# Penalty Blog
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The **penaltyblog** Python package contains lots of useful code from [pena.lt/y/blog](http://pena.lt/y/blog.html) 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](https://penaltyblog.readthedocs.io/en/latest/) and look at the
examples for:
- [Scraping football data](https://penaltyblog.readthedocs.io/en/latest/scrapers/index.html)
- [Predicting football matches and betting markets](https://penaltyblog.readthedocs.io/en/latest/models/index.html)
- [Estimating the implied odds from bookmakers odds](https://penaltyblog.readthedocs.io/en/latest/implied/index.html)
- [Calculate Massey, Colley and Elo ratings](https://penaltyblog.readthedocs.io/en/latest/ratings/index.html)
## 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
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"description": "# Penalty Blog\n\n<div align=\"center\">\n\n <a href=\"\">[](https://pypi.org/project/penaltyblog/)</a>\n <a href=\"\">[](https://coveralls.io/repos/github/martineastwood/penaltyblog/badge.svg?branch=master&service=github)</a>\n <a href=\"\">[](https://pypi.org/project/penaltyblog/)</a>\n <a href=\"\">[](https://pepy.tech/project/penaltyblog)</a>\n <a href=\"\">[](https://opensource.org/licenses/MIT)</a>\n <a href=\"\">[](https://github.com/psf/black)</a>\n <a href=\"\">[](https://github.com/pre-commit/pre-commit)</a>\n\n</div>\n\n\nThe **penaltyblog** Python package contains lots of useful code from [pena.lt/y/blog](http://pena.lt/y/blog.html) for working with football (soccer) data.\n\n**penaltyblog** includes functions for:\n\n- Scraping football data from sources such as football-data.co.uk, FBRef, ESPN, Club Elo, Understat, SoFifa and Fantasy Premier League\n- Modelling of football matches using Poisson-based models, such as Dixon and Coles, and Bayesian models\n- Predicting probabilities for many betting markets, e.g. Asian handicaps, over/under, total goals etc\n- Modelling football team's abilities using Massey ratings, Colley ratings and Elo ratings\n- Estimating the implied odds from bookmaker's odds by removing the overround using multiple different methods\n- Mathematically optimising your fantasy football team\n\n## Installation\n\n`pip install penaltyblog`\n\n\n## Documentation\n\nTo learn how to use penaltyblog, you can read the [documentation](https://penaltyblog.readthedocs.io/en/latest/) and look at the\nexamples for:\n\n- [Scraping football data](https://penaltyblog.readthedocs.io/en/latest/scrapers/index.html)\n- [Predicting football matches and betting markets](https://penaltyblog.readthedocs.io/en/latest/models/index.html)\n- [Estimating the implied odds from bookmakers odds](https://penaltyblog.readthedocs.io/en/latest/implied/index.html)\n- [Calculate Massey, Colley and Elo ratings](https://penaltyblog.readthedocs.io/en/latest/ratings/index.html)\n\n## References\n\n- Mark J. Dixon and Stuart G. Coles (1997) Modelling Association Football Scores and Inefficiencies in the Football Betting Market\n- H\u00e5vard Rue and \u00d8yvind Salvesen (1999) Prediction and Retrospective Analysis of Soccer Matches in a League\n- Anthony C. Constantinou and Norman E. Fenton (2012) Solving the problem of inadequate scoring rules for assessing probabilistic football forecast models\n- Hyun Song Shin (1992) Prices of State Contingent Claims with Insider Traders, and the Favourite-Longshot Bias\n- Hyun Song Shin (1993) Measuring the Incidence of Insider Trading in a Market for State-Contingent Claims\n- Joseph Buchdahl (2015) The Wisdom of the Crowd\n- Gianluca Baio and Marta A. Blangiardo (2010) Bayesian Hierarchical Model for the Prediction of Football Results\n",
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