Taha, Z. and Musa, R.M. and Majeed, A.P.P.A and Abdullah, M.R. and Alim, M.M. and Ab Nasir, A.F. (2018) The application of k-Nearest Neighbour in the identification of high potential archers based on relative psychological coping skills variables. In: International Conference on Innovative Technology, Engineering and Sciences 2018, 01-02 May 2018, Universiti Malaysia Pahang.
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Abstract
The present study aims at classifying and predicting high and low potential archers from a collection of psychological coping skills variables trained on different k-Nearest Neighbour (k-NN) kernels. 50 youth archers with the average age and standard deviation of (17.0 +/-.056) gathered from various archery programmes completed a one end shooting score test. Psychological coping skills inventory which evaluates the archers level of related coping skills were filled out by the archers prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed k-NN models, i.e. fine, medium, coarse, cosine, cubic and weighted kernel functions, were trained on the psychological variables. The k-means clustered the archers into high psychologically prepared archers (HPPA) and low psychologically prepared archers (LPPA), respectively. It was demonstrated that the cosine k-NN model exhibited good accuracy and precision throughout the exercise with an accuracy of 94% and considerably fewer error rate for the prediction of the HPPA and the LPPA as compared to the rest of the models. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected psychological coping skills variables examined which would consequently save time and energy during talent identification and development programme.
Item Type: | Conference or Workshop Item (Paper) |
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Uncontrolled Keywords: | bayesian network, performance, sport |
Subjects: | B Philosophy. Psychology. Religion > BF Psychology Q Science > Q Science (General) T Technology > T Technology (General) |
Divisions: | Faculty of Applied Social Sciences |
Depositing User: | Muhammad Akmal Azhar |
Date Deposited: | 22 Nov 2020 01:55 |
Last Modified: | 22 Nov 2020 01:55 |
URI: | http://eprints.unisza.edu.my/id/eprint/1702 |
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