goals_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict number of goals in ice hockey games\n",
    "\n",
    "This workbook explains how I built a machine learning model to predict if the number of goals in an ice hockey game will be over or under 5.5 goals. The reason for choosing that exact number is that this is the most frequently number you can bet on. \n",
    "\n",
    "I will work with data from ice hockey games in the 1st and 2nd league in Sweden (SHL and Hockeyallsvenskan). \n",
    "The process will look like this:\n",
    "1. Import and explore data\n",
    "2. Prepare data for modeling\n",
    "3. Train model\n",
    "4. Evaluate performance\n",
    "5. Apply model to current season results \n",
    "\n",
    "So let's start!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Import and explore data\n",
    "I will use a dataset I have put together through webscraping (origin: www.stats.swehockey.se) \n",
    "The packages to scrape this site for the data can be found in my [github project for ice-hockey-analysis](https://github.com/msjoelin/icehockey-analysis)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# sklearn will be used to build the model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from scipy.stats import kendalltau, pearsonr, spearmanr\n",
    "\n",
    "# To save the model\n",
    "import pickle\n",
    "\n",
    "# For visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "import os\n",
    "notebook_path = os.path.abspath(\"game_prediction.ipynb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9184  rows imported\n"
     ]
    }
   ],
   "source": [
    "# Import data from csv file\n",
    "df_raw = pd.read_csv(os.path.join(os.path.dirname(notebook_path),\"data/swehockey_game_trend.csv\"))\n",
    "\n",
    "print(df_raw.shape[0], \" rows imported\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'game', 'score', 'periodscore', 'spectators', 'schedule_id',\n",
       "       'game_id', 'league', 'season', 'home', 'away', 'score_home',\n",
       "       'score_away', 'p1score', 'p2score', 'p3score', 'p4score', 'p5score',\n",
       "       'p1score_home', 'p1score_away', 'p2score_home', 'p2score_away',\n",
       "       'p3score_home', 'p3score_away', 'p4score_home', 'p4score_away',\n",
       "       'p5score_home', 'p5score_away', 'result', 'result_p1', 'result_p2',\n",
       "       'result_p3', 'result_p4', 'result_p5', 'created', 'win_R5_home',\n",
       "       'draw_R5_home', 'lost_R5_home', 'scored_avg_R5_home',\n",
       "       'conceded_avg_R5_home', 'scored_cum_prev_avg_home',\n",
       "       'conceded_cum_prev_avg_home', 'points_cum_prev_avg_home',\n",
       "       'points_cum_h_a_prev_avg_home', 'H2H_W_home', 'H2H_D_home',\n",
       "       'H2H_L_home', 'win_R5_away', 'draw_R5_away', 'lost_R5_away',\n",
       "       'scored_avg_R5_away', 'conceded_avg_R5_away',\n",
       "       'scored_cum_prev_avg_away', 'conceded_cum_prev_avg_away',\n",
       "       'points_cum_prev_avg_away', 'points_cum_h_a_prev_avg_away'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data set contains several columns. We will use data from previous matches, and fortunately there are metrics in the data set precalculated. \n",
    "Example : \n",
    "* conceded_cum_prev_avg: Average number of conceded goals per game, total entire season so far (excluding current game)\n",
    "* points_cum_prev_avg_away: Average number of points per game for away team when playing away, total entire season so far (excluding current game)\n",
    "\n",
    "Lets extract the relevant columns for us. For example, the period score or number of spectators are being left out.\n",
    "Thereafter binary variables for our target variable is created. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>scored_avg_R5_home</th>\n",
       "      <th>conceded_avg_R5_home</th>\n",
       "      <th>scored_cum_prev_avg_home</th>\n",
       "      <th>conceded_cum_prev_avg_home</th>\n",
       "      <th>scored_avg_R5_away</th>\n",
       "      <th>conceded_avg_R5_away</th>\n",
       "      <th>scored_cum_prev_avg_away</th>\n",
       "      <th>conceded_cum_prev_avg_away</th>\n",
       "      <th>win_R5_home</th>\n",
       "      <th>draw_R5_home</th>\n",
       "      <th>...</th>\n",
       "      <th>lost_R5_away</th>\n",
       "      <th>H2H_W_home</th>\n",
       "      <th>H2H_D_home</th>\n",
       "      <th>H2H_L_home</th>\n",
       "      <th>points_cum_prev_avg_home</th>\n",
       "      <th>points_cum_prev_avg_away</th>\n",
       "      <th>points_cum_h_a_prev_avg_home</th>\n",
       "      <th>points_cum_h_a_prev_avg_away</th>\n",
       "      <th>goals</th>\n",
       "      <th>goals_over_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9184.000000</td>\n",
       "      <td>9184.000000</td>\n",
       "      <td>9009.000000</td>\n",
       "      <td>9009.000000</td>\n",
       "      <td>9184.000000</td>\n",
       "      <td>9184.000000</td>\n",
       "      <td>9007.000000</td>\n",
       "      <td>9007.000000</td>\n",
       "      <td>9184.000000</td>\n",
       "      <td>9184.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9184.000000</td>\n",
       "      <td>9184.000000</td>\n",
       "      <td>9184.000000</td>\n",
       "      <td>9184.00000</td>\n",
       "      <td>9009.000000</td>\n",
       "      <td>9007.000000</td>\n",
       "      <td>8832.000000</td>\n",
       "      <td>8832.000000</td>\n",
       "      <td>9184.000000</td>\n",
       "      <td>9184.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.603061</td>\n",
       "      <td>2.657571</td>\n",
       "      <td>2.656266</td>\n",
       "      <td>2.683019</td>\n",
       "      <td>2.652134</td>\n",
       "      <td>2.596833</td>\n",
       "      <td>2.681612</td>\n",
       "      <td>2.655131</td>\n",
       "      <td>0.375528</td>\n",
       "      <td>0.209776</td>\n",
       "      <td>...</td>\n",
       "      <td>0.373214</td>\n",
       "      <td>0.363284</td>\n",
       "      <td>0.202434</td>\n",
       "      <td>0.38169</td>\n",
       "      <td>1.467175</td>\n",
       "      <td>1.493979</td>\n",
       "      <td>1.739727</td>\n",
       "      <td>1.218418</td>\n",
       "      <td>5.365309</td>\n",
       "      <td>0.414852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.899425</td>\n",
       "      <td>0.913987</td>\n",
       "      <td>0.586097</td>\n",
       "      <td>0.605769</td>\n",
       "      <td>0.921188</td>\n",
       "      <td>0.894860</td>\n",
       "      <td>0.608752</td>\n",
       "      <td>0.582008</td>\n",
       "      <td>0.242882</td>\n",
       "      <td>0.195283</td>\n",
       "      <td>...</td>\n",
       "      <td>0.241922</td>\n",
       "      <td>0.227754</td>\n",
       "      <td>0.174017</td>\n",
       "      <td>0.23556</td>\n",
       "      <td>0.473430</td>\n",
       "      <td>0.476068</td>\n",
       "      <td>0.579692</td>\n",
       "      <td>0.566392</td>\n",
       "      <td>2.200685</td>\n",
       "      <td>0.492723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.307692</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>2.311806</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.25000</td>\n",
       "      <td>1.181818</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.409091</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.615385</td>\n",
       "      <td>2.641026</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.636364</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.37500</td>\n",
       "      <td>1.480769</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.214286</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>2.954545</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>2.974022</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.764706</td>\n",
       "      <td>2.090909</td>\n",
       "      <td>1.533333</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       scored_avg_R5_home  conceded_avg_R5_home  scored_cum_prev_avg_home  \\\n",
       "count         9184.000000           9184.000000               9009.000000   \n",
       "mean             2.603061              2.657571                  2.656266   \n",
       "std              0.899425              0.913987                  0.586097   \n",
       "min              0.000000              0.000000                  0.000000   \n",
       "25%              2.000000              2.000000                  2.307692   \n",
       "50%              2.600000              2.600000                  2.615385   \n",
       "75%              3.200000              3.200000                  2.954545   \n",
       "max              8.000000             10.000000                  8.000000   \n",
       "\n",
       "       conceded_cum_prev_avg_home  scored_avg_R5_away  conceded_avg_R5_away  \\\n",
       "count                 9009.000000         9184.000000           9184.000000   \n",
       "mean                     2.683019            2.652134              2.596833   \n",
       "std                      0.605769            0.921188              0.894860   \n",
       "min                      0.000000            0.000000              0.000000   \n",
       "25%                      2.333333            2.000000              2.000000   \n",
       "50%                      2.641026            2.600000              2.600000   \n",
       "75%                      3.000000            3.200000              3.200000   \n",
       "max                     10.000000           10.000000              7.500000   \n",
       "\n",
       "       scored_cum_prev_avg_away  conceded_cum_prev_avg_away  win_R5_home  \\\n",
       "count               9007.000000                 9007.000000  9184.000000   \n",
       "mean                   2.681612                    2.655131     0.375528   \n",
       "std                    0.608752                    0.582008     0.242882   \n",
       "min                    0.000000                    0.000000     0.000000   \n",
       "25%                    2.333333                    2.311806     0.200000   \n",
       "50%                    2.636364                    2.625000     0.400000   \n",
       "75%                    2.974022                    3.000000     0.600000   \n",
       "max                   10.000000                    7.500000     1.000000   \n",
       "\n",
       "       draw_R5_home  ...  lost_R5_away   H2H_W_home   H2H_D_home  H2H_L_home  \\\n",
       "count   9184.000000  ...   9184.000000  9184.000000  9184.000000  9184.00000   \n",
       "mean       0.209776  ...      0.373214     0.363284     0.202434     0.38169   \n",
       "std        0.195283  ...      0.241922     0.227754     0.174017     0.23556   \n",
       "min        0.000000  ...      0.000000     0.000000     0.000000     0.00000   \n",
       "25%        0.000000  ...      0.200000     0.230769     0.071429     0.25000   \n",
       "50%        0.200000  ...      0.400000     0.363636     0.200000     0.37500   \n",
       "75%        0.400000  ...      0.600000     0.500000     0.285714     0.50000   \n",
       "max        1.000000  ...      1.000000     1.000000     1.000000     1.00000   \n",
       "\n",
       "       points_cum_prev_avg_home  points_cum_prev_avg_away  \\\n",
       "count               9009.000000               9007.000000   \n",
       "mean                   1.467175                  1.493979   \n",
       "std                    0.473430                  0.476068   \n",
       "min                    0.000000                  0.000000   \n",
       "25%                    1.181818                  1.200000   \n",
       "50%                    1.480769                  1.500000   \n",
       "75%                    1.750000                  1.764706   \n",
       "max                    3.000000                  3.000000   \n",
       "\n",
       "       points_cum_h_a_prev_avg_home  points_cum_h_a_prev_avg_away  \\\n",
       "count                   8832.000000                   8832.000000   \n",
       "mean                       1.739727                      1.218418   \n",
       "std                        0.579692                      0.566392   \n",
       "min                        0.000000                      0.000000   \n",
       "25%                        1.409091                      0.875000   \n",
       "50%                        1.750000                      1.214286   \n",
       "75%                        2.090909                      1.533333   \n",
       "max                        3.000000                      3.000000   \n",
       "\n",
       "             goals  goals_over_5  \n",
       "count  9184.000000   9184.000000  \n",
       "mean      5.365309      0.414852  \n",
       "std       2.200685      0.492723  \n",
       "min       0.000000      0.000000  \n",
       "25%       4.000000      0.000000  \n",
       "50%       5.000000      0.000000  \n",
       "75%       7.000000      1.000000  \n",
       "max      15.000000      1.000000  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set relevant columns\n",
    "relevant_cols = ['score', 'date', 'league', 'season', 'home', 'away', 'score_home', 'score_away',\n",
    "                 'scored_avg_R5_home', 'conceded_avg_R5_home', \n",
    "                 'scored_cum_prev_avg_home', 'conceded_cum_prev_avg_home', \n",
    "                 'scored_avg_R5_away', 'conceded_avg_R5_away',\n",
    "                 'scored_cum_prev_avg_away', 'conceded_cum_prev_avg_away',\n",
    "                 'win_R5_home', 'draw_R5_home', 'lost_R5_home',\n",
    "                 'win_R5_away', 'draw_R5_away', 'lost_R5_away',\n",
    "                 'H2H_W_home', 'H2H_D_home', 'H2H_L_home', \n",
    "                 'points_cum_prev_avg_home', 'points_cum_prev_avg_away',\n",
    "                 'points_cum_h_a_prev_avg_home', 'points_cum_h_a_prev_avg_away'\n",
    "                ]\n",
    "\n",
    "df_games = df_raw[relevant_cols].reset_index(drop=True)\n",
    "\n",
    "# Define target column (total number of goals per game)\n",
    "df_games['goals'] = df_games['score_home'] + df_games['score_away']\n",
    "df_games = df_games.drop(['score_home', 'score_away'], axis=1)\n",
    "\n",
    "# Introduce binary outcome variables if over \n",
    "df_games['goals_over_5'] = 0\n",
    "df_games.loc[(df_games['goals']>5), 'goals_over_5'] = 1\n",
    "\n",
    "df_games.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last column ```goals_over_5``` shows that about 41% of the games ends up with more than 5 goals. \n",
    "\n",
    "There are some ```nan``` values that should be removed before modeling. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove null values\n",
    "df_games = df_games[~df_games.isnull().any(axis=1)].reset_index(drop=True)\n",
    "\n",
    "# Heatmap for correlations\n",
    "plt.figure(figsize = (12, 9))\n",
    "s = sns.heatmap(df_games.corr()[['goals']],\n",
    "               annot = True, \n",
    "               cmap = 'RdBu',\n",
    "               vmin = -0.1, \n",
    "               vmax = 0.1)\n",
    "s.set_yticklabels(s.get_yticklabels(), rotation = 0, fontsize = 12)\n",
    "s.set_xticklabels(s.get_xticklabels(), rotation = 90, fontsize = 12)\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The heatmap shows that - unsurprisingly - number of scored / conceded are the variables with highest correlation to number of goals in a game. Correlation for the home team is slightly higher, and the total scored goals over the season is also higher then the trend the last 5 games. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Prepare data for modeling\n",
    "\n",
    "The data for current season is saved to another dataframe, as this will be used to evaluate the final performance of the model and thus should not be used when training the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data current season: 697\n",
      "Data for modeling: 8107\n"
     ]
    }
   ],
   "source": [
    "# Split data\n",
    "df_2019 = df_games[df_games['season']=='2019/20']\n",
    "df_model = df_games[df_games['season']!='2019/20'].reset_index(drop=True)\n",
    "\n",
    "print(\"Data current season: \" + str(df_2019.shape[0]))\n",
    "print(\"Data for modeling: \" + str(df_model.shape[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution data in model\n",
      "0    0.584063\n",
      "1    0.415937\n",
      "Name: goals_over_5, dtype: float64\n",
      "Distribution data current season\n",
      "0    0.586801\n",
      "1    0.413199\n",
      "Name: goals_over_5, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Distribution of variable to be predicted\n",
    "print('Distribution data in model')\n",
    "print(df_model['goals_over_5'].value_counts(normalize=True))\n",
    "print('Distribution data current season')\n",
    "print(df_2019['goals_over_5'].value_counts(normalize=True))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution is very similar the current season as before. \n",
    "\n",
    "#### Dependent and independent variables\n",
    "Time to split our data into independent and dependent variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8107 entries, 0 to 8106\n",
      "Data columns (total 29 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   score                         8107 non-null   object \n",
      " 1   date                          8107 non-null   object \n",
      " 2   league                        8107 non-null   object \n",
      " 3   season                        8107 non-null   object \n",
      " 4   home                          8107 non-null   object \n",
      " 5   away                          8107 non-null   object \n",
      " 6   conceded_avg_R5_home          8107 non-null   float64\n",
      " 7   scored_cum_prev_avg_home      8107 non-null   float64\n",
      " 8   conceded_cum_prev_avg_home    8107 non-null   float64\n",
      " 9   points_cum_prev_avg_home      8107 non-null   float64\n",
      " 10  scored_avg_R5_away            8107 non-null   float64\n",
      " 11  conceded_avg_R5_away          8107 non-null   float64\n",
      " 12  scored_cum_prev_avg_away      8107 non-null   float64\n",
      " 13  conceded_cum_prev_avg_away    8107 non-null   float64\n",
      " 14  win_R5_home                   8107 non-null   float64\n",
      " 15  draw_R5_home                  8107 non-null   float64\n",
      " 16  lost_R5_home                  8107 non-null   float64\n",
      " 17  win_R5_away                   8107 non-null   float64\n",
      " 18  draw_R5_away                  8107 non-null   float64\n",
      " 19  lost_R5_away                  8107 non-null   float64\n",
      " 20  H2H_W_home                    8107 non-null   float64\n",
      " 21  H2H_D_home                    8107 non-null   float64\n",
      " 22  H2H_L_home                    8107 non-null   float64\n",
      " 23  points_cum_prev_avg_home      8107 non-null   float64\n",
      " 24  points_cum_prev_avg_away      8107 non-null   float64\n",
      " 25  points_cum_h_a_prev_avg_home  8107 non-null   float64\n",
      " 26  points_cum_h_a_prev_avg_away  8107 non-null   float64\n",
      " 27  goals                         8107 non-null   int64  \n",
      " 28  goals_over_5                  8107 non-null   int64  \n",
      "dtypes: float64(21), int64(2), object(6)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_model.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dependent variable (what should be predicted) is in column 28, and the independent variables are in columns 7 to 26. We save the attribute values in the variable ```info```.\n",
    "\n",
    "Thereafter the data is splitted into train and test data (proportion 80/20).   \n",
    "Considering our imbalanced data set, the input ``stratify =y`` is used. \n",
    "Finally the data is scaled so that all variables are weighted equal in the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the data into independent and dependent variables\n",
    "X = df_model.iloc[:,7:27].values\n",
    "y = df_model.iloc[:,28].values\n",
    "info = df_model.iloc[:,0:6].values\n",
    "\n",
    "X_2019 = df_2019.iloc[:,7:27].values\n",
    "y_2019 = df_2019.iloc[:,28].values\n",
    "info_2019 = df_2019.iloc[:,0:6].values\n",
    "\n",
    "# Split in test and train data\n",
    "X_train, X_test, y_train, y_test, info_train, info_test  = train_test_split(X, y, info, \n",
    "                                                                            test_size = 0.2, stratify = y, random_state = 21)\n",
    "# Scaling data\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "X_2019 = scaler.transform(X_2019)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Train model\n",
    "\n",
    "The data is prepared and ready to be modeled. \n",
    "I choose a standard randomforest classifier, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting Random Forest Classification to the Training set\n",
    "rf = RandomForestClassifier(n_estimators = 50, criterion = 'entropy', random_state = 42)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# save the model to disk\n",
    "filename = os.path.join(os.path.dirname(notebook_path),\"ml_models/rf_predict_nbr_goals.sav\")\n",
    "pickle.dump(rf, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Evaluate performance\n",
    "\n",
    "Time to evaluate the performance of the model. We are looking at following metric:\n",
    "* Precision: Proportion of positive predictions that was correct?\n",
    "* Recall: Proportion of actual positives that was identified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.59      0.81      0.68       947\n",
      "           1       0.43      0.21      0.28       675\n",
      "\n",
      "    accuracy                           0.56      1622\n",
      "   macro avg       0.51      0.51      0.48      1622\n",
      "weighted avg       0.52      0.56      0.51      1622\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_rf = rf.predict(X_test)\n",
    "y_pred_rf_prob = rf.predict_proba(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred_rf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep in mind that the overall probability that the number of goals would be under 5.5 is about 58%. \n",
    "This means that a \"stupid\" model would just suggest 0 as output all the time. Let's see what performance this model would give. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      1.00      0.74       947\n",
      "           1       0.00      0.00      0.00       675\n",
      "\n",
      "    accuracy                           0.58      1622\n",
      "   macro avg       0.29      0.50      0.37      1622\n",
      "weighted avg       0.34      0.58      0.43      1622\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\marcu\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "# Create baseline array with only 0\n",
    "y_pred_baseline = np.array([0] * y_pred_rf.size)\n",
    "\n",
    "print(classification_report(y_test, y_pred_baseline))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the output our model performs clearly better then the stupid one. \n",
    "We are identifying same number of games below 5.5, but still having a pretty decent identification of games above 5.5. Looks like the model might be useful on real data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Apply model to current season results\n",
    "\n",
    "So let us now apply the model to the game this current season. This data was not part of the training data, so overfitting will not be an issue when evaluating performance. We will also estimate if this model would bring any money if we would have used it over the entire season. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>score</th>\n",
       "      <th>date</th>\n",
       "      <th>league</th>\n",
       "      <th>season</th>\n",
       "      <th>home</th>\n",
       "      <th>away</th>\n",
       "      <th>actual</th>\n",
       "      <th>predicted</th>\n",
       "      <th>baseline</th>\n",
       "      <th>correct</th>\n",
       "      <th>prob_under</th>\n",
       "      <th>prob_over</th>\n",
       "      <th>correct_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4 - 2</td>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>shl</td>\n",
       "      <td>2019/20</td>\n",
       "      <td>Djurgårdens IF</td>\n",
       "      <td>Örebro HK</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4 - 2</td>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>shl</td>\n",
       "      <td>2019/20</td>\n",
       "      <td>Färjestad BK</td>\n",
       "      <td>Växjö Lakers HC</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4 - 0</td>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>shl</td>\n",
       "      <td>2019/20</td>\n",
       "      <td>Frölunda HC</td>\n",
       "      <td>Linköping HC</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3 - 2</td>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>shl</td>\n",
       "      <td>2019/20</td>\n",
       "      <td>Luleå HF</td>\n",
       "      <td>HV 71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0 - 1</td>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>shl</td>\n",
       "      <td>2019/20</td>\n",
       "      <td>Rögle BK</td>\n",
       "      <td>IK Oskarshamn</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   score        date league   season            home             away  actual  \\\n",
       "0  4 - 2  2019-09-19    shl  2019/20  Djurgårdens IF        Örebro HK       1   \n",
       "1  4 - 2  2019-09-19    shl  2019/20    Färjestad BK  Växjö Lakers HC       1   \n",
       "2  4 - 0  2019-09-19    shl  2019/20     Frölunda HC     Linköping HC       0   \n",
       "3  3 - 2  2019-09-19    shl  2019/20        Luleå HF            HV 71       0   \n",
       "4  0 - 1  2019-09-19    shl  2019/20        Rögle BK    IK Oskarshamn       0   \n",
       "\n",
       "   predicted  baseline  correct  prob_under  prob_over  correct_baseline  \n",
       "0          1         0        1        0.32       0.68                 0  \n",
       "1          1         0        1        0.48       0.52                 0  \n",
       "2          0         0        1        0.70       0.30                 1  \n",
       "3          0         0        1        0.68       0.32                 1  \n",
       "4          0         0        1        0.56       0.44                 1  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred2019_rf = rf.predict(X_2019)\n",
    "y_pred2019_rf_prob = rf.predict_proba(X_2019)\n",
    "\n",
    "# Baseline model: Only 0 all the time\n",
    "y_pred2019_baseline = np.array([0] * y_pred2019_rf.size)\n",
    "\n",
    "# Create dataframe with data for 2019\n",
    "df_2019 = pd.DataFrame(info_2019)\n",
    "df_2019.columns = [\"score\", \"date\", \"league\", \"season\", \"home\",\"away\"]\n",
    "\n",
    "# Set the outcome variables and probabilities\n",
    "df_2019['actual'] = y_2019\n",
    "df_2019['predicted'] = y_pred2019_rf\n",
    "df_2019['baseline'] = y_pred2019_baseline\n",
    "\n",
    "df_2019['correct']=0\n",
    "df_2019.loc[df_2019['actual']==df_2019['predicted'],'correct']=1\n",
    "df_2019['prob_under']=y_pred2019_rf_prob[:,0]\n",
    "df_2019['prob_over']=y_pred2019_rf_prob[:,1]\n",
    "\n",
    "df_2019['correct_baseline']=0\n",
    "df_2019.loc[df_2019['actual']==df_2019['baseline'],'correct_baseline']=1\n",
    "\n",
    "df_2019.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted\n",
      "0    561\n",
      "1    136\n",
      "Name: actual, dtype: int64\n",
      "baseline\n",
      "0    697\n",
      "Name: actual, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check mean values by sign\n",
    "print(df_2019.groupby(['predicted'])['actual'].count())\n",
    "print(df_2019.groupby(['baseline'])['actual'].count())"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I dont have any historical odds data for over/under results, but looking at my betting history a \"normal\" odds above 5.5 is around 2.2. Keep in mind I probably picked out odds which were high, so lets set a typical odds to 2.1 above and 1.6 below. \n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>score</th>\n",
       "      <th>date</th>\n",
       "      <th>league</th>\n",
       "      <th>season</th>\n",
       "      <th>home</th>\n",
       "      <th>away</th>\n",
       "      <th>actual</th>\n",
       "      <th>predicted</th>\n",
       "      <th>baseline</th>\n",
       "      <th>correct</th>\n",
       "      <th>prob_under</th>\n",
       "      <th>prob_over</th>\n",
       "      <th>correct_baseline</th>\n",
       "      <th>odds_under</th>\n",
       "      <th>odds_over</th>\n",
       "      <th>odds_outcome</th>\n",
       "      <th>stake</th>\n",
       "      <th>return</th>\n",
       "      <th>return_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4 - 2</td>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>shl</td>\n",
       "      <td>2019/20</td>\n",
       "      <td>Djurgårdens IF</td>\n",
       "      <td>Örebro HK</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4 - 2</td>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>shl</td>\n",
       "      <td>2019/20</td>\n",
       "      <td>Färjestad BK</td>\n",
       "      <td>Växjö Lakers HC</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4 - 0</td>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>shl</td>\n",
       "      <td>2019/20</td>\n",
       "      <td>Frölunda HC</td>\n",
       "      <td>Linköping HC</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3 - 2</td>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>shl</td>\n",
       "      <td>2019/20</td>\n",
       "      <td>Luleå HF</td>\n",
       "      <td>HV 71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0 - 1</td>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>shl</td>\n",
       "      <td>2019/20</td>\n",
       "      <td>Rögle BK</td>\n",
       "      <td>IK Oskarshamn</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   score        date league   season            home             away  actual  \\\n",
       "0  4 - 2  2019-09-19    shl  2019/20  Djurgårdens IF        Örebro HK       1   \n",
       "1  4 - 2  2019-09-19    shl  2019/20    Färjestad BK  Växjö Lakers HC       1   \n",
       "2  4 - 0  2019-09-19    shl  2019/20     Frölunda HC     Linköping HC       0   \n",
       "3  3 - 2  2019-09-19    shl  2019/20        Luleå HF            HV 71       0   \n",
       "4  0 - 1  2019-09-19    shl  2019/20        Rögle BK    IK Oskarshamn       0   \n",
       "\n",
       "   predicted  baseline  correct  prob_under  prob_over  correct_baseline  \\\n",
       "0          1         0        1        0.32       0.68                 0   \n",
       "1          1         0        1        0.48       0.52                 0   \n",
       "2          0         0        1        0.70       0.30                 1   \n",
       "3          0         0        1        0.68       0.32                 1   \n",
       "4          0         0        1        0.56       0.44                 1   \n",
       "\n",
       "   odds_under  odds_over  odds_outcome  stake  return  return_baseline  \n",
       "0         1.6        2.3           2.3      1     1.3             -1.0  \n",
       "1         1.6        2.3           2.3      1     1.3             -1.0  \n",
       "2         1.6        2.3           1.6      1     0.6              0.6  \n",
       "3         1.6        2.3           1.6      1     0.6              0.6  \n",
       "4         1.6        2.3           1.6      1     0.6              0.6  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odds_under = 1.6\n",
    "odds_over = 2.3\n",
    "\n",
    "df_2019['odds_under'] = odds_under\n",
    "df_2019['odds_over'] = odds_over\n",
    "\n",
    "df_2019['odds_outcome'] = odds_under\n",
    "df_2019.loc[df_2019['actual']==1,'odds_outcome'] = odds_over\n",
    "\n",
    "df_2019['stake'] = 1\n",
    "df_2019['return'] = df_2019['odds_outcome']*df_2019['correct']*df_2019['stake'] - df_2019['stake']\n",
    "df_2019['return_baseline'] = df_2019['odds_outcome']*df_2019['correct_baseline']*df_2019['stake'] - df_2019['stake']\n",
    "\n",
    "df_2019.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted\n",
      "0   -33.0\n",
      "1    -4.9\n",
      "Name: return, dtype: float64\n",
      "baseline\n",
      "0   -42.6\n",
      "Name: return_baseline, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#print(df_2019.groupby(['actual'])['return'].sum())\n",
    "#print(df_2019.groupby(['actual'])['return_baseline'].sum())\n",
    "\n",
    "print(df_2019.groupby(['predicted'])['return'].sum())\n",
    "print(df_2019.groupby(['baseline'])['return_baseline'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2019.to_csv(\"C:/Users/marcu/Documents/Python/icehockey/data/df_2019_nbr_goals.csv\", index=False)"
   ]
  }
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