     ERA_K   HR/AB_R   SO/AB_R     ERA_R   SO/IP_R\n",
       "0    0.0  0.000000  0.233863  0.124615  0.412548  0.209096  0.474680\n",
       "1    1.0  0.616766  0.046773  0.046154  0.410646  0.823314  0.542404\n",
       "2    1.0  1.000000  0.000000  0.000000  0.247148  1.000000  0.694936\n",
       "3    0.0  0.000000  0.280636  0.040000  0.290875  0.000000  0.271507\n",
       "4    1.0  0.365269  0.000000  0.000000  0.560837  0.411918  0.466138\n",
       "..   ...       ...       ...       ...       ...       ...       ...\n",
       "549  0.0  0.000000  0.158092  0.049231  0.564639  0.052274  0.745577\n",
       "550  1.0  0.000000  0.093545  0.049231  0.256654  0.156822  0.406955\n",
       "551  1.0  0.185629  0.140318  0.087692  0.275665  0.261370  0.406955\n",
       "552  0.0  0.173653  0.374181  0.075385  0.136882  0.261370  0.677852\n",
       "553  0.0  0.191617  0.280636  0.044615  0.222433  0.052274  0.406955\n",
       "\n",
       "[554 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def vif(x):\n",
    "    # vif 10 초과시 drop을 위한 임계값 설정\n",
    "    thresh = 10\n",
    "    # Filter method로 feature selection 진행 후 최종 도출 될 데이터 프레임 형성\n",
    "    output = pd.DataFrame()\n",
    "    # 데이터의 컬럼 개수 설정\n",
    "    k = x.shape[1]\n",
    "    # VIF 측정\n",
    "    vif = [variance_inflation_factor(x.values, i) for i in range(x.shape[1])]\n",
    "    for i in range(1,k):\n",
    "        print(f'{i}번째 VIF 측정')\n",
    "        # VIF 최대 값 선정\n",
    "        a = np.argmax(vif)\n",
    "        print(f'Max VIF feature & value : {x.columns[a]}, {vif[a]}')\n",
    "        # VIF 최대 값이 임계치를 넘지 않는 경우 break\n",
    "        if (vif[a] <= thresh):\n",
    "            print('\\n')\n",
    "            for q in range(output.shape[1]):\n",
    "                print(f'{output.columns[q]}의 vif는 {np.round(vif[q],2)}입니다.')\n",
    "            break\n",
    "        # VIF 최대 값이 임계치를 넘는 경우, + 1번째 시도인 경우 : if 문으로 해당 feature 제거 후 다시 vif 측정\n",
    "        if (i == 1):\n",
    "            output = x.drop(x.columns[a], axis = 1)\n",
    "            vif = [variance_inflation_factor(output.values, j) for j in range(output.shape[1])]\n",
    "        # VIF 최대 값이 임계치를 넘는 경우, + 1번째 이후 시도인 경우 : if 문으로 해당 feature 제거 후 다시 vif 측정\n",
    "        elif (i > 1):\n",
    "            output = output.drop(output.columns[a], axis = 1)\n",
    "            vif = [variance_inflation_factor(output.values, j) for j in range(output.shape[1])]\n",
    "    return(output)\n",
    "vif(df_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f95a06a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>승패</th>\n",
       "      <th>HR/AB_K</th>\n",
       "      <th>SO/AB_K</th>\n",
       "      <th>ERA_K</th>\n",
       "      <th>HR/AB_R</th>\n",
       "      <th>SO/AB_R</th>\n",
       "      <th>ERA_R</th>\n",
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       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.247148</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.365269</td>\n",
       "      <td>0.407002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.560837</td>\n",
       "      <td>0.411918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.564639</td>\n",
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       "      <th>550</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.380744</td>\n",
       "      <td>0.093545</td>\n",
       "      <td>0.049231</td>\n",
       "      <td>0.256654</td>\n",
       "      <td>0.156822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.185629</td>\n",
       "      <td>0.422319</td>\n",
       "      <td>0.140318</td>\n",
       "      <td>0.087692</td>\n",
       "      <td>0.275665</td>\n",
       "      <td>0.261370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.173653</td>\n",
       "      <td>0.630197</td>\n",
       "      <td>0.374181</td>\n",
       "      <td>0.075385</td>\n",
       "      <td>0.136882</td>\n",
       "      <td>0.261370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.437637</td>\n",
       "      <td>0.280636</td>\n",
       "      <td>0.044615</td>\n",
       "      <td>0.222433</td>\n",
       "      <td>0.052274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>554 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      승패   HR/AB_K   SO/AB_K     ERA_K   HR/AB_R   SO/AB_R     ERA_R\n",
       "0    0.0  0.000000  0.428884  0.233863  0.124615  0.412548  0.209096\n",
       "1    1.0  0.616766  0.391685  0.046773  0.046154  0.410646  0.823314\n",
       "2    1.0  1.000000  0.551422  0.000000  0.000000  0.247148  1.000000\n",
       "3    0.0  0.000000  0.284464  0.280636  0.040000  0.290875  0.000000\n",
       "4    1.0  0.365269  0.407002  0.000000  0.000000  0.560837  0.411918\n",
       "..   ...       ...       ...       ...       ...       ...       ...\n",
       "549  0.0  0.000000  0.671772  0.158092  0.049231  0.564639  0.052274\n",
       "550  1.0  0.000000  0.380744  0.093545  0.049231  0.256654  0.156822\n",
       "551  1.0  0.185629  0.422319  0.140318  0.087692  0.275665  0.261370\n",
       "552  0.0  0.173653  0.630197  0.374181  0.075385  0.136882  0.261370\n",
       "553  0.0  0.191617  0.437637  0.280636  0.044615  0.222433  0.052274\n",
       "\n",
       "[554 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# r- square + 최적의 vif\n",
    "# data 쪼개기\n",
    "\n",
    "df_2 = df_sc.loc[:, ['승패', 'HR/AB_K', 'SO/AB_K', 'ERA_K', 'HR/AB_R', 'SO/AB_R', 'ERA_R']]\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6a5148d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>승패</td>        <th>  R-squared (uncentered):</th>      <td>   0.762</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.760</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   293.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 06 Dec 2022</td> <th>  Prob (F-statistic):</th>          <td>2.01e-167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:11:07</td>     <th>  Log-Likelihood:    </th>          <td> -181.57</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   554</td>      <th>  AIC:               </th>          <td>   375.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   548</td>      <th>  BIC:               </th>          <td>   401.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HR/AB_K</th> <td>   -0.0717</td> <td>    0.104</td> <td>   -0.686</td> <td> 0.493</td> <td>   -0.277</td> <td>    0.134</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SO/AB_K</th> <td>    0.2862</td> <td>    0.075</td> <td>    3.831</td> <td> 0.000</td> <td>    0.139</td> <td>    0.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ERA_K</th>   <td>   -1.0977</td> <td>    0.085</td> <td>  -12.986</td> <td> 0.000</td> <td>   -1.264</td> <td>   -0.932</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HR/AB_R</th> <td>   -0.0349</td> <td>    0.254</td> <td>   -0.137</td> <td> 0.891</td> <td>   -0.535</td> <td>    0.465</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SO/AB_R</th> <td>    0.7118</td> <td>    0.076</td> <td>    9.319</td> <td> 0.000</td> <td>    0.562</td> <td>    0.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ERA_R</th>   <td>    1.5636</td> <td>    0.089</td> <td>   17.665</td> <td> 0.000</td> <td>    1.390</td> <td>    1.737</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>16.027</td> <th>  Durbin-Watson:     </th> <td>   1.888</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>   8.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.005</td> <th>  Prob(JB):          </th> <td>  0.0172</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.407</td> <th>  Cond. No.          </th> <td>    11.4</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                     승패   R-squared (uncentered):                   0.762\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.760\n",
       "Method:                 Least Squares   F-statistic:                              293.1\n",
       "Date:                Tue, 06 Dec 2022   Prob (F-statistic):                   2.01e-167\n",
       "Time:                        10:11:07   Log-Likelihood:                         -181.57\n",
       "No. Observations:                 554   AIC:                                      375.1\n",
       "Df Residuals:                     548   BIC:                                      401.1\n",
       "Df Model:                           6                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "HR/AB_K       -0.0717      0.104     -0.686      0.493      -0.277       0.134\n",
       "SO/AB_K        0.2862      0.075      3.831      0.000       0.139       0.433\n",
       "ERA_K         -1.0977      0.085    -12.986      0.000      -1.264      -0.932\n",
       "HR/AB_R       -0.0349      0.254     -0.137      0.891      -0.535       0.465\n",
       "SO/AB_R        0.7118      0.076      9.319      0.000       0.562       0.862\n",
       "ERA_R          1.5636      0.089     17.665      0.000       1.390       1.737\n",
       "==============================================================================\n",
       "Omnibus:                       16.027   Durbin-Watson:                   1.888\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                8.126\n",
       "Skew:                           0.005   Prob(JB):                       0.0172\n",
       "Kurtosis:                       2.407   Cond. No.                         11.4\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test2 = df_2.drop(['승패'], axis=1)\n",
    "lm = sm.OLS(df_2['승패'], df_test2)\n",
    "results = lm.fit()\n",
    "results.summary()\n",
    "\n",
    "# but.. 특징들을 다 사용하는 것이 더 정확한 측정방법이 아닐까 라는 생각이 든다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db37578f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='승패', ylabel='HR/AB_K'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df_2['승패'], y=df_2['HR/AB_K'], data=df_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81eba97b",
   "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": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}