For example, Mulholland and Jensen (2014) analyze the success of tight ends in the NFL draft, Clark, Johnson, and Stimpson (2013) and Pasteur and Cunningham-Rhoads (2014) both provide improved metrics for kicker evaluation, Martin, Timmons, and Powell (2017) examine the NFL’s change in overtime rules, and Snyder and Lopez (2015) focus on discretionary penalties from referees. Additionally, with the notable exception of Lock and Nettleton (2014), recent research relating to on-field or player personnel decisions in football is narrowly focused.
NFL PLAY BY PLAY DATA TV
Recent work in football addresses topics such as fantasy football ( Becker and Sun 2016), predicting game outcomes ( Balreira, Miceli, and Tegtmeyer 2014), NFL TV ratings ( Grimshaw and Burwell 2014), the effect of “fan passion” and league sponsorship on brand recognition ( Wakefield and Rivers 2012), and realignment in college football ( Jensen and Turner 2014). 2013), there is limited new research that addresses on-field or player personnel decisions for National Football League (NFL) teams. 2007 Deshpande and Jensen 2016), and hockey ( Macdonald 2011 Gramacy, Taddy, and Jensen 2013 Thomas et al. While new statistical research involving player and team evaluation is regularly published in baseball ( Albert 2006 Jensen, Shirley, and Wyner 2009 Piette and Jensen 2012 Baumer, Jensen, and Matthews 2015), basketball ( Kubatko et al. Finally, we discuss the potential implications of this work for NFL teams.ĭespite the sport’s popularity in the United States, public statistical analysis of American football (“football”) has lagged behind that of other major sports. 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. We assess the uncertainty in WAR through a resampling approach specifically designed for football, and we present results for the 2017 NFL season. 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). Third, we use the expected points as input into a generalized additive model for estimating the win probability for each play. Second, we introduce a novel multinomial logistic regression approach for estimating the expected points for each play.
First, we develop the R package nflscrapR to provide easy access to publicly available play-by-play data from the National Football League (NFL). We present four contributions to the study of football statistics to address these issues. 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.
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