Down, set, hut! Explaining variability in snap timing on plays with motion

Music City Center 

Player tracking data have provided great opportunities to generate novel insights into understudied areas of American football, such as pre-snap motion. Using a Bayesian multilevel model, we provide an assessment of a quarterback's ability to adapt and align the ball snap with pre-snap motion from their teammates. We focus on pass plays with receivers in motion at snap and running a route, and define the snap timing as the time between the start of the receiver's motion and the ball snap. We assume a Gamma distribution for the play-level snap timing and model the mean parameter with player and team random effects, along with relevant fixed effects such as the motion type identified via a Gaussian mixture model. Most importantly, we model the shape parameter with quarterback random effects, which enables us to estimate the differences in snap timing variability among NFL quarterbacks. We demonstrate that higher variability in snap timing is beneficial for the passing game, as it relates to facing less havoc created by the opposing defense. We also obtain a quarterback leaderboard based on our snap timing variability measure, and Patrick Mahomes stands out as the top rated player.

Bayesian statistics

mixed-effects model

uncertainty quantification

tracking data

American football

statistics in sports 

Section on Statistics in Sports