TL;DR
This paper introduces nflWAR, a reproducible framework for evaluating offensive players in football using publicly available data, novel statistical models, and a new R package, enabling transparent and extendable player assessment.
Contribution
The paper develops a reproducible, open-source method for offensive player evaluation in football, combining new statistical models and a comprehensive R package for data access.
Findings
nflWAR provides player-specific wins above replacement estimates.
The framework is based on publicly available NFL play data.
Uncertainty in player WAR estimates is quantified through resampling.
Abstract
Unlike other major professional sports, American football lacks comprehensive statistical ratings for player evaluation that are both reproducible and easily interpretable in terms of game outcomes. Existing methods for player evaluation in football depend heavily on proprietary data, are not reproducible, and lag behind those of other major sports. We present four contributions to the study of football statistics in order 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) dating back to 2009. 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…
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