A machine learning approach of predicting high potential archers by means of physical fitness indicators

k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. However, the application of k-NN for prediction and classification in specific sport is still in its infancy. The present study classified and predicted high and low potential archers f...

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Main Authors:	Musa, Rabiu Muazu, Anwar, P. P. Abdul Majeed, Zahari, Taha
Format:	Article
Language:	English
Published:	Public Library of Science 2019
Subjects:	RC1200 Sports Medicine TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/24884/ http://umpir.ump.edu.my/id/eprint/24884/ http://umpir.ump.edu.my/id/eprint/24884/ http://umpir.ump.edu.my/id/eprint/24884/1/A%20machine%20learning%20approach%20of%20predicting%20high%20potential%20archers.pdf

id	ump-24884
recordtype	eprints
spelling	ump-248842019-07-09T04:55:56Z http://umpir.ump.edu.my/id/eprint/24884/ A machine learning approach of predicting high potential archers by means of physical fitness indicators Musa, Rabiu Muazu Anwar, P. P. Abdul Majeed Zahari, Taha RC1200 Sports Medicine TS Manufactures k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. However, the application of k-NN for prediction and classification in specific sport is still in its infancy. The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. Standard fitness measurements of the handgrip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were conducted. Multiple linear regression was utilised to ascertain the significant variables that affect the shooting score. It was demonstrated from the analysis that core muscle strength and vertical jump were statistically significant. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. The HACA clustered the archers into high potential archers (HPA) and low potential archers (LPA). The weighted k-NN outperformed all the tested models at itdemonstrated reasonably good classification on the evaluated indicators with an accuracy of 82.5 ± 4.75% for the prediction of the HPA and the LPA. Moreover, the performance of the classifiers was further investigated against fresh data, which also indicates the efficacy of the weighted k-NN model. These findings could be valuable to coaches and sports managers to recognise high potential archers from a combination of the selected few physical fitness performance indicators identified which would subsequently save cost, time and energy for a talent identification programme. Public Library of Science 2019-01-03 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/24884/1/A%20machine%20learning%20approach%20of%20predicting%20high%20potential%20archers.pdf Musa, Rabiu Muazu and Anwar, P. P. Abdul Majeed and Zahari, Taha (2019) A machine learning approach of predicting high potential archers by means of physical fitness indicators. PLoS ONE, 14 (1). pp. 1-12. ISSN 1932-6203 https://doi.org/10.1371/journal.pone.0209638 https://doi.org/10.1371/journal.pone.0209638
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	RC1200 Sports Medicine TS Manufactures
spellingShingle	RC1200 Sports Medicine TS Manufactures Musa, Rabiu Muazu Anwar, P. P. Abdul Majeed Zahari, Taha A machine learning approach of predicting high potential archers by means of physical fitness indicators
description	k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. However, the application of k-NN for prediction and classification in specific sport is still in its infancy. The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. Standard fitness measurements of the handgrip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were conducted. Multiple linear regression was utilised to ascertain the significant variables that affect the shooting score. It was demonstrated from the analysis that core muscle strength and vertical jump were statistically significant. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. The HACA clustered the archers into high potential archers (HPA) and low potential archers (LPA). The weighted k-NN outperformed all the tested models at itdemonstrated reasonably good classification on the evaluated indicators with an accuracy of 82.5 ± 4.75% for the prediction of the HPA and the LPA. Moreover, the performance of the classifiers was further investigated against fresh data, which also indicates the efficacy of the weighted k-NN model. These findings could be valuable to coaches and sports managers to recognise high potential archers from a combination of the selected few physical fitness performance indicators identified which would subsequently save cost, time and energy for a talent identification programme.
format	Article
author	Musa, Rabiu Muazu Anwar, P. P. Abdul Majeed Zahari, Taha
author_facet	Musa, Rabiu Muazu Anwar, P. P. Abdul Majeed Zahari, Taha
author_sort	Musa, Rabiu Muazu
title	A machine learning approach of predicting high potential archers by means of physical fitness indicators
title_short	A machine learning approach of predicting high potential archers by means of physical fitness indicators
title_full	A machine learning approach of predicting high potential archers by means of physical fitness indicators
title_fullStr	A machine learning approach of predicting high potential archers by means of physical fitness indicators
title_full_unstemmed	A machine learning approach of predicting high potential archers by means of physical fitness indicators
title_sort	machine learning approach of predicting high potential archers by means of physical fitness indicators
publisher	Public Library of Science
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/24884/ http://umpir.ump.edu.my/id/eprint/24884/ http://umpir.ump.edu.my/id/eprint/24884/ http://umpir.ump.edu.my/id/eprint/24884/1/A%20machine%20learning%20approach%20of%20predicting%20high%20potential%20archers.pdf
first_indexed	2023-09-18T22:37:54Z
last_indexed	2023-09-18T22:37:54Z
_version_	1777416719059386368

A machine learning approach of predicting high potential archers by means of physical fitness indicators

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