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doc/examples/MatchHistory - Home advantage.ipynb.
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[2]:
import soccerdata as sd
[3]:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set_context("notebook")
sns.set_style("whitegrid")
Home team advantage in the Italian Serie A¶
We all know sports teams have an advantage when playing at home. Here’s a look at home team advantage for 5 years of the Serie A.
[4]:
seriea_hist = sd.MatchHistory('ITA-Serie A', range(2018, 2023))
games = seriea_hist.read_games()
games.sample(5)
/cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccerdata/soccerdata/_common.py:471: UserWarning: Season id "2021" is ambiguous: interpreting as "20-21"
warnings.warn(msg, stacklevel=1)
[4]:
| date | home_team | away_team | FTHG | FTAG | FTR | HTHG | HTAG | HTR | HS | ... | AvgC<2.5 | AHCh | B365CAHH | B365CAHA | PCAHH | PCAHA | MaxCAHH | MaxCAHA | AvgCAHH | AvgCAHA | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| league | season | game | |||||||||||||||||||||
| ITA-Serie A | 1819 | 2018-12-22 Lazio-Cagliari | 2018-12-22 12:00:00 | Lazio | Cagliari | 3 | 1 | H | 2 | 0 | H | 15 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2021 | 2020-12-20 Torino-Bologna | 2020-12-20 11:30:00 | Torino | Bologna | 1 | 1 | D | 0 | 0 | D | 13 | ... | 2.25 | 0.0 | 1.91 | 2.02 | 1.92 | 2.0 | 1.94 | 2.11 | 1.86 | 2.00 | |
| 1819 | 2019-03-03 Udinese-Bologna | 2019-03-03 12:00:00 | Udinese | Bologna | 2 | 1 | H | 1 | 1 | D | 11 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | |
| 2021 | 2021-02-20 Genoa-Verona | 2021-02-20 17:00:00 | Genoa | Verona | 2 | 2 | D | 0 | 1 | A | 12 | ... | 1.64 | 0.0 | 1.95 | 1.98 | 1.93 | 2.0 | 2.07 | 2.00 | 1.93 | 1.93 | |
| 1819 | 2019-03-10 Fiorentina-Lazio | 2019-03-10 12:00:00 | Fiorentina | Lazio | 1 | 1 | D | 0 | 1 | A | 7 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 121 columns
[5]:
def home_away_results(games: pd.DataFrame):
"""Returns aggregated home/away results per team"""
res = (
pd.melt(games.reset_index(),
id_vars=['date', 'FTR'],
value_name='team', var_name='is_home',
value_vars=['home_team', 'away_team']))
res.is_home = res.is_home.replace(['home_team', 'away_team'], ['Home', 'Away'])
res['win'] = res['lose'] = res['draw'] = 0
res.loc[(res['is_home'] == 'Home') & (res['FTR'] == 'H'), 'win'] = 1
res.loc[(res['is_home'] == 'Away') & (res['FTR'] == 'A'), 'win'] = 1
res.loc[(res['is_home'] == 'Home') & (res['FTR'] == 'A'), 'lose'] = 1
res.loc[(res['is_home'] == 'Away') & (res['FTR'] == 'H'), 'lose'] = 1
res.loc[res['FTR'] == 'D', 'draw'] = 1
g = res.groupby(['team', 'is_home'])
w = (g.win.agg(['sum', 'mean'])
.rename(columns={'sum': 'n_win', 'mean': 'win_pct'}))
l = (g.lose.agg(['sum', 'mean'])
.rename(columns={'sum': 'n_lose', 'mean': 'lose_pct'}))
d = (g.draw.agg(['sum', 'mean'])
.rename(columns={'sum': 'n_draw', 'mean': 'draw_pct'}))
res = pd.concat([w, l, d], axis=1)
return res
[6]:
results = home_away_results(games)
results.head(6)
[6]:
| n_win | win_pct | n_lose | lose_pct | n_draw | draw_pct | ||
|---|---|---|---|---|---|---|---|
| team | is_home | ||||||
| Atalanta | Away | 52 | 0.547368 | 16 | 0.168421 | 27 | 0.284211 |
| Home | 56 | 0.589474 | 23 | 0.242105 | 16 | 0.168421 | |
| Benevento | Away | 10 | 0.263158 | 18 | 0.473684 | 10 | 0.263158 |
| Home | 4 | 0.105263 | 20 | 0.526316 | 14 | 0.368421 | |
| Bologna | Away | 22 | 0.231579 | 48 | 0.505263 | 25 | 0.263158 |
| Home | 35 | 0.368421 | 29 | 0.305263 | 31 | 0.326316 |
The overall picture shows most teams have a clear advantage at home:
[7]:
g = sns.FacetGrid(results.reset_index(), hue='team', palette='Set2', height=6, aspect=.5)
g.map(sns.pointplot, 'is_home', 'win_pct', order=["Away", "Home"])
g.set_axis_labels('', 'win %');
/cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccerdata/.venv/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight
self._figure.tight_layout(*args, **kwargs)
But there are a few exceptions.
[8]:
g = sns.FacetGrid(results.reset_index(), col='team', col_wrap=5)
g.map(sns.pointplot, 'is_home', 'win_pct', order=["Away", "Home"])
g.set_axis_labels('', 'win %');
/cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccerdata/.venv/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight
self._figure.tight_layout(*args, **kwargs)
