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Hybrid Machine Learning Forecasts for the FIFA Women's World Cup 2019

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arxiv 1906.01131 v1 pith:7KDAYRQF submitted 2019-06-03 stat.ML cs.LGstat.AP

Andreas Groll , Christophe Ley , Gunther Schauberger , Hans Van Eetvelde , Achim Zeileis This is my paper · ORCID
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keywords fifarankingwomenworldapproachestimatesmethodteams
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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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