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

Author

Listed:
  • Lock Dennis
  • Nettleton Dan

    (Department of Statistics, Iowa State University, Ames, IA 50011, USA)

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.

Suggested Citation

  • Lock Dennis & Nettleton Dan, 2014. "Using random forests to estimate win probability before each play of an NFL game," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 1-9, June.
    • Handle: RePEc:bpj:jqsprt:v:10:y:2014:i:2:p:9:n:10
    DOI: 10.1515/jqas-2013-0100
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    References listed on IDEAS

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    1. Mills Brian M. & Salaga Steven, 2011. "Using Tree Ensembles to Analyze National Baseball Hall of Fame Voting Patterns: An Application to Discrimination in BBWAA Voting," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-32, October.
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    Cited by:

    1. S. E. Hill, 2022. "In-game win probability models for Canadian football," Journal of Business Analytics, Taylor & Francis Journals, vol. 5(2), pages 164-178, July.
    2. Daniel Cervone & Alex D’Amour & Luke Bornn & Kirk Goldsberry, 2016. "A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 585-599, April.
    3. Yurko Ronald & Ventura Samuel & Horowitz Maksim, 2019. "nflWAR: a reproducible method for offensive player evaluation in football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 163-183, September.

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