Video analysis and data-driven tactical optimization of sports football matches: Visual recognition and strategy analysis algorithm

For the purpose of this research, an original technique to assess football matches is described. The strategy makes use of a set of innovative algorithms for Strategic Analysis (SA) and Visual Recognition (VR). The approach, as mentioned above, has been designed around a virtual reality (VR) platform that is centered on YOLOv5 and successfully monitors the actions of both players and the ball in real-time. With the guidance of Markov Chain Models (MCM), the resulting information is processed and evaluated in order to find correlations in player location and actions. This enables an in-depth comprehension of the tactics and plans the team’s management executes. One of the most significant components of the research project is the exploration of multiple approximation techniques with the aim of enhancing frame analysis performance. Furthermore, threshold scaling was executed in order to attain maximum accuracy in detection, and an approach for Steady-State Analysis (SSA) is being created in order to analyze the long-term strategic positions of teammates. This complete method can run on sophisticated knowledge of in-game tactics, and it also serves as a tool for trainers and players who want to increase the effectiveness of the teams they coach and counteract strategies used by the opposing team.