Prediction of player position for talent identification in association netball: a regression-based approach

(1) Nur Hazwani Jasni Mail (Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)
(2) Aida Mustapha Mail (Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)
(3) Siti Solehah Tenah Mail (Research Management Centre, University Tun Hussein Onn Malaysia, Malaysia)
(4) * Salama A Mostafa Mail (Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)
(5) Nazim Razali Mail (Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)
*corresponding author


Among the challenges in industrial revolutions, 4.0 is managing organizations’ talents, especially to ensure the right person for the position can be selected. This study is set to introduce a predictive approach for talent identification in the sport of netball using individual player qualities in terms of physical fitness, mental capacity, and technical skills. A data mining approach is proposed using three data mining algorithms, which are Decision Tree (DT), Neural Network (NN), and Linear Regressions (LR). All the models are then compared based on the Relative Absolute Error (RAE), Mean Absolute Error (MAE), Relative Square Error (RSE), Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Relative Square Error (RSE). The findings are presented and discussed in light of early talent spotting and selection. Generally, LR has the best performance in terms of MAE and RMSE as it has the lowest values among the three models.



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