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brendansudol/epl-outcome-probs.ipynb

Created April 12, 2015 23:43
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epl-outcome-probs.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import statsmodels.api as sm\n",
"\n",
"pd.set_option('display.width', 500)\n",
"pd.set_option('display.max_columns', 30)\n",
"\n",
"idx = pd.IndexSlice"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(4940, 10)\n",
"season 2001-2002\n",
"date 2002-06-11\n",
"home_team Blackburn Rovers\n",
"home_score 3\n",
"home_scorers Andy Cole,53,g;Damien Duff,66,g;Andy Cole,81,g\n",
"away_team Fulham\n",
"away_score 0\n",
"away_scorers NULL\n",
"venue Ewood Park\n",
"game_id 1\n",
"Name: 0, dtype: object\n"
]
}
],
"source": [
"data_file = \"../data/input-cleaned/combined.tsv\"\n",
"df = pd.read_csv(data_file, sep=\"\\t\")\n",
"\n",
"df['game_id'] = range(1, len(df) + 1)\n",
"\n",
"for col in ['home_scorers', 'away_scorers']:\n",
" df[col] = df[col].fillna('NULL')\n",
"\n",
"print df.shape\n",
"print df.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" games freq\n",
"result \n",
"home 2286 0.462753\n",
"away 1374 0.278138\n",
"draw 1280 0.259109\n"
]
}
],
"source": [
"# add column denoting who won\n",
"def get_result(row):\n",
" h, a = row.home_score, row.away_score\n",
" return 'draw' if h == a else 'home' if h > a else 'away'\n",
"\n",
"df['result'] = df.apply(get_result, axis=1)\n",
"\n",
"# result counts\n",
"results = pd.DataFrame()\n",
"results['games'] = df.groupby('result').size().order(ascending=False)\n",
"results['freq'] = (results.games / sum(results.games)).astype(float)\n",
"\n",
"print results"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9880\n",
"4940\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>game_id</th>\n",
" <th>date</th>\n",
" <th>season</th>\n",
" <th>team</th>\n",
" <th>score</th>\n",
" <th>scorers</th>\n",
" <th>side</th>\n",
" <th>result</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> 2002-06-11</td>\n",
" <td> 2001-2002</td>\n",
" <td> Blackburn Rovers</td>\n",
" <td> 3</td>\n",
" <td> Andy Cole,53,g;Damien Duff,66,g;Andy Cole,81,g</td>\n",
" <td> home</td>\n",
" <td> win</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> 2002-06-11</td>\n",
" <td> 2001-2002</td>\n",
" <td> Fulham</td>\n",
" <td> 0</td>\n",
" <td> NULL</td>\n",
" <td> away</td>\n",
" <td> loss</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" game_id date season team score scorers side result\n",
"0 1 2002-06-11 2001-2002 Blackburn Rovers 3 Andy Cole,53,g;Damien Duff,66,g;Andy Cole,81,g home win\n",
"0 1 2002-06-11 2001-2002 Fulham 0 NULL away loss"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# transpose df so that there is only 1 team column per row\n",
"dfs = {}\n",
"for side in ['home', 'away']: \n",
" df_side = df.copy()\n",
" \n",
" new_names = {}\n",
" for col in ['team', 'score', 'scorers', 'g1']:\n",
" new_names['%s_%s' % (side, col)] = col\n",
" df_side.rename(columns=new_names, inplace=True)\n",
"\n",
" df_side['side'] = side\n",
" df_side['result'] = df_side['result'].apply(lambda x: \n",
" 'draw' if x == 'draw' else 'win' if x == side else 'loss')\n",
"\n",
" cols_to_keep = ['game_id', 'date', 'season', 'team', 'score', 'scorers', 'side', 'result']\n",
" df_side = df_side[cols_to_keep]\n",
" \n",
" dfs[side] = df_side\n",
"\n",
"dg = dfs['home'].append(dfs['away'])\n",
"\n",
"# check that it worked\n",
"print len(dg)\n",
"print len(dg.game_id.unique())\n",
"\n",
"dg[dg.game_id == 1]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"score 0 1 2 3 4 5 6 7 8 9\n",
"team \n",
"Arsenal 63 150 128 86 43 13 7 4 0 0\n",
"Aston Villa 151 175 105 40 16 5 2 0 0 0\n",
"Birmingham 60 49 30 10 2 1 0 0 0 0\n",
"Birmingham City 32 52 23 5 2 0 0 0 0 0\n",
"Blackburn Rovers 126 143 94 39 13 2 0 1 0 0\n",
"Blackpool 8 11 15 2 2 0 0 0 0 0\n",
"Bolton Wanderers 132 147 92 27 17 3 0 0 0 0\n",
"Burnley 13 15 5 3 2 0 0 0 0 0\n",
"Cardiff City 19 10 5 4 0 0 0 0 0 0\n",
"Charlton Athletic 77 81 47 16 7 0 0 0 0 0\n"
]
}
],
"source": [
"# goals scored in a game by team\n",
"print dg.groupby(['team', 'score']).size().unstack().fillna(0).head(10)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13001\n",
" game_id season team side result tot_goals player time type\n",
"0 1 2001-2002 Blackburn Rovers home win 3 Andy Cole 53 g\n",
"1 1 2001-2002 Blackburn Rovers home win 3 Damien Duff 66 g\n",
"2 1 2001-2002 Blackburn Rovers home win 3 Andy Cole 81 g\n",
"3 2 2001-2002 Leicester City home win 2 Paul Dickov 60 g\n",
"4 2 2001-2002 Leicester City home win 2 Matthew Piper 71 g\n"
]
}
],
"source": [
"# new df around indiv goals\n",
"data = []\n",
"for i, r in dg.iterrows():\n",
" if r.scorers != 'NULL':\n",
" goals = r.scorers.split(';')\n",
" for goal in goals:\n",
" goal_info = goal.split(',')\n",
" other_info = [r.game_id, r.season, r.team, r.side, r.result, r.score]\n",
" combined = other_info + goal_info\n",
" data.append(combined)\n",
"\n",
"cols = ['game_id', 'season', 'team', 'side', 'result', 'tot_goals', 'player', 'time', 'type']\n",
"goals = pd.DataFrame(data, columns=cols)\n",
"\n",
"goals['time'] = goals['time'].astype(int)\n",
"\n",
"print len(goals)\n",
"print goals.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 13001.000000\n",
"mean 50.486501\n",
"std 26.406922\n",
"min 0.000000\n",
"25% 29.000000\n",
"50% 51.000000\n",
"75% 74.000000\n",
"max 95.000000\n",
"Name: time, dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"goals.time.describe()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10fdc53d0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10fec7b10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# goals scored by 15 min time frames\n",
"# 30-45 + 75-90 includes stoppage time before half and end of game\n",
"def time_norm(t, bin=5):\n",
" t_start = max(min(t, 90) - 1, 0) / bin * bin\n",
" return '%d-%d' % (t_start, t_start + bin)\n",
"\n",
"goals['time_norm'] = goals['time'].apply(lambda x: time_norm(x, 15))\n",
"\n",
"goals.groupby('time_norm').size().plot(kind='barh')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"side away home\n",
"game_id time \n",
"1 53 0 1\n",
" 66 0 2\n",
" 81 0 3\n",
"2 54 1 0\n",
" 60 1 1\n",
" 71 1 2\n",
"3 13 0 1\n",
" 35 0 2\n",
" 46 0 3\n",
" 57 0 4\n"
]
}
],
"source": [
"# transpose goals df so that index is game and time\n",
"goals = goals.sort(['game_id', 'time'], ascending=[1, 1])\n",
"\n",
"# 1/0 for home/away goals at each score times\n",
"score_by_time = goals.groupby(['game_id', 'time', 'side']).size().unstack().fillna(0)\n",
"\n",
"# cumulative goals for home/away by game\n",
"score_by_time = score_by_time.groupby(level=[0]).cumsum()\n",
"\n",
"print score_by_time.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"side away home\n",
"game_id time \n",
"10 4 0 1\n",
" 20 1 1\n",
" 31 2 1\n",
" 33 2 2\n",
" 72 2 3\n",
" 83 2 4\n",
" 89 3 4\n"
]
}
],
"source": [
"# check a specific game to make sure things look good\n",
"print score_by_time.loc[idx[[10],:], :]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" home away diff\n",
"game_id time \n",
"10 85 4 2 2\n",
" 86 4 2 2\n",
" 87 4 2 2\n",
" 88 4 2 2\n",
" 89 4 3 1\n",
" 90 4 3 1\n",
" 91 4 3 1\n",
" 93 4 3 1\n",
" 94 4 3 1\n",
" 95 4 3 1\n"
]
}
],
"source": [
"# densify so that all times are in index for each game\n",
"home = score_by_time.unstack()['home'].fillna(method='ffill', axis=1).fillna(0).stack()\n",
"away = score_by_time.unstack()['away'].fillna(method='ffill', axis=1).fillna(0).stack()\n",
"\n",
"score_by_time_dense = pd.DataFrame()\n",
"\n",
"score_by_time_dense['home'] = home\n",
"score_by_time_dense['away'] = away\n",
"\n",
"# add goal diff column\n",
"score_by_time_dense['diff'] = score_by_time_dense['home'] - score_by_time_dense['away']\n",
"\n",
"# check that it worked\n",
"print score_by_time_dense.loc[idx[[10], :], :].tail(10)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"result away draw home tot_games\n",
"time diff \n",
"0 -3 NaN NaN NaN 0\n",
" -2 NaN NaN NaN 0\n",
" -1 0.000000 0.000000 1.000000 2\n",
" 0 0.303125 0.192188 0.504687 4480\n",
" 1 1.000000 0.000000 0.000000 1\n",
" 2 NaN NaN NaN 0\n",
" 3 NaN NaN NaN 0\n",
"1 -3 NaN NaN NaN 0\n",
" -2 NaN NaN NaN 0\n",
" -1 0.500000 0.250000 0.250000 28\n",
" 0 0.303030 0.191542 0.505427 4422\n",
" 1 0.151515 0.212121 0.636364 33\n",
" 2 NaN NaN NaN 0\n",
" 3 NaN NaN NaN 0\n",
"2 -3 NaN NaN NaN 0\n"
]
}
],
"source": [
"# add final result to this df\n",
"score_by_time_dense2 = pd.merge(score_by_time_dense.reset_index(), df[['game_id', 'result']], on='game_id')\n",
"\n",
"# tally results by time and goal diff\n",
"outcomes = score_by_time_dense2.groupby(['time', 'diff', 'result']).size()\n",
"outcomes = outcomes.unstack().unstack().fillna(0).stack().loc[idx[:, -3:3], :]\n",
"\n",
"# turn counts in percentages\n",
"outcomes[\"tot_games\"] = outcomes.sum(axis=1)\n",
"outcomes.loc[:, \"away\":\"home\"] = outcomes.loc[:, \"away\":\"home\"].div(outcomes[\"tot_games\"], axis=0)\n",
"\n",
"print outcomes.head(15)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"text/plain": [
"diff -3 -2 -1 0 1 2 3 \n",
"result away draw home tot_games away draw home tot_games away draw home tot_games away draw home tot_games away draw home tot_games away draw home tot_games away draw home tot_games\n",
"time \n",
"0 NaN NaN NaN 0 NaN NaN NaN 0 0.000000 0.000000 1.000000 2 0.303125 0.192188 0.504687 4480 1.000000 0.000000 0.000000 1 NaN NaN NaN 0 NaN NaN NaN 0\n",
"1 NaN NaN NaN 0 NaN NaN NaN 0 0.500000 0.250000 0.250000 28 0.303030 0.191542 0.505427 4422 0.151515 0.212121 0.636364 33 NaN NaN NaN 0 NaN NaN NaN 0\n",
"2 NaN NaN NaN 0 NaN NaN NaN 0 0.538462 0.200000 0.261538 65 0.303023 0.192707 0.504270 4333 0.129412 0.152941 0.717647 85 NaN NaN NaN 0 NaN NaN NaN 0\n",
"3 NaN NaN NaN 0 0.5 0.5 0 2 0.486957 0.165217 0.347826 115 0.304595 0.193273 0.502132 4222 0.111888 0.174825 0.713287 143 0 0.000000 1.000000 1 NaN NaN NaN 0\n",
"4 NaN NaN NaN 0 0.5 0.5 0 2 0.469880 0.180723 0.349398 166 0.305151 0.194121 0.500729 4116 0.124352 0.150259 0.725389 193 0 0.333333 0.666667 6 NaN NaN NaN 0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pivot data so index is only time\n",
"outcomes2 = outcomes.stack().unstack(level=1).unstack(level=1)\n",
"\n",
"outcomes2.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1111c8490>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1111ccf10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"outcomes2[2].ix[:, 'away':'home'].plot()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"result away away_smooth draw draw_smooth home home_smooth tot_games\n",
"time \n",
"0 0.303125 0.303486 0.192188 0.192624 0.504687 0.503849 4480\n",
"1 0.303030 0.303999 0.191542 0.192699 0.505427 0.503266 4422\n",
"2 0.303023 0.304492 0.192707 0.192767 0.504270 0.502711 4333\n",
"3 0.304595 0.304962 0.193273 0.192828 0.502132 0.502185 4222\n",
"4 0.305151 0.305411 0.194121 0.192886 0.500729 0.501684 4116\n"
]
}
],
"source": [
"# add smoothed series for each outcome (using local regression) \n",
"outcomes3 = outcomes2.copy()\n",
"\n",
"for col in outcomes3.columns:\n",
" if col[1] in ['away', 'draw', 'home']: \n",
" s = outcomes3[col]\n",
" x, y = s.index.values, s.values\n",
"\n",
" y_smooth = sm.nonparametric.lowess(y, x, frac=0.2, return_sorted=False)\n",
" outcomes3[col[0], '%s_smooth' % col[1]] = y_smooth\n",
"\n",
"outcomes3 = outcomes3.sort_index(axis=1)\n",
"\n",
"print outcomes3[0].head()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x117a95ed0>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x117a98210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# check that things seem reasonable\n",
"outcomes3[0].ix[:, 'away':'home_smooth'].plot()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# output final dataset\n",
"outcomes3.to_csv('../ipy-data/outcome-probs.csv', index=True, tupleize_cols=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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