Determining Added Value of Defensive NFL Players

Turn on any of the sports talk shows popular on TV today and you will hear sports commentators debating whether Tom Brady is better than Aaron Rodgers in the NFL or if Bryce Harper is superior to Mike Trout in the MLB. The latter debate is dominated by objective stats, such as Wins Above Replacement (WAR) and Runs Created (RC), to show individual value. However, the former is dominated by the analysis of ‘clutchness’ and descriptive statistics such as Passer Rating and touchdown passes completed. Advanced statistical analysis has begun to be applied to sports like basketball and hockey with some success. This revolution, however, has not arrived at the NFL for several reasons. Among these, the output of a single player is hard to measure because of the interdependency of teammates (i.e. for a running back to do well he both needs to run well and have an offensive line that can create space for him) and the high variability in player usage, playcalling, and formation.

This project intends to answer three questions: (1) what are the challenges in measuring individual value in NFL football through sabermetric analysis; (2) what specifications need to be made to create an accurate model of individual defensive players; and (3) what model needs to be created to create a comprehensive WAR statistic for defensive football players. This project aims to build upon prior research in the field to fill a large and important gap in the knowledge of sports analytics.