Clemente Filipe Manuel, Couceiro Micael Santos, Martins Fernando Manuel Lourenço, Mendes Rui Sousa
Polytechnic Institute of Coimbra, Coimbra College of Education, Department of Education, Portugal. ; Faculty of Sport Sciences and Physical Education - University of Coimbra, Portugal.
Ingeniarius, Lda., Coimbra, Portugal.
J Hum Kinet. 2015 Apr 7;45:123-34. doi: 10.1515/hukin-2015-0013. eCollection 2015 Mar 29.
The aim of this study was to propose a set of network methods to measure the specific properties of a team. These metrics were organised at macro-analysis levels. The interactions between teammates were collected and then processed following the analysis levels herein announced. Overall, 577 offensive plays were analysed from five matches. The network density showed an ambiguous relationship among the team, mainly during the 2nd half. The mean values of density for all matches were 0.48 in the 1st half, 0.32 in the 2nd half and 0.34 for the whole match. The heterogeneity coefficient for the overall matches rounded to 0.47 and it was also observed that this increased in all matches in the 2nd half. The centralisation values showed that there was no 'star topology'. The results suggest that each node (i.e., each player) had nearly the same connectivity, mainly in the 1st half. Nevertheless, the values increased in the 2nd half, showing a decreasing participation of all players at the same level. Briefly, these metrics showed that it is possible to identify how players connect with each other and the kind and strength of the connections between them. In summary, it may be concluded that network metrics can be a powerful tool to help coaches understand team's specific properties and support decision-making to improve the sports training process based on match analysis.
本研究的目的是提出一套网络方法来衡量团队的特定属性。这些指标是在宏观分析层面进行组织的。收集队友之间的互动，然后按照本文公布的分析层面进行处理。总体而言，对五场比赛中的577次进攻战术进行了分析。网络密度显示出团队之间存在模糊的关系，主要是在下半场。所有比赛上半场的密度平均值为0.48，下半场为0.32，全场比赛为0.34。全场比赛的异质性系数约为0.47，并且还观察到下半场所有比赛中该系数都有所增加。中心性值表明不存在“星型拓扑”。结果表明，每个节点（即每个球员）的连通性几乎相同，主要是在上半场。然而，下半场这些值有所增加，表明所有球员在同一水平上的参与度在下降。简而言之，这些指标表明可以识别球员之间的连接方式以及他们之间连接的种类和强度。总之，可以得出结论，网络指标可以成为一个强大的工具，帮助教练了解团队的特定属性，并支持基于比赛分析的决策，以改进运动训练过程。
