The Relationship Between Numerical Values of Some Kinematic Variables Contribution Ratios Using Artificial Neural Networks and Young Javelin Throwers’ Performance

Abstract

The present study aims to demonstrate the importance of using artificial intelligence to identify the most important kinematic variables contributing to the achievement of the javelin throwing competition. This study also aims to improve the quality of performance and assist coaches in the success of the training process, given its positive role in achieving the longest throwing distance. The study aims to identify the most important kinematic variables for young javelin throwers and to identify their numerical values using artificial neural networks. The descriptive correlational relationships approach is employed in order to obtain the objectives of the present study. The research population and sample consist of (8) young javelin throwers participating in the Iraqi Central Clubs Championship. Having conducted video recordings during the tournament, analyzed the data using advanced analytical programs, and processed it using artificial neural networks, the researcher concludes that the use of advanced technology, represented by artificial neural networks, contributes to identifying the most important kinematic variables for javelin throwers. Identifying the contribution ratios of these variables helps coaches save time and effort in the training process. Coaches shall use advanced technology in processing the variables understudy, and focusing training on the contribution ratios of the specific kinematic variables.