Cao Shun
Department of Information Science Technology, University of Houston, Houston, TX, USA.
J Sports Sci. 2025 Jan;43(1):33-47. doi: 10.1080/02640414.2023.2229154. Epub 2023 Jun 27.
Complex networks have been widely used in studying collective behaviours in soccer sports, such as examining tactical strategies, recognizing team characteristics, and discovering topological determinants for high team performance. The passing network of a team evolves and displays different temporal patterns, that are strongly linked to team status, tactical strategies, attacking/defending transitions, etc. Nevertheless, existing research has not illuminated the state dynamics of team passing networks, whereas similar methods have been extensively used in examining the dynamical brain networks constructed from human brain neuroimage data. This study aims to investigate the state dynamics of team passing networks in soccer sports. The introduced method incorporates multiple techniques, including sliding time window, network modeling, graph distance measure, clustering, and cluster validation. The final match of the FIFA World Cup 2018 was taken as an example, and the state dynamics of teams Croatia and France were analyzed respectively. Additionally, the effects of the time windows and graph distance measures on the results were briefly discussed. This study presents a novel outlook on examining the dynamics of team passing networks, as it facilitates the recognition of important team states or state transitions in soccer and other team ball-passing sports for further analysis.
复杂网络已被广泛应用于研究足球运动中的集体行为，例如审视战术策略、识别球队特征以及发现高球队表现的拓扑决定因素。球队的传球网络会演变并呈现出不同的时间模式，这些模式与球队状态、战术策略、攻防转换等密切相关。然而，现有研究尚未阐明球队传球网络的状态动态，而类似方法已被广泛用于研究由人类脑影像数据构建的动态脑网络。本研究旨在探究足球运动中球队传球网络的状态动态。所引入的方法融合了多种技术，包括滑动时间窗口、网络建模、图距离度量、聚类和聚类验证。以2018年国际足联世界杯决赛为例，分别分析了克罗地亚队和法国队的状态动态。此外，还简要讨论了时间窗口和图距离度量对结果的影响。本研究为审视球队传球网络的动态提供了一种新颖的视角，因为它有助于识别足球及其他团队传球运动中重要的球队状态或状态转换，以供进一步分析。
