The Identification of High Potential Archers Based on Fitness and Motor Ability Variables: A Support Vector Machine Approach

Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study...

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Main Authors:	Zahari, Taha, Rabiu Muazu, Musa, Anwar, P. P. Abdul Majeed, Muhammad Muaz, Alim, Mohamad Razali, Abdullah
Format:	Article
Language:	English
Published:	Elsevier Ltd 2018
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/20341/ http://umpir.ump.edu.my/id/eprint/20341/ http://umpir.ump.edu.my/id/eprint/20341/ http://umpir.ump.edu.my/id/eprint/20341/1/The%20Identification%20of%20High%20Potential%20Archers%20Based%20on%20Fitness%20and%20Motor1.pdf

id	ump-20341
recordtype	eprints
spelling	ump-203412018-07-31T01:39:39Z http://umpir.ump.edu.my/id/eprint/20341/ The Identification of High Potential Archers Based on Fitness and Motor Ability Variables: A Support Vector Machine Approach Zahari, Taha Rabiu Muazu, Musa Anwar, P. P. Abdul Majeed Muhammad Muaz, Alim Mohamad Razali, Abdullah TS Manufactures Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. Standard fitness and ability measurements namely hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were also recorded. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. SVM models with linear, quadratic, cubic, fine RBF, medium RBF, as well as the coarse RBF kernel functions, were trained based on the measured performance variables. The HACA clustered the archers into high-potential archers (HPA) and low-potential archers (LPA), respectively. The linear, quadratic, cubic, as well as the medium RBF kernel functions models, demonstrated reasonably excellent classification accuracy of 97.5% and 2.5% error rate for the prediction of the HPA and the LPA. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from a combination of the selected few measured fitness and motor ability performance variables examined which would consequently save cost, time and effort during talent identification programme. Elsevier Ltd 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/20341/1/The%20Identification%20of%20High%20Potential%20Archers%20Based%20on%20Fitness%20and%20Motor1.pdf Zahari, Taha and Rabiu Muazu, Musa and Anwar, P. P. Abdul Majeed and Muhammad Muaz, Alim and Mohamad Razali, Abdullah (2018) The Identification of High Potential Archers Based on Fitness and Motor Ability Variables: A Support Vector Machine Approach. Human Movement Science, 57. pp. 184-193. ISSN 0167-9457 https://doi.org/10.1016/j.humov.2017.12.008 https://doi.org/10.1016/j.humov.2017.12.008
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TS Manufactures
spellingShingle	TS Manufactures Zahari, Taha Rabiu Muazu, Musa Anwar, P. P. Abdul Majeed Muhammad Muaz, Alim Mohamad Razali, Abdullah The Identification of High Potential Archers Based on Fitness and Motor Ability Variables: A Support Vector Machine Approach
description	Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. Standard fitness and ability measurements namely hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were also recorded. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. SVM models with linear, quadratic, cubic, fine RBF, medium RBF, as well as the coarse RBF kernel functions, were trained based on the measured performance variables. The HACA clustered the archers into high-potential archers (HPA) and low-potential archers (LPA), respectively. The linear, quadratic, cubic, as well as the medium RBF kernel functions models, demonstrated reasonably excellent classification accuracy of 97.5% and 2.5% error rate for the prediction of the HPA and the LPA. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from a combination of the selected few measured fitness and motor ability performance variables examined which would consequently save cost, time and effort during talent identification programme.
format	Article
author	Zahari, Taha Rabiu Muazu, Musa Anwar, P. P. Abdul Majeed Muhammad Muaz, Alim Mohamad Razali, Abdullah
author_facet	Zahari, Taha Rabiu Muazu, Musa Anwar, P. P. Abdul Majeed Muhammad Muaz, Alim Mohamad Razali, Abdullah
author_sort	Zahari, Taha
title	The Identification of High Potential Archers Based on Fitness and Motor Ability Variables: A Support Vector Machine Approach
title_short	The Identification of High Potential Archers Based on Fitness and Motor Ability Variables: A Support Vector Machine Approach
title_full	The Identification of High Potential Archers Based on Fitness and Motor Ability Variables: A Support Vector Machine Approach
title_fullStr	The Identification of High Potential Archers Based on Fitness and Motor Ability Variables: A Support Vector Machine Approach
title_full_unstemmed	The Identification of High Potential Archers Based on Fitness and Motor Ability Variables: A Support Vector Machine Approach
title_sort	identification of high potential archers based on fitness and motor ability variables: a support vector machine approach
publisher	Elsevier Ltd
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/20341/ http://umpir.ump.edu.my/id/eprint/20341/ http://umpir.ump.edu.my/id/eprint/20341/ http://umpir.ump.edu.my/id/eprint/20341/1/The%20Identification%20of%20High%20Potential%20Archers%20Based%20on%20Fitness%20and%20Motor1.pdf
first_indexed	2023-09-18T22:29:17Z
last_indexed	2023-09-18T22:29:17Z
_version_	1777416176489463808

The Identification of High Potential Archers Based on Fitness and Motor Ability Variables: A Support Vector Machine Approach

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