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

TL;DR

This paper compares various count data models to predict handball match scores, finding that a Gaussian response model performs best and can effectively simulate tournament outcomes and team probabilities.

Contribution

The study introduces a comparative analysis of count data models for handball score prediction, identifying the Gaussian response model as the most effective for tournament simulation.

Findings

Gaussian response model outperforms others in prediction accuracy

Model favors Denmark over France for the 2019 tournament

Provides detailed team survival and qualification probabilities

Abstract

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