  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.
