Intelligent Prediction of Soccer Technical Skill on Youth Soccer Player’s Relative Performance Using Multivariate Analysis and Artificial Neural Network Techniques

This study aims to predict the potential pattern of soccer technical skill on Malaysia youth soccer players relative performance using multivariate analysis and artificial neural network techniques. 184 male youth soccer players were recruited in Malaysia soccer academy (average age = 15.2±2.0) underwent to, physical fitness test, anthropometric, maturity, motivation and the level of skill related soccer. Unsupervised pattern recognition of principal component analysis (PCA) was used to identify the most significant parameters in soccer for the current study and intelligent prediction of artificial neural network (ANN) was developed to determine its predictive ability for the soccer relative performance index (SRPI). The PCA has indicated sit up, agility, 5m speed, 10m speed, 20m speed, weight, height, sitting height, bicep, tricep, subscapular, suprailiac, calf circumference, maturity, task, ego, short pass, shooting right top corner and shooting left top corner are the most significant parameters in soccer. Meanwhile, the PCA-ANN showed better predictive ability in the determination of SRPI with fewer parameters such as R2 and root mean square error (RMSE) values of 0.922 and 0.190, respectively. The current study indicated that only a few parameters are needed to improve and enhanced the performance of novice group. Nevertheless, the prediction method techniques for the present study show very high and strong ability in prediction of the player’s performance. It has highlighted the possibility of defining the optimum number of parameters for the player's relative performance evaluation, which in turn will reduce the costs, energy and time of the measurement.