CIFI2D, Centre of Research, Education, Innovation and Intervention in Sport, Faculdade de Desporto, Universidade do Porto, Rua Dr. Plácido Costa, 91, 4200-450, Porto, Portugal.
Shanghai SIPG FC, Xangai, China.
Sports Med. 2017 Sep;47(9):1689-1696. doi: 10.1007/s40279-017-0695-1.
This paper discusses how social network analyses and graph theory can be implemented in team sports performance analyses to evaluate individual (micro) and collective (macro) performance data, and how to use this information for designing practice tasks. Moreover, we briefly outline possible limitations of social network studies and provide suggestions for future research. Instead of cataloguing discrete events or player actions, it has been argued that researchers need to consider the synergistic interpersonal processes emerging between teammates in competitive performance environments. Theoretical assumptions on team coordination prompted the emergence of innovative, theoretically driven methods for assessing collective team sport behaviours. Here, we contribute to this theoretical and practical debate by re-conceptualising sports teams as complex social networks. From this perspective, players are viewed as network nodes, connected through relevant information variables (e.g. a ball-passing action), sustaining complex patterns of interaction between teammates (e.g. a ball-passing network). Specialised tools and metrics related to graph theory could be applied to evaluate structural and topological properties of interpersonal interactions of teammates, complementing more traditional analysis methods. This innovative methodology moves beyond the use of common notation analysis methods, providing a richer understanding of the complexity of interpersonal interactions sustaining collective team sports performance. The proposed approach provides practical applications for coaches, performance analysts, practitioners and researchers by establishing social network analyses as a useful approach for capturing the emergent properties of interactions between players in sports teams.
本文讨论了如何在团队运动表现分析中实施社会网络分析和图论，以评估个体（微观）和集体（宏观）表现数据，以及如何利用这些信息来设计练习任务。此外，我们还简要概述了社会网络研究可能存在的局限性，并为未来的研究提供了建议。研究人员需要考虑到在竞争环境中队友之间出现的协同人际过程，而不是记录离散事件或球员动作。理论上对团队协调的假设促使人们提出了创新的、理论驱动的方法来评估集体团队运动行为。在这里，我们通过将运动队重新概念化为复杂的社会网络，为这一理论和实践辩论做出了贡献。从这个角度来看，球员被视为网络节点，通过相关信息变量（例如传球动作）连接在一起，维持着队友之间复杂的相互作用模式（例如传球网络）。与图论相关的专门工具和指标可用于评估队友之间人际互动的结构和拓扑性质，补充更传统的分析方法。这种创新的方法超越了使用常见的符号分析方法，提供了对维持集体团队运动表现的人际相互作用复杂性的更深入理解。通过将社会网络分析确立为一种捕捉运动队中球员之间交互的新兴属性的有用方法，该方法为教练、表现分析师、从业者和研究人员提供了实际应用。
