  This article is motivated by soccer positional passing networks collected
across multiple games. We refer to these data as replicated spatial passing
networks---to accurately model such data it is necessary to take into account
the spatial positions of the passer and receiver for each passing event. This
spatial registration and replicates that occur across games represent key
differences with usual social network data. As a key step before investigating
how the passing dynamics influence team performance, we focus on developing
methods for summarizing different team's passing strategies. Our proposed
approach relies on a novel multiresolution data representation framework and
Poisson nonnegative block term decomposition model, which automatically
produces coarse-to-fine low-rank network motifs. The proposed methods are
applied to detailed passing record data collected from the 2014 FIFA World Cup.
