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SpringerBriefs in Applied Sciences and Technology

Machine Learning in Sports

Identifying Potential Archers

Authors: Muazu Musa, R., Taha, Z., P.P.Abdul Majeed, A., Abdullah, M.R.

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About this book

This brief highlights the association of different performance variables that influences archery performance and the employment of different machine learning algorithms in the identification of potential archers. The sport of archery is often associated with a myriad of performance indicators namely bio-physiological, psychological, anthropometric as well as physical fitness. Traditionally, the determination of potential archers is carried out by means of conventional statistical techniques. Nonetheless, such methods often fall short in associating non-linear relationships between the variables. This book explores the notion of machine learning that is capable of mitigating the aforesaid issue. This book is valuable for coaches and managers in identifying potential archers during talent identification programs.​

About the authors

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.

Mr Anwar P.P. Abdul Majeed graduated with a first class honours in B.Eng. Mechanical Engineering from Universiti Teknologi MARA (UiTM), Malaysia. He obtained an MSc. in Nuclear Engineering from Imperial College London, United Kingdom. He is currently pursuing his PhD studies at the Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing Engineering, University Malaysia Pahang. His research interest includes computational mechanics, dynamics, vibration, control, acoustics, sports engineering as well as machine learning.

Prof. Dr. Zahari Taha graduated with a BSc in Aeronautical Engineering with Honours from the University of Bath, United Kingdom. He obtained his PhD in Dynamics and Control of Robots from the University of Wales Institute of Science and Technology in 1987. He is currently a Professor of Manufacturing Engineering at Universiti Malaysia Pahang and he is also the founder and advisor of the Innovative Manufacturing, Mechatronics and Sports Laboratory. Prof Zahari teaches and conducts research in the areas of industrial automation, robotics, ergonomics, sustainable manufacturing, machine learning and sports engineering.

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. He is currently an Associate Professor at Faculty of Applied Social Sciences, Universiti Sultan Zainal Abidin, Kuala Terengganu, Malaysia.​

Table of contents (6 chapters)

Table of contents (6 chapters)

Buy this book

eBook $54.99
price for USA in USD (gross)
  • ISBN 978-981-13-2592-2
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for USA in USD
  • ISBN 978-981-13-2591-5
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning in Sports
Book Subtitle
Identifying Potential Archers
Authors
Series Title
SpringerBriefs in Applied Sciences and Technology
Copyright
2019
Publisher
Springer Singapore
Copyright Holder
The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
eBook ISBN
978-981-13-2592-2
DOI
10.1007/978-981-13-2592-2
Softcover ISBN
978-981-13-2591-5
Series ISSN
2191-530X
Edition Number
1
Number of Pages
XII, 45
Number of Illustrations
11 illustrations in colour
Topics

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