Formation Analysis Method for Team Sports Using Deep Graph Convolutional Neural Network with Geometric Formation Features as Input

In the last decade, player and ball tracking data have increasingly been gathered and employed in various team sports, in particular invasion team sports, such as soccer, basketball, rugby, and American football. Additionally, the effectiveness of geometric analysis of formations using such tracking data has been demonstrated. Moreover, deep neural network-based methods for analyzing such tracking data have also been proposed. However, to the best of our knowledge, no research has been found that combines both, even though the benefits are seemingly apparent. In this study, against this background, we propose a formation analysis method for team sports using a deep graph neural network with geometric formation features. This method uses geometric formation features derived from a geometric formation analysis for tracking data. For initial validation, we also conduct an experiment applying this method to a soccer shooting scene classification problem with real soccer tracking data in Japan. Based on the results, we will discuss the method’s effectiveness.