A Naive Bayes Approach for NFL Passing Evaluation using Tracking Data Extracted from Images
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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 that visualize the locations of pass attempts for players throughout a game, encoding their outcome (complete, incomplete, interception, or touchdown). Our work aims to partially close the gap between what data is available privately (to NFL teams) and publicly, and our contribution is twofold. First, we introduce an image processing tool designed specifically for extracting the raw data from the NGS pass chart images. We extract the outcome of the pass, the on-field location, and other metadata. Second, we analyze the resulting dataset and examine NFL passing tendencies and the spatial performance of individual quarterbacks and defenses. We introduce a generalized additive model for completion percentages by field location. We use a Naive Bayes approach for adjusting the 2-D completion percentage surfaces of individual teams and quarterbacks based on the number of their pass attempts. We find that our pass location data matches the NFL's official ball tracking data.
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