Turnovers in the National Football League (NFL) occur whenever a team loses possession of the ball due to a fumble, or an interception. Turnovers disrupt momentum of the offensive team, and represent lost opportunities to advance downfield and score. Teams with a positive differential turnover margin in a given game win
of the time. Turnovers are statistically rare events, occurring apparently randomly. These characteristics make them difficult to predict. This investigation advances the hypothesis that turnovers are predictable in NFL football. Machine learning models are developed to learn the concept: At any point within a football game, what is the likelihood that a turnover will be observed on the next play from scrimmage? NFL play-by-play data for 32 teams spanning seven full seasons were used to train the models. Results presented suggest evidence to support the working hypothesis. Under certain conditions, both fumbles and interceptions can be anticipated at low false discovery rates (less than
). When a turnover is predicted on the impending play from scrimmage, a high degree of confidence is associated with that prediction. The ability to anticipate catastrophic in-game events may lead to their management and control, ultimately improving the performance of individual athletes and their teams. This investigation contributes to the sports science literature by demonstrating the predictability of in-game events often considered to be essentially random in their occurrence. To the author’s knowledge, direct prediction of turnovers has not previously appeared in the literature, which has focused on retrospective statistical analyses of turnover margin in football games.
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