Event analysis in football aims to provide a detailed and accurate understanding of the performance of both teams and individual players through the processing and interpreting data generated during matches. This analysis allows for extracting relevant information about specific aspects of the game, such as playing patterns, performance trends, and areas for improvement. With this data, coaches and analysts can identify opportunities to optimize tactics and strategy for future matches. The ability to make evidence-based decisions makes data analysis an essential tool for modern football. It provides key information for selecting players, adjusting strategies based on strengths and weaknesses, and identifying effective tactics against specific opponents. Additionally, the use of advanced techniques such as machine learning significantly expands the possibilities of analysis by enabling the creation of predictive models that, based on historical data, anticipate behaviors, performance trends, and recurring game patterns. The analysis process involves several stages, including collecting real-time data during matches, processing, storage, and visualization in a clear and accessible way. These methods allow for the evaluation of not only the collective performance of teams but also a deeper insight into the individual performance of each player. Using this information, coaching staff can design personalized training plans to enhance specific skills. The integration of technologies such as machine learning has revolutionized professional football. Predictive models allow for continuous monitoring and improvement of player performance, facilitate the creation of personalized strategies, and maximize the potential of each team member. These advancements transform how talent is managed, and tactics are designed, making technology an indispensable ally in tackling tactical challenges and achieving competitive success.