A Deep Learning Approach to Possession Pattern Recognition in the 2026 FIFA World Cup

Abstract

Ball possession in a soccer match is the most important factor. A comprehensive analysis is required to understand the initial direction of the ball, corner kicks, and the random patterns created by players in terms of passing, as well as how the ball moves from one foot to another. If a foul occurs, an analysis of free kicks, penalty kicks, and other situations is necessary. This is the type of analysis that must be conducted. With the help of deep learning—utilizing parameters and mathematical equations—detailed analysis can be performed using heatmaps for individual players or the entire match. Algorithmic approaches such as XGBoost, LSTM, or other methods should be employed. Detailed analysis can also be performed using the Spatio-Temporal Graph-Sequence Network (ST-GSN).