Feeling the pressure: A unified framework for automating pass rushing statistics in NFL games

In professional football, the pass rush has become an increasingly important aspect of the game, with pass rushers being some of the top paid defensive players in the league. In spite of the importance of the pass rush, pass rushing statistics only include the final outcomes of a play, e.g., sack and pass-made. They do not capture the dynamics of the pass rush or fine-grained insights throughout a play on how much pressure a rusher generates during the rush [1-7]. This lack of in-play insight prevents a post-game analysis from taking into account an individual player’s contribution on defense and fails to quantify pressures generated by each pass rusher. Even if a player does not record a sack, they can still generate pressure and impact the play. There are a few challenges that need to be tackled to enable such a fine-grained pressure estimation throughout a play. First, blockers and rushers who create a pocket must be identified from among all the players on the field. This is crucial for ensuring that players are only evaluated when they are in a rusher or blocker role. Second, the match-up between blockers and rushers needs to be known to take into account how much resistance a rusher gets when they rush toward a quarterback. Finally, the pressure scores of each rusher and a team needs to be estimated throughout a play. In this paper, we propose a unified framework that tackles these challenges by leveraging machine learning (ML) models and the National Football League (NFL) Next Gen Stats (NGS) data with positional and kinematic information from player sensors [1].