Improvement of Visualization Method for Attack Patterns in Team Sports

In this study, we improved the visualization method for attack patterns in team sports that was proposed in our previous work to visualize not only patterns of offense, but also patterns of defense. In the improved method, dictionary learning for nonnegative sparse coding is performed to obtain a dictionary of base player densities, which can be considered as components of player formations for both teams. Next, nonnegative sparse coding is utilized to evaluate coefficient vectors as features in corresponding scenes. Finally, co-occurrence analysis based on mutual self-information is utilized to construct a co-occurrence network for shooting and formation components for both teams. For validation, the improved method was applied to player position data from five matches from the 2011 J. League.