Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

2014

Journal or Book Title

Journal of Quantitative Analysis in Sports

Volume

10

Issue

2

First Page

197

Last Page

205

DOI

10.1515/jqas-2013-0100

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.

Comments

This article is published as Lock, Dennis, and Dan Nettleton. "Using random forests to estimate win probability before each play of an NFL game." Journal of Quantitative Analysis in Sports 10, no. 2 (2014): 197-205. doi: 10.1515/jqas-2013-0100. Posted with permission.

Copyright Owner

Walter de Gruyter Berlin/Boston

Language

en

File Format

application/pdf

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