Automatic recognition of sport events from spatio-temporal data: An application for virtual reality-based training in basketball

Data analysis in the field of sport is growing rapidly due to the availability of datasets containing spatio-temporal positional data of the players and other sport equipment collected during the game. This paper investigates the use of machine learning for the automatic recognition of small-scale sport events in a basketball-related dataset. The results of the method discussed in this paper have been exploited to extend the functionality of an existing Virtual Reality (VR)-based tool supporting training in basketball. The tool allows the coaches to draw game tactics on a touchscreen, which can be then visualized and studies in an immersive VR environment by multiple players. Events recognized by the proposed system can be used to let the tool manage also previous matches, which can be automatically recreated by activating different animations for the virtual players and the ball based on the particular game situation, thus increasing the realism of the simulation.