Neural Network Approach to NFL Position Classification

Abstract

With an ever-increasing captivation of the United States sports-viewing audience, the National Football League continues to produce some of the world’s most capable, physical athletes. In this work, athletes’ positions C, OG, OT, DE, and DT were categorized as on the line , while the remaining positions were categorized as not on the line . In this work, a predictive neural network is applied to classify 2,022 National Football League players into the two classifications using scouting combine data of height, weight, and 40-Yard dash time, outperforming the current standard logistic regression. The two measures utilized to compare the strength of the methods were total accuracy and area under ROC curve, with the neural network yielding a slightly higher average in both. In terms of total accuracy, the neural network had an accuracy of 0.9134 to the logistic model’s 0.9065, and in terms of area under ROC curve, the neural network had an area of 0.9578 compared to the logistic model’s 0.9567. As a head-to-head iteration-wise comparison, the neural network had a winning Win-Loss-Tie ratio of 7-2-1 and 5-5-0 in the two measures respectively.