  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 framework, using multilevel models to isolate
the contributions of individual offensive skill players, and providing
estimates for their individual wins above replacement (WAR). We estimate the
uncertainty in each player's WAR through a resampling approach specifically
designed for football, and we present these results for the 2017 NFL season. We
discuss how our reproducible WAR framework, built entirely on publicly
available data, can be easily extended to estimate WAR for players at any
position, provided that researchers have access to data specifying which
players are on the field during each play. Finally, we discuss the potential
implications of this work for NFL teams.
