nflWAR: a reproducible method for offensive player evaluation in football
Ronald Yurko, Samuel Ventura, Maksim Horowitz
Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
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.
Results & Benchmarks
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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.
Implementation Evidence Summary
ryurko/nflWAR is the closest maintained adjacent implementation (Matches contextual method/domain keyword: offensive). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 36 GitHub stars.
Reproduction Risks
- Adjacent implementations are not paper-verified
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Hardware Notes
Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Evidence disclosure
Evidence graph: 3 refs, 3 links.
Utility signals: depth 70/100, grounding 75/100, status medium.
Implementation Status
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Reproduction readiness
Hardware requirements
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No verified implementation available
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Closest related implementations
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- ryurko/nflWARAdjacentConfidence: LowStars: 36
Matches contextual method/domain keyword: offensive
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Research context
53
Citations
37
References
Tasks
Football, Offensive, League, Computer science, Resampling, American football, Multinomial logistic regression, Operations research
Methods
None detected
Domains
Economics, Econometrics and Finance
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