Use of Kernel Density Estimation to understand the spatial trends of attacking possessions in rugby league

Abstract: Despite having the potential to provide significant insights into tactical preparations for future matches, very few studies have considered the spatial trends of team attacking possessions in rugby league. Those which have considered these trends have used grid based aggregation methods, which provide a discrete understanding of rugby league match play but may fail to provide a complete understanding of the spatial trends of attacking possessions due to the dynamic nature of the sport. In this study, we use Kernel Density Estimation (KDE) to provide a continuous understanding of the spatial trends of attacking possessions in rugby league on a team by team basis. We use the Wasserstein distance to understand the differences between teams (i.e. using all of each team's data) and within teams (i.e. using a single team's data against different opponents). Our results show that KDEs are able to provide interesting tactical insights at the between team level. Furthermore, at the within team level, the results are able to show patterns of spatial trends for attacking teams, which are present against some opponents but not others. The results could help sports practitioners to understand opposition teams' previous performances and prepare tactical strategies for matches against them.