• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

足球比赛中的常见和独特网络动态。

Common and unique network dynamics in football games.

机构信息

Research Center of Health, Physical Fitness and Sports, Nagoya University, Chikusa, Nagoya, Japan.

出版信息

PLoS One. 2011;6(12):e29638. doi: 10.1371/journal.pone.0029638. Epub 2011 Dec 28.

DOI:10.1371/journal.pone.0029638
PMID:22216336
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3247158/
Abstract

The sport of football is played between two teams of eleven players each using a spherical ball. Each team strives to score by driving the ball into the opposing goal as the result of skillful interactions among players. Football can be regarded from the network perspective as a competitive relationship between two cooperative networks with a dynamic network topology and dynamic network node. Many complex large-scale networks have been shown to have topological properties in common, based on a small-world network and scale-free network models. However, the human dynamic movement pattern of this network has never been investigated in a real-world setting. Here, we show that the power law in degree distribution emerged in the passing behavior in the 2006 FIFA World Cup Final and an international "A" match in Japan, by describing players as vertices connected by links representing passes. The exponent values γ ~ 3.1 are similar to the typical values that occur in many real-world networks, which are in the range of 2<γ<3, and are larger than that of a gene transcription network, γ ~ 1. Furthermore, we reveal the stochastically switched dynamics of the hub player throughout the game as a unique feature in football games. It suggests that this feature could result not only in securing vulnerability against intentional attack, but also in a power law for self-organization. Our results suggest common and unique network dynamics of two competitive networks, compared with the large-scale networks that have previously been investigated in numerous works. Our findings may lead to improved resilience and survivability not only in biological networks, but also in communication networks.

摘要

足球运动由两支各有 11 名球员组成的队伍进行,使用一个球形球。每个队都试图通过球员之间的熟练互动将球打入对方球门来得分。从网络的角度来看,足球可以被视为两个合作网络之间的竞争关系,具有动态的网络拓扑和动态的网络节点。许多复杂的大规模网络已经被证明具有共同的拓扑性质,基于小世界网络和无标度网络模型。然而,这个网络的人类动态运动模式从未在真实环境中被调查过。在这里,我们表明,在 2006 年世界杯决赛和日本的一场国际"A"级比赛中,传球行为的度分布呈幂律分布,通过将球员描述为通过代表传球的链接连接的顶点。指数值γ3.1 与许多真实网络中发生的典型值相似,范围在 2<γ<3 之间,并且大于基因转录网络的γ1。此外,我们揭示了游戏中枢纽球员的随机切换动态,这是足球比赛中的一个独特特征。这表明,这种特征不仅可以确保对故意攻击的脆弱性,而且可以实现自我组织的幂律。与之前在众多作品中研究过的大规模网络相比,我们的研究结果表明了两个竞争网络的共同和独特的网络动态。我们的研究结果不仅可能导致生物网络和通信网络的弹性和生存能力的提高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df63/3247158/f9b57556f0a9/pone.0029638.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df63/3247158/3d619ed129d6/pone.0029638.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df63/3247158/7f7508792714/pone.0029638.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df63/3247158/068e5637ffa7/pone.0029638.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df63/3247158/f9b57556f0a9/pone.0029638.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df63/3247158/3d619ed129d6/pone.0029638.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df63/3247158/7f7508792714/pone.0029638.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df63/3247158/068e5637ffa7/pone.0029638.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df63/3247158/f9b57556f0a9/pone.0029638.g004.jpg

相似文献

1
Common and unique network dynamics in football games.足球比赛中的常见和独特网络动态。
PLoS One. 2011;6(12):e29638. doi: 10.1371/journal.pone.0029638. Epub 2011 Dec 28.
2
Influence of Match Status on Players' Prominence and Teams' Network Properties During 2018 FIFA World Cup.2018年国际足联世界杯期间比赛状态对球员知名度和球队网络属性的影响
Front Psychol. 2019 Mar 28;10:695. doi: 10.3389/fpsyg.2019.00695. eCollection 2019.
3
Exploring Team Passing Networks and Player Movement Dynamics in Youth Association Football.探索青少年英式足球中的团队传球网络和球员移动动态
PLoS One. 2017 Jan 31;12(1):e0171156. doi: 10.1371/journal.pone.0171156. eCollection 2017.
4
Study State Dynamics of Team Passing Networks in Soccer Games.研究足球比赛中团队传球网络的状态动态。
J Sports Sci. 2025 Jan;43(1):33-47. doi: 10.1080/02640414.2023.2229154. Epub 2023 Jun 27.
5
Complex networks untangle competitive advantage in Australian football.复杂网络揭示澳大利亚足球的竞争优势。
Chaos. 2018 May;28(5):053105. doi: 10.1063/1.5006986.
6
Passing path predicts shooting outcome in football.传球路线可预测足球比赛中的射门结果。
Sci Rep. 2024 Apr 26;14(1):9572. doi: 10.1038/s41598-024-60183-7.
7
Determinants of international football performance: Empirical evidence from the 1994-2022 FIFA World Cup.国际足球比赛成绩的决定因素:1994年至2022年国际足联世界杯的实证证据
Heliyon. 2023 Sep 17;9(10):e20252. doi: 10.1016/j.heliyon.2023.e20252. eCollection 2023 Oct.
8
Spatial and Temporal Entropies in the Spanish Football League: A Network Science Perspective.西班牙足球联赛中的时空熵:网络科学视角
Entropy (Basel). 2020 Feb 2;22(2):172. doi: 10.3390/e22020172.
9
Practice effects on intra-team synergies in football teams.练习对足球队内部协同效应的影响。
Hum Mov Sci. 2016 Apr;46:39-51. doi: 10.1016/j.humov.2015.11.017. Epub 2015 Dec 18.
10
Neural network modelling and dynamical system theory: are they relevant to study the governing dynamics of association football players?神经网络建模和动力系统理论:它们与研究足球运动员的控制动力学有关吗?
Sports Med. 2011 Dec 1;41(12):1003-17. doi: 10.2165/11593950-000000000-00000.

引用本文的文献

1
Analysis of player speed and angle toward the ball in soccer.足球运动员相对于球的速度和角度分析。
Sci Rep. 2024 May 23;14(1):11780. doi: 10.1038/s41598-024-62480-7.
2
Temporal and Spatial Structure of Collective Pass-Chaining Action Performed by Japanese Top-Level Field Hockey Players.日本顶级曲棍球运动员集体传球链式动作的时空结构
Front Sports Act Living. 2022 Apr 12;4:867743. doi: 10.3389/fspor.2022.867743. eCollection 2022.
3
Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study.

本文引用的文献

1
Three people can synchronize as coupled oscillators during sports activities.在体育活动中,三个人可以作为耦合振荡器同步。
PLoS Comput Biol. 2011 Oct;7(10):e1002181. doi: 10.1371/journal.pcbi.1002181. Epub 2011 Oct 6.
2
Adaptive self-organization in a realistic neural network model.现实神经网络模型中的自适应自组织
Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Dec;80(6 Pt 1):061917. doi: 10.1103/PhysRevE.80.061917. Epub 2009 Dec 23.
3
Coevolutionary games--a mini review.协同进化博弈——一篇综述短文
基于球恢复和被攻击预测模型的足球队防守评估:一项初步研究。
PLoS One. 2022 Jan 27;17(1):e0263051. doi: 10.1371/journal.pone.0263051. eCollection 2022.
4
Identification of skill in an online game: The case of Fantasy Premier League.在线游戏技能的识别:以 Fantasy Premier League 为例。
PLoS One. 2021 Mar 3;16(3):e0246698. doi: 10.1371/journal.pone.0246698. eCollection 2021.
5
Passing Networks and Tactical Action in Football: A Systematic Review.足球中的传球网络和战术行为:系统综述。
Int J Environ Res Public Health. 2020 Sep 11;17(18):6649. doi: 10.3390/ijerph17186649.
6
Clarifying the structure of serious head and spine injury in youth Rugby Union players.阐明青少年橄榄球联盟球员严重头部和脊柱损伤的结构。
PLoS One. 2020 Jul 15;15(7):e0235035. doi: 10.1371/journal.pone.0235035. eCollection 2020.
7
How Training Tools Physically Linking Soccer Players Improve Interpersonal Coordination.训练工具如何在物理上连接足球运动员以提高人际协调能力。
J Sports Sci Med. 2020 May 1;19(2):245-255. eCollection 2020 Jun.
8
Play-by-Play Network Analysis in Football.足球比赛逐帧网络分析
Front Psychol. 2019 Jul 25;10:1738. doi: 10.3389/fpsyg.2019.01738. eCollection 2019.
9
The Role of Hypernetworks as a Multilevel Methodology for Modelling and Understanding Dynamics of Team Sports Performance.超网络作为一种多层次方法在团队运动表现的建模和理解中的作用。
Sports Med. 2019 Sep;49(9):1337-1344. doi: 10.1007/s40279-019-01104-x.
10
Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game.运用网络科学分析足球传球网络:比赛的动态性、空间、时间及多层性质
Front Psychol. 2018 Oct 8;9:1900. doi: 10.3389/fpsyg.2018.01900. eCollection 2018.
Biosystems. 2010 Feb;99(2):109-25. doi: 10.1016/j.biosystems.2009.10.003. Epub 2009 Oct 29.
4
Generalized outer synchronization between complex dynamical networks.复杂动态网络之间的广义外部同步
Chaos. 2009 Mar;19(1):013109. doi: 10.1063/1.3072787.
5
Anomaly of fractal dimensions observed in stochastically switched systems.在随机切换系统中观察到的分形维异常。
Phys Rev E Stat Nonlin Soft Matter Phys. 2008 Mar;77(3 Pt 2):036210. doi: 10.1103/PhysRevE.77.036210. Epub 2008 Mar 18.
6
Synchronization between two coupled complex networks.两个耦合复杂网络之间的同步。
Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Oct;76(4 Pt 2):046204. doi: 10.1103/PhysRevE.76.046204. Epub 2007 Oct 4.
7
Adaptive coevolutionary networks: a review.自适应协同进化网络:综述
J R Soc Interface. 2008 Mar 6;5(20):259-71. doi: 10.1098/rsif.2007.1229.
8
Self-organized critical neural networks.自组织临界神经网络。
Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Jun;67(6 Pt 2):066118. doi: 10.1103/PhysRevE.67.066118. Epub 2003 Jun 27.
9
Self-organized scale-free networks.自组织无标度网络
Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Aug;72(2 Pt 2):026131. doi: 10.1103/PhysRevE.72.026131. Epub 2005 Aug 26.
10
Finding and evaluating community structure in networks.在网络中寻找并评估社区结构。
Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Feb;69(2 Pt 2):026113. doi: 10.1103/PhysRevE.69.026113. Epub 2004 Feb 26.