Overview
- Highlights the application of performance analysis tools in data acquisition, and various ML algorithms for evaluating team performance in sports
- Discusses how key performance indicators as well as human performance parameters are used in talent identification
- Will help coaches, managers and professionals to evaluate athletes’ performance
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
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About this book
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.
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Keywords
Table of contents (7 chapters)
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Front Matter
Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Machine Learning in Team Sports
Book Subtitle: Performance Analysis and Talent Identification in Beach Soccer & Sepak-takraw
Authors: Rabiu Muazu Musa, Anwar P.P. Abdul Majeed, Norlaila Azura Kosni, Mohamad Razali Abdullah
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-981-15-3219-1
Publisher: Springer Singapore
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020
Softcover ISBN: 978-981-15-3218-4Published: 18 February 2020
eBook ISBN: 978-981-15-3219-1Published: 17 February 2020
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: X, 61
Number of Illustrations: 2 b/w illustrations, 24 illustrations in colour
Topics: Computational Intelligence, Sport, Simulation and Modeling, Sport Psychology, Biomedical Engineering and Bioengineering, Computer Appl. in Social and Behavioral Sciences