Employing Analytical Artificial Intelligence to Analyze Skill Patterns and Classify Performance Levels among Junior Handball Players

Handball is considered one of the team sports characterized by high speed and continuous changes in game situations, making performance evaluation a relatively complex process due to the interaction of physical, technical, tactical, and psychological aspects during a single match. Artificial intelligence is important in handball because it can account for individual differences among players and the fundamental variations between playing positions on the court.

This study aimed to develop an intelligent model based on artificial intelligence techniques to evaluate the skill performance of junior handball players objectively and accurately, away from the subjective judgments associated with traditional assessment methods. The study is significant because artificial intelligence applications in sports are advancing rapidly and have substantial potential for performance analysis and extracting accurate indicators that help coaches make decisions about talent identification, training, and player development.

The researcher adopted the descriptive survey method. The research sample consisted of (80) junior players from sports talent centers aged between (14–16) years, representing various playing positions except goalkeepers. Expert questionnaires identified a set of fundamental handball skills, including passing and receiving, shooting, dribbling, feinting, off-the-ball movement, defensive blocking, ball interception and individual defense, defensive coverage, and complex offensive movements.

The study used an artificial intelligence system based on image and video analysis to compare players’ actual performances with ideal skill models trained with machine learning algorithms. The system classified performance into three main levels: excellent performance, average performance, and weak performance, according to matching percentages determined by the system.