Abstract The NFL collects detailed tracking data capturing the location of all players and the ball during each play. Although the raw form of this data is not publicly available, the NFL releases a set of aggregated statistics via their Next Gen Stats (NGS) platform. They also provide charts showing the locations of pass attempts and outcomes for individual quarterbacks. Our work aims to partially close the gap between what data is available privately (to NFL teams) and publicly, and our contribution is two-fold. First, we introduce an image processing tool designed specifically for extracting the raw data from the NGS pass charts. We extract the pass outcome, coordinates, and other metadata. Second, we analyze the resulting dataset, examining the spatial tendencies and performances of individual quarterbacks and defenses. We use a generalized additive model for completion percentages by field location. We introduce a naive Bayes approach for estimating the 2-D completion percentage surfaces of individual teams and quarterbacks, and we provide a one-number summary, completion percentage above expectation (CPAE), for evaluating quarterbacks and team defenses. We find that our pass location data closely matches the NFL’s tracking data, and that our CPAE metric closely matches the NFL’s proprietary CPAE metric.

中文翻译：

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从图像中提取NFL跟踪数据以评估四分卫并通过防守

摘要NFL收集详细的跟踪数据，以捕获每次比赛中所有球员和球的位置。尽管此数据的原始形式尚未公开，但NFL通过其下一代统计（NGS）平台发布了一组汇总统计信息。他们还提供了图表，显示了各个四分卫的传球尝试和结果的位置。我们的工作旨在部分缩小私人（可供NFL团队使用）和公开获得的数据之间的差距，我们的贡献是双重的。首先，我们介绍一种图像处理工具，该工具专门设计用于从NGS通道图中提取原始数据。我们提取通过结果，坐标和其他元数据。其次，我们分析结果数据集，检查单个四分卫和防守的空间趋势和表现。我们使用通用的加性模型按字段位置完成百分比。我们引入了朴素的贝叶斯方法来估计单个团队和四分卫的二维完成率表面，并且我们提供了一个单数摘要，高于预期的完成率（CPAE），用于评估四分卫和团队防守。我们发现我们的通过位置数据与NFL的跟踪数据非常匹配，并且我们的CPAE指标与NFL的专有CPAE指标非常匹配。