NFL Career Success as Predicted by NFL Scouting Combine

Abstract: The National Football League (NFL) Scouting Combine serves as a tool to evaluate the skills of prospective players and assess their readiness to play in the NFL. The development of machine learning brings new opportunities in assessing the utility of the Scouting Combine. Using machine and statistical learning, it may be possible to predict future success of prospective athletes, as well as predict which Scouting Combine tests are the most important. Results from statistical learning research have been contradicting whether the Scouting combine is a useful metric for player success. In this study, we investigate if machine learning can be used to determine matriculation and future success in the NFL. Using Scouting Combine data, we evaluate six different algorithms' ability to predict whether a potential draft pick will play a single NFL snap (matriculation). If a player is drafted, we predict how many snaps they go on to play (success). We are able to predict matriculation with 83% accuracy; however, we are unable to predict later success. Our best performing algorithm returns large error and low explained variance (RMSE=1,210 snaps; ${R}^2$=0.17). These findings indicate that while the Scouting Combine can predict NFL matriculation, it may not be a reliable predictor of long-term player success.