2025, Vol. 12, Issue 5, Part H

Traditionally, identifying sports talent has depended on subjective assessments, regional tryouts, and competition results. Although these methods can identify naturally gifted athletes, they lack consistency, scalability, and scientific rigor. This paper proposes using the Normal Probability Curve (NPC) as a statistical tool to create standardized benchmarks for talent identification. By matching athlete test scores to the Normal Probability Curve, researchers and coaches can categorize performance using percentile-based zones, set cut-off points, and differentiate between athletes of varying abilities. The methodology uses hypothetical data, longitudinal tracking, and sport-specific comparisons to demonstrate how the Normal Probability Curve can be applied in real-world talent identification programs. The findings demonstrate that the Normal Probability Curve offers a clear, objective, and adaptable framework that minimizes bias, enhances predictive accuracy, and supports evidence-based decisions in athlete development. The study concludes that incorporating the Normal Probability Curve into talent identification systems can strengthen talent pipelines, optimize resource allocation, and provide a fairer foundation for developing future sports excellence.