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In this work, we compare several different modeling approaches for count dataapplied to the scores of handball matches with regard to their predictiveperformances based on all matches from the four previous IHF World Men'sHandball Championships 2011 - 2017: (underdispersed) Poisson regression models,Gaussian response models and negative binomial models. All models are based onthe teams' covariate information. Within this comparison, the Gaussian responsemodel turns out to be the best-performing prediction method on the trainingdata and is, therefore, chosen as the final model. Based on its estimates, theIHF World Men's Handball Championship 2019 is simulated repeatedly and winningprobabilities are obtained for all teams. The model clearly favors Denmarkbefore France. Additionally, we provide survival probabilities for all teamsand at all tournament stages as well as probabilities for all teams to qualifyfor the main round.

Prediction of the 2019 IHF World Men's Handball Championship - An underdispersed sparse count data regression model