nflWAR: a reproducible method for offensive player evaluation in football

Yurko Ronald (), Ventura Samuel () and Horowitz Maksim ()
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Yurko Ronald: Carnegie Mellon University, Statistics and Data Science, Pittsburgh, PA 15213, USA
Ventura Samuel: Carnegie Mellon University, Statistics and Data Science, Pittsburgh, PA 15213, USA
Horowitz Maksim: Carnegie Mellon University, Statistics and Data Science, Pittsburgh, PA 15213, USA

Journal of Quantitative Analysis in Sports, 2019, vol. 15, issue 3, 163-183

Abstract: Existing methods for player evaluation in American football rely heavily on proprietary data, are often not reproducible, lag behind those of other major sports, and are not interpretable in terms of game outcomes. We present four contributions to the study of football statistics to address these issues. First, we develop the R package nflscrapR to provide easy access to publicly available play-by-play data from the National Football League (NFL). Second, we introduce a novel multinomial logistic regression approach for estimating the expected points for each play. Third, we use the expected points as input into a generalized additive model for estimating the win probability for each play. Fourth, we introduce our nflWAR framework, using multilevel models to isolate the contributions of individual offensive skill players in terms of their wins above replacement (WAR). We assess the uncertainty in WAR through a resampling approach specifically designed for football, and we present results for the 2017 NFL season. We discuss how our reproducible WAR framework can be extended to estimate WAR for players at any position if researchers have data specifying the players on the field during each play. Finally, we discuss the potential implications of this work for NFL teams.

Keywords: generalized additive models; multilevel models; multinomial logistic regression; R; reproducibility (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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DOI: 10.1515/jqas-2018-0010

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