{"paper":{"title":"Prediction of the 2019 IHF World Men's Handball Championship - An underdispersed sparse count data regression model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Andreas Groll, Gunther Schauberger, Jonas Heiner, J\\\"orn Uhrmeister","submitted_at":"2019-01-17T10:41:16Z","abstract_excerpt":"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 "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.05722","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}