Three state-of-the-art statistical ranking methods for forecasting football matches are combined with several other predictors in a hybrid machine learning model. Namely an ability estimate for every team based on historic matches; an ability estimate for every team based on bookmaker consensus; average plus-minus player ratings based on their individual performances in their home clubs and national teams; and further team covariates (e.g., market value, team structure) and country-specific socio-economic factors (population, GDP). The proposed combined approach is used for learning the number of goals scored in the matches from the four previous UEFA EUROs 2004-2016 and then applied to current information to forecast the upcoming UEFA EURO 2020. Based on the resulting estimates, the tournament is simulated repeatedly and winning probabilities are obtained for all teams. A random forest model favors the current World Champion France with a winning probability of 14.8% before England (13.5%) and Spain (12.3%). Additionally, we provide survival probabilities for all teams and at all tournament stages.

中文翻译：

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UEFA EURO 2020 的混合机器学习预测

三种用于预测足球比赛的最先进的统计排名方法与混合机器学习模型中的其他几个预测器相结合。即基于历史比赛对每支球队的能力估计；基于博彩公司共识对每个团队的能力评估；根据他们在主场俱乐部和国家队的个人表现得出的平均正负球员评分；以及进一步的团队协变量（例如，市场价值、团队结构）和国家特定的社会经济因素（人口、GDP）。提议的组合方法用于从 2004-2016 年之前的四场 UEFA EURO 中学习在比赛中的进球数，然后将其应用于当前信息以预测即将到来的 UEFA EURO 2020。基于由此产生的估计，比赛被反复模拟，并获得所有球队的获胜概率。随机森林模型有利于现任世界冠军法国的获胜概率为 14.8%，领先于英格兰（13.5%）和西班牙（12.3%）。此外，我们为所有球队和所有锦标赛阶段提供生存概率。