Evaluating Individual Player Value and Positional Spending Efficiency in the National Football League

Abstract

This thesis introduces a data-driven framework for evaluating player value in the National Football League (NFL) by linking advanced performance metrics to player salaries. Despite the proliferation of advanced metrics in professional football, translating measures such as Wins Above Replacement, Expected Points Added, and Pro Football Focus grades into fair salary valuations remains challenging. The proposed framework addresses this gap by combining unsupervised learning with predictive modeling. Specifically, we use k-means to group players into performance-based archetypes. Then we train XGBoost regression models for each archetype to predict players’ expected average per-year salary. Finally, we design a constrained roster optimization model in order to maximize expected team wins under the salary cap. This segmented modeling approach enables a fine-grained evaluation of cost-efficiency across player roles and reveals systematic market inefficiencies. Results indicate that certain roles are consistently undervalued, whereas others are overvalued relative to their on-field contributions. We acknowledge that our findings are limited when considering an entire NFL roster due to the scarcity of advanced tracking data. We further acknowledge that our results are subject to uncertainty due to insufficient robustness checks and validation. Nevertheless, our results are intriguing and they immediately provide a practical application for researchers or general managers who want to improve their spending efficiency.