Problem
Research questions and friction points this paper is trying to address.
relegation
promotion
water polo
Bayesian hierarchical model
Relegation, promotion and the components of scoring in water polo
🤖 AI Summary
本文通过构建首个水球贝叶斯层次模型，分析了欧洲水球联赛中的降级决策及新升俱乐部的能力评估问题。
📝 Abstract
Relegation, not the championship, decides the outcome of many European water polo leagues: one club has won every recent national title in Italy, Spain and Hungary. We make the relegation decision our object of inference. Serie A1 relegates one club for finishing last and a second through a three-match play-out, and we ask at each stage how much can be known and when. Promoted clubs arrive with no top-flight record, so we also compare four ways of setting their prior. These analyses rest on the first Bayesian hierarchical model of water polo, which splits scoring into even-strength, man-up and penalty components, models opportunities and conversions separately, and lets abilities evolve between seasons. Treating the components as independent proves untenable, since they compete for a common budget of possessions. Fitted to a new dataset of 940 matches, the model identifies the directly relegated club from the ninth round, before the league table does, predicts the play-out field better than the table, and shows the play-out itself to be close to a coin toss. Promoted clubs start below the league on every component except drawing exclusions: knowing a club was promoted improves forecasts; knowing how does not.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian hierarchical model
scoring components
water polo
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