Multi-hierarchical pattern recognition of athlete's relative performance as a criterion for predicting potential athletes

• Main Authors: Mohamad Razali, Abdullah, Mainul, Haque, Hafizan, Juahir
• Format: Article
• Language: English; English
• Published: EManuscript Services 2016
• Subjects: L Education (General)
• Online Access: http://eprints.unisza.edu.my/7546/1/FH02-FSSG-16-06506.jpg; http://eprints.unisza.edu.my/7546/2/FH02-FSSG-17-08125.jpg; http://eprints.unisza.edu.my/7546/

Objective: This study investigates the relative performance quality pattern of athletes that trains under Terengganu sports development program based on physical fitness and psychological components. Methods: Relative performance data (223x7) were obtained from various types of sport, and its main tributaries were evaluated for physical fitness and TEOSQ instrument. Multivariate methods of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), and principal factor analysis (PFA), were used to study the relative performance variations of the most significant performance quality variables and to determine the origin of relative performance components. Results: Three clusters of performance were shaped in view of HACA. Forward and backward stepwise DA discriminates six and five performance quality variables from the first seven variables. PCA and FA were used to identify the origin of each quality performance variables based on three clustered groups. Three PCs were obtained with 67% total variation for the high-performance group (HPG) region, three PCs with 72% and 64% total variances were obtained for the moderate-performance group (MPG) and low-performance group (LPG) regions, respectively. The general performance sources for the three groups are from cardiovascular and ego orientation sources. The differences between groups are from flexibility for LPG, task orientation, muscle strength and endurance for MPG and for HPG is flexibility, strength and task orientation. Conclusion: Multivariate methods reveal meaningful information on the relative performance variability of a large and complex athlete's performance quality data and can be used to determine the significant source and predict potential athletes.