Citation

Pelechrinis K, Papalexakis E. PLoS One 2016; 11(12): e0168716.

Affiliation

Department of Computer Science and Engineering, University of California Riverside, Riverside, CA, United States of America.

Copyright

(Copyright © 2016, Public Library of Science)

DOI

10.1371/journal.pone.0168716

PMID

28005971

Abstract

How much does a fumble affect the probability of winning an American football game? How balanced should your offense be in order to increase the probability of winning by 10%? These are questions for which the coaching staff of National Football League teams have a clear qualitative answer. Turnovers are costly; turn the ball over several times and you will certainly lose. Nevertheless, what does "several" mean? How "certain" is certainly? In this study, we collected play-by-play data from the past 7 NFL seasons, i.e., 2009-2015, and we build a descriptive model for the probability of winning a game. Despite the fact that our model incorporates simple box score statistics, such as total offensive yards, number of turnovers etc., its overall cross-validation accuracy is 84%. Furthermore, we combine this descriptive model with a statistical bootstrap module to build FPM (short for Football Prediction Matchup) for predicting future match-ups. The contribution of FPM is pertinent to its simplicity and transparency, which however does not sacrifice the system's performance. In particular, our evaluations indicate that our prediction engine performs on par with the current state-of-the-art systems (e.g., ESPN's FPI and Microsoft's Cortana). The latter are typically proprietary but based on their components described publicly they are significantly more complicated than FPM. Moreover, their proprietary nature does not allow for a head-to-head comparison in terms of the core elements of the systems but it should be evident that the features incorporated in FPM are able to capture a large percentage of the observed variance in NFL games.

Language: en