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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| Format: | Article |
| Language: | English |
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Elsevier Ltd
2018
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| Online Access: | 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 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T02:19:38Z |
| format | Article |
| id | ump-20341 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:19:38Z |
| publishDate | 2018 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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. (Published) https://doi.org/10.1016/j.humov.2017.12.008 https://doi.org/10.1016/j.humov.2017.12.008 |
| 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 |
| title | 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_short | 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 |
| topic | TS Manufactures |
| 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 |