Library from http://pena.lt/y/blog for modelling and working with football (soccer) data
Project description
Penalty Blog
The penaltyblog package contains code from http://pena.lt/y/blog for working with football (soccer) data.
Requirements
- python >=3.6
- numpy >=1.19.2
- pandas >=1.1.3
- scipy >=1.5.0
Installation
pip install penaltyblog
Example
There are examples of all the functions available in the Examples section.
Download Data from football-data.co.uk
penaltyblog contains some helper functions for downloading data from football-data.co.uk.
List the countries available
import penaltyblog as pb
pd.footballdata.list_countries()
['belgium',
'england',
'france',
'germany',
'greece',
'italy',
'portugal',
'scotland',
'spain',
'turkey']
Fetch the data
The first parameter is the country of interest, the second is the starting year of the season and the third paramater is the level of the division of interest, where 0 is the highest division (e.g. England's Premier League), 1 is the second highest (e.g. England's Championship) etc.
df = pb.footballdata.fetch_data("england", 2018, 0)
df[["Date", "HomeTeam", "AwayTeam", "FTHG", "FTAG"]].head()
| Date | HomeTeam | AwayTeam | FTHG | FTAG | |
|---|---|---|---|---|---|
| 0 | 2018-08-10 00:00:00 | Man United | Leicester | 2 | 1 |
| 1 | 2018-08-11 00:00:00 | Bournemouth | Cardiff | 2 | 0 |
| 2 | 2018-08-11 00:00:00 | Fulham | Crystal Palace | 0 | 2 |
| 3 | 2018-08-11 00:00:00 | Huddersfield | Chelsea | 0 | 3 |
| 4 | 2018-08-11 00:00:00 | Newcastle | Tottenham | 1 | 2 |
Predicting Goals
penaltyblog contains models designed for predicting the number of goals scored in football (soccer) games. Although aimed at football (soccer), they may also be useful for other sports, such as hockey.
The Basic Poisson Model
Let's start off by downloading some example scores from the awesome football-data website.
import penaltyblog as pb
df = pb.footballdata.fetch_data("England", 2018, 0)
df[["Date", "HomeTeam", "AwayTeam", "FTHG", "FTAG"]].head()
| Date | HomeTeam | AwayTeam | FTHG | FTAG | |
|---|---|---|---|---|---|
| 0 | 2018-08-10 00:00:00 | Man United | Leicester | 2 | 1 |
| 1 | 2018-08-11 00:00:00 | Bournemouth | Cardiff | 2 | 0 |
| 2 | 2018-08-11 00:00:00 | Fulham | Crystal Palace | 0 | 2 |
| 3 | 2018-08-11 00:00:00 | Huddersfield | Chelsea | 0 | 3 |
| 4 | 2018-08-11 00:00:00 | Newcastle | Tottenham | 1 | 2 |
Next, we create a basic Poisson model and fit it to the data.
pois = pb.poisson.PoissonGoalsModel(
df["FTHG"], df["FTAG"], df["HomeTeam"], df["AwayTeam"])
pois.fit()
Let's take a look at the fitted parameters.
pois
Module: Penaltyblog
Model: Poisson
Number of parameters: 41
Log Likelihood: -1065.077
AIC: 2212.154
Team Attack Defence
------------------------------------------------------------
Arsenal 1.362 -0.856
Bournemouth 1.115 -0.555
Brighton 0.634 -0.731
Burnley 0.894 -0.595
Cardiff 0.614 -0.592
Chelsea 1.202 -1.135
Crystal Palace 1.004 -0.839
Everton 1.055 -0.978
Fulham 0.626 -0.431
Huddersfield 0.184 -0.507
Leicester 1.0 -0.939
Liverpool 1.532 -1.683
Man City 1.598 -1.633
Man United 1.249 -0.807
Newcastle 0.805 -0.948
Southampton 0.891 -0.641
Tottenham 1.264 -1.131
Watford 1.03 -0.731
West Ham 1.026 -0.801
Wolves 0.916 -0.985
------------------------------------------------------------
Home Advantage: 0.225
The Dixon and Coles Adjustment
The basic Poisson model struggles somewhat with the probabilities for low scoring games. Dixon and Coles (1997) added in an adjustment factor (rho) that modifies the probabilities for 0-0, 1-0 and 0-1 scorelines to account for this.
dc = pb.poisson.DixonColesGoalModel(
df["FTHG"], df["FTAG"], df["HomeTeam"], df["AwayTeam"])
dc.fit()
dc
Module: Penaltyblog
Model: Dixon and Coles
Number of parameters: 42
Log Likelihood: -1064.943
AIC: 2213.886
Team Attack Defence
------------------------------------------------------------
Arsenal 1.36 -0.858
Bournemouth 1.115 -0.555
Brighton 0.632 -0.733
Burnley 0.897 -0.592
Cardiff 0.615 -0.591
Chelsea 1.205 -1.13
Crystal Palace 1.007 -0.837
Everton 1.054 -0.977
Fulham 0.625 -0.433
Huddersfield 0.18 -0.507
Leicester 0.996 -0.94
Liverpool 1.534 -1.679
Man City 1.599 -1.638
Man United 1.251 -0.807
Newcastle 0.806 -0.946
Southampton 0.897 -0.636
Tottenham 1.259 -1.137
Watford 1.031 -0.729
West Ham 1.023 -0.803
Wolves 0.914 -0.988
------------------------------------------------------------
Home Advantage: 0.225
Rho: -0.041
The Rue and Salvesen Adjustment
Rue and Salvesen (1999) added in an additional psycological effect factor (gamma) where Team A will under-estimate Team B if Team A is stronger than team B. They also truncate scorelines to a maximum of five goals, e.g. a score of 7-3 becomes 5-3, stating that any goals above 5 are non-informative.
rs = pb.poisson.RueSalvesenGoalModel(
df["FTHG"], df["FTAG"], df["HomeTeam"], df["AwayTeam"])
rs.fit()
rs
Module: Penaltyblog
Model: Rue Salvesen
Number of parameters: 43
Log Likelihood: -1061.167
AIC: 2208.334
Team Attack Defence
------------------------------------------------------------
Arsenal 1.496 -0.988
Bournemouth 1.27 -0.705
Brighton 0.559 -0.655
Burnley 0.964 -0.654
Cardiff 0.585 -0.556
Chelsea 1.183 -1.129
Crystal Palace 1.027 -0.852
Everton 1.034 -0.974
Fulham 0.653 -0.455
Huddersfield 0.023 -0.359
Leicester 0.978 -0.916
Liverpool 1.445 -1.585
Man City 1.503 -1.571
Man United 1.367 -0.917
Newcastle 0.72 -0.854
Southampton 0.942 -0.693
Tottenham 1.243 -1.131
Watford 1.097 -0.79
West Ham 1.062 -0.836
Wolves 0.85 -0.919
------------------------------------------------------------
Home Advantage: 0.222
Rho: -0.04
Gamma: 0.692
Making Predictions
To make a prediction using any of the above models, just pass the name of the home and away teams to the predict function. This returns the FootballProbabilityGrid class that can convert the output from the model into probabilities for various betting markets.
probs = rs.predict("Watford", "Wolves")
probs
Module: Penaltyblog
Class: FootballProbabilityGrid
Home Goal Expectation: 1.3094663275662697
Away Goal Expectation: 1.2096084394688094
Home Win: 0.3843462702759874
Draw: 0.2787256663458056
Away Win: 0.3369280633610962
Home / Draw / Away
# also known as 1x2
probs.home_draw_away
[0.3843462702759874, 0.2787256663458056, 0.3369280633610962]
Total Goals
probs.total_goals("over", 2.5)
0.4610704441088047
probs.total_goals("under", 2.5)
0.5389295558740843
Asian Handicaps
probs.asian_handicap("home", 1.5)
0.17531437781532913
probs.asian_handicap("away", -1.5)
0.8246856221675609
Model Parameters
You can access the model's parameters via the get_params function.
from pprint import pprint
params = my_model.get_params()
pprint(params)
{'attack_Arsenal': 1.4960574633781003,
'attack_Bournemouth': 1.2701540413261327,
'attack_Brighton': 0.559186251363228,
'attack_Burnley': 0.9644520899122194,
'attack_Cardiff': 0.5847648397569006,
'attack_Chelsea': 1.1828466188120765,
'attack_Crystal Palace': 1.0273361069287597,
'attack_Everton': 1.0335248035400801,
'attack_Fulham': 0.6531864264818924,
'attack_Huddersfield': 0.023109559960240708,
'attack_Leicester': 0.977933119588144,
'attack_Liverpool': 1.4451581320799645,
'attack_Man City': 1.5025454369883477,
'attack_Man United': 1.366845541477835,
'attack_Newcastle': 0.720009733703693,
'attack_Southampton': 0.9416226570416543,
'attack_Tottenham': 1.2427047093744437,
'attack_Watford': 1.096790079793436,
'attack_West Ham': 1.06163359275858,
'attack_Wolves': 0.8501387957342722,
'defence_Arsenal': -0.9879049844176601,
'defence_Bournemouth': -0.704968272653022,
'defence_Brighton': -0.6545658993274335,
'defence_Burnley': -0.6541299575160815,
'defence_Cardiff': -0.5555542344325824,
'defence_Chelsea': -1.128898069212659,
'defence_Crystal Palace': -0.8518829374985971,
'defence_Everton': -0.9742632965054263,
'defence_Fulham': -0.45545035895833286,
'defence_Huddersfield': -0.3586836043107179,
'defence_Leicester': -0.915653821531362,
'defence_Liverpool': -1.5850200706445228,
'defence_Man City': -1.5713140731733608,
'defence_Man United': -0.9165982110339421,
'defence_Newcastle': -0.8538889602642802,
'defence_Southampton': -0.6925502345992922,
'defence_Tottenham': -1.1307038809506598,
'defence_Watford': -0.7898505955782175,
'defence_West Ham': -0.8356435683761823,
'defence_Wolves': -0.9188112323803922,
'home_advantage': 0.22164932659641978,
'rho': -0.04033232667301132,
'rue_salvesen': 0.6922490800541602}
Ratings
Massey Ratings
Calculates the overall Massey ratings, plus Massey attack and defence ratings too.
import penaltyblog as pb
df = pb.footballdata.fetch_data("england", 2020, 0)
pb.ratings.massey(df["FTHG"], df["FTAG"], df["HomeTeam"], df["AwayTeam"])
| team | rating | offence | defence | |
|---|---|---|---|---|
| 0 | Man City | 1.275 | 1.48618 | -0.211184 |
| 1 | Man United | 0.725 | 1.23896 | -0.513962 |
| 2 | Liverpool | 0.65 | 1.10424 | -0.45424 |
| 3 | Tottenham | 0.575 | 1.10841 | -0.533406 |
| 4 | Chelsea | 0.55 | 0.832018 | -0.282018 |
| 5 | Leicester | 0.45 | 1.11535 | -0.665351 |
| 6 | Arsenal | 0.4 | 0.757018 | -0.357018 |
| 7 | West Ham | 0.375 | 0.952851 | -0.577851 |
| 8 | Aston Villa | 0.225 | 0.76674 | -0.54174 |
| 9 | Leeds | 0.2 | 0.962573 | -0.762573 |
| 10 | Everton | -0.025 | 0.558406 | -0.583406 |
| 11 | Brighton | -0.15 | 0.370906 | -0.520906 |
| 12 | Wolves | -0.4 | 0.273684 | -0.673684 |
| 13 | Newcastle | -0.4 | 0.551462 | -0.951462 |
| 14 | Southampton | -0.525 | 0.586184 | -1.11118 |
| 15 | Burnley | -0.55 | 0.198684 | -0.748684 |
| 16 | Crystal Palace | -0.625 | 0.425073 | -1.05007 |
| 17 | Fulham | -0.65 | 0.0375731 | -0.687573 |
| 18 | West Brom | -1.025 | 0.280629 | -1.30563 |
| 19 | Sheffield United | -1.075 | -0.13326 | -0.94174 |
Colley Ratings
Calculates Colley ratings. Since Colley ratings don't explicitly define how to handle tied results, you can set whether to include them and how much weighting they should receive compared to a win.
import penaltyblog as pb
df = pb.footballdata.fetch_data("england", 2020, 0)
pb.ratings.colley(df["FTHG"], df["FTAG"], df["HomeTeam"], df["AwayTeam"], include_draws=True, draw_weight=1/3)
| team | rating | |
|---|---|---|
| 0 | Man City | 1.42857 |
| 1 | Man United | 1.38095 |
| 2 | Liverpool | 1.3254 |
| 3 | Chelsea | 1.31746 |
| 4 | West Ham | 1.28571 |
| 5 | Leicester | 1.27778 |
| 6 | Tottenham | 1.2619 |
| 7 | Arsenal | 1.24603 |
| 8 | Everton | 1.2381 |
| 9 | Leeds | 1.21429 |
| 10 | Aston Villa | 1.19841 |
| 11 | Brighton | 1.14286 |
| 12 | Newcastle | 1.13492 |
| 13 | Wolves | 1.13492 |
| 14 | Crystal Palace | 1.11905 |
| 15 | Southampton | 1.10317 |
| 16 | Burnley | 1.0873 |
| 17 | Fulham | 1.03175 |
| 18 | West Brom | 1 |
| 19 | Sheffield United | 0.904762 |
import penaltyblog as pb
df = pb.footballdata.fetch_data("england", 2020, 0)
pb.ratings.colley(df["FTHG"], df["FTAG"], df["HomeTeam"], df["AwayTeam"], include_draws=False)
| team | rating | |
|---|---|---|
| 0 | Man City | 0.75 |
| 1 | Man United | 0.678571 |
| 2 | Liverpool | 0.630952 |
| 3 | Chelsea | 0.619048 |
| 4 | Leicester | 0.595238 |
| 5 | West Ham | 0.595238 |
| 6 | Tottenham | 0.571429 |
| 7 | Arsenal | 0.559524 |
| 8 | Everton | 0.547619 |
| 9 | Leeds | 0.535714 |
| 10 | Aston Villa | 0.511905 |
| 11 | Newcastle | 0.440476 |
| 12 | Wolves | 0.440476 |
| 13 | Brighton | 0.428571 |
| 14 | Crystal Palace | 0.428571 |
| 15 | Southampton | 0.416667 |
| 16 | Burnley | 0.392857 |
| 17 | Fulham | 0.321429 |
| 18 | West Brom | 0.297619 |
| 19 | Sheffield United | 0.238095 |
Implied Probabilities
Removes the overround and gets the implied probabilities from odds via a variety of methods
Multiplicative
Normalizes the probabilites so they sum to 1.0 by dividing the inverse of the odds by the sum of the inverse of the odds
import penaltyblog as pb
odds = [2.7, 2.3, 4.4]
pb.implied.multiplicative(odds)
{'implied_probabilities': array([0.35873804, 0.42112726, 0.2201347 ]),
'margin': 0.03242570633874986,
'method': 'multiplicative'}
Additive
Normalizes the probabilites so they sum to 1.0 by removing an equal amount from each
import penaltyblog as pb
odds = [2.7, 2.3, 4.4]
pb.implied.additive(odds)
{'implied_probabilities': array([0.3595618 , 0.42397404, 0.21646416]),
'margin': 0.03242570633874986,
'method': 'additive'}
Power
Solves for the power coefficient that normalizes the inverse of the odds to sum to 1.0
import penaltyblog as pb
odds = [2.7, 2.3, 4.4]
pb.implied.power(odds)
{'implied_probabilities': array([0.3591711 , 0.42373075, 0.21709815]),
'margin': 0.03242570633874986,
'method': 'power',
'k': 1.0309132393169356}
Shin
Uses the Shin (1992, 1993) method to calculate the implied probabilities
import penaltyblog as pb
odds = [2.7, 2.3, 4.4]
pb.implied.shin(odds)
{'implied_probabilities': array([0.35934392, 0.42324385, 0.21741223]),
'margin': 0.03242570633874986,
'method': 'shin',
'z': 0.016236442857291165}
Differential Margin Weighting
Uses the differential margin approach described by Joesph Buchdahl in his wisdom of the crowds article
import penaltyblog as pb
odds = [2.7, 2.3, 4.4]
pb.implied.differential_margin_weighting(odds)
{'implied_probabilities': array([0.3595618 , 0.42397404, 0.21646416]),
'margin': 0.03242570633874986,
'method': 'differential_margin_weighting'}
Odds Ratio
Uses Keith Cheung's odds ratio approach, as discussed by Joesph Buchdahl's in his wisdom of the crowds article, to calculate the implied probabilities
import penaltyblog as pb
odds = [2.7, 2.3, 4.4]
pb.implied.odds_ratio(odds)
{'implied_probabilities': array([0.35881036, 0.42256142, 0.21862822]),
'margin': 0.03242570633874986,
'method': 'odds_ratio',
'c': 1.05116912729218}
Rank Probability Scores
Based on Constantinou and Fenton (2021), penaltyblog contains a function for calculating Rank Probability Scores for assessing home, draw, away probability forecasts.
predictions is a list of home, draw, away probabilities and observed is the zero-based index for which outcome actually occurred.
import penaltyblog as pb
predictions = [
[1, 0, 0],
[0.9, 0.1, 0],
[0.8, 0.1, 0.1],
[0.5, 0.25, 0.25],
[0.35, 0.3, 0.35],
[0.6, 0.3, 0.1],
[0.6, 0.25, 0.15],
[0.6, 0.15, 0.25],
[0.57, 0.33, 0.1],
[0.6, 0.2, 0.2],
]
observed = [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0]
for p, o in zip(predictions, observed):
rps = pb.metrics.rps(p, o)
print(round(rps, 4))
0.0
0.005
0.025
0.1562
0.1225
0.185
0.0913
0.1113
0.0975
0.1
Download ELO rating from clubelo.com
Download ELO ratings for a given date
import penaltyblog as pb
df = pb.clubelo.fetch_rankings_by_date(2010, 1, 1)
df.head()
| Rank | Club | Country | Level | Elo | From | To | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | Barcelona | ESP | 1 | 1987.68 | 2009-12-18 00:00:00 | 2010-01-02 00:00:00 |
| 1 | 2 | Chelsea | ENG | 1 | 1945.54 | 2009-12-29 00:00:00 | 2010-01-16 00:00:00 |
| 2 | 3 | Man United | ENG | 1 | 1928.53 | 2009-12-31 00:00:00 | 2010-01-09 00:00:00 |
| 3 | 4 | Real Madrid | ESP | 1 | 1902.72 | 2009-12-20 00:00:00 | 2010-01-03 00:00:00 |
| 4 | 5 | Inter | ITA | 1 | 1884.49 | 2009-12-21 00:00:00 | 2010-01-06 00:00:00 |
List all teams with ratings available
import penaltyblog as pb
teams = pb.clubelo.list_all_teams()
teams[:5]
['Man City', 'Bayern', 'Liverpool', 'Real Madrid', 'Man United']
Download Historical ELO ratings for a given team
import penaltyblog as pb
df = pb.clubelo.fetch_rankings_by_team("barcelona")
df.head()
| Rank | Club | Country | Level | Elo | From | To | |
|---|---|---|---|---|---|---|---|
| 0 | None | Barcelona | ESP | 1 | 1636.7 | 1939-10-22 00:00:00 | 1939-12-03 00:00:00 |
| 1 | None | Barcelona | ESP | 1 | 1626.1 | 1939-12-04 00:00:00 | 1939-12-10 00:00:00 |
| 2 | None | Barcelona | ESP | 1 | 1636.73 | 1939-12-11 00:00:00 | 1939-12-17 00:00:00 |
| 3 | None | Barcelona | ESP | 1 | 1646.95 | 1939-12-18 00:00:00 | 1939-12-24 00:00:00 |
| 4 | None | Barcelona | ESP | 1 | 1637.42 | 1939-12-25 00:00:00 | 1939-12-31 00:00:00 |
References
- Mark J. Dixon and Stuart G. Coles (1997) Modelling Association Football Scores and Inefficiencies in the Football Betting Market.
- Håvard Rue and Øyvind Salvesen (1999) Prediction and Retrospective Analysis of Soccer Matches in a League.
- Anthony C. Constantinou and Norman E. Fenton (2012) Solving the problem of inadequate scoring rules for assessing probabilistic football forecast models
- Hyun Song Shin (1992) Prices of State Contingent Claims with Insider Traders, and the Favourite-Longshot Bias
- Hyun Song Shin (1993) Measuring the Incidence of Insider Trading in a Market for State-Contingent Claims
- Joseph Buchdahl (2015) The Wisdom of the Crowd
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