A Bayesian football goal model uses period-specific spike-and-slab priors to adaptively weight how much team attack and defense strengths change over time, improving prediction relative to standard dynamic models.
Hybrid Machine Learning Forecasts for the FIFA Women's World Cup 2019
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abstract
In this work, we combine two different ranking methods together with several other predictors in a joint random forest approach for the scores of soccer matches. The first ranking method is based on the bookmaker consensus, the second ranking method estimates adequate ability parameters that reflect the current strength of the teams best. The proposed combined approach is then applied to the data from the two previous FIFA Women's World Cups 2011 and 2015. Finally, based on the resulting estimates, the FIFA Women's World Cup 2019 is simulated repeatedly and winning probabilities are obtained for all teams. The model clearly favors the defending champion USA before the host France.
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Bayesian weighted discrete-time dynamic models for association football prediction
A Bayesian football goal model uses period-specific spike-and-slab priors to adaptively weight how much team attack and defense strengths change over time, improving prediction relative to standard dynamic models.