谁阻挡谁？美式橄榄球中阻挡者和冲传者的概率性阻挡分配评估
Who Blocks Whom? Probabilistic Pass-Blocking Assignments for Evaluating Blockers and Pass Rushers in American Football
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中文总结 AI 辅助
本研究将篮球防守对位隐马尔可夫模型适配至橄榄球传球保护，利用高维时空数据生成概率性阻挡分配，量化冲传者注意力并升级评估指标，拟合NFL数据后识别出精英球员。
中文摘要 AI 辅助
历史上，对进攻线卫的统计分析一直因缺乏易于测量的数据而受阻。最近，随着球员追踪数据的引入，新的方法论进展成为可能。利用高维时空数据，我们将弗兰克斯等人（2015）的防守对位隐马尔可夫模型从篮球适应到橄榄球传球保护，为每个传球阻挡者生成逐帧的概率性冲传者分配。我们展示了这种概率性分配作为一种可用的建模产物，如何增强现有的球员评估框架。我们直接量化了冲传者所吸引的注意力，将调整后的正负值（Macdonald+2012）从全有或全无的时段升级为连续时间内的部分、连续阻挡信用，产出摆脱阻挡的生存指标，并衡量冲传者为队友创造的空间。拟合2021年NFL赛季前八周的数据，所得指标识别出广泛认可的精英冲传者和传球保护者，并与独立图表分析结果一致。
英文摘要
Historically, statistical analysis of offensive lineman has been hindered by the lack of easily measurable quantities. More recently, with the introduction of player tracking data new methodological advances are now possible. Using high-dimensional spatio-temporal data, we adapt the defensive-matchup hidden Markov model of \cite{franks2015characterizing} from basketball to football pass protection, producing frame-by-frame probabilistic assignments of each pass blocker to the rushers. We show how this probabilistic assignment is a usable modeling artifact that augments existing player-evaluation frameworks. We directly quantify the attention a rusher commands, upgrade adjusted plus-minus \citep{Macdonald+2012} from all-or-nothing stints to partial, continuous blocking credit in continuous time, yield block-shedding survival metrics, and measure the space a rusher generates for his teammates. Fit to the first eight weeks of the 2021 NFL season, the resulting metrics recover widely-recognized elite rushers and pass protectors and align with independent charting.
发表机构
- University of California Santa Barbara（加州大学圣塔芭芭拉分校）
- Loyola University Chicago（芝加哥洛约拉大学）
机构由 AI 辅助整理，请以论文原文为准。
