DeepQB: Deep Learning with Player Tracking to Quantify Quarterback Decision-Making & Performance

Research Paper will be posted in the coming weeks. Check back soon!
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Brian Burke


Abstract: DeepQB is a proposed application of deep neural networks to player tracking data from over two full seasons of American professional football. This novel approach demonstrates the ability to successfully understand complex aspects of the passing game, most notably quarterback decision-making. It can assess and compare individual quarterback pass target selection based on a snapshot presented to the passer by the receivers and defenders. Assessments of quarterback decision-making are made by comparing actual target selection to that predicted by our model. The model performs well, correctly identifying the targeted receiver in 60% of cross-validated cases. When passers target the predicted receiver, passes are completed 74% of the time, compared to 55% when the QB targets any other receiver. This performance is surprisingly strong, given that the offense often conceals its intent by design, while defenses try not to allow any single receiver to be open. Further, quarterback passing skills separate and apart from his receivers and defense are isolated and assessed by comparing metrics of actual play success to the metrics of success predicted by the situation presented to the passer. This approach represents a new way for teams, media, and fans to understand and quantitatively assess quarterback decision-making, an aspect of the sport which has previously been opaque and inaccessible.