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

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