Leveraging machine learning to predict playcalling tendencies in the NFL

Abstract

In this thesis, we apply four machine learning models to NFL play-by-play data from 2009-2018 to predict whether a team will run or pass the ball on a given play. We tested our models using league-wide and team-specific data in five different situations on the field. Our best league-wide models achieved a test accuracy of 80% and our best team-specific models achieved a test accuracy of 86%. Relative to the baseline of the run-to-pass ratio, the best league-wide models achieved an increase in accuracy of 25% and the best team-specific models achieved an increase of 27%. Our models showed that the Tennessee Titans, the New York Jets, and the Cincinnati Bengals have been the most predictable offenses in the NFL over 10 years. We found that a team's in-game run-to-pass ratio and their win and score probabilities are the driving factors for offensive play-calling. Additionally, our results show that teams are more predictable later in games, and that less predictable teams tend to experience greater success offensively.