NFL’s Next Gen Stats (NGS) powered by AWS accurately captures player and ball data in real time for every play and every NFL game—over 300 million data points per season—through the extensive use of sensors in players’ pads and the ball. With this rich set of tracking data, NGS uses AWS machine learning (ML) technology to uncover deeper insights and develop a better understanding of various aspects and trends of the game. To date, NGS metrics have focused on helping fans better appreciate and understand the offense and defense in gameplay through the application of advanced analytics, particularly in the passing game. Thanks to tracking data, it’s possible to quantify the difficulty of passes, model expected yards after catch, and determine the value of various play outcomes. A logical next step with this analytical information is to evaluate quarterback decision-making, such as whether the quarterback has considered all eligible receivers and evaluated tradeoffs accurately.
To effectively model quarterback decision-making, we considered a few key metrics—mainly the probability of different events occurring on a pass, and the value of said events. A pass can result in three outcomes: completion, incompletion, or interception. NGS has already created models that provide probabilities of these outcomes, but these events rely on information that’s available at only two points during the play: when the ball is thrown (termed as pass-forward), and when the ball arrives to a receiver (pass-arrived). Because of this, creating accurate probabilities requires modeling the trajectory of players between those two points in time.
For these probabilities, the quarterback’s decision is heavily influenced by the quality of defensive coverage on various receivers, because a receiver with a closely covered defender has a lower likelihood of pass completion compared to a receiver who is wide open due to blown coverage. Furthermore, defenders are inherently reactive to how the play progresses. Defenses move in completely different ways depending on which receiver is targeted on the pass. This means that a trajectory model for defenders has to similarly be reactive to the specified targeted receiver in a believable manner.
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