Machine Learning in Youth Badminton: Predictive Analytics for Talent Detection and Individualised Training

This book explores the application of machine learning techniques to model the interplay between psycho-physiological, anthropometric, and fitness variables in youth badminton athletes. This book explores the application of machine learning techniques to model the interplay between psycho-physiological, anthropometric, and fitness variables in youth badminton athletes. The data presented in this book were collected across multiple youth badminton development programs, encompassing a broad spectrum of athletes aged 11 to 17. Key parameters include maturity offset, neuromuscular fitness (e.g., jump performance, balance, coordination), psychological indicators (e.g., training and competitive strategies), and internal/external training loads. Through classification models, clustering techniques, and predictive analytics, the book examines how these variables interact to inform talent identification and design individualised training strategies. The findings from this work are envisioned to support evidence-based decision-making for coaches, sport scientists, and talent development experts by offering actionable insights into the profiling, monitoring, and development of youth badminton players. This approach holds promise for enhancing athlete development pipelines, minimising injury risk, and facilitating early identification of future elite badminton players.