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.
翻译:这项研究的目的是改进以前对低数据可用性体育中预期占有权值(EPV)模型的分区办法,方法是对橄榄球中的EPV模型采用Bayesian Mixture模型方法。 使用了2021年超级联盟季节的99,966次观测,分布在球场各处的33个中心(30个在游戏场,3个在试验区),每个中心都拥有5个占有权结果的概率(反向/无反向尝试、罚款、下降目标及无点)。利用线性与双线性内插法,为每个球场的每个地点提供了模型的重量。每个中心都使用Bayesian方法估算了概率,并对球场上的所有地点进行了外推。一个EPV测量来自拥有结果概率及其点值。模型产生了一个平滑的球表面,能够提供不同占有权结果的概率和每个球场上每个地点的EPV。攻击队和防御场之间的差别是可视化的,并开发了一个实际与预期的玩家评级系统之间的差别。模型提供了比以往要差得多的灵活性。