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The Anatomy of American Football: Evidence from 7 Years of NFL Game Data

Eriksson, Kimmo and Pelechrinis, Konstantinos and Papalexakis, Evangelos (2016) The Anatomy of American Football: Evidence from 7 Years of NFL Game Data. PLOS ONE, 11 (12). e0168716. ISSN 1932-6203

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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.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Eriksson, Kimmo
Papalexakis, Evangelos
Date: 22 December 2016
Date Type: Publication
Journal or Publication Title: PLOS ONE
Volume: 11
Number: 12
Publisher: Public Library of Science
Page Range: e0168716
DOI or Unique Handle: 10.1371/journal.pone.0168716
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 1932-6203
Official URL:
Article Type: Research Article
Date Deposited: 08 Jun 2020 13:09
Last Modified: 08 Jun 2020 13:09


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