Artificial Neural Networks for Enhancing Soccer Team Performance Through Tactical Data Analysis

This research investigates using artificial neural networks (ANNs) to improve football team performance via tactical data analysis. The growing amount of data produced during football matches poses challenges and possibilities for coaches and analysts. ANNs, as a category of machine learning, provide a strong foundation for recognizing patterns and extracting actionable insights from data. It created a prediction algorithm that evaluates team performance and recommends ideal tactics using player data, game video, and tactical formations. It indicates that ANNs can proficiently assess intricate relationships inside the game, enhancing coaches' decision-making. The model was also evaluated using past match data, demonstrating its capacity to forecast results and suggesting tactical modifications in real time. This research's ramifications beyond performance improvement players underscore the significance of data-driven methodologies in contemporary sports. This novel use of technology enhances player development and cultivates a profound comprehension of game dynamics, providing a competitive advantage in football.