Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks
This work addresses the need for accurate modeling of spatial passing networks in soccer to analyze team strategies, representing an incremental advancement in network analysis methods for sports data.
The paper tackles the problem of summarizing soccer teams' passing strategies from spatial passing network data across multiple games, using a novel multiresolution framework and Poisson nonnegative block term decomposition model to automatically extract low-rank network motifs, applied to data from the 2014 FIFA World Cup.
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.