Machine Learning in Elite Volleyball - Integrating Performance Analysis, Competition and Training Strategies

This brief highlights the use of various Machine Learning (ML) algorithms to evaluate training and competitional strategies in Volleyball, as well as to identify high-performance players in the sport. Several psychological elements/strategies coupled with human performance parameters are discussed in view to ascertain their impact on performance in elite Volleyball competitions. It presents key performance indicators as well as human performance parameters that can be used in future evaluation of team performance and players. The details outlined in this brief are vital to coaches, club managers, talent identification experts, performance analysts as well as other important stakeholders in the evaluation of performance and to foster improvement in this sport.

List of contents

Chapter 1. Nature of Volleyball Sport, Performance Analysis in Volleyball, and the Recent Advances of Machine Learning Application in Sports.- Chapter 2. The Effect of Competition strategies in influencing Volleyball performance.- Chapter 3. Identification of psychological training strategies essential for Volleyball performance.- Chapter 4. The Strategic competitional elements contributing to Volleyball performance.- Chapter 5. Anthropometric variables in the identification of high-performance Volleyball players.- Chapter 6. Performance Indicators predicting medalists and non-medalists in elite men Volleyball competition.- Chapter 7. Summary, Conclusion and Future Direction.

About the author

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

Dr Anwar P.P. Abdul Majeed graduated with a first-class honours B.Eng. in Mechanical Engineering from Universiti Teknologi MARA (UiTM), Malaysia. He obtained an MSc. in Nuclear Engineering from Imperial College London, United Kingdom. He then received his PhD in Rehabilitation Robotics from the UniversitiMalaysia Pahang (UMP). He is currently serving as a senior lecturer and the Head of Programme (Bachelor of Manufacturing Engineering Technology (Industrial Automation)) at the Faculty of Manufacturing and Mechatronics Engineering Technology, UMP. Dr Anwar is a Chartered Engineer, registered with the Institute of Mechanical Engineers (IMechE), UK, a Member of the Institute of Engineering and Technology (IET), UK as well as a Member of the Institute of Electrical and Electronics Engineers (IEEE). He is an active research member at the Innovative Manufacturing, Mechatronics and Sports Laboratory (iMAMS), UMP. His research interest includes rehabilitation robotics, computational mechanics, applied mechanics, sports engineering, renewable and nuclear energy, sports performance analysis as well as machine learning. He has authored over 60 papers in different journals, conference proceedings as well as books. He serves as a reviewer in a number of prolific journals such as IEEE Access, Frontiers in Bioengineering and Biotechnology, SN Applied Sciences, PeerJ Computer Science, Applied Computing and Informatics amongst others. He has also served as a Guest Editor for SN Applied Sciences as well as an Editor for several Springer book series. Dr Anwar is also an affiliate member with the Young Scientists Network of the Academy of Sciences Malaysia (YSN - ASM).

Muhammad Zuhaili Suhaimi is a lecturer at Universiti Malaysia Terengganu, Malaysia. He obtained his degree in Bachelor of Health Sciences (Exercise & Sports Science) and Master of Science (Sports Science), both at University Science of Malaysia (USM). His research interests include exercise-induced oxidative stress, health-related fitness, and high intensity interval training (HIIT). In volleyball, he is an ex-state athlete who participated in Sukan Malaysia (SUKMA) 2008 and currently he was certified as a volleyball coach (Level 1) by the Malaysia Volleyball Association (MAVA).

Dr. Mohd Azraai Mohd Razman graduated from the University of Sheffield, UK in Mechatronics Engineering. He then obtained his MEng. from Universiti Malaysia Pahang (UMP) in Mechatronics Engineering. He completed his PhD at UMP specifically in the application of machine learning in aquaculture. His research interest includes optimization techniques, control systems, signal processing, instrumentation in aquaculture, sports engineering as well as machine learning. He is currently serving as a guest editor for SN Applied Sciences in a number of topical collections.

Assoc. Prof. Dr. Mohamad Razali Abdullah obtained his Bachelor of Physical Education in 1989 from Universiti Putra Malaysia (UPM). He obtained his MSc in Sport and Exercise Science from the University of Wales Institute, Cardiff in 1998 and in 2007 he received his PhD in Sports Science from UPM. His research interests include motor control, sports biomechanics,motor performance and machine learning in sports. Heis currently an Associate Professor at East Coast Environmental R

Product details

• Authors: Anwar P Abdul Majeed, Anwar P P Abdul Majeed, Anwar P. P. Abdul Majeed, Mohamad Razali Abdullah, Noor Azuan Abu Osman, Mohd Azraai Mohd Razman, Rabi Muazu Musa, Rabiu Muazu Musa, Suhaimi, Muhammad Zuhaili Suhaimi
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 25.07.2021
• EAN: 9789811631917
• ISBN: 978-981-1631-91-7
• No. of pages: 53
• Dimensions: 155 mm x 3 mm x 235 mm
• Illustrations: X, 53 p. 13 illus., 12 illus. in color.
• Series: SpringerBriefs in Applied Sciences and Technology; Springerbriefs in Applied Scie
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > IT