The “Moneyball” Myth: Evaluating Public Pre-Draft Data Against Expert Scouting in the NFL

Abstract

The National Football League (NFL) Draft is a high-stakes selection process where teams commit multi-million dollar investments to unproven talent under high uncertainty of future production. “Moneyball” methodology transformed baseball and sports analytics as a whole, offering the potential to capitalize on inefficient traditional approaches to player valuation. This thesis tests whether a similar approach can improve NFL Draft decisions. To do this, a comprehensive feature set with in-depth college statistics, combine results, and additional physical and school-context measurements is designed. Linear and nonlinear models are trained to predict Approximate Value, a position-independent output metric of on-field contribution, across various time horizons. The predictive power of this set is evaluated against ESPN scouting variables, which are aggregated expert grades and rankings of incoming prospects. Predicted target values are utilized to test player selection in historical drafts and reconstruct traditional pick-value curves to better estimate drafting capital. Results showed that without supplemental scouting information, public data alone could not predict performance on any time horizon. Furthermore, a simple calibration of a single ESPN scouting variable outperformed the full feature set machine learning pipeline across all horizons. Reranking historical drafts through model predictions achieved modest results. This implies that scout-level knowledge, although subjective, already absorbs the signal in publicly available data.