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Full Description
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.
Contents
Introduction.- Maturity Status as a Predictor of Fitness Efficiency and Neuromotor Control in Youth Badminton Players.- Decision Tree Model for Designing Training Programs in Young Badminton Players Based on Bio-Fitness and Motor Ability Parameters.- Classification of External Load Based on Fitness and Motor Ability Parameters in Youth Badminton.- Identification of Important Anthropometric, Fitness and Motor Ability Parameters for Young Badminton Players.- Essential Competitive and Training Psychological Strategies for Performance in Young Badminton Players.- Fitness and Motor Ability Parameters in Classifying Winners and Losers During Badminton Match Play.- Concluding Remarks.
