AI 中文总结
本研究提出一种贝叶斯图形建模框架，构建三种纵向贝叶斯网络模型，结合NBA费城76人队2005-06赛季数据开展案例分析，用于评估篮球队表现。
AI 中文摘要
评估篮球队表现需要考虑多方面信息。近年来，体育领域产生的数据量和数据质量大幅提升，篮球领域尤为明显。本研究提出一种用于篮球球队表现纵向分析的贝叶斯图形建模框架，该框架基于贝叶斯网络，将节点（即关注的随机变量）明确表示为纵向随机过程。研究提出三种基线纵向模型：静态贝叶斯网络、具有连续比赛间自回归结构的动态贝叶斯网络，以及基于隐马尔可夫结构的动态贝叶斯网络。通过针对2005-06赛季美国职业篮球联赛（NBA）费城76人队的真实体育分析案例研究对所提框架进行说明，分析内容涵盖球员参与情况、上场时间、被犯规次数，以及1分、2分和3分投篮的出手次数与命中次数。
英文摘要
Assessing the performance of a basketball team requires the consideration of multiple sources of information. In recent years, the volume and the quality of data generated in sport has increased considerably, particularly in basketball. In this work, we propose a Bayesian graphical modelling framework for the longitudinal analysis of basketball team performance. The framework is based on Bayesian networks that explicitly represent the nodes, that is, the random variables of interest, as longitudinal stochastic processes. We propose three baseline longitudinal models: a static Bayesian network, a dynamic Bayesian network with an autoregressive structure between successive games, and a dynamic Bayesian network based on a hidden Markov structure. We illustrate the proposed framework through a real-world sports analytics case study involving the Philadelphia 76ers of the National Basketball Association (NBA) during the 2005--06 season. The analysis includes player participation, minutes played, fouls drawn, and one-, two-, and three-point shots attempted and made.
