Utilizing Artificial Intelligence to Identify a Model for Muscular Strength and Accuracy index of Shooting Skill in Handball

In light of rapid advancements across various fields, the domain of sports stands out for its growing emphasis on scientific planning grounded in testing and measurement. These tools play a critical role in evaluating athletes’ performance and identifying their physical and technical capabilities. Periodic testing serves as an effective means of assessing these attributes and enhancing athletic performance. Within this context, artificial intelligence—particularly artificial neural networks—emerges as a sophisticated method for analyzing sports data and constructing precise models that link various performance variables. Problem of Study lies in the absence of a scientifically accurate model based on neural networks to analyze the relationship between muscular strength and shooting performance in handball. The study aims to develop a model that contributes to the scientific evaluation and development of athletes by identifying the relationship between muscular strength and different types of shooting. The researchers employed a descriptive methodology using correlational analysis. Sample consisted of (30) players from the Premier League during the 2024–2025 season. Muscular strength and shooting types were measured, and the data were processed using neural network techniques. Results demonstrated the effectiveness of neural networks in identifying correlational relationships and the relative importance of the studied variables. This enabled the construction of a precise model linking muscular strength to shooting types. The study recommends the application of this model in sports training and the adoption of modern statistical programs, such as neural networks, to enhance athletic performance