Machine Learning in Team Sports (eBook)

This brief highlights the application of performance analysis tools in data acquisition, and various machine learning algorithms for evaluating team performance as well as talent identification in beach soccer and sepak takraw. Numerous performance indicators and human performance parameters are considered based on their relevance to each sport. The findings presented here demonstrate that the key performance indicators as well as human performance parameters can be used in the future evaluation of team performance as well as talent identification in these sports. Accordingly, they offer a valuable resource for coaches, club managers, talent identification experts, performance analysts and other relevant stakeholders involved in performance assessments. 

Dr. Rabiu Muazu Musa holds a PhD degree from the Universiti Sultan Zainal Abidin (UniSZA), Malaysia. He obtained his MSc in Sports Science from the UniSZA in 2015 and his BSc in Physical and Health Education from Bayero University Kano, Nigeria in 2011. His PhD research focused on the development of multivariate and machine learning models for gauging athletic performance. His research interests include performance analysis, health promotion, sports psychology, exercise science, talent identification, testing and measurement, as well as machine learning. He is currently a lecturer at the Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu.

Dr. Anwar P.P. Abdul Majeed holds a B.Eng. in Mechanical Engineering from the Universiti Teknologi MARA (UiTM), Malaysia; an MSc in Nuclear Engineering from Imperial College London, UK; and a PhD in Rehabilitation Robotics from the Universiti Malaysia Pahang (UMP). He is currently serving as a senior lecturer at the Faculty of Manufacturing and Mechatronics Engineering Technology, UMP and is an active research member of the Innovative Manufacturing, Mechatronics and Sports (iMAMS) Laboratory, UMP. His research interests include rehabilitation robotics, computational mechanics, applied mechanics, sports engineering, sports performance analysis, and machine learning.

Dr. Norlaila Azura Kosni obtained her first degree and Master's degree in Sports Science from the Universiti Malaysia Sabah (UMS). She attained her PhD in Sports Science at the Universiti Sultan Zainal Abidin, with a focus on performance models for youth athletes. Her research interests include sports biomechanics, athletic performance, sports analytics, and sports talent identification and development.

Assoc. Prof. Dr. Mohamad Razali Abdullah obtained his Bachelor of Physical Education from the Universiti Putra Malaysia (UPM) in 1989; his MSc in Sport and Exercise Science from the University of Wales Institute, Cardiff in 1998; and his PhD in Sports Science from the UPM in 2007. His research interests include motor control, sports biomechanics, motor performance and machine learning in sports.

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This brief highlights the application of performance analysis tools in data acquisition, and various machine learning algorithms for evaluating team performance as well as talent identification in beach soccer and sepak takraw. Numerous performance indicators and human performance parameters are considered based on their relevance to each sport. The findings presented here demonstrate that the key performance indicators as well as human performance parameters can be used in the future evaluation of team performance as well as talent identification in these sports. Accordingly, they offer a valuable resource for coaches, club managers, talent identification experts, performance analysts and other relevant stakeholders involved in performance assessments.