Abstract In this work, we propose a new hybrid modeling approach for the scores of international soccer matches which combines random forests with Poisson ranking methods. While the random forest is based on the competing teams’ covariate information, the latter method estimates ability parameters on historical match data that adequately reflect the current strength of the teams. We compare the new hybrid random forest model to its separate building blocks as well as to conventional Poisson regression models with regard to their predictive performance on all matches from the four FIFA World Cups 2002–2014. It turns out that by combining the random forest with the team ability parameters from the ranking methods as an additional covariate the predictive power can be improved substantially. Finally, the hybrid random forest is used (in advance of the tournament) to predict the FIFA World Cup 2018. To complete our analysis on the previous World Cup data, the corresponding 64 matches serve as an independent validation data set and we are able to confirm the compelling predictive potential of the hybrid random forest which clearly outperforms all other methods including the betting odds.

中文翻译：

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混合随机森林可预测国际比赛中的足球比赛

摘要在这项工作中，我们为国际足球比赛的得分提出了一种新的混合建模方法，该方法将随机森林与泊松排名方法相结合。尽管随机森林基于竞争团队的协变量信息，但后一种方法会根据历史比赛数据估算能力参数，以充分反映团队当前的实力。我们将新的混合随机森林模型与其单独的构建基块以及常规的Poisson回归模型进行了比较，比较了它们在2002-2014年四届FIFA世界杯所有比赛中的预测表现。事实证明，通过将随机森林与排名方法的团队能力参数相结合作为附加协变量，可以大大提高预测能力。最后，