Analyzing Players' Tactical Positions and Movement Trajectories in Soccer Matches Using Machine Vision Algorithms

Abstract

Tactical positions and movement trajectories in soccer matches using machine vision algorithms involves leveraging computer vision techniques to track players and the ball throughout the game. By processing video feeds from multiple cameras, machine vision algorithms can identify players, recognize their positions on the field, and map out their movement trajectories over time. This analysis provides valuable insights into team formations, player positioning, and tactical strategies employed during matches. Coaches and analysts can use this information to assess team performance, identify patterns in gameplay, and make data-driven decisions to optimize tactics and training regimes. Machine vision algorithms enhance the understanding of soccer dynamics, facilitating more strategic and effective approaches to coaching and gameplay.. This paper presents a comprehensive analysis of player movement, tactical positions, and detection in soccer matches using machine learning algorithms. Leveraging a simulation environment built upon a Recurrent Neural Network (RNN) model trained on historical match data, we investigate the efficacy of the model in accurately predicting future player positions based on contextual features such as player trajectories and ball movement. Furthermore, we explore the model's ability to estimate tactical movements during specific time intervals, providing valuable insights for coaches and analysts in understanding team strategies and adapting to match dynamics. Additionally, we evaluate player detection systems to assess their capabilities and limitations, laying the groundwork for future improvements in player tracking technologies. Our findings underscore the potential of machine learning in soccer analytics, offering actionable insights for enhancing performance analysis, strategic decision-making, and overall understanding of the game. Our results show a high prediction accuracy, with an average Mean Squared Error (MSE) of 0.05. Furthermore, we explore the model's ability to estimate tactical movements during specific time intervals, achieving an overall prediction accuracy of 85%. Additionally, we evaluate player detection systems to assess their capabilities and limitations, achieving a detection accuracy of 90%.