文献类型：期刊文献

英文题名：Quantifying contextual influences on spatial community structure in FC Barcelona's passing networks

作者：Li, Ming-Xia[1,2];Zhou, Wei-Xing[2,3,4]

机构：[1]East China Univ Sci & Technol, Sch Sports Sci & Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Res Ctr Econophys, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China;[4]East China Univ Sci & Technol, Sch Math, Shanghai 200237, Peoples R China

年份：2026

卷号：209

外文期刊名：CHAOS SOLITONS & FRACTALS

收录：;EI(收录号：20261820629586);WOS:【SCI-EXPANDED(收录号:WOS:001761591400001)】；

基金：This work was supported by the Fundamental Research Funds for the Central Universities, China.

语种：英文

外文关键词：Passing network; Community detection; Normalized Mutual Information (NMI); Tactical analysis; Football

摘要：Understanding how contextual factors are associated with the spatial organization of football passing remains a key challenge in performance analysis. While network approaches reveal tactical structures, their interpretability is often limited by a lack of statistical validation and unclear links to match context. This study bridges these gaps by first establishing the statistical robustness of spatial passing communities and then quantifying their association with contextual variables. We analyzed 532 FC Barcelona matches, constructing directed passing networks on a discretized pitch. Community structures were detected using the Infomap algorithm and validated against null models and the Directed Degree-Corrected Stochastic Block Model. The association between communities and contextual factors was measured using Normalized Mutual Information (NMI). Our results, validated by permutation tests, demonstrate that Barcelona's own coach exhibits the most robust statistical associations with passing community structure, while opponent coach shows only marginal significance. In contrast, match venue and scoreline showed negligible association. Barcelona's networks consistently exhibited high connectivity, clustering, and a tendency to form two dominant spatial modules, reflecting a stable, possession-based core adaptable to specific threats. This work provides a scalable, context-aware framework for football analytics, advancing from descriptive mapping to diagnostic modeling. Metrics like clustering coefficient and module size offer structure-sensitive tools for tactical evaluation, linking network architecture to actionable insights.