用于预测排球比赛结果的贝叶斯层次模型。
Bayesian hierarchical models for the prediction of volleyball results.
作者信息
Gabrio Andrea
机构信息
Department of Statistical Science, University College London, London, UK.
出版信息
J Appl Stat. 2020 Feb 6;48(2):301-321. doi: 10.1080/02664763.2020.1723506. eCollection 2021.
Statistical modelling of sports data has become more and more popular in the recent years and different types of models have been proposed to achieve a variety of objectives: from identifying the key characteristics which lead a team to win or lose to predicting the outcome of a game or the team rankings in national leagues. Although not as popular as football or basketball, volleyball is a team sport with both national and international level competitions in almost every country. However, there is almost no study investigating the prediction of volleyball game outcomes and team rankings in national leagues. We propose a Bayesian hierarchical model for the prediction of the rankings of volleyball national teams, which also allows to estimate the results of each match in the league. We consider two alternative model specifications of different complexity which are validated using data from the women's volleyball Italian Serie A1 2017-2018 season.
近年来，体育数据的统计建模越来越受欢迎，人们提出了不同类型的模型以实现各种目标：从确定导致球队胜负的关键特征到预测比赛结果或国家联赛中的球队排名。排球虽然不像足球或篮球那样受欢迎，但它是一项团队运动，几乎在每个国家都有国内和国际水平的比赛。然而，几乎没有研究调查排球比赛结果的预测以及国家联赛中的球队排名。我们提出了一种贝叶斯分层模型来预测排球国家队的排名，该模型还可以估计联赛中每场比赛的结果。我们考虑了两种不同复杂程度的替代模型规格，并使用2017 - 2018赛季意大利女子排球甲级联赛的数据进行了验证。
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