penaltyblog 0.6.1

Library from http://pena.lt/y/blog for scraping and modelling football (soccer) data

Penalty Blog

The penaltyblog Python package contains lots of useful code from pena.lt/y/blog for working with football (soccer) data.

penaltyblog includes functions for:

• Scraping football data from sources such as football-data.co.uk, ESPN, Club Elo, Understat and Fantasy Premier League
• Modelling of football matches using Poisson-based models, such as Dixon and Coles, and Bayesian models
• Predicting probabilities for many betting markets, e.g. Asian handicaps, over/under, total goals etc
• Modelling football team's abilities using Massey ratings and Colley ratings
• Estimating the implied odds from bookmaker's odds by removing the overround using multiple different methods
• Mathematically optimising your fantasy football team

Installation

pip install penaltyblog

Documentation

To learn how to use penaltyblog, you can read the documentation and look at the examples for:

• Scraping football data
• Predicting football matches and betting markets
• Estimating the implied odds from bookmakers odds
• Calculate Massey and Colley ratings

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
• Gianluca Baio and Marta A. Blangiardo (2010) Bayesian Hierarchical Model for the Prediction of Football Results