Library from http://pena.lt/y/blog for scraping and modelling football (soccer) data
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
- 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
pip install penaltyblog
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
- 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
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Hashes for penaltyblog-0.4.0-py3-none-any.whl