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.
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.
Dr. Rabiu Muazu Musa holds a PhD in Sports Science from Universiti Sultan Zainal Abidin (UniSZA), Malaysia. His research activity is focused on the development of multivariate and machine learning models for athletic performance. His research interests include sports performance analysis, health promotion, sport and exercise science, talent identification, test, and measurement as well as machine learning in sports.
Dr. Anwar P.P. Abdul Majeed is an Associate Professor and Head of Department at the Department of Data Science and Artificial Intelligence at Sunway University, bringing extensive experience in transnational education. He received his PhD in Rehabilitation Robotics from Universiti Malaysia Pahang (UMP). Prior to his current position, he served as an Associate Professor at the School of Robotics, Xi'an Jiaotong Liverpool University, and previously held roles as Senior Lecturer and Head of Programme for Bachelor of Manufacturing Engineering Technology (Industrial Automation) at the Faculty of Manufacturing and Mechatronics Engineering Technology, UMP. As a Chartered Engineer registered with the Institute of Mechanical Engineers (IMechE), UK, he is also a member of the Institute of Engineering and Technology (IET), UK, and a Senior Member of IEEE. His research encompasses rehabilitation robotics, computational mechanics, applied mechanics, sports engineering, renewable and nuclear energy, sports performance analysis, and machine learning applications across various domains.
