The Computational Evolution of Athletic Selection: Integrating Artificial Neural Networks into Talent Identification and Scouting Systems

Abstract

The traditional landscape of sports scouting, long defined by the subjective intuition of seasoned
professionals, is undergoing a profound transformation driven by the proliferation of high-dimensional
data and the sophisticated application of Artificial Neural Networks (ANNs). Historically, talent
identification—the process of recognizing young or unknown athletes with the potential to perform at
the elite level—relied heavily on personal experience, qualitative criteria, and geographic proximity.1
These methods, while culturally entrenched, are inherently prone to cognitive biases, human error, and
the "Relative Age Effect" (RAE), where physical maturity is frequently mistaken for latent talent.1 As
professional sports organizations move toward a model of "talent supply chain management," the
integration of computational models provides a mechanism to secure competitive advantages before
rivals discover specific athletes, while simultaneously optimizing the distribution of training resources
and minimizing financial losses associated with unsuccessful development programs.1