论文标题
橄榄球联盟中预期拥有价值的贝叶斯混合模型方法
A Bayesian Mixture Model Approach to Expected Possession Values in Rugby League
论文作者
论文摘要
这项研究的目的是通过在橄榄球联盟中引入贝叶斯混合模型方法来改善低数据可用性体育中预期拥有价值（EPV）模型的先前区域方法。使用了2021年超级联赛赛季的99,966次观察。在整个球场上，一组33个中心（在比赛领域30个，在Try区域中有3个）。每个中心都有发生五种占有结果的可能性（转换/未转变的尝试，罚款，落后目标和没有分数）。使用线性和双线性插值技术为球场上的每个位置提供了模型的权重。使用贝叶斯方法估算了每个中心的概率，并推断到球场上的所有位置。 EPV措施源自拥有结果概率及其点价值。该模型产生了光滑的音高表面，该表面能够为球场上的每个位置提供不同的拥有结果概率和EPV。可视化团队进攻和防守地块之间的差异，并开发了实际与预期的球员评分系统。该模型比以前的方法提供了明显更高的灵活性，并且可以适应其他类似数据的运动。
The aim of this study was to improve previous zonal approaches to expected possession value (EPV) models in low data availability sports by introducing a Bayesian Mixture Model approach to an EPV model in rugby league. 99,966 observations from the 2021 Super League season were used. A set of 33 centres (30 in the field of play, 3 in the try area) were located across the pitch. Each centre held the probability of five possession outcomes occurring (converted/unconverted try, penalty, drop goal and no points). Weights for the model were provided for each location on the pitch using linear and bilinear interpolation techniques. Probabilities at each centre were estimated using a Bayesian approach and extrapolated to all locations on the pitch. An EPV measure was derived from the possession outcome probabilities and their points value. The model produced a smooth pitch surface, which was able to provide different possession outcome probabilities and EPVs for every location on the pitch. Differences between team attacking and defensive plots were visualised and an actual vs expected player rating system was developed. The model provides significantly more flexibility than previous approaches and could be adapted to other sports where data is similarly sparse.
