This study aimed to develop an artificial intelligence-based model for evaluating the fundamental skills of junior handball players objectively and accurately.

Abstract

The importance of the study stems from the growing role of artificial intelligence technologies in sports performance analysis and assessment, providing modern tools capable of improving decision-making processes in training, talent identification, and player development. The researcher employed the descriptive survey method. The research sample consisted of (80) junior handball players aged (14 16) years from sports talent centers, representing different playing positions except goalkeepers. The study focused on a set of fundamental handball skills, including passing and receiving, shooting, dribbling, feinting, movement without the ball, defensive blocking, ball interception and individual defense, defensive coverage, and complex offensive movements. An artificial intelligence program based on image and video analysis was utilized to compare the actual performance of players with pre-defined ideal performance models. The system was trained using machine learning algorithms to recognize movement patterns and classify performance levels according to matching percentages. Performance was categorized into three levels: excellent, moderate, and weak. The results revealed that offensive skills achieved higher performance percentages than defensive skills. Passing and receiving recorded the highest performance level, followed by shooting and dribbling, while defensive coverage and complex offensive movements obtained the lowest scores. The findings also demonstrated the effectiveness of the artificial intelligence system in providing accurate and objective evaluations while reducing the influence of subjective judgments commonly associated with traditional assessment methods. The study concluded that artificial intelligence technologies can serve as an effective and reliable tool for evaluating the skill performance of junior handball players. Furthermore, they can assist coaches in identifying strengths and weaknesses, monitoring player development, and supporting talent selection processes. The researcher recommends expanding the use of artificial intelligence applications in sports training and evaluation and conducting further studies that integrate skill, physical, tactical, and psychological performance indicators in handball.