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

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