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Description:
penaltyblog 0.8.1
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, FBRef, ESPN, Club Elo, Understat, SoFifa 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, Colley ratings and Elo 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, Colley and Elo 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
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Customer Reviews
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