03-Copy1.ipynb

{
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   "cell_type": "code",
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   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn import tree\n",
    "from subprocess import call\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "\n",
    "league = 'spanish'\n",
    "\n",
    "df = None\n",
    "files = os.listdir(f'fulldata/{league}')\n",
    "files.sort()\n",
    "for file in files:\n",
    "    year = int(file.strip('.csv'))\n",
    "    df_year = pd.read_csv(f'fulldata/{league}/' + file\n",
    "                          # skiprows=1,\n",
    "                          # index_col=None,\n",
    "                          # names=['Div', 'Date', 'HomeTeam', 'AwayTeam', 'FTHG', 'FTAG', 'FTR', 'HTHG', 'HTAG', 'HTR', 'Referee', 'HS', 'AS', 'HST', 'AST', 'HF', 'AF', 'HC', 'AC', 'HY', 'AY', 'HR', 'AR', 'B365H', 'B365D', 'B365A']\n",
    "                          )\n",
    "    df_year.reset_index(drop=True, inplace=True)\n",
    "    df_year['Year'] = year\n",
    "    df_year['Match'] = df_year.index + 1\n",
    "\n",
    "    if df is None:\n",
    "        df = df_year\n",
    "    else:\n",
    "        df = df.append(df_year, ignore_index=True, sort=False)\n",
    "\n",
    "# print(len(df))\n",
    "# print(df.shape)\n",
    "\n",
    "# remove unused columns\n",
    "df_league = None\n",
    "df.reset_index(inplace=True)\n",
    "# df = df[['Year', 'Date', 'HomeTeam', 'AwayTeam', 'FTHG', 'FTAG', 'FTR', 'HTHG', 'HTAG', 'HTR', 'HS', 'AS', 'HST', 'AST', 'HF', 'AF', 'HC', 'AC', 'HY', 'AY', 'HR', 'AR',\n",
    "#          \"B365H\", \"B365D\", \"B365A\"]]\n",
    "\n",
    "validate_year = 2017\n",
    "predict_year = 2015\n",
    "train_year = 2011\n",
    "teams = ['Ath Madrid']\n",
    "# teams = np.unique(df.loc[df['Year'] == predict_year, 'HomeTeam'].values)\n",
    "teams.sort()\n",
    "for team in teams:\n",
    "\n",
    "    df_team = df[(df['HomeTeam'] == team) | (df['AwayTeam'] == team)]\n",
    "    X = pd.DataFrame(\n",
    "        data={\n",
    "            'Year': df_team['Year'],\n",
    "            'Date': df_team['Date'],\n",
    "            'Team': team,\n",
    "            'HomeMatch': df_team['HomeTeam'] == team\n",
    "        }\n",
    "    )\n",
    "    X['Opponent'] = np.where(X['HomeMatch'], df_team['AwayTeam'], df_team['HomeTeam'])\n",
    "    # X['HalfTimeGoals'] = np.where(X['HomeMatch'], df_team['HTHG'], df_team['HTAG'])\n",
    "    # X['HalfTimeOpponentGoals'] = np.where(X['HomeMatch'], df_team['HTAG'], df_team['HTHG'])\n",
    "    # X['HalfTimeLead'] = X['HalfTimeGoals'] > X['HalfTimeOpponentGoals']\n",
    "    # X['HalfTimeLeadMoreThanTwo'] = (X['HalfTimeGoals'] - X['HalfTimeOpponentGoals']) > 2\n",
    "    # X['FullTimeGoals'] = np.where(X['HomeMatch'], ath_madrid['FTHG'], ath_madrid['FTAG'])\n",
    "    # X['FullTimeOpponentGoals'] = np.where(X['HomeMatch'], ath_madrid['FTAG'], ath_madrid['FTHG'])\n",
    "    X['FTR'] = df_team['FTR']\n",
    "    X['Won'] = np.where(X['HomeMatch'], df_team['FTR'] == 'H', df_team['FTR'] == 'A')\n",
    "    X['Draw'] = df_team['FTR'] == 'D'\n",
    "    X['Lost'] = np.where(X['HomeMatch'], df_team['FTR'] == 'A', df_team['FTR'] == 'H')\n",
    "    X['Result'] = np.where(X['Won'], 'Win', (np.where(X['Lost'], 'Lose', 'Draw')))\n",
    "    # X['SumGoals'] = X.groupby('Opponent')['FullTimeGoals'].transform(sum)\n",
    "    X['B365Max'] = np.maximum(np.maximum(df_team['B365H'], df_team['B365A']), df_team['B365D'])\n",
    "    X['B365Min'] = np.minimum(np.minimum(df_team['B365H'], df_team['B365A']), df_team['B365D'])\n",
    "    X['B365Say'] = np.where(X['HomeMatch'],\n",
    "                    # home match\n",
    "                    np.where(X['B365Max'] == df_team['B365H'], -1,\n",
    "                             np.where(X['B365Max'] == df_team['B365A'], 1,\n",
    "                                      0)),\n",
    "                    # away match\n",
    "                    np.where(X['B365Max'] == df_team['B365H'], 1,\n",
    "                             np.where(X['B365Max'] == df_team['B365A'], -1,\n",
    "                                      0))\n",
    "                    )\n",
    "    X['B365Diff'] = np.where(X['B365Say'] == 1, X['B365Max'] - X['B365Min'], X['B365Min'] - X['B365Max'])\n",
    "\n",
    "    # find number of times won against this opponent in last 5 meetings\n",
    "    for key, groupByOpponent in X.groupby('Opponent'):\n",
    "        # keep index as new a column, will be restored and assigned back to X later\n",
    "        idx = groupByOpponent.index\n",
    "\n",
    "        # make match day an index because rolling need an index date\n",
    "        xx = groupByOpponent.set_index('Date')\n",
    "        xx['idx'] = idx\n",
    "\n",
    "        # shift to exclude self\n",
    "        xx['Last5AgainstThisOpponentWon'] = xx['Won'].rolling(6).apply(lambda x: np.nansum(x.shift()), raw=False)\n",
    "        xx['Last5AgainstThisOpponentDraw'] = xx['Draw'].rolling(6).apply(lambda x: np.nansum(x.shift()), raw=False)\n",
    "\n",
    "        xx['Last3AgainstThisOpponentWon'] = xx['Won'].rolling(4).apply(lambda x: np.nansum(x.shift()), raw=False)\n",
    "        xx['Last3AgainstThisOpponentDraw'] = xx['Draw'].rolling(4).apply(lambda x: np.nansum(x.shift()), raw=False)\n",
    "\n",
    "        xx['LastAgainstThisOpponentWon'] = xx['Won'].rolling(2).apply(lambda x: np.nansum(x.shift()), raw=False)\n",
    "        xx['LastAgainstThisOpponentDraw'] = xx['Draw'].rolling(2).apply(lambda x: np.nansum(x.shift()), raw=False)\n",
    "\n",
    "        # restore index\n",
    "        xx = xx.set_index('idx')\n",
    "\n",
    "        # assign back to the big dataframe\n",
    "        X.loc[xx.index, 'Last5AgainstThisOpponentWon'] = xx['Last5AgainstThisOpponentWon']\n",
    "        X.loc[xx.index, 'Last5AgainstThisOpponentDraw'] = xx['Last5AgainstThisOpponentDraw']\n",
    "        X.loc[xx.index, 'Last3AgainstThisOpponentWon'] = xx['Last3AgainstThisOpponentWon']\n",
    "        X.loc[xx.index, 'Last3AgainstThisOpponentDraw'] = xx['Last3AgainstThisOpponentDraw']\n",
    "        X.loc[xx.index, 'LastAgainstThisOpponentWon'] = xx['LastAgainstThisOpponentWon']\n",
    "        X.loc[xx.index, 'LastAgainstThisOpponentDraw'] = xx['LastAgainstThisOpponentDraw']\n",
    "\n",
    "    # find recent forms\n",
    "    idx = X.index\n",
    "    xx = X.set_index('Date')\n",
    "    xx['idx'] = idx\n",
    "    xx['Last5Won'] = xx['Won'].rolling(6).apply(lambda x: np.nansum(x.shift()), raw=False)\n",
    "    xx['Last5Draw'] = xx['Draw'].rolling(6).apply(lambda x: np.nansum(x.shift()), raw=False)\n",
    "    xx['Last3Won'] = xx['Won'].rolling(4).apply(lambda x: np.nansum(x.shift()), raw=False)\n",
    "    xx['Last3Draw'] = xx['Draw'].rolling(4).apply(lambda x: np.nansum(x.shift()), raw=False)\n",
    "    xx['LastWon'] = xx['Won'].rolling(2).apply(lambda x: np.nansum(x.shift()), raw=False)\n",
    "    xx['LastDraw'] = xx['Draw'].rolling(2).apply(lambda x: np.nansum(x.shift()), raw=False)\n",
    "    xx['LastMatch'] = np.where(xx['LastWon'] == 1, 1, np.where(xx['LastDraw'] == 1, 0, -1))\n",
    "    \n",
    "    # restore index\n",
    "    xx = xx.set_index('idx')\n",
    "\n",
    "    # assign back to the big dataframe\n",
    "    X.loc[xx.index, 'Last5Won'] = xx['Last5Won']\n",
    "    X.loc[xx.index, 'Last5Draw'] = xx['Last5Draw']\n",
    "    X.loc[xx.index, 'Last3Won'] = xx['Last3Won']\n",
    "    X.loc[xx.index, 'Last3Draw'] = xx['Last3Draw']\n",
    "    X.loc[xx.index, 'LastWon'] = xx['LastWon']\n",
    "    X.loc[xx.index, 'LastDraw'] = xx['LastDraw']\n",
    "    X.loc[xx.index, 'LastMatch'] = xx['LastMatch']\n",
    "\n",
    "    # replace nan with 0\n",
    "    # TODO: better way to handle nan\n",
    "    X.loc[np.isnan(X['Last5AgainstThisOpponentWon']), 'Last5AgainstThisOpponentWon'] = 0\n",
    "    X.loc[np.isnan(X['Last5AgainstThisOpponentDraw']), 'Last5AgainstThisOpponentDraw'] = 0\n",
    "    X.loc[np.isnan(X['Last3AgainstThisOpponentWon']), 'Last3AgainstThisOpponentWon'] = 0\n",
    "    X.loc[np.isnan(X['Last3AgainstThisOpponentDraw']), 'Last3AgainstThisOpponentDraw'] = 0\n",
    "    X.loc[np.isnan(X['LastAgainstThisOpponentWon']), 'LastAgainstThisOpponentWon'] = 0\n",
    "    X.loc[np.isnan(X['LastAgainstThisOpponentDraw']), 'LastAgainstThisOpponentDraw'] = 0\n",
    "    #     X.loc[np.isnan(X['Last5Won']), 'Last5Won'] = 0\n",
    "    #     X.loc[np.isnan(X['Last5Draw']), 'Last5Draw'] = 0\n",
    "    #     X.loc[np.isnan(X['Last3Won']), 'Last3Won'] = 0\n",
    "    #     X.loc[np.isnan(X['Last3Draw']), 'Last3Draw'] = 0\n",
    "    #     X.loc[np.isnan(X['LastWon']), 'LastWon'] = 0\n",
    "    #     X.loc[np.isnan(X['LastDraw']), 'LastDraw'] = 0\n",
    "\n",
    "    # restrict training data (too old data may not be irrelevance)\n",
    "    X = X.loc[X['Year'] >= train_year]\n",
    "#     Y = X[['Opponent', 'Result']]\n",
    "    Y = X[['Result']]\n",
    "    \n",
    "#     X_full = X\n",
    "\n",
    "    # remove duplicate features\n",
    "    del X['LastWon']\n",
    "    del X['LastDraw']\n",
    "\n",
    "    # prevent future leaks\n",
    "    result = pd.DataFrame(X['Result'])\n",
    "    del X['Result']\n",
    "    del X['Lost']\n",
    "    del X['Draw']\n",
    "    del X['Won']\n",
    "    del X['FTR']\n",
    "    del X['Date']\n",
    "    del X['Opponent']\n",
    "    del X['Team']\n",
    "    del X['B365Max']\n",
    "    del X['B365Min']\n",
    "\n",
    "    # split data into train - test sets\n",
    "    x_train = X[(X['Year'] < predict_year)]\n",
    "    y_train = Y[(X['Year'] < predict_year)]\n",
    "    x_test = X[((X['Year'] >= predict_year) & (X['Year'] < validate_year))]\n",
    "    y_test = Y[((X['Year'] >= predict_year) & (X['Year'] < validate_year))]\n",
    "    x_validate = X[(X['Year'] >= predict_year)]\n",
    "    y_validate = Y[(X['Year'] >= predict_year)]\n",
    "    \n",
    "\n",
    "    # split prediction by opponent\n",
    "    # construct decision tree\n",
    "#     x_train_opponents = x_train.groupby('Opponent')\n",
    "#     y_train_opponents = y_train.groupby('Opponent')\n",
    "#     x_test_opponents = x_test.groupby('Opponent')\n",
    "#     y_test_opponents = y_test.groupby('Opponent')\n",
    "    #     x_test_teams = X_test_opponents.groups.keys()\n",
    "#     x_test_teams = ['Villarreal']\n",
    "#     X['Predict'] = ''\n",
    "    #     os.makedirs(f'decision_tree/{league}/{predict_year}/{team}/', exist_ok=True)\n",
    "    #     for key, x_train_opponent in x_train_opponents:\n",
    "#     for key in x_test_teams:\n",
    "        #         if key not in x_test_teams:\n",
    "        #             continue\n",
    "#         x_train_opponent = x_train_opponents.get_group(key)\n",
    "#         x_test_opponent = x_test_opponents.get_group(key)\n",
    "#         y_train_opponent = y_train_opponents.get_group(key)\n",
    "#         y_test_opponent = y_test_opponents.get_group(key)\n",
    "\n",
    "#         del y_train_opponent['Opponent']\n",
    "#         del y_test_opponent['Opponent']\n",
    "#         del x_train_opponent['Opponent']\n",
    "#         del x_test_opponent['Opponent']\n",
    "#         del x_train_opponent['Year']\n",
    "#         del x_test_opponent['Year']\n",
    "#         del x_train_opponent['Team']\n",
    "#         del x_test_opponent['Team']\n",
    "\n",
    "#         clf = DecisionTreeClassifier(\n",
    "#             criterion=\"entropy\",\n",
    "#             random_state=100,\n",
    "#             min_samples_leaf=3\n",
    "#         )\n",
    "#         clf.fit(x_train_opponent, y_train_opponent)\n"
   ]
  },
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   "cell_type": "code",
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       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2385</th>\n",
       "      <td>2011</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2393</th>\n",
       "      <td>2011</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.93</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2400</th>\n",
       "      <td>2011</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "      <td>-15.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>2011</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2425</th>\n",
       "      <td>2011</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2435</th>\n",
       "      <td>2011</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2441</th>\n",
       "      <td>2011</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2456</th>\n",
       "      <td>2011</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463</th>\n",
       "      <td>2011</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2479</th>\n",
       "      <td>2011</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2485</th>\n",
       "      <td>2011</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2492</th>\n",
       "      <td>2011</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2508</th>\n",
       "      <td>2011</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2.09</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2514</th>\n",
       "      <td>2011</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>-5.56</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2523</th>\n",
       "      <td>2011</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2534</th>\n",
       "      <td>2011</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>8.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2547</th>\n",
       "      <td>2011</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2552</th>\n",
       "      <td>2011</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>2.70</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2568</th>\n",
       "      <td>2011</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2.70</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2575</th>\n",
       "      <td>2011</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.88</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</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>3507</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>5.47</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3511</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>13.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3528</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2.53</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>6.06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3545</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>15.82</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3551</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>5.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3565</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.88</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3575</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.35</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3579</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>15.80</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3596</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3604</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>15.82</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3609</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>10.75</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3621</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>4.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3631</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3646</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.70</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>13.80</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3667</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3675</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>4.38</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3683</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3692</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>8.67</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3702</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>8.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3710</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>9.70</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3722</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3733</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2.65</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3741</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>15.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3757</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3761</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>7.10</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3777</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>5.97</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3780</th>\n",
       "      <td>2014</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3795</th>\n",
       "      <td>2014</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2.03</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  HomeMatch  B365Say  B365Diff  Last5AgainstThisOpponentWon  \\\n",
       "2284  2011       True        1      4.97                          3.0   \n",
       "2292  2011      False       -1     -1.84                          2.0   \n",
       "2306  2011       True        1      6.06                          0.0   \n",
       "2313  2011       True        1      6.10                          3.0   \n",
       "2321  2011      False       -1    -11.80                          2.0   \n",
       "2335  2011       True        1      1.84                          1.0   \n",
       "2342  2011      False        1      1.40                          0.0   \n",
       "2354  2011       True        1      6.06                          2.0   \n",
       "2368  2011      False        0     -1.02                          3.0   \n",
       "2374  2011       True        1      6.60                          3.0   \n",
       "2385  2011      False        0     -0.87                          1.0   \n",
       "2393  2011       True        1      3.93                          4.0   \n",
       "2400  2011      False       -1    -15.83                          0.0   \n",
       "2414  2011       True        1      3.93                          0.0   \n",
       "2425  2011      False        0     -0.75                          2.0   \n",
       "2435  2011       True        1      5.00                          3.0   \n",
       "2441  2011      False       -1     -1.05                          1.0   \n",
       "2456  2011       True        1      3.33                          2.0   \n",
       "2463  2011      False        0     -0.90                          3.0   \n",
       "2479  2011      False        0     -1.00                          3.0   \n",
       "2485  2011       True        1      1.75                          1.0   \n",
       "2492  2011      False        1      1.75                          1.0   \n",
       "2508  2011      False        1      2.09                          3.0   \n",
       "2514  2011       True       -1     -5.56                          1.0   \n",
       "2523  2011      False        0     -0.87                          1.0   \n",
       "2534  2011       True        1      8.67                          0.0   \n",
       "2547  2011      False        0     -0.87                          2.0   \n",
       "2552  2011       True        1      2.70                          2.0   \n",
       "2568  2011      False        1      2.70                          4.0   \n",
       "2575  2011       True        1      3.88                          1.0   \n",
       "...    ...        ...      ...       ...                          ...   \n",
       "3507  2014      False        1      5.47                          4.0   \n",
       "3511  2014       True        1     13.82                          0.0   \n",
       "3528  2014      False        1      2.53                          3.0   \n",
       "3531  2014       True        1      6.06                          3.0   \n",
       "3545  2014       True        1     15.82                          4.0   \n",
       "3551  2014      False        1      5.43                          0.0   \n",
       "3565  2014       True        1      3.88                          4.0   \n",
       "3575  2014      False        1      1.35                          4.0   \n",
       "3579  2014       True        1     15.80                          2.0   \n",
       "3596  2014      False       -1     -3.93                          0.0   \n",
       "3604  2014       True        1     15.82                          5.0   \n",
       "3609  2014       True        1     10.75                          3.0   \n",
       "3621  2014      False        1      4.93                          0.0   \n",
       "3631  2014       True        0     -0.95                          2.0   \n",
       "3646  2014      False        1      1.70                          4.0   \n",
       "3651  2014       True        1     13.80                          2.0   \n",
       "3667  2014      False        0     -0.75                          4.0   \n",
       "3675  2014       True        1      4.38                          2.0   \n",
       "3683  2014      False        1      1.65                          4.0   \n",
       "3692  2014       True        1      8.67                          4.0   \n",
       "3702  2014      False        1      8.10                          0.0   \n",
       "3710  2014       True        1      9.70                          3.0   \n",
       "3722  2014      False        1      1.80                          3.0   \n",
       "3733  2014      False        1      2.65                          4.0   \n",
       "3741  2014       True        1     15.82                          0.0   \n",
       "3757  2014      False        1      1.20                          3.0   \n",
       "3761  2014       True        1      7.10                          4.0   \n",
       "3777  2014      False        1      5.97                          3.0   \n",
       "3780  2014       True       -1     -3.00                          0.0   \n",
       "3795  2014      False        1      2.03                          5.0   \n",
       "\n",
       "      Last5AgainstThisOpponentDraw  Last3AgainstThisOpponentWon  \\\n",
       "2284                           0.0                          3.0   \n",
       "2292                           2.0                          1.0   \n",
       "2306                           3.0                          0.0   \n",
       "2313                           1.0                          1.0   \n",
       "2321                           0.0                          1.0   \n",
       "2335                           1.0                          0.0   \n",
       "2342                           0.0                          0.0   \n",
       "2354                           1.0                          2.0   \n",
       "2368                           0.0                          2.0   \n",
       "2374                           1.0                          2.0   \n",
       "2385                           2.0                          1.0   \n",
       "2393                           0.0                          2.0   \n",
       "2400                           1.0                          0.0   \n",
       "2414                           0.0                          0.0   \n",
       "2425                           1.0                          0.0   \n",
       "2435                           1.0                          2.0   \n",
       "2441                           1.0                          1.0   \n",
       "2456                           0.0                          1.0   \n",
       "2463                           1.0                          2.0   \n",
       "2479                           1.0                          2.0   \n",
       "2485                           2.0                          0.0   \n",
       "2492                           3.0                          1.0   \n",
       "2508                           1.0                          2.0   \n",
       "2514                           0.0                          0.0   \n",
       "2523                           2.0                          0.0   \n",
       "2534                           0.0                          0.0   \n",
       "2547                           2.0                          2.0   \n",
       "2552                           0.0                          1.0   \n",
       "2568                           1.0                          3.0   \n",
       "2575                           1.0                          1.0   \n",
       "...                            ...                          ...   \n",
       "3507                           1.0                          2.0   \n",
       "3511                           0.0                          0.0   \n",
       "3528                           1.0                          2.0   \n",
       "3531                           2.0                          1.0   \n",
       "3545                           1.0                          2.0   \n",
       "3551                           0.0                          0.0   \n",
       "3565                           1.0                          2.0   \n",
       "3575                           0.0                          2.0   \n",
       "3579                           1.0                          2.0   \n",
       "3596                           2.0                          0.0   \n",
       "3604                           0.0                          3.0   \n",
       "3609                           1.0                          2.0   \n",
       "3621                           0.0                          0.0   \n",
       "3631                           1.0                          2.0   \n",
       "3646                           1.0                          2.0   \n",
       "3651                           2.0                          2.0   \n",
       "3667                           1.0                          2.0   \n",
       "3675                           1.0                          2.0   \n",
       "3683                           0.0                          2.0   \n",
       "3692                           1.0                          3.0   \n",
       "3702                           0.0                          0.0   \n",
       "3710                           0.0                          2.0   \n",
       "3722                           2.0                          2.0   \n",
       "3733                           1.0                          2.0   \n",
       "3741                           0.0                          3.0   \n",
       "3757                           1.0                          1.0   \n",
       "3761                           0.0                          3.0   \n",
       "3777                           1.0                          2.0   \n",
       "3780                           2.0                          0.0   \n",
       "3795                           0.0                          3.0   \n",
       "\n",
       "      Last3AgainstThisOpponentDraw  LastAgainstThisOpponentWon  \\\n",
       "2284                           0.0                         1.0   \n",
       "2292                           1.0                         0.0   \n",
       "2306                           2.0                         0.0   \n",
       "2313                           1.0                         0.0   \n",
       "2321                           0.0                         0.0   \n",
       "2335                           1.0                         0.0   \n",
       "2342                           0.0                         0.0   \n",
       "2354                           0.0                         1.0   \n",
       "2368                           0.0                         0.0   \n",
       "2374                           1.0                         1.0   \n",
       "2385                           1.0                         0.0   \n",
       "2393                           0.0                         1.0   \n",
       "2400                           0.0                         0.0   \n",
       "2414                           0.0                         0.0   \n",
       "2425                           1.0                         0.0   \n",
       "2435                           0.0                         1.0   \n",
       "2441                           0.0                         0.0   \n",
       "2456                           0.0                         1.0   \n",
       "2463                           0.0                         1.0   \n",
       "2479                           1.0                         0.0   \n",
       "2485                           1.0                         0.0   \n",
       "2492                           1.0                         1.0   \n",
       "2508                           0.0                         1.0   \n",
       "2514                           0.0                         0.0   \n",
       "2523                           2.0                         0.0   \n",
       "2534                           0.0                         0.0   \n",
       "2547                           1.0                         0.0   \n",
       "2552                           0.0                         0.0   \n",
       "2568                           0.0                         1.0   \n",
       "2575                           1.0                         0.0   \n",
       "...                            ...                         ...   \n",
       "3507                           1.0                         1.0   \n",
       "3511                           0.0                         0.0   \n",
       "3528                           0.0                         1.0   \n",
       "3531                           2.0                         0.0   \n",
       "3545                           1.0                         0.0   \n",
       "3551                           0.0                         1.0   \n",
       "3565                           1.0                         1.0   \n",
       "3575                           0.0                         1.0   \n",
       "3579                           0.0                         0.0   \n",
       "3596                           2.0                         0.0   \n",
       "3604                           0.0                         1.0   \n",
       "3609                           1.0                         0.0   \n",
       "3621                           0.0                         1.0   \n",
       "3631                           1.0                         1.0   \n",
       "3646                           1.0                         0.0   \n",
       "3651                           0.0                         1.0   \n",
       "3667                           1.0                         1.0   \n",
       "3675                           0.0                         0.0   \n",
       "3683                           0.0                         1.0   \n",
       "3692                           0.0                         1.0   \n",
       "3702                           0.0                         1.0   \n",
       "3710                           0.0                         0.0   \n",
       "3722                           1.0                         1.0   \n",
       "3733                           1.0                         1.0   \n",
       "3741                           0.0                         1.0   \n",
       "3757                           1.0                         0.0   \n",
       "3761                           0.0                         1.0   \n",
       "3777                           0.0                         1.0   \n",
       "3780                           2.0                         0.0   \n",
       "3795                           0.0                         1.0   \n",
       "\n",
       "      LastAgainstThisOpponentDraw  Last5Won  Last5Draw  Last3Won  Last3Draw  \\\n",
       "2284                          0.0       3.0        0.0       2.0        0.0   \n",
       "2292                          0.0       2.0        1.0       2.0        1.0   \n",
       "2306                          0.0       2.0        1.0       1.0        1.0   \n",
       "2313                          0.0       3.0        1.0       1.0        1.0   \n",
       "2321                          0.0       3.0        1.0       2.0        0.0   \n",
       "2335                          1.0       2.0        1.0       2.0        0.0   \n",
       "2342                          0.0       2.0        1.0       1.0        1.0   \n",
       "2354                          0.0       2.0        2.0       0.0        2.0   \n",
       "2368                          0.0       1.0        3.0       0.0        3.0   \n",
       "2374                          0.0       0.0        3.0       0.0        2.0   \n",
       "2385                          1.0       1.0        3.0       1.0        1.0   \n",
       "2393                          0.0       1.0        2.0       1.0        0.0   \n",
       "2400                          0.0       2.0        1.0       2.0        0.0   \n",
       "2414                          0.0       2.0        0.0       1.0        0.0   \n",
       "2425                          1.0       3.0        0.0       2.0        0.0   \n",
       "2435                          0.0       2.0        0.0       1.0        0.0   \n",
       "2441                          0.0       2.0        0.0       1.0        0.0   \n",
       "2456                          0.0       1.0        1.0       0.0        1.0   \n",
       "2463                          0.0       2.0        1.0       1.0        1.0   \n",
       "2479                          1.0       2.0        1.0       2.0        1.0   \n",
       "2485                          0.0       3.0        1.0       3.0        0.0   \n",
       "2492                          0.0       3.0        2.0       2.0        1.0   \n",
       "2508                          0.0       3.0        2.0       1.0        2.0   \n",
       "2514                          0.0       2.0        3.0       0.0        3.0   \n",
       "2523                          1.0       1.0        3.0       0.0        2.0   \n",
       "2534                          1.0       0.0        4.0       0.0        2.0   \n",
       "2547                          1.0       1.0        3.0       1.0        1.0   \n",
       "2552                          0.0       1.0        2.0       1.0        1.0   \n",
       "2568                          0.0       2.0        1.0       2.0        0.0   \n",
       "2575                          0.0       2.0        1.0       1.0        0.0   \n",
       "...                           ...       ...        ...       ...        ...   \n",
       "3507                          0.0       3.0        1.0       2.0        0.0   \n",
       "3511                          0.0       4.0        0.0       2.0        0.0   \n",
       "3528                          0.0       4.0        0.0       3.0        0.0   \n",
       "3531                          1.0       3.0        0.0       2.0        0.0   \n",
       "3545                          1.0       4.0        0.0       2.0        0.0   \n",
       "3551                          0.0       4.0        0.0       2.0        0.0   \n",
       "3565                          0.0       4.0        0.0       3.0        0.0   \n",
       "3575                          0.0       3.0        0.0       2.0        0.0   \n",
       "3579                          0.0       4.0        0.0       2.0        0.0   \n",
       "3596                          1.0       4.0        0.0       2.0        0.0   \n",
       "3604                          0.0       3.0        0.0       2.0        0.0   \n",
       "3609                          1.0       3.0        0.0       2.0        0.0   \n",
       "3621                          0.0       4.0        0.0       2.0        0.0   \n",
       "3631                          0.0       4.0        0.0       3.0        0.0   \n",
       "3646                          1.0       4.0        0.0       3.0        0.0   \n",
       "3651                          0.0       4.0        0.0       2.0        0.0   \n",
       "3667                          0.0       4.0        0.0       2.0        0.0   \n",
       "3675                          0.0       3.0        1.0       1.0        1.0   \n",
       "3683                          0.0       2.0        2.0       1.0        2.0   \n",
       "3692                          0.0       1.0        3.0       0.0        3.0   \n",
       "3702                          0.0       2.0        3.0       1.0        2.0   \n",
       "3710                          0.0       2.0        3.0       2.0        1.0   \n",
       "3722                          0.0       3.0        2.0       3.0        0.0   \n",
       "3733                          0.0       3.0        2.0       2.0        1.0   \n",
       "3741                          0.0       4.0        1.0       2.0        1.0   \n",
       "3757                          0.0       4.0        1.0       2.0        1.0   \n",
       "3761                          0.0       4.0        1.0       3.0        0.0   \n",
       "3777                          0.0       3.0        2.0       2.0        1.0   \n",
       "3780                          0.0       3.0        2.0       1.0        2.0   \n",
       "3795                          0.0       2.0        2.0       0.0        2.0   \n",
       "\n",
       "      LastMatch  \n",
       "2284          1  \n",
       "2292          0  \n",
       "2306         -1  \n",
       "2313          1  \n",
       "2321          1  \n",
       "2335         -1  \n",
       "2342          0  \n",
       "2354          0  \n",
       "2368          0  \n",
       "2374         -1  \n",
       "2385          1  \n",
       "2393         -1  \n",
       "2400          1  \n",
       "2414         -1  \n",
       "2425          1  \n",
       "2435         -1  \n",
       "2441         -1  \n",
       "2456          0  \n",
       "2463          1  \n",
       "2479          1  \n",
       "2485          1  \n",
       "2492          0  \n",
       "2508          0  \n",
       "2514          0  \n",
       "2523         -1  \n",
       "2534          0  \n",
       "2547          1  \n",
       "2552         -1  \n",
       "2568          1  \n",
       "2575         -1  \n",
       "...         ...  \n",
       "3507          1  \n",
       "3511          1  \n",
       "3528          1  \n",
       "3531         -1  \n",
       "3545          1  \n",
       "3551          1  \n",
       "3565          1  \n",
       "3575         -1  \n",
       "3579          1  \n",
       "3596          1  \n",
       "3604         -1  \n",
       "3609          1  \n",
       "3621          1  \n",
       "3631          1  \n",
       "3646          1  \n",
       "3651         -1  \n",
       "3667          1  \n",
       "3675          0  \n",
       "3683          0  \n",
       "3692          0  \n",
       "3702          1  \n",
       "3710          1  \n",
       "3722          1  \n",
       "3733          0  \n",
       "3741          1  \n",
       "3757          1  \n",
       "3761          1  \n",
       "3777          0  \n",
       "3780          0  \n",
       "3795         -1  \n",
       "\n",
       "[152 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "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>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2306</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2313</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2321</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2342</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2368</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2374</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2385</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2393</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2400</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2425</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2435</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2441</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2456</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2463</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2479</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2485</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2492</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2508</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2514</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2523</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2534</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2547</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2552</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2568</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2575</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3507</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3511</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3528</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3545</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3551</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3565</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3575</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3579</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3596</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3604</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3609</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3621</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3631</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3646</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3667</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3675</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3683</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3692</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3702</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3710</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3722</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3733</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3741</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3757</th>\n",
       "      <td>Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3761</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3777</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3780</th>\n",
       "      <td>Lose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3795</th>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Result\n",
       "2284   Draw\n",
       "2292   Lose\n",
       "2306    Win\n",
       "2313    Win\n",
       "2321   Lose\n",
       "2335   Draw\n",
       "2342   Draw\n",
       "2354   Draw\n",
       "2368   Lose\n",
       "2374    Win\n",
       "2385   Lose\n",
       "2393    Win\n",
       "2400   Lose\n",
       "2414    Win\n",
       "2425   Lose\n",
       "2435   Lose\n",
       "2441   Draw\n",
       "2456    Win\n",
       "2463    Win\n",
       "2479    Win\n",
       "2485   Draw\n",
       "2492   Draw\n",
       "2508   Draw\n",
       "2514   Lose\n",
       "2523   Draw\n",
       "2534    Win\n",
       "2547   Lose\n",
       "2552    Win\n",
       "2568   Lose\n",
       "2575    Win\n",
       "...     ...\n",
       "3507    Win\n",
       "3511    Win\n",
       "3528   Lose\n",
       "3531    Win\n",
       "3545    Win\n",
       "3551    Win\n",
       "3565   Lose\n",
       "3575    Win\n",
       "3579    Win\n",
       "3596   Lose\n",
       "3604    Win\n",
       "3609    Win\n",
       "3621    Win\n",
       "3631    Win\n",
       "3646   Lose\n",
       "3651    Win\n",
       "3667   Draw\n",
       "3675   Draw\n",
       "3683   Draw\n",
       "3692    Win\n",
       "3702    Win\n",
       "3710    Win\n",
       "3722   Draw\n",
       "3733    Win\n",
       "3741    Win\n",
       "3757    Win\n",
       "3761   Draw\n",
       "3777   Draw\n",
       "3780   Lose\n",
       "3795   Draw\n",
       "\n",
       "[152 rows x 1 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=3, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=100,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(\n",
    "    criterion=\"entropy\",\n",
    "    random_state=100,\n",
    "    min_samples_leaf=3\n",
    ")\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.8421052631579"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in-sample test\n",
    "y_insample_pred = clf.predict(x_train)\n",
    "accuracy_score(y_train, y_insample_pred)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.68421052631579"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# out-sample test\n",
    "y_pred = clf.predict(x_test)\n",
    "accuracy_score(y_test, y_pred)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.526315789473685"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# validate\n",
    "y_validate_pred = clf.predict(x_validate)\n",
    "accuracy_score(y_validate, y_validate_pred)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=4, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=2, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=10, \n",
    "                             criterion='entropy', \n",
    "                             min_samples_leaf=2,\n",
    "                            max_depth=4)\n",
    "clf.fit(x_train, y_train['Result'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71.71052631578947"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in-sample test\n",
    "y_insample_pred = clf.predict(x_train)\n",
    "accuracy_score(y_train, y_insample_pred)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68.42105263157895"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# out-sample test\n",
    "y_pred = clf.predict(x_test)\n",
    "accuracy_score(y_test, y_pred)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67.54385964912281"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# validate test\n",
    "y_validate_pred = clf.predict(x_validate)\n",
    "accuracy_score(y_validate, y_validate_pred)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute confusion matrix\n",
    "labels = ['Draw', 'Lose', 'Win']\n",
    "cnf_matrix = confusion_matrix(y_test, y_pred, labels=labels)\n",
    "np.set_printoptions(precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[ 0  2 11]\n",
      " [ 0  3  9]\n",
      " [ 1  1 49]]\n",
      "Normalized confusion matrix\n",
      "[[0.   0.15 0.85]\n",
      " [0.   0.25 0.75]\n",
      " [0.02 0.02 0.96]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=labels,\n",
    "                      title='Confusion matrix, without normalization')\n",
    "# Plot normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=labels, normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}