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.
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.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: 42
Log Likelihood: -1065.077
AIC: 2214.154
Team Attack Defence
------------------------------------------------------------
Arsenal 1.362 -1.062
Bournemouth 1.115 -0.761
Brighton 0.634 -0.937
Burnley 0.894 -0.801
Cardiff 0.614 -0.798
Chelsea 1.202 -1.341
Crystal Palace 1.004 -1.045
Everton 1.055 -1.184
Fulham 0.626 -0.637
Huddersfield 0.184 -0.712
Leicester 0.999 -1.145
Liverpool 1.532 -1.889
Man City 1.598 -1.839
Man United 1.249 -1.013
Newcastle 0.805 -1.153
Southampton 0.891 -0.846
Tottenham 1.264 -1.337
Watford 1.03 -0.937
West Ham 1.026 -1.007
Wolves 0.916 -1.191
------------------------------------------------------------
Home Advantage: 0.225
Intercept: 0.206
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.DixonColesGoalModel(
df["FTHG"], df["FTAG"], df["HomeTeam"], df["AwayTeam"])
dc.fit()
dc
Module: Penaltyblog
Model: Dixon and Coles
Number of parameters: 43
Log Likelihood: -1064.943
AIC: 2215.886
Team Attack Defence
------------------------------------------------------------
Arsenal 1.36 -0.982
Bournemouth 1.115 -0.679
Brighton 0.632 -0.858
Burnley 0.897 -0.717
Cardiff 0.615 -0.715
Chelsea 1.205 -1.254
Crystal Palace 1.007 -0.961
Everton 1.054 -1.102
Fulham 0.625 -0.557
Huddersfield 0.18 -0.631
Leicester 0.996 -1.064
Liverpool 1.534 -1.803
Man City 1.599 -1.762
Man United 1.251 -0.931
Newcastle 0.806 -1.07
Southampton 0.897 -0.761
Tottenham 1.259 -1.261
Watford 1.031 -0.854
West Ham 1.023 -0.927
Wolves 0.914 -1.113
------------------------------------------------------------
Home Advantage: 0.225
Intercept: 0.124
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.RueSalvesenGoalModel(
df["FTHG"], df["FTAG"], df["HomeTeam"], df["AwayTeam"])
rs.fit()
rs
Module: Penaltyblog
Model: Rue Salvesen
Number of parameters: 44
Log Likelihood: -1061.167
AIC: 2210.334
Team Attack Defence
------------------------------------------------------------
Arsenal 1.435 -1.068
Bournemouth 1.2 -0.776
Brighton 0.594 -0.831
Burnley 0.935 -0.766
Cardiff 0.6 -0.712
Chelsea 1.194 -1.281
Crystal Palace 1.019 -0.985
Everton 1.044 -1.126
Fulham 0.641 -0.585
Huddersfield 0.096 -0.573
Leicester 0.988 -1.067
Liverpool 1.487 -1.768
Man City 1.533 -1.743
Man United 1.315 -1.006
Newcastle 0.761 -1.036
Southampton 0.921 -0.814
Tottenham 1.244 -1.274
Watford 1.067 -0.902
West Ham 1.045 -0.961
Wolves 0.881 -1.091
------------------------------------------------------------
Home Advantage: 0.222
Intercept: 0.141
Rho: -0.04
Gamma: 0.373
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 = my_model.predict("Liverpool", "Stoke")
Home / Draw / Away
# also known as 1x2
probs.home_draw_away
[0.5193995875820345, 0.3170596913687951, 0.1635407210315597]
Total Goals
probs.total_goals("over", 2.5)
0.31911650768322447
probs.total_goals("under", 2.5)
0.680883492299145
Asian Handicaps
probs.asian_handicap("home", 1.5)
0.2602616248461783
probs.asian_handicap("away", -1.5)
0.7397383751361912
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': 1.3650671020694474,
'attack_Aston Villa': 0.6807140182913024,
'attack_Blackburn': 0.971135574781119,
'attack_Bolton': 0.9502712140456423,
'attack_Chelsea': 1.235466344414206,
'attack_Everton': 0.9257685468926837,
'attack_Fulham': 0.9122902202053228,
'attack_Liverpool': 0.8684673939949753,
'attack_Man City': 1.543379586931267,
'attack_Man United': 1.4968564161865994,
'attack_Newcastle': 1.1095636706231062,
'attack_Norwich': 1.0424304866584615,
'attack_QPR': 0.827439335780754,
'attack_Stoke': 0.6248927873330669,
'attack_Sunderland': 0.8510292333101492,
'attack_Swansea': 0.8471368133406263,
'attack_Tottenham': 1.2496040004504756,
'attack_West Brom': 0.8625207332372105,
'attack_Wigan': 0.8177807129177644,
'attack_Wolves': 0.8181858085358248,
'defence_Arsenal': -1.2192247076852236,
'defence_Aston Villa': -1.0566859588325535,
'defence_Blackburn': -0.7430288162188969,
'defence_Bolton': -0.7268011436918458,
'defence_Chelsea': -1.2065700516830344,
'defence_Everton': -1.3564763976122773,
'defence_Fulham': -1.1159544166204092,
'defence_Liverpool': -1.3293118049518535,
'defence_Man City': -1.6549894606952225,
'defence_Man United': -1.5728126940204685,
'defence_Newcastle': -1.1186158411320268,
'defence_Norwich': -0.8865413401238464,
'defence_QPR': -0.9124617361500764,
'defence_Stoke': -1.0766419199030601,
'defence_Sunderland': -1.2049421203955355,
'defence_Swansea': -1.1077243368907703,
'defence_Tottenham': -1.3160823704397775,
'defence_West Brom': -1.1014569193066301,
'defence_Wigan': -0.932997180492951,
'defence_Wolves': -0.6618461794219439,
'home_advantage': 0.2655860528422758,
'intercept': 0.23467961435272489,
'rho': -0.1375912978446625,
'rue_salvesen': 0.1401430558820631}
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.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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