Predicting play calls in the National Football League using hidden Markov models

Abstract In recent years, data-driven approaches have become a popular tool in a variety of sports to gain an advantage by, for example, analysing potential strategies of opponents. Whereas the availability of play-by-play or player tracking data in sports such as basketball and baseball has led to an increase of sports analytics studies, equivalent data sets for the National Football League (NFL) were not freely available for a long time. In this contribution, we consider a comprehensive play-by-play NFL dataset provided by www.kaggle.com, comprising 289,191 observations in total, to predict play calls in the NFL using hidden Markov models. The resulting out-of-sample prediction accuracy for the 2018 NFL season is 71.6%, which is similar compared to existing studies on play call predictions in the NFL. In practice, such predictions are helpful for NFL teams, especially for defense coordinators, to make adjustments in real time on the field.