CornersChallenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5fbfe37f",
   "metadata": {},
   "source": [
    "## Import Libraries and Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a94a7982",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "from scipy.stats import chisquare, ks_2samp, poisson, norm\n",
    "import math\n",
    "from sklearn.model_selection import train_test_split\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ef746ca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train (15).csv')\n",
    "test = pd.read_csv('test (15).csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4729c8fc",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc0301e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MatchId</th>\n",
       "      <th>LeagueId</th>\n",
       "      <th>Date</th>\n",
       "      <th>HomeTeamId</th>\n",
       "      <th>AwayTeamId</th>\n",
       "      <th>Home_Goals</th>\n",
       "      <th>Away_Goals</th>\n",
       "      <th>Home_Corners</th>\n",
       "      <th>Away_Corners</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2826</td>\n",
       "      <td>793</td>\n",
       "      <td>02/04/2005</td>\n",
       "      <td>410</td>\n",
       "      <td>908</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2827</td>\n",
       "      <td>793</td>\n",
       "      <td>02/04/2005</td>\n",
       "      <td>338</td>\n",
       "      <td>597</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2828</td>\n",
       "      <td>793</td>\n",
       "      <td>02/04/2005</td>\n",
       "      <td>1364</td>\n",
       "      <td>246</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2829</td>\n",
       "      <td>793</td>\n",
       "      <td>02/04/2005</td>\n",
       "      <td>1088</td>\n",
       "      <td>1397</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2830</td>\n",
       "      <td>793</td>\n",
       "      <td>02/04/2005</td>\n",
       "      <td>830</td>\n",
       "      <td>1412</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MatchId  LeagueId        Date  HomeTeamId  AwayTeamId  Home_Goals  \\\n",
       "0     2826       793  02/04/2005         410         908         2.0   \n",
       "1     2827       793  02/04/2005         338         597         3.0   \n",
       "2     2828       793  02/04/2005        1364         246         2.0   \n",
       "3     2829       793  02/04/2005        1088        1397         1.0   \n",
       "4     2830       793  02/04/2005         830        1412         2.0   \n",
       "\n",
       "   Away_Goals  Home_Corners  Away_Corners  \n",
       "0         0.0            15             1  \n",
       "1         2.0             3             6  \n",
       "2         3.0            13             2  \n",
       "3         0.0             2             5  \n",
       "4         3.0             3             6  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "795c00e5",
   "metadata": {},
   "source": [
    "Split train to train and validation around 80:20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c743f7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, val = train_test_split(train, test_size = 0.2, random_state = 111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4e32bc2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MatchId</th>\n",
       "      <th>LeagueId</th>\n",
       "      <th>Date</th>\n",
       "      <th>HomeTeamId</th>\n",
       "      <th>AwayTeamId</th>\n",
       "      <th>Home_Goals</th>\n",
       "      <th>Away_Goals</th>\n",
       "      <th>Home_Corners</th>\n",
       "      <th>Away_Corners</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8191</th>\n",
       "      <td>111379</td>\n",
       "      <td>800</td>\n",
       "      <td>26/08/2007</td>\n",
       "      <td>3592</td>\n",
       "      <td>473</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14904</th>\n",
       "      <td>185532</td>\n",
       "      <td>813</td>\n",
       "      <td>23/03/2009</td>\n",
       "      <td>1404</td>\n",
       "      <td>771</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10846</th>\n",
       "      <td>142750</td>\n",
       "      <td>816</td>\n",
       "      <td>03/05/2008</td>\n",
       "      <td>662</td>\n",
       "      <td>1107</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22774</th>\n",
       "      <td>286768</td>\n",
       "      <td>734</td>\n",
       "      <td>08/10/2010</td>\n",
       "      <td>1212</td>\n",
       "      <td>1061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12498</th>\n",
       "      <td>153496</td>\n",
       "      <td>801</td>\n",
       "      <td>17/08/2008</td>\n",
       "      <td>570</td>\n",
       "      <td>1416</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7443</th>\n",
       "      <td>107634</td>\n",
       "      <td>816</td>\n",
       "      <td>30/06/2007</td>\n",
       "      <td>604</td>\n",
       "      <td>197</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4182</th>\n",
       "      <td>73234</td>\n",
       "      <td>793</td>\n",
       "      <td>29/07/2006</td>\n",
       "      <td>691</td>\n",
       "      <td>1049</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4820</th>\n",
       "      <td>77255</td>\n",
       "      <td>813</td>\n",
       "      <td>16/09/2006</td>\n",
       "      <td>1404</td>\n",
       "      <td>2603</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10196</th>\n",
       "      <td>138013</td>\n",
       "      <td>800</td>\n",
       "      <td>22/03/2008</td>\n",
       "      <td>382</td>\n",
       "      <td>834</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12116</th>\n",
       "      <td>152173</td>\n",
       "      <td>800</td>\n",
       "      <td>26/07/2008</td>\n",
       "      <td>369</td>\n",
       "      <td>944</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19064 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MatchId  LeagueId        Date  HomeTeamId  AwayTeamId  Home_Goals  \\\n",
       "8191    111379       800  26/08/2007        3592         473         1.0   \n",
       "14904   185532       813  23/03/2009        1404         771         1.0   \n",
       "10846   142750       816  03/05/2008         662        1107         2.0   \n",
       "22774   286768       734  08/10/2010        1212        1061         0.0   \n",
       "12498   153496       801  17/08/2008         570        1416         2.0   \n",
       "...        ...       ...         ...         ...         ...         ...   \n",
       "7443    107634       816  30/06/2007         604         197         2.0   \n",
       "4182     73234       793  29/07/2006         691        1049         1.0   \n",
       "4820     77255       813  16/09/2006        1404        2603         2.0   \n",
       "10196   138013       800  22/03/2008         382         834         0.0   \n",
       "12116   152173       800  26/07/2008         369         944         1.0   \n",
       "\n",
       "       Away_Goals  Home_Corners  Away_Corners  \n",
       "8191          0.0             5             6  \n",
       "14904         3.0             5             1  \n",
       "10846         1.0             3             2  \n",
       "22774         0.0             7             6  \n",
       "12498         0.0             2             3  \n",
       "...           ...           ...           ...  \n",
       "7443          2.0             2             4  \n",
       "4182          4.0             5             3  \n",
       "4820          0.0             7             0  \n",
       "10196         2.0             5             2  \n",
       "12116         1.0             3             7  \n",
       "\n",
       "[19064 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8099e1da",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Total_Corners'] = train['Home_Corners'] + train['Away_Corners']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b399abd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19a5cc538d0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols = 3, figsize = (15, 5))\n",
    "sns.histplot(data = train['Home_Corners'], color = 'skyblue', ax = axs[0])\n",
    "x = np.arange(0, train['Home_Corners'].max(), 1)\n",
    "y = poisson.pmf(x, mu = train['Home_Corners'].mean()) * len(train['Home_Corners'])\n",
    "axs[0].plot(x, y)\n",
    "\n",
    "sns.histplot(data = train['Away_Corners'], color = 'skyblue', ax = axs[1])\n",
    "x = np.arange(0, train['Away_Corners'].max(), 1)\n",
    "y = poisson.pmf(x, mu = train['Away_Corners'].mean()) * len(train['Away_Corners'])\n",
    "axs[1].plot(x, y)\n",
    "\n",
    "sns.histplot(data = train['Total_Corners'], color = 'skyblue', ax = axs[2])\n",
    "x = np.arange(0, train['Total_Corners'].max(), 1)\n",
    "y = poisson.pmf(x, mu = train['Total_Corners'].mean()) * len(train['Total_Corners'])\n",
    "axs[2].plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7e638a0",
   "metadata": {},
   "source": [
    "## Checking Distribution for Corners with KS Test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a79e06cc",
   "metadata": {},
   "source": [
    "We use Kolmogorov-Smirnov goodness-of-fit test with the following hypothesis:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e14c9c",
   "metadata": {},
   "source": [
    "H0: the data follow the specified distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af7b073",
   "metadata": {},
   "source": [
    "H1: the data does not follow the specified distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "685e0b9e",
   "metadata": {},
   "source": [
    "### Check whether home corners for each team is well fitted by poisson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3d714109",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_values = []\n",
    "for leagueid, teamid in zip(train['LeagueId'], train['HomeTeamId']):\n",
    "    data = train[(train['LeagueId'] == leagueid) & (train['HomeTeamId'] == teamid)]['Home_Corners']\n",
    "    poisson_dist = poisson.rvs(size = len(data), mu = data.mean())\n",
    "    ks_statistic, p_value = ks_2samp(data, poisson_dist)\n",
    "    p_values.append(p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "786aff3c",
   "metadata": {},
   "source": [
    "Average p-value for poisson distribution (home corners)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e2e1dbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7660973323983239"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(p_values) / len(p_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea627a3a",
   "metadata": {},
   "source": [
    "### Check whether away corners for each team is well fitted by poisson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "625a68dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_values2 = []\n",
    "for leagueid, teamid in zip(train['LeagueId'], train['AwayTeamId']):\n",
    "    data = train[(train['LeagueId'] == leagueid) & (train['AwayTeamId'] == teamid)]['Away_Corners']\n",
    "    poisson_dist = poisson.rvs(size = len(data), mu = data.mean())\n",
    "    ks_statistic, p_value = ks_2samp(data, poisson_dist)\n",
    "    p_values2.append(p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18978cb2",
   "metadata": {},
   "source": [
    "Average p-value for poisson distribution (away corners)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "82f02090",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7843226667265067"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(p_values2) / len(p_values2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee5c8178",
   "metadata": {},
   "source": [
    "Since both averages for p-value is very high, we failed to reject null hypothesis and the data follows poisson distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "870c6ea0",
   "metadata": {},
   "source": [
    "## Testing the data using validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7dc4191d",
   "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>MatchId</th>\n",
       "      <th>LeagueId</th>\n",
       "      <th>Date</th>\n",
       "      <th>HomeTeamId</th>\n",
       "      <th>AwayTeamId</th>\n",
       "      <th>Home_Goals</th>\n",
       "      <th>Away_Goals</th>\n",
       "      <th>Home_Corners</th>\n",
       "      <th>Away_Corners</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23251</th>\n",
       "      <td>294047</td>\n",
       "      <td>813</td>\n",
       "      <td>30/10/2010</td>\n",
       "      <td>988</td>\n",
       "      <td>1404</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11636</th>\n",
       "      <td>149392</td>\n",
       "      <td>800</td>\n",
       "      <td>20/06/2008</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12009</th>\n",
       "      <td>151652</td>\n",
       "      <td>801</td>\n",
       "      <td>20/07/2008</td>\n",
       "      <td>570</td>\n",
       "      <td>238</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>20086</td>\n",
       "      <td>729</td>\n",
       "      <td>14/11/2005</td>\n",
       "      <td>601</td>\n",
       "      <td>1185</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15718</th>\n",
       "      <td>191969</td>\n",
       "      <td>741</td>\n",
       "      <td>09/05/2009</td>\n",
       "      <td>1424</td>\n",
       "      <td>1117</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MatchId  LeagueId        Date  HomeTeamId  AwayTeamId  Home_Goals  \\\n",
       "23251   294047       813  30/10/2010         988        1404         3.0   \n",
       "11636   149392       800  20/06/2008        1279        1351         1.0   \n",
       "12009   151652       801  20/07/2008         570         238         3.0   \n",
       "2304     20086       729  14/11/2005         601        1185         0.0   \n",
       "15718   191969       741  09/05/2009        1424        1117         3.0   \n",
       "\n",
       "       Away_Goals  Home_Corners  Away_Corners  \n",
       "23251         0.0             1             5  \n",
       "11636         2.0             5             3  \n",
       "12009         0.0             6             7  \n",
       "2304          2.0             1             6  \n",
       "15718         1.0             5             3  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f4354237",
   "metadata": {},
   "outputs": [],
   "source": [
    "val['pred_home_corners'] = 0\n",
    "val['pred_away_corners'] = 0\n",
    "for i, row in val.iterrows():\n",
    "    leagueid, homeid, awayid = row['LeagueId'], row['HomeTeamId'], row['AwayTeamId']\n",
    "    \n",
    "    # find home corners for the home team\n",
    "    data = train[(train['LeagueId'] == leagueid) & (train['HomeTeamId'] == homeid)]['Home_Corners']\n",
    "    val.at[i, 'pred_home_corners'] = poisson.rvs(mu = data.mean(), size = 1)[0]\n",
    "    \n",
    "    # find away corners for the away team\n",
    "    data2 = train[(train['LeagueId'] == leagueid) & (train['AwayTeamId'] == awayid)]['Away_Corners']\n",
    "    val.at[i, 'pred_away_corners'] = poisson.rvs(mu = data2.mean(), size = 1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1159d235",
   "metadata": {},
   "outputs": [],
   "source": [
    "val['total_corners'] = val['Home_Corners'] + val['Away_Corners']\n",
    "val['pred_total_corners'] = val['pred_home_corners'] + val['pred_away_corners']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c372b271",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MatchId</th>\n",
       "      <th>LeagueId</th>\n",
       "      <th>Date</th>\n",
       "      <th>HomeTeamId</th>\n",
       "      <th>AwayTeamId</th>\n",
       "      <th>Home_Goals</th>\n",
       "      <th>Away_Goals</th>\n",
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       "      <th>Away_Corners</th>\n",
       "      <th>pred_home_corners</th>\n",
       "      <th>pred_away_corners</th>\n",
       "      <th>total_corners</th>\n",
       "      <th>pred_total_corners</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23251</th>\n",
       "      <td>294047</td>\n",
       "      <td>813</td>\n",
       "      <td>30/10/2010</td>\n",
       "      <td>988</td>\n",
       "      <td>1404</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>11636</th>\n",
       "      <td>149392</td>\n",
       "      <td>800</td>\n",
       "      <td>20/06/2008</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12009</th>\n",
       "      <td>151652</td>\n",
       "      <td>801</td>\n",
       "      <td>20/07/2008</td>\n",
       "      <td>570</td>\n",
       "      <td>238</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>20086</td>\n",
       "      <td>729</td>\n",
       "      <td>14/11/2005</td>\n",
       "      <td>601</td>\n",
       "      <td>1185</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15718</th>\n",
       "      <td>191969</td>\n",
       "      <td>741</td>\n",
       "      <td>09/05/2009</td>\n",
       "      <td>1424</td>\n",
       "      <td>1117</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>18</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21049</th>\n",
       "      <td>263077</td>\n",
       "      <td>801</td>\n",
       "      <td>21/06/2010</td>\n",
       "      <td>771</td>\n",
       "      <td>489</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4110</th>\n",
       "      <td>72871</td>\n",
       "      <td>793</td>\n",
       "      <td>22/07/2006</td>\n",
       "      <td>548</td>\n",
       "      <td>1090</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18971</th>\n",
       "      <td>223069</td>\n",
       "      <td>800</td>\n",
       "      <td>06/12/2009</td>\n",
       "      <td>1135</td>\n",
       "      <td>369</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15526</th>\n",
       "      <td>190348</td>\n",
       "      <td>800</td>\n",
       "      <td>02/05/2009</td>\n",
       "      <td>369</td>\n",
       "      <td>3592</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11198</th>\n",
       "      <td>145486</td>\n",
       "      <td>801</td>\n",
       "      <td>21/05/2008</td>\n",
       "      <td>758</td>\n",
       "      <td>1035</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4766 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MatchId  LeagueId        Date  HomeTeamId  AwayTeamId  Home_Goals  \\\n",
       "23251   294047       813  30/10/2010         988        1404         3.0   \n",
       "11636   149392       800  20/06/2008        1279        1351         1.0   \n",
       "12009   151652       801  20/07/2008         570         238         3.0   \n",
       "2304     20086       729  14/11/2005         601        1185         0.0   \n",
       "15718   191969       741  09/05/2009        1424        1117         3.0   \n",
       "...        ...       ...         ...         ...         ...         ...   \n",
       "21049   263077       801  21/06/2010         771         489         0.0   \n",
       "4110     72871       793  22/07/2006         548        1090         1.0   \n",
       "18971   223069       800  06/12/2009        1135         369         1.0   \n",
       "15526   190348       800  02/05/2009         369        3592         4.0   \n",
       "11198   145486       801  21/05/2008         758        1035         3.0   \n",
       "\n",
       "       Away_Goals  Home_Corners  Away_Corners  pred_home_corners  \\\n",
       "23251         0.0             1             5                  7   \n",
       "11636         2.0             5             3                  8   \n",
       "12009         0.0             6             7                 11   \n",
       "2304          2.0             1             6                  8   \n",
       "15718         1.0             5             3                  9   \n",
       "...           ...           ...           ...                ...   \n",
       "21049         2.0             5             3                 12   \n",
       "4110          1.0             7             4                  8   \n",
       "18971         2.0             0             3                  3   \n",
       "15526         0.0             6             4                  7   \n",
       "11198         1.0             5            10                 10   \n",
       "\n",
       "       pred_away_corners  total_corners  pred_total_corners  \n",
       "23251                  9              6                  16  \n",
       "11636                  2              8                  10  \n",
       "12009                  4             13                  15  \n",
       "2304                   5              7                  13  \n",
       "15718                  9              8                  18  \n",
       "...                  ...            ...                 ...  \n",
       "21049                  5              8                  17  \n",
       "4110                   4             11                  12  \n",
       "18971                  4              3                   7  \n",
       "15526                  6             10                  13  \n",
       "11198                  2             15                  12  \n",
       "\n",
       "[4766 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ebdb94f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "val['error'] = abs(val['total_corners'] - val['pred_total_corners'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac59227e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4766.000000\n",
       "mean        3.706043\n",
       "std         2.919312\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         3.000000\n",
       "75%         5.000000\n",
       "max        19.000000\n",
       "Name: error, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val['error'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deebeb3d",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ea3d397f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MatchId</th>\n",
       "      <th>LeagueId</th>\n",
       "      <th>Date</th>\n",
       "      <th>HomeTeamId</th>\n",
       "      <th>AwayTeamId</th>\n",
       "      <th>Line</th>\n",
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       "      <th>Under</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>P(Under)</th>\n",
       "      <th>P(At)</th>\n",
       "      <th>P(Over)</th>\n",
       "      <th>Bet (U/O)</th>\n",
       "      <th>Stake</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>342</td>\n",
       "      <td>694</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1.790</td>\n",
       "      <td>1.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>1424</td>\n",
       "      <td>270</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1.920</td>\n",
       "      <td>2.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>729</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>691</td>\n",
       "      <td>1137</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.970</td>\n",
       "      <td>1.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>729</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>787</td>\n",
       "      <td>808</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.075</td>\n",
       "      <td>1.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>784</td>\n",
       "      <td>1117</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.020</td>\n",
       "      <td>1.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MatchId  LeagueId        Date  HomeTeamId  AwayTeamId  Line   Over  Under  \\\n",
       "0        1       741  01/04/2011         342         694   9.5  1.790   1.80   \n",
       "1        2       741  01/04/2011        1424         270  11.5  1.920   2.00   \n",
       "2        3       729  01/04/2011         691        1137  10.5  1.970   1.87   \n",
       "3        4       729  01/04/2011         787         808  11.0  2.075   1.77   \n",
       "4        5       741  01/04/2011         784        1117  12.0  2.020   1.86   \n",
       "\n",
       "   Unnamed: 8  P(Under)  P(At)  P(Over)  Bet (U/O)  Stake  \n",
       "0         NaN       NaN    NaN      NaN        NaN    NaN  \n",
       "1         NaN       NaN    NaN      NaN        NaN    NaN  \n",
       "2         NaN       NaN    NaN      NaN        NaN    NaN  \n",
       "3         NaN       NaN    NaN      NaN        NaN    NaN  \n",
       "4         NaN       NaN    NaN      NaN        NaN    NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6c5c2566",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.drop(columns=['Unnamed: 8'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "893214c8",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MatchId</th>\n",
       "      <th>LeagueId</th>\n",
       "      <th>Date</th>\n",
       "      <th>HomeTeamId</th>\n",
       "      <th>AwayTeamId</th>\n",
       "      <th>Line</th>\n",
       "      <th>Over</th>\n",
       "      <th>Under</th>\n",
       "      <th>P(Under)</th>\n",
       "      <th>P(At)</th>\n",
       "      <th>P(Over)</th>\n",
       "      <th>Bet (U/O)</th>\n",
       "      <th>Stake</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>342</td>\n",
       "      <td>694</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1.790</td>\n",
       "      <td>1.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>1424</td>\n",
       "      <td>270</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1.920</td>\n",
       "      <td>2.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>729</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>691</td>\n",
       "      <td>1137</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.970</td>\n",
       "      <td>1.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>729</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>787</td>\n",
       "      <td>808</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.075</td>\n",
       "      <td>1.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>784</td>\n",
       "      <td>1117</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.020</td>\n",
       "      <td>1.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MatchId  LeagueId        Date  HomeTeamId  AwayTeamId  Line   Over  Under  \\\n",
       "0        1       741  01/04/2011         342         694   9.5  1.790   1.80   \n",
       "1        2       741  01/04/2011        1424         270  11.5  1.920   2.00   \n",
       "2        3       729  01/04/2011         691        1137  10.5  1.970   1.87   \n",
       "3        4       729  01/04/2011         787         808  11.0  2.075   1.77   \n",
       "4        5       741  01/04/2011         784        1117  12.0  2.020   1.86   \n",
       "\n",
       "   P(Under)  P(At)  P(Over)  Bet (U/O)  Stake  \n",
       "0       NaN    NaN      NaN        NaN    NaN  \n",
       "1       NaN    NaN      NaN        NaN    NaN  \n",
       "2       NaN    NaN      NaN        NaN    NaN  \n",
       "3       NaN    NaN      NaN        NaN    NaN  \n",
       "4       NaN    NaN      NaN        NaN    NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2c09fd3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in test.iterrows():\n",
    "    leagueid, homeid, awayid, line = row['LeagueId'], row['HomeTeamId'], row['AwayTeamId'], row['Line']\n",
    "    under, at, over = 0, 0, 0\n",
    "    # find home corners for the home team\n",
    "    mu_home = train[(train['LeagueId'] == leagueid) &\n",
    "                    (train['HomeTeamId'] == homeid)]['Home_Corners'].mean()\n",
    "    if math.isnan(mu_home):\n",
    "        mu_home = train[train['HomeTeamId'] == homeid]['Home_Corners'].mean()\n",
    "    \n",
    "    # find away corners for the away team\n",
    "    mu_away = train[(train['LeagueId'] == leagueid) &\n",
    "                    (train['AwayTeamId'] == awayid)]['Away_Corners'].mean()\n",
    "    if math.isnan(mu_away):\n",
    "        mu_away = train[train['AwayTeamId'] == awayid]['Away_Corners'].mean()\n",
    "    \n",
    "    if abs(line - math.floor(line)) == 0:\n",
    "        under = int(line) - 1\n",
    "        at = int(line)\n",
    "        over = int(line) + 1\n",
    "    else:\n",
    "        under = int(math.floor(line))\n",
    "        at = -1\n",
    "        over = int(math.ceil(line))\n",
    "    \n",
    "    # Find probability of At\n",
    "    p = 0\n",
    "    if at == -1:\n",
    "        test.at[i, 'P(At)'] = 0\n",
    "    else:\n",
    "        for x in range(at + 1):\n",
    "            p += (poisson.pmf(k = x, mu = mu_home) * poisson.pmf(k = at - x, mu = mu_away))\n",
    "        test.at[i, 'P(At)'] = p\n",
    "        \n",
    "    # Find probability of Under\n",
    "    p2 = 0\n",
    "    for y in range(under + 1):\n",
    "        for z in range(under - y + 1):\n",
    "            p2 += (poisson.pmf(k = y, mu = mu_home) * poisson.pmf(k = z, mu = mu_away))\n",
    "    test.at[i, 'P(Under)'] = p2\n",
    "    \n",
    "    # Find probability of Over\n",
    "    test.at[i, 'P(Over)'] = 1 - p - p2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b51e2916",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['EV Under'] = test['Under'] * test['P(Under)']\n",
    "test['EV Over'] = test['Over'] * test['P(Over)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1d1d5fb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MatchId</th>\n",
       "      <th>LeagueId</th>\n",
       "      <th>Date</th>\n",
       "      <th>HomeTeamId</th>\n",
       "      <th>AwayTeamId</th>\n",
       "      <th>Line</th>\n",
       "      <th>Over</th>\n",
       "      <th>Under</th>\n",
       "      <th>P(Under)</th>\n",
       "      <th>P(At)</th>\n",
       "      <th>P(Over)</th>\n",
       "      <th>Bet (U/O)</th>\n",
       "      <th>Stake</th>\n",
       "      <th>EV Under</th>\n",
       "      <th>EV Over</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
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       "      <td>9.5</td>\n",
       "      <td>1.790</td>\n",
       "      <td>1.800</td>\n",
       "      <td>0.265385</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.477692</td>\n",
       "      <td>1.314962</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>1424</td>\n",
       "      <td>270</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1.920</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.531832</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.063664</td>\n",
       "      <td>0.898883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>729</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>691</td>\n",
       "      <td>1137</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.970</td>\n",
       "      <td>1.870</td>\n",
       "      <td>0.594537</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.405463</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.111784</td>\n",
       "      <td>0.798763</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>729</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>787</td>\n",
       "      <td>808</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.075</td>\n",
       "      <td>1.770</td>\n",
       "      <td>0.386553</td>\n",
       "      <td>0.117285</td>\n",
       "      <td>0.496161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.684199</td>\n",
       "      <td>1.029535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>784</td>\n",
       "      <td>1117</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.020</td>\n",
       "      <td>1.860</td>\n",
       "      <td>0.565861</td>\n",
       "      <td>0.110484</td>\n",
       "      <td>0.323655</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.052501</td>\n",
       "      <td>0.653783</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>336</th>\n",
       "      <td>337</td>\n",
       "      <td>729</td>\n",
       "      <td>22/05/2011</td>\n",
       "      <td>1211</td>\n",
       "      <td>1118</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.961</td>\n",
       "      <td>0.263292</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.736708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.516315</td>\n",
       "      <td>1.436581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>338</td>\n",
       "      <td>795</td>\n",
       "      <td>22/05/2011</td>\n",
       "      <td>1116</td>\n",
       "      <td>876</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.920</td>\n",
       "      <td>1.909</td>\n",
       "      <td>0.467767</td>\n",
       "      <td>0.125071</td>\n",
       "      <td>0.407161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.892968</td>\n",
       "      <td>0.781750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>339</td>\n",
       "      <td>801</td>\n",
       "      <td>22/05/2011</td>\n",
       "      <td>1223</td>\n",
       "      <td>771</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.020</td>\n",
       "      <td>1.840</td>\n",
       "      <td>0.461865</td>\n",
       "      <td>0.125104</td>\n",
       "      <td>0.413031</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.849831</td>\n",
       "      <td>0.834323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>340</td>\n",
       "      <td>801</td>\n",
       "      <td>22/05/2011</td>\n",
       "      <td>238</td>\n",
       "      <td>570</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.020</td>\n",
       "      <td>1.900</td>\n",
       "      <td>0.344035</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.655965</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.653667</td>\n",
       "      <td>1.325048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>341</td>\n",
       "      <td>776</td>\n",
       "      <td>23/05/2011</td>\n",
       "      <td>412</td>\n",
       "      <td>1089</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.220</td>\n",
       "      <td>1.729</td>\n",
       "      <td>0.663661</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.336339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.147471</td>\n",
       "      <td>0.746672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>341 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     MatchId  LeagueId        Date  HomeTeamId  AwayTeamId  Line   Over  \\\n",
       "0          1       741  01/04/2011         342         694   9.5  1.790   \n",
       "1          2       741  01/04/2011        1424         270  11.5  1.920   \n",
       "2          3       729  01/04/2011         691        1137  10.5  1.970   \n",
       "3          4       729  01/04/2011         787         808  11.0  2.075   \n",
       "4          5       741  01/04/2011         784        1117  12.0  2.020   \n",
       "..       ...       ...         ...         ...         ...   ...    ...   \n",
       "336      337       729  22/05/2011        1211        1118  10.5  1.950   \n",
       "337      338       795  22/05/2011        1116         876  10.0  1.920   \n",
       "338      339       801  22/05/2011        1223         771  10.0  2.020   \n",
       "339      340       801  22/05/2011         238         570  10.5  2.020   \n",
       "340      341       776  23/05/2011         412        1089  10.5  2.220   \n",
       "\n",
       "     Under  P(Under)     P(At)   P(Over)  Bet (U/O)  Stake  EV Under   EV Over  \n",
       "0    1.800  0.265385  0.000000  0.734615        NaN    NaN  0.477692  1.314962  \n",
       "1    2.000  0.531832  0.000000  0.468168        NaN    NaN  1.063664  0.898883  \n",
       "2    1.870  0.594537  0.000000  0.405463        NaN    NaN  1.111784  0.798763  \n",
       "3    1.770  0.386553  0.117285  0.496161        NaN    NaN  0.684199  1.029535  \n",
       "4    1.860  0.565861  0.110484  0.323655        NaN    NaN  1.052501  0.653783  \n",
       "..     ...       ...       ...       ...        ...    ...       ...       ...  \n",
       "336  1.961  0.263292  0.000000  0.736708        NaN    NaN  0.516315  1.436581  \n",
       "337  1.909  0.467767  0.125071  0.407161        NaN    NaN  0.892968  0.781750  \n",
       "338  1.840  0.461865  0.125104  0.413031        NaN    NaN  0.849831  0.834323  \n",
       "339  1.900  0.344035  0.000000  0.655965        NaN    NaN  0.653667  1.325048  \n",
       "340  1.729  0.663661  0.000000  0.336339        NaN    NaN  1.147471  0.746672  \n",
       "\n",
       "[341 rows x 15 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d7c6d7fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bet(row):\n",
    "    if row['EV Under'] > row['EV Over']:\n",
    "        return 'U'\n",
    "    else:\n",
    "        return 'O'\n",
    "    \n",
    "def kelly(row):\n",
    "    if row['Bet (U/O)'] == 'U':\n",
    "        return max(((row['Under']-1) * row['P(Under)'] - row['P(Over)'])/(row['Under']-1), 0)\n",
    "    else:\n",
    "        return max(((row['Over']-1) * row['P(Over)'] - row['P(Under)'])/(row['Over']-1), 0)\n",
    "\n",
    "test['Bet (U/O)'] = test.apply(lambda x: bet(x), axis = 1)\n",
    "test['Kelly'] = test.apply(lambda x: kelly(x), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c6d4458d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>MatchId</th>\n",
       "      <th>LeagueId</th>\n",
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       "      <th>P(Under)</th>\n",
       "      <th>P(At)</th>\n",
       "      <th>P(Over)</th>\n",
       "      <th>Bet (U/O)</th>\n",
       "      <th>Stake</th>\n",
       "      <th>EV Under</th>\n",
       "      <th>EV Over</th>\n",
       "      <th>Kelly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>342</td>\n",
       "      <td>694</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1.790</td>\n",
       "      <td>1.800</td>\n",
       "      <td>0.265385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.734615</td>\n",
       "      <td>O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.477692</td>\n",
       "      <td>1.314962</td>\n",
       "      <td>0.398686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>1424</td>\n",
       "      <td>270</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1.920</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.531832</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468168</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.063664</td>\n",
       "      <td>0.898883</td>\n",
       "      <td>0.063664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>729</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>691</td>\n",
       "      <td>1137</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.970</td>\n",
       "      <td>1.870</td>\n",
       "      <td>0.594537</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.405463</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.111784</td>\n",
       "      <td>0.798763</td>\n",
       "      <td>0.128487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>729</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>787</td>\n",
       "      <td>808</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.075</td>\n",
       "      <td>1.770</td>\n",
       "      <td>0.386553</td>\n",
       "      <td>0.117285</td>\n",
       "      <td>0.496161</td>\n",
       "      <td>O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.684199</td>\n",
       "      <td>1.029535</td>\n",
       "      <td>0.136577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>784</td>\n",
       "      <td>1117</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.020</td>\n",
       "      <td>1.860</td>\n",
       "      <td>0.565861</td>\n",
       "      <td>0.110484</td>\n",
       "      <td>0.323655</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.052501</td>\n",
       "      <td>0.653783</td>\n",
       "      <td>0.189518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>337</td>\n",
       "      <td>729</td>\n",
       "      <td>22/05/2011</td>\n",
       "      <td>1211</td>\n",
       "      <td>1118</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.961</td>\n",
       "      <td>0.263292</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.736708</td>\n",
       "      <td>O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.516315</td>\n",
       "      <td>1.436581</td>\n",
       "      <td>0.459559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>338</td>\n",
       "      <td>795</td>\n",
       "      <td>22/05/2011</td>\n",
       "      <td>1116</td>\n",
       "      <td>876</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.920</td>\n",
       "      <td>1.909</td>\n",
       "      <td>0.467767</td>\n",
       "      <td>0.125071</td>\n",
       "      <td>0.407161</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.892968</td>\n",
       "      <td>0.781750</td>\n",
       "      <td>0.019845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>339</td>\n",
       "      <td>801</td>\n",
       "      <td>22/05/2011</td>\n",
       "      <td>1223</td>\n",
       "      <td>771</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.020</td>\n",
       "      <td>1.840</td>\n",
       "      <td>0.461865</td>\n",
       "      <td>0.125104</td>\n",
       "      <td>0.413031</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.849831</td>\n",
       "      <td>0.834323</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>340</td>\n",
       "      <td>801</td>\n",
       "      <td>22/05/2011</td>\n",
       "      <td>238</td>\n",
       "      <td>570</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.020</td>\n",
       "      <td>1.900</td>\n",
       "      <td>0.344035</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.655965</td>\n",
       "      <td>O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.653667</td>\n",
       "      <td>1.325048</td>\n",
       "      <td>0.318675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>341</td>\n",
       "      <td>776</td>\n",
       "      <td>23/05/2011</td>\n",
       "      <td>412</td>\n",
       "      <td>1089</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.220</td>\n",
       "      <td>1.729</td>\n",
       "      <td>0.663661</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.336339</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.147471</td>\n",
       "      <td>0.746672</td>\n",
       "      <td>0.202292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>341 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     MatchId  LeagueId        Date  HomeTeamId  AwayTeamId  Line   Over  \\\n",
       "0          1       741  01/04/2011         342         694   9.5  1.790   \n",
       "1          2       741  01/04/2011        1424         270  11.5  1.920   \n",
       "2          3       729  01/04/2011         691        1137  10.5  1.970   \n",
       "3          4       729  01/04/2011         787         808  11.0  2.075   \n",
       "4          5       741  01/04/2011         784        1117  12.0  2.020   \n",
       "..       ...       ...         ...         ...         ...   ...    ...   \n",
       "336      337       729  22/05/2011        1211        1118  10.5  1.950   \n",
       "337      338       795  22/05/2011        1116         876  10.0  1.920   \n",
       "338      339       801  22/05/2011        1223         771  10.0  2.020   \n",
       "339      340       801  22/05/2011         238         570  10.5  2.020   \n",
       "340      341       776  23/05/2011         412        1089  10.5  2.220   \n",
       "\n",
       "     Under  P(Under)     P(At)   P(Over) Bet (U/O)  Stake  EV Under   EV Over  \\\n",
       "0    1.800  0.265385  0.000000  0.734615         O    NaN  0.477692  1.314962   \n",
       "1    2.000  0.531832  0.000000  0.468168         U    NaN  1.063664  0.898883   \n",
       "2    1.870  0.594537  0.000000  0.405463         U    NaN  1.111784  0.798763   \n",
       "3    1.770  0.386553  0.117285  0.496161         O    NaN  0.684199  1.029535   \n",
       "4    1.860  0.565861  0.110484  0.323655         U    NaN  1.052501  0.653783   \n",
       "..     ...       ...       ...       ...       ...    ...       ...       ...   \n",
       "336  1.961  0.263292  0.000000  0.736708         O    NaN  0.516315  1.436581   \n",
       "337  1.909  0.467767  0.125071  0.407161         U    NaN  0.892968  0.781750   \n",
       "338  1.840  0.461865  0.125104  0.413031         U    NaN  0.849831  0.834323   \n",
       "339  1.900  0.344035  0.000000  0.655965         O    NaN  0.653667  1.325048   \n",
       "340  1.729  0.663661  0.000000  0.336339         U    NaN  1.147471  0.746672   \n",
       "\n",
       "        Kelly  \n",
       "0    0.398686  \n",
       "1    0.063664  \n",
       "2    0.128487  \n",
       "3    0.136577  \n",
       "4    0.189518  \n",
       "..        ...  \n",
       "336  0.459559  \n",
       "337  0.019845  \n",
       "338  0.000000  \n",
       "339  0.318675  \n",
       "340  0.202292  \n",
       "\n",
       "[341 rows x 16 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6e5ccb65",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Stake'] = test['Kelly']/sum(test['Kelly'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "76a8fff5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MatchId</th>\n",
       "      <th>LeagueId</th>\n",
       "      <th>Date</th>\n",
       "      <th>HomeTeamId</th>\n",
       "      <th>AwayTeamId</th>\n",
       "      <th>Line</th>\n",
       "      <th>Over</th>\n",
       "      <th>Under</th>\n",
       "      <th>P(Under)</th>\n",
       "      <th>P(At)</th>\n",
       "      <th>P(Over)</th>\n",
       "      <th>Bet (U/O)</th>\n",
       "      <th>Stake</th>\n",
       "      <th>EV Under</th>\n",
       "      <th>EV Over</th>\n",
       "      <th>Kelly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>342</td>\n",
       "      <td>694</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1.790</td>\n",
       "      <td>1.800</td>\n",
       "      <td>0.265385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.734615</td>\n",
       "      <td>O</td>\n",
       "      <td>0.008097</td>\n",
       "      <td>0.477692</td>\n",
       "      <td>1.314962</td>\n",
       "      <td>0.398686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>1424</td>\n",
       "      <td>270</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1.920</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.531832</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468168</td>\n",
       "      <td>U</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>1.063664</td>\n",
       "      <td>0.898883</td>\n",
       "      <td>0.063664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>729</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>691</td>\n",
       "      <td>1137</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.970</td>\n",
       "      <td>1.870</td>\n",
       "      <td>0.594537</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.405463</td>\n",
       "      <td>U</td>\n",
       "      <td>0.002609</td>\n",
       "      <td>1.111784</td>\n",
       "      <td>0.798763</td>\n",
       "      <td>0.128487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>729</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>787</td>\n",
       "      <td>808</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.075</td>\n",
       "      <td>1.770</td>\n",
       "      <td>0.386553</td>\n",
       "      <td>0.117285</td>\n",
       "      <td>0.496161</td>\n",
       "      <td>O</td>\n",
       "      <td>0.002774</td>\n",
       "      <td>0.684199</td>\n",
       "      <td>1.029535</td>\n",
       "      <td>0.136577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>741</td>\n",
       "      <td>01/04/2011</td>\n",
       "      <td>784</td>\n",
       "      <td>1117</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.020</td>\n",
       "      <td>1.860</td>\n",
       "      <td>0.565861</td>\n",
       "      <td>0.110484</td>\n",
       "      <td>0.323655</td>\n",
       "      <td>U</td>\n",
       "      <td>0.003849</td>\n",
       "      <td>1.052501</td>\n",
       "      <td>0.653783</td>\n",
       "      <td>0.189518</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>337</td>\n",
       "      <td>729</td>\n",
       "      <td>22/05/2011</td>\n",
       "      <td>1211</td>\n",
       "      <td>1118</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.961</td>\n",
       "      <td>0.263292</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.736708</td>\n",
       "      <td>O</td>\n",
       "      <td>0.009333</td>\n",
       "      <td>0.516315</td>\n",
       "      <td>1.436581</td>\n",
       "      <td>0.459559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>338</td>\n",
       "      <td>795</td>\n",
       "      <td>22/05/2011</td>\n",
       "      <td>1116</td>\n",
       "      <td>876</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.920</td>\n",
       "      <td>1.909</td>\n",
       "      <td>0.467767</td>\n",
       "      <td>0.125071</td>\n",
       "      <td>0.407161</td>\n",
       "      <td>U</td>\n",
       "      <td>0.000403</td>\n",
       "      <td>0.892968</td>\n",
       "      <td>0.781750</td>\n",
       "      <td>0.019845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>339</td>\n",
       "      <td>801</td>\n",
       "      <td>22/05/2011</td>\n",
       "      <td>1223</td>\n",
       "      <td>771</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.020</td>\n",
       "      <td>1.840</td>\n",
       "      <td>0.461865</td>\n",
       "      <td>0.125104</td>\n",
       "      <td>0.413031</td>\n",
       "      <td>U</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.849831</td>\n",
       "      <td>0.834323</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>340</td>\n",
       "      <td>801</td>\n",
       "      <td>22/05/2011</td>\n",
       "      <td>238</td>\n",
       "      <td>570</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.020</td>\n",
       "      <td>1.900</td>\n",
       "      <td>0.344035</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.655965</td>\n",
       "      <td>O</td>\n",
       "      <td>0.006472</td>\n",
       "      <td>0.653667</td>\n",
       "      <td>1.325048</td>\n",
       "      <td>0.318675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>341</td>\n",
       "      <td>776</td>\n",
       "      <td>23/05/2011</td>\n",
       "      <td>412</td>\n",
       "      <td>1089</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.220</td>\n",
       "      <td>1.729</td>\n",
       "      <td>0.663661</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.336339</td>\n",
       "      <td>U</td>\n",
       "      <td>0.004108</td>\n",
       "      <td>1.147471</td>\n",
       "      <td>0.746672</td>\n",
       "      <td>0.202292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>341 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     MatchId  LeagueId        Date  HomeTeamId  AwayTeamId  Line   Over  \\\n",
       "0          1       741  01/04/2011         342         694   9.5  1.790   \n",
       "1          2       741  01/04/2011        1424         270  11.5  1.920   \n",
       "2          3       729  01/04/2011         691        1137  10.5  1.970   \n",
       "3          4       729  01/04/2011         787         808  11.0  2.075   \n",
       "4          5       741  01/04/2011         784        1117  12.0  2.020   \n",
       "..       ...       ...         ...         ...         ...   ...    ...   \n",
       "336      337       729  22/05/2011        1211        1118  10.5  1.950   \n",
       "337      338       795  22/05/2011        1116         876  10.0  1.920   \n",
       "338      339       801  22/05/2011        1223         771  10.0  2.020   \n",
       "339      340       801  22/05/2011         238         570  10.5  2.020   \n",
       "340      341       776  23/05/2011         412        1089  10.5  2.220   \n",
       "\n",
       "     Under  P(Under)     P(At)   P(Over) Bet (U/O)     Stake  EV Under  \\\n",
       "0    1.800  0.265385  0.000000  0.734615         O  0.008097  0.477692   \n",
       "1    2.000  0.531832  0.000000  0.468168         U  0.001293  1.063664   \n",
       "2    1.870  0.594537  0.000000  0.405463         U  0.002609  1.111784   \n",
       "3    1.770  0.386553  0.117285  0.496161         O  0.002774  0.684199   \n",
       "4    1.860  0.565861  0.110484  0.323655         U  0.003849  1.052501   \n",
       "..     ...       ...       ...       ...       ...       ...       ...   \n",
       "336  1.961  0.263292  0.000000  0.736708         O  0.009333  0.516315   \n",
       "337  1.909  0.467767  0.125071  0.407161         U  0.000403  0.892968   \n",
       "338  1.840  0.461865  0.125104  0.413031         U  0.000000  0.849831   \n",
       "339  1.900  0.344035  0.000000  0.655965         O  0.006472  0.653667   \n",
       "340  1.729  0.663661  0.000000  0.336339         U  0.004108  1.147471   \n",
       "\n",
       "      EV Over     Kelly  \n",
       "0    1.314962  0.398686  \n",
       "1    0.898883  0.063664  \n",
       "2    0.798763  0.128487  \n",
       "3    1.029535  0.136577  \n",
       "4    0.653783  0.189518  \n",
       "..        ...       ...  \n",
       "336  1.436581  0.459559  \n",
       "337  0.781750  0.019845  \n",
       "338  0.834323  0.000000  \n",
       "339  1.325048  0.318675  \n",
       "340  0.746672  0.202292  \n",
       "\n",
       "[341 rows x 16 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "252fe9d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop(columns=['EV Under', 'EV Over', 'Kelly']).to_csv('test_result.csv')"
   ]
  }
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