Using random forests to estimate win probability before each play of an NFL game

Lock Dennis () and Nettleton Dan
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Lock Dennis: Department of Statistics, Iowa State University, Ames, IA 50011, USA
Nettleton Dan: Department of Statistics, Iowa State University, Ames, IA 50011, USA

Journal of Quantitative Analysis in Sports, 2014, vol. 10, issue 2, 197-205

Abstract: Before any play of a National Football League (NFL) game, the probability that a given team will win depends on many situational variables (such as time remaining, yards to go for a first down, field position and current score) as well as the relative quality of the two teams as quantified by the Las Vegas point spread. We use a random forest method to combine pre-play variables to estimate Win Probability (WP) before any play of an NFL game. When a subset of NFL play-by-play data for the 12 seasons from 2001 to 2012 is used as a training dataset, our method provides WP estimates that resemble true win probability and accurately predict game outcomes, especially in the later stages of games. In addition to being intrinsically interesting in real time to observers of an NFL football game, our WP estimates can provide useful evaluations of plays and, in some cases, coaching decisions.

Keywords: random forest; NFL; win probability (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (3)

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DOI: 10.1515/jqas-2013-0100

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