Integrating physics models and machine learning for the soccer shot prediction: a trajectory analysis approach

This study will combine machine learning models with physics-based simulations to predict the most optimal soccer shot trajectories to help player win the match. The Machine learning models used in this paper will include the regression analysis, are utilized to identify the best shooting strategies, such as optimal angles and forces. The identified strategies are then verified by using physics models, such as projectile motion equations, drag force, and the Magnus effect, which simulate real-world shooting conditions. This approach not only increase the shot success rates but also provides a deeper understanding of the physical factors influencing soccer shot trajectories. The combination of machine learning and physics models offers a novel and reliable method for enhancing soccer performance through data-driven decision-making and scientific validation. Besides, if the physical equations are difficult to solve because of many factors, we will use the numerical method like Fourth-Order Runge-Kutta to approximate the result. Numerical methods have these advantages in solving Differential equations. Experimental results demonstrate that this integrated approach significantly improves the prediction accuracy and practical applicability of shot strategies.