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When analyzing and modeling the results of soccer matches, one important aspect is to account for the correct dependence of the scores of two competing teams. Several studies have found that, marginally, these scores are moderately negatively correlated. Even though many approaches that analyze the results of soccer matches are based on two (conditionally) independent pairwise Poisson distributions, a certain amount of (mostly negative) dependence between the scores of the competing teams can simply be induced by the inclusion of covariate information of both teams in a suitably structured linear predictor. One objective of this article is to analyze if this type of modeling is appropriate or if additional explicit modeling of the dependence structure for the joint score of a soccer match needs to be taken into account. Therefore, a specific bivariate Poisson model for the two numbers of goals scored by national teams competing in UEFA European football championship matches is fitted to all matches from the three previous European championships, including covariate information of both competing teams. A boosting approach is then used to select the relevant covariates. Based on the estimates, the tournament is simulated 1,000,000 times to obtain winning probabilities for all participating national teams.

On the dependency of soccer scores – a sparse bivariate Poisson model for the UEFA European football championship 2016