Calculate feature weights such as 2 Point percentage, steals and others to determine win probability. It uses the sklearn linear regression to estimate the feature weights.
- Install python
- Install requirements
- pip install -r requirements.txt
- Download the repo here
- Command line
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Generate ranking after group phase but before quarter-final.
python basketball_perf.py ./data/tokyo2021_olympics_basketball_team_stats.csv -
Generate ranking after quarter-final but before semi-final.
python basketball_perf.py ./data/tokyo2021_olympics_basketball_team_stats_2.csv -
Generate ranking after semi-final but before final.
python basketball_perf.py ./data/tokyo2021_olympics_basketball_team_stats_3.csv -
Generate ranking after the final.
python basketball_perf.py ./data/tokyo2021_olympics_basketball_team_stats_4.csv
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There is a selenium python script crawler that can be found under tool folder, that can get data from FIBA. It saves data to csv file similar to files in data folder but without average records.
The data file tokyo2021_olympics_basketball_team_stats.csv is in data folder. There are team records after 3 games each team in the preliminary stage.
PHASE CAT NAME OPP PTS FGM FGA FG% 2PM 2PA 2P% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST FO TO STL BLK +/- EFF RES
0 Group Single Iran Czech Republic 78 31 62 0.50 25 43 0.58 6 19 0.32 10 17 0.59 7 26 33 20 18 21 9 1 -6 79 0.0
1 Group Single Czech Republic Iran 84 33 74 0.45 27 44 0.61 6 30 0.20 12 18 0.67 16 27 43 29 17 15 12 2 6 105 1.0
2 Group Single Germany Italy 82 30 64 0.47 15 32 0.47 15 32 0.47 7 9 0.78 9 25 34 15 16 14 6 2 -10 84 0.0
3 Group Single Italy Germany 92 34 68 0.50 19 37 0.51 15 31 0.48 9 12 0.75 9 26 35 20 15 9 11 4 10 112 1.0
4 Group Single Australia Nigeria 84 28 71 0.39 17 47 0.36 11 24 0.46 17 19 0.90 13 31 44 21 20 21 12 1 17 90 1.0
...
X = df[['2P%', '3P%', 'FT%', 'AST', 'OREB', 'DREB', 'TO', 'STL']]You can change the features to include the Fouls and EFF using
X = df[['2P%', '3P%', 'FT%', 'AST', 'OREB', 'DREB', 'TO', 'STL', 'FO', 'EFF']]y = df['RES']2PM : 2 Points Made
2PA : 2 Points Attempt
2P% : 2 Points percentage
3P% : 3 Points percentage
FT% : Free Throw percentage
AST : num assists
REB : num rebounds
TO : num turnovers
STL : num steals
2P%_we : 2 Point percentage weight
Scatter plot from all data points in the the data file. You need to install matplotlib with the command pip install matplotlib.
# Plot win probability vs 2 point percentage.
ax = df.plot.scatter(x='RES', y='2P%', alpha=0.5, title='2 Point Percentage on Win Probability')
ax.set_xlabel("Win Probability")
ax.set_ylabel("2 Point Percentage")
ax.figure.savefig('2p-winprob.pdf')
ax = df.plot.scatter(x='RES', y='TO', alpha=0.5, title='Turnovers on Win Probability')
ax.set_xlabel("Win Probability")
ax.set_ylabel("Turnovers")
ax.figure.savefig('to-winprob.pdf')
2P%_we: 109.95%
3P%_we: 54.63%
FT%_we: 55.01%
AST_we: 1.55%
OREB_we: 1.36%
DREB_we: 1.96%
TO_we: 0.03%
STL_we: 0.81%
intercept: -1.743135327438301
winprob = 2P%_we*2p + 3P%_we*3p + FT%_we*ft + AST_we*as + OREB_we*oreb + DREB_we*dreb + TO_we*to + STL_we*stl + intercept
Model win probability ranking based on team average stats after preliminaries but before quarter-finals.
Slovenia team average in 3 games on the features:
slovenia_ave = [0.613,0.36,0.71,23,17,38,12,5]
p2% = 0.613
p3% = 0.36
ft% = 0.71
ast = 23
oreb = 17
dreb = 38
to = 12
st = 5
winprob = 1.1*0.613 + 0.546*0.36 + 0.55*0.71 + 0.016*23 + 0.0136*17 + 0.0196*38 + 0.0003*12 + 0.0081*5 - 1.74
Win Probability Ranking Summary
team winprob
0 Slovenia 0.895120
1 USA 0.785697
2 France 0.686635
3 Australia 0.628305
4 Spain 0.557405
5 Argentina 0.525046
6 Germany 0.490459
7 Italy 0.458668
match: Slovenia - Germany, score: 94 - 70
match: Spain - USA, score: 81 - 95
match: Italy - France, score: 75 - 84
match: Australia - Argentina, score: 97 - 59
The regression uses the data file tokyo2021_olympics_basketball_team_stats_2.csv.
python basketball_perf.py ./data/tokyo2021_olympics_basketball_team_stats_2.csv
2P%_we: 108.01%
3P%_we: 56.97%
FT%_we: 54.93%
AST_we: 1.65%
OREB_we: 1.56%
DREB_we: 2.02%
TO_we: -0.27%
STL_we: 1.00%
intercept: -1.7868833598225142
Win Probability Ranking Summary
team winprob
0 Slovenia 0.874822
1 USA 0.735948
2 France 0.720355
3 Australia 0.694822
4 Spain 0.497407
5 Italy 0.416237
6 Argentina 0.415951
7 Germany 0.396465
match: USA - Australia, score: 97 - 78
match: France - Slovenia, score: 90 - 89
The regression uses the data file tokyo2021_olympics_basketball_team_stats_3.csv.
python basketball_perf.py ./data/tokyo2021_olympics_basketball_team_stats_3.csv
2P%_we: 99.18%
3P%_we: 73.70%
FT%_we: 64.72%
AST_we: 1.29%
OREB_we: 1.56%
DREB_we: 2.08%
TO_we: -0.29%
STL_we: 1.32%
intercept: -1.8408441099337223
Win Probability Ranking Summary
team winprob
0 Slovenia 0.781881
1 USA 0.745022
2 France 0.670351
3 Australia 0.609665
4 Spain 0.495007
5 Italy 0.433284
6 Germany 0.418237
7 Argentina 0.417896
Gold/Silver:
match: France - USA, score: 82 - 87
Bronze:
match: Slovenia - Australia, score: 93 - 107
python basketball_perf.py ./data/tokyo2021_olympics_basketball_team_stats_4.csv
The script uses sklearn.linear_model.Ridge model.
reg_features = ['2P%', '3P%', 'FT%', 'OREB', 'DREB', 'AST', 'FO', 'TO', 'STL', 'BLK']
2P%_we: 105.94%
3P%_we: 75.38%
FT%_we: 50.95%
OREB_we: 1.56%
DREB_we: 1.93%
AST_we: 1.16%
FO_we: -0.06%
TO_we: -0.57%
STL_we: 1.49%
BLK_we: 0.54%
intercept: -1.6896029023867132
This ranking is based on average stats per team applying the feature weight results.
team winprob
0 Slovenia 0.764157
1 USA 0.696443
2 France 0.630511
3 Australia 0.618170
4 Spain 0.496014
5 Italy 0.445323
6 Argentina 0.396582
7 Germany 0.396354
8 Czech Republic 0.384329
9 Nigeria 0.293762
10 Japan 0.288418
11 Iran 0.186147