估计女子大学排球团队成功中的个人贡献
Estimating individual contributions to team success in women's college volleyball
AI总结：
本文利用2022年NCAA女子排球赛季大规模数据，将排球得分过程建模为马尔可夫链，并通过分层线性模型估计球员个人对团队获胜概率的贡献，解决了传统统计无法跨技能比较和衡量个人影响的问题。
摘要：
排球中一分的进程从发球开始，然后在两队之间交替进行，每队最多允许触球三次。利用2022年NCAA第一级别女子排球赛季的图表数据（4,147场比赛，超过600,000分，超过500万次记录触球），我们将一分的进程建模为马尔可夫链，其状态空间由当前回合中的触球序列定义。我们估计每支球队赢得该分的概率，该概率在每次触球时都会发生变化。我们将得分概率的变化归因于负责每次触球的一名或多名球员，从而便于在得分尺度上衡量不同技能的表现。传统的排球统计数据不允许在不同技能之间进行同类比较，并且它们不衡量表现对团队成功的影响。对于对抗性触球（发球/接发球和进攻/拦网/防守），我们为结果估计了一个分层线性模型，其中涉及球员的随机效应；并且我们不仅在会议/球队层面，而且在个人球员层面上，根据赛程强度调整表现。我们可以利用这些结果来回答排球教练的实际问题。
英文摘要：
The progression of a single point in volleyball starts with a serve and then alternates between teams, each team allowed up to three contacts with the ball. Using charted data from the 2022 NCAA Division I women's volleyball season (4,147 matches, 600,000+ points, more than 5 million recorded contacts), we model the progression of a point as a Markov chain with the state space defined by the sequence of contacts in the current volley. We estimate the probability of each team winning the point, which changes on each contact. We attribute changes in point probability to the player(s) responsible for each contact, facilitating measurement of performance on the point scale for different skills. Traditional volleyball statistics do not allow apples-to-apples comparisons across skills, and they do not measure the impact of the performances on team success. For adversarial contacts (serve/receive and attack/block/dig), we estimate a hierarchical linear model for the outcome, with random effects for the players involved; and we adjust performance for strength of schedule not only on the conference/team level but on the individual player level. We can use the results to answer practical questions for volleyball coaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
