2019/01/17 by Andreas Groll, Groll, Andreas, Jonas Heiner +6
Computer Science · Economics, Econometrics and Finance · Environmental Science · Medicine · #Applications (stat.AP) #Data Analysis with R #FOS: Computer and information sciences #Forest ecology and management #Sports Analytics and Performance #Sports Performance and Training
paper · pdf · doi:10.48550/arxiv.1901.05722
openalex publication_date 2019/01/17 · openalex created_date 2022/07/30 · openalex updated_date 2026/07/28
In this work, we compare several different modeling approaches for count data\napplied to the scores of handball matches with regard to their predictive\nperformances based on all matches from the four previous IHF World Men's\nHandball Championships 2011 - 2017: (underdispersed) Poisson regression models,\nGaussian response models and negative binomial models. All models are based on\nthe teams' covariate information. Within this comparison, the Gaussian response\nmodel turns out to be the best-performing prediction method on the training\ndata and is, therefore, chosen as the final model. Based on its estimates, the\nIHF World Men's Handball Championship 2019 is simulated repeatedly and winning\nprobabilities are obtained for all teams. The model clearly favors Denmark\nbefore France. Additionally, we provide survival probabilities for all teams\nand at all tournament stages as well as probabilities for all teams to qualify\nfor the main round.\n