Article
Version 1
Preserved in Portico This version is not peer-reviewed
Empirical Prediction of Turnovers in NFL Football
Version 1
: Received: 1 November 2016 / Approved: 2 November 2016 / Online: 2 November 2016 (07:07:06 CET)
A peer-reviewed article of this Preprint also exists.
Bock, J.R. Empirical Prediction of Turnovers in NFL Football. Sports 2017, 5, 1. Bock, J.R. Empirical Prediction of Turnovers in NFL Football. Sports 2017, 5, 1.
Abstract
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 $70\%$ 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? 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 $15\%$). This approach may be useful to inform in-game strategies to mitigate the negative consequences of turnovers by an offensive team, or to maximize their probability by a defensive squad.
Keywords
machine learning; unbalanced data; predicting rare events; NFL football; sports analytics
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment