TY - JOUR
T1 - The identification of high potential archers based on relative psychological coping skills variables
T2 - 4th Asia Pacific Conference on Manufacturing Systems and the 3rd International Manufacturing Engineering Conference, APCOMS-iMEC 2017
AU - Taha, Zahari
AU - Musa, Rabiu Muazu
AU - Abdul Majeed, A. P.P.
AU - Abdullah, Mohamad Razali
AU - Zakaria, Muhammad Aizzat
AU - Alim, Muhammad Muaz
AU - Jizat, Jessnor Arif Mat
AU - Ibrahim, Mohamad Fauzi
N1 - Funding Information:
This work is funded by the National Sports Institute of Malaysia (ISNRG: 8/2014-12/2014).
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2018/3/21
Y1 - 2018/3/21
N2 - Support Vector Machine (SVM) has been revealed to be a powerful learning algorithm for classification and prediction. However, the use of SVM for prediction and classification in sport is at its inception. The present study classified and predicted high and low potential archers from a collection of psychological coping skills variables trained on different SVMs. 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. SVM models, i.e. linear and fine radial basis function (RBF) 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 linear SVM exhibited good accuracy and precision throughout the exercise with an accuracy of 92% and considerably fewer error rate for the prediction of the HPPA and the LPPA as compared to the fine RBF SVM. 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.
AB - Support Vector Machine (SVM) has been revealed to be a powerful learning algorithm for classification and prediction. However, the use of SVM for prediction and classification in sport is at its inception. The present study classified and predicted high and low potential archers from a collection of psychological coping skills variables trained on different SVMs. 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. SVM models, i.e. linear and fine radial basis function (RBF) 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 linear SVM exhibited good accuracy and precision throughout the exercise with an accuracy of 92% and considerably fewer error rate for the prediction of the HPPA and the LPPA as compared to the fine RBF SVM. 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.
UR - http://www.scopus.com/inward/record.url?scp=85045620375&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/319/1/012027
DO - 10.1088/1757-899X/319/1/012027
M3 - Conference article
AN - SCOPUS:85045620375
SN - 1757-8981
VL - 319
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
IS - 1
M1 - 012027
Y2 - 7 December 2017 through 8 December 2017
ER -