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.
CSER, Sheffield Hallam University, Broomgrove Teaching Block, Broomgrove Road, Sheffield, S10 2LX, UK.
Sports Med. 2019 Sep;49(9):1337-1344. doi: 10.1007/s40279-019-01104-x.
Despite its importance in many academic fields, traditional scientific methodologies struggle to cope with analysis of interactions in many complex adaptive systems, including team sports. Inherent features of such systems (e.g. emergent behaviours) require a more holistic approach to measurement and analysis for understanding system properties. Complexity sciences encompass a holistic approach to research on collective adaptive systems, which integrates concepts and tools from other theories and methods (e.g. ecological dynamics and social network analysis) to explain functioning of such systems in their natural environments. Multilevel networks and hypernetworks comprise novel and potent methodological tools for assessing team dynamics at more sophisticated levels of analysis, increasing their potential to impact on competitive performance in team sports. Here, we discuss how concepts and tools derived from studies of multilevel networks and hypernetworks have the potential for revealing key properties of sports teams as complex, adaptive social systems. This type of analysis can provide valuable information on team performance, which can be used by coaches, sport scientists and performance analysts for enhancing practice and training. We examine the relevance of network sciences, as a sub-discipline of complexity sciences, for studying the dynamics of relational structures of sports teams during practice and competition. Specifically, we explore the benefits of implementing multilevel networks, in contrast to traditional network techniques, highlighting future research possibilities. We conclude by recommending methods for enhancing the applicability of hypernetworks in analysing team dynamics at multiple levels.
尽管在许多学术领域都很重要，但传统的科学方法在分析许多复杂适应系统（包括团队运动）中的相互作用时，往往难以应对。这些系统的固有特征（例如，涌现行为）需要更全面的方法来进行测量和分析，以了解系统的特性。复杂性科学涵盖了对集体自适应系统的整体研究方法，它整合了来自其他理论和方法的概念和工具（例如，生态动力学和社会网络分析），以解释这些系统在其自然环境中的功能。多层次网络和超网络是评估团队动态的新颖而有力的方法工具，可以在更复杂的分析层次上提高其对团队运动竞争表现的影响潜力。在这里，我们讨论了从多层次网络和超网络研究中得出的概念和工具如何有可能揭示作为复杂自适应社会系统的运动队的关键特性。这种分析可以提供有关团队表现的有价值信息，教练、运动科学家和表现分析师可以利用这些信息来提高实践和训练效果。我们研究了网络科学作为复杂性科学的一个分支，对于研究团队在实践和比赛中关系结构的动态的相关性。具体来说，我们探讨了实施多层次网络的好处，与传统网络技术相比，突出了未来的研究可能性。最后，我们建议了增强超网络在分析多个层次团队动态中的适用性的方法。
