In the current study, a total of 86 soccer’s players with mean age of 14 years drawn from Terengganu soccer academy were tested in performing 10 parameters aiming at determining the performance of those players based on assessing the contribution of each activity and its corresponding significant level. The 10 performance related parameters involved anthropometry (BMI), fitness test (agility, coordination, muscular endurance (push and sit up), power, YoYo level), and football skill test (dribbling with ball, dribbling without ball and juggling). All the parameters testing is carried out based on international standard and performed by well-trained staff. The Pearson correlation analysis was used to achieve the objective in this study. Result shows a positive correlation between the two types of muscular parameters; the power is influenced by BMI and coordination; the specific football tests are highly impacted by the power and agility. The coefficient of determination R^2 and the significance level p-values show that the parameters that can be significantly considered are the anthropometric BMI (0.020), agility (0.025), muscular endurance (0.039 and 0.043), power (0.039), special football test without the ball (0.041), and juggling (0.046). The coordination, YoYo, football special test with the ball were not found to be significantly accounted for preparing the young players to achieve the required performance. Based on the results of the coefficient of determination and the significance p-values of the parameters, a model was proposed to determine the highest and lowest parameters that play important roles in the selection of young players.
The purpose of this study is to determine spatial pattern recognition of school performance based on
children’s anthropometric and motor skills component. This study involved 94 primary schools with a
total 2237 male students aged 7.30±0.28 years in Pahang, Malaysia. The parameters of anthropometric
(weight and height) and motor component included lower muscular power (standing broad jump),
flexibility (sit and reach), coordination (hand wall toss) and speed (20 meter run) were selected. Cluster
Analysis (CA) and Discriminant Analysis (DA) under Multivariate Method and technique of Kriging
Interpolation in Geographic Interpolation Software (GIS) were used. CA revealed two clusters of school
performance. There are a total 34 high performance schools (HPS) and 60 low performance schools
(LPS). Then, the assigned groups were treated as independent variable (IV) while anthropometric and
motor parameters were treated as dependent variable (DV) in DA. Standard mode of DA obtained
95.74% correctness of classification matrix with three discriminated variables (height, standing broad
jump and 20 meter run) out of six variables. Meanwhile, forward and backward stepwise mode of DA
discriminated only one (standing broad jump) out of six variables with 96.81% of classification
correctness. The map output of Kriging interpolation has shown graphically the pattern of discriminated
variables that greatly influence school performance. It exposed the ability of children motor skills
development in particular region is higher than another region.
The study attempts to use multivariate analysis to evaluate the profile of male player for developments of Long-Term Talent in Sports (LT-TiS) model based on anthropometric and motor fitness components. Data of anthropometric and motor fitness included power, flexibility, coordination and speed were obtained from 2019 respondents aged 7.32±0.52 year. Data interpretations were carried out using multivariate analysis of Principle Components Analysis (PCA) and Discriminant analysis (DA). The adequacy of sampling has been measured using Bartletts tests on sphericity and Kaiser-Meyer-Olkin (KMO) has been used, with this conformance of running the Principal Component Analysis (PCA). Then, Discriminant Analysis (DA) were used to validate the correctness of group classification by LT-TiS model. Then, Discriminant Analysis (DA) were used to validate the correctness of group classification by LT-TiS. As a result, two factors with eigenvalues greater than 1 were extracted which accounted for 55.00% of the variations present in the original variables was found. The two factors were used to obtain the factor score coefficients explained by 27.86% and 27.21% of the variations in player performance respectively. Factor 1 revealed high factor loading on motor fitness compared to factor 2 as it was significantly related to anthropometrics. A model was obtained using standardized coefficient of factor 1. Three clusters of performance were shaped in view by categorizing; LT−TiS≥65%, 40%≤LT−TiS