Abstract Statistical analysis of defensive players in football has lagged behind that of offensive players, special teams, and coaching decisions, largely because data on individual defensive players has historically been lacking. With the introduction of player tracking data from the NFL, researchers can now tackle these problems. However, event and strategy annotations in the NFL’s tracking data are limited, especially when it comes to describing what defensive players do on each play. Moreover, methods for creating these annotations typically require extensive human labeling, which is difficult and expensive. Because of the importance of the passing game and the limited prior research on the defensive side of passing, we provide annotations for the pass coverage types of cornerbacks using unsupervised learning techniques, which require no training data. We define a set of features from the tracking data that distinguish between “man” and “zone” coverage. We use mixture models to create clusters corresponding to each group, allowing us to provide probabilistic assignments to each coverage type (or cluster). Additionally, we quantify each feature’s influence in distinguishing defensive pass coverage types. Our work makes possible several potential avenues of future NFL research into defensive backs and pass coverage strategies.

中文翻译：

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利用NFL球员跟踪数据在防守后卫之间识别传球覆盖率的无监督方法

摘要足球中防守球员的统计分析落后于进攻球员，特殊球队和教练的决策，这在很大程度上是因为历史上一直缺乏有关单个防守球员的数据。随着NFL球员追踪数据的引入，研究人员现在可以解决这些问题。但是，NFL跟踪数据中的事件和策略注释是有限的，特别是在描述防守球员在每场比赛中的表现时。此外，用于创建这些注释的方法通常需要广泛的人工标记，这既困难又昂贵。由于传球游戏的重要性以及传球防守方面的先验研究有限，我们使用无监督学习技术为角卫的传球覆盖类型提供注释，不需要培训数据。我们从跟踪数据中定义了一组可区分“人”和“区域”覆盖范围的特征。我们使用混合模型来创建与每个组相对应的聚类，从而使我们能够为每种覆盖类型（或聚类）提供概率分配。此外，我们在区分防御性传球覆盖类型时量化了每个功能的影响。我们的工作为未来NFL研究防守后卫和传球覆盖策略提供了几种潜在的途径。我们在区分防御性传球覆盖类型时量化每个功能的影响。我们的工作为未来NFL研究防守后卫和传球覆盖策略提供了几种潜在的途径。我们在区分防御性传球覆盖类型时量化每个功能的影响。我们的工作为未来NFL研究防守后卫和传球覆盖策略提供了几种潜在的途径。