Team invasion games are sports in which individual team members interact and exchange information to coordinate their behaviours and actions in pursuit of the common goal of winning matches. Researchers have used social network analysis to quantify the cooperative behaviours of sports teams (cooperative network analysis), yet this research exists across an array of disciplines and uses various methods. Therefore, accessibility for practitioners and researchers interested in using it to quantify team cooperation in team invasion games may be limited. This systematic mapping review aimed to identify, report and discuss research in this emerging research area. Articles were systematically searched in electronic databases and reference list scans resulting in 112 papers included. Football was the most studied sport (n = 91), and passing was the most observed interaction between players within a sports team (n = 107). This review further revealed a lack of consistency in reporting between the included studies with respect to nomenclature and network measures. A comprehensive map of the current literature on the use of cooperative network analysis in team invasion games is provided which can be used by practitioners and researchers tasked with or interested in analysing team performance.
FransenJTriboletRSheehanWB, et al.Cooperative passing network features are associated with successful match outcomes in the Australian Football League. Int J Sports Sci2021; 17(5): 17479541211052760.
2.
BrahamCSmallM. Complex networks untangle competitive advantage in Australian football. Chaos2018; 28: 053105.
3.
HughesMDBartlettRM. The use of performance indicators in performance analysis. J Sports Sci2002; 20: 739–754.
4.
BorgattiSPMehraABrassDJ, et al.Network analysis in the social sciences. Science2009; 323: 892–895.
5.
StokmanFNZeggelinkEP. ‘Self-organizing’ friendship networks. In WBG Liebrand and DM Messick (eds) Title of host publication Frontiers in Social Dilemmas Research New York: Springer, pp. 385–418.
6.
KatzNLazerDArrowH, et al.Network theory and small groups. Small Group Res2004; 35: 307–332.
7.
McLeanSSalmonPMGormanAD, et al.Do inter-continental playing styles exist? Using social network analysis to compare goals from the 2016 EURO and COPA football tournaments knock-out stages. Theor Issues Ergon Sci2017; 18: 370–383.
8.
RibeiroJSilvaPDuarteR, et al.Team sports performance analysed through the lens of social network theory: Implications for research and practice. Sports Med2017; 47: 1689–1696.
9.
CintiaPCosciaMPappalardoL. The haka network: Evaluating rugby team performance with dynamic graph analysis. In: 2016 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), 18-21 August 2016, IEEE, San Francisco, CA, USA, 2016, pp.1095–1102.
10.
McLeanSSalmonPMGormanAD, et al.Integrating communication and passing networks in football using social network analysis. Sci Med Footb2019; 3: 29–35.
11.
McLarenCDSpinkKS. Member communication as network structure: Relationship with task cohesion in sport. Int J Sport Exerc Psychol2020; 18: 764–778.
12.
GouldPGatrellA. A structural analysis of a game: The Liverpool v Manchester United Cup Final of 1977. Soc Networks1979; 2: 253–273.
13.
PassosPDavidsKAraujoD, et al.Networks as a novel tool for studying team ball sports as complex social systems. J Sci Med Sport2011; 14: 170–176.
14.
TriboletRSheehanWBNovakAR, et al.Match simulation practice may not represent competitive match play in professional Australian football. J Sports Sci2022; 40: 413–421.
15.
YoungCMLuoWGastinP, et al.Understanding effective tactics in Australian football using network analysis. Int J Perform Anal Sport2019; 19: 331–341.
16.
SheehanWBTriboletRWatsfordML, et al.Using cooperative networks to analyse behaviour in professional Australian Football. J Sci Med Sport2020; 23: 291–296.
17.
NovakARRichardsonMJImpellizzeriFM, et al.Speed, spacing and synchrony: An exploratory, quantitative analysis of collective team behaviour in elite Rugby Union. J Sci Med Sport2022; 25: S17.
18.
SasakiKYamamotoTMiyaoM, et al.Network centrality analysis to determine the tactical leader of a sports team. Int J Perform Anal Sport2017; 17: 822–831.
19.
SasakiK. Defense performance analysis: Social network analysis. J Hum Sport Exerc2013; 10: 747–752.
20.
DuchJWaitzmanJSAmaralLAN. Quantifying the performance of individual players in a team activity. PLoS One2010; 5: e10937.
21.
NovakARPalmerSImpellizzeriFM, et al.Description of collective team behaviours and team performance analysis of elite rugby competition via cooperative network analysis. Int J Perform Anal Sport2021; 21: 804–819.
22.
NewellKM. Constraints on the Development of Coordination. In MG Wade and HTA Whiting (eds) Motor Development in Children: Aspects of Coordination and Control. The Netherlands: Martinus Nijhoff, Dordrecht, 1986, pp. 341-360. http://dx.doi.org/10.1007/978-94-009-4460-2_19
23.
MunnZSternCAromatarisE, et al.What kind of systematic review should I conduct? A proposed typology and guidance for systematic reviewers in the medical and health sciences. BMC Med Res Methodol2018; 18: 5.
24.
XuC. ProQuest Information & Learning. PhD Thesis, University of Washington, 2018.
25.
McLeanSSalmonPMGormanAD, et al.The communication and passing contributions of playing positions in a professional soccer team. J Hum Kinet2021; 77: 223–234.
26.
YoungCMLuoWGastinPB, et al.Understanding the relative contribution of technical and tactical performance to match outcome in Australian Football. J Sports Sci2020; 38: 676–681.
27.
McLeanSSalmonPMGormanAD, et al.A social network analysis of the goal scoring passing networks of the 2016 European Football Championships. Hum Mov Sci2018; 57: 400–408.
28.
CottaCMoraAMMereloJJ, et al.A network analysis of the 2010 FIFA world cup champion team play. J Syst Sci Complex2013; 26: 21–42.
29.
TaylorNGastinPBMillsO, et al.Network analysis of kick-in possession chains in elite Australian football. J Sports Sci2020; 38: 1053–1061.
30.
ChoYYoonJLeeS. Using social network analysis and gradient boosting to develop a soccer win–lose prediction model. Eng Appl Artif Intell2018; 72: 228–240.
AhmadiAHNooriATeimourpourB. Social network analysis of passes and communication graph in football by mining frequent subgraphs. In: 2020 6th international conference on web research (ICWR), Tehran, Iran, 22-23 April, 2020, pp.1–7. IEEE.
33.
GamaJDiasGCouceiroM, et al.Networks metrics and ball possession in professional football. Complex2016; 21: 342–354.
34.
MaltaPTravassosB. Caraterizaæo da transiÆo defesa-ataque de uma equipa de Futebol. / Characterization of the defense-attack transition of a soccer team. Motricidade2014; 10: 27–37.
35.
EliakimEMorgulevELidorR, et al.The development of metrics for measuring the level of symmetry in team formation and ball movement flow, and their association with performance. Sci Med Footb2022; 6: 189–202.
36.
KusmakarSShelyagSZhuY, et al.Machine learning enabled team performance analysis in the dynamical environment of soccer. IEEE Access2020; 8: 90266–90279.
37.
ZhaoYZhangH. Eigenvalues make the difference – a network analysis of the Chinese Super League. Int J Sports Sci Coach2020; 15: 184–194.
38.
ClementeFMCouceiroMSMartinsFML, et al.Using network metrics in soccer: A macro-analysis. J Hum Kinet2015; 45: 123–134.
39.
CouceiroMDiasGSilvaJP, et al.Adaptive collective behavior of the players of the Portuguese national team in the 2016 European Football Championship. Rev Bras Futsal Futebol2018; 10: 523–530.
40.
SrinivasanB. A social network analysis of football – evaluating player and team performance. In: 2017 ninth international conference on advanced computing, Chennai, India, IEEE, 14-16 December 2017, pp.242–246.
41.
KorteFLamesM. Passing network analysis of positional attack formations in handball. J Hum Kinet2019; 70: 209–221.
42.
PracaGMLimaBBBredtS, et al.Influence of match status on players’ prominence and teams’ network properties during 2018 FIFA World Cup. Front Psychol2019; 10: 695.
43.
YuQGaiYGongB, et al.Using passing network measures to determine the performance difference between foreign and domestic outfielder players in Chinese Football Super League. Int J Sports Sci Coach2020; 15: 398–404.
44.
GamaJDiasGPassosP, et al.Homogeneous distribution of passing between players of a team predicts attempts to shoot at goal in association football: A case study with 10 matches. Nonlinear Dyn Psychol Life Sci2020; 24: 353–365.
45.
Arriaza-ArdilesEMartin-GonzalezJMZunigaMD, et al.Applying graphs and complex networks to football metric interpretation. Hum Mov Sci2018; 57: 236–243.
46.
BelliRJFigueiredo DiasGNTravassos Ventura GamaJM, et al.Network and collective behavior in professional football teams. Rev Bras Futsal Futebol2017; 9: 84–94.
47.
Uchoa Maia RodriguesDCMouraFACunhaSA, et al.Graph visual rhythms in temporal network analyses. Graph Models2019: 103: 101021.
48.
CintiaPRinzivilloSPappalardoL. A network-based approach to evaluate the performance of football teams. In: Machine learning and data mining for sports analytics workshop, Porto, Portugal, September 11, 2015, pp. 1–9.
49.
IchinoseGTsuchiyaTWatanabeS. Robustness of football passing networks against continuous node and link removals. Chaos, Solitons Fractals2021; 147: 110973.
50.
GrundTU. Network structure and team performance: The case of English Premier League soccer teams. Soc Netw2012; 34: 682–690.
51.
CastellanoJEcheazarraI. Network-based centrality measures and physical demands in football regarding player position: Is there a connection? A preliminary study. J Sports Sci2019; 37: 2631–2638.
52.
ClementeFMLourenço MartinsFMSantos CouceiroM, et al.A network approach to characterize the teammates’ interactions on football: A single match analysis. Cuad Psicol Deporte2014; 14: 141–148.
53.
ZhangTHuGLiaoQ, et al.Analysis of offense tactics of basketball games using link prediction. In: 2013 IEEE/ACIS 12th international conference on computer and information science (ICIS),Niigataa, Japan, IEEE, 16-20 June, 2013, pp.207–212.
54.
TrequattriniRLombardiRBattistaM. Network analysis and football team performance: A first application. Team Perform Manag2015; 21: 85–110.
55.
YamamotoKNarizukaT. Examination of Markov-chain approximation in football games based on time evolution of ball-passing networks. Phys Rev E2018; 98: 052314.
56.
ClementeFMSarmentoHAquinoR. Player position relationships with centrality in the passing network of world cup soccer teams: Win/loss match comparisons. Chaos Solitons Fractals2020; 133: 109625.
57.
GrundTU. The relational value of network experience in teams. Am Behav Sci2016; 60: 1260–1280.
58.
DavidGKWilsonRS. Cooperation improves success during intergroup competition: An analysis using data from professional soccer tournaments. PLoS One2015; 10: e0136503.
59.
ClementeFM. Performance outcomes and their associations with network measures during FIFA World Cup 2018. Int J Perform Anal Sport2018; 18: 1010–1023.
60.
McLeanSSalmonPM. The weakest link: A novel use of network analysis for the broken passing links in football. Sci Med Footb2019; 3: 255–258.
61.
PinaTJPauloAAraújoD. Network characteristics of successful performance in association football. A study on the UEFA champions league. Front Psychol2017; 8: 1173.
62.
MedinaPCarrascoSRoganJ, et al.Is a social network approach relevant to football results?Chaos Solitons Fractals2021; 142: 110369.
63.
ClementeFMJoséFOliveiraN, et al.Network structure and centralization tendencies in professional football teams from Spanish La Liga and English Premier Leagues. J Hum Sport Exerc2016; 11: 376–389.
64.
NarizukaTYamamotoKYamazakiY. Statistical properties of position-dependent ball-passing networks in football games. Physica A2014; 412: 157–168.
65.
NeumanYNobleDCohenY. Is the whole different from the sum of its parts? A proposed procedure for measuring divergence from additivity. Int J Gen Syst2018; 47: 665–678.
66.
CaoF-xChengKLiX,et al.The relationship between passing performance and match win–loss for teams from Chinese football super league under the different match situations. J Phys Educ Sport2021; 1: 111–118.
67.
MoraesGRMoratoMP. Tactical-technical analysis in football: Comparison of European champion 2018–19 with the champion South American 2019. Rev Bras Futsal Futebol2021; 13: 284–296.
68.
RamosJLopesRJAraujoD. What's next in complex networks? Capturing the concept of attacking play in invasive team sports. Sports Med2018; 48: 17–28.
69.
HålandEMWiigASHvattumLM, et al.Evaluating the effectiveness of different network flow motifs in association football. J Quant Anal Sports2020; 16: 311–323.
70.
XuC. Social network analysis of college and professional basketball. ProQuest Information & Learning. PhD Thesis2018, University of Washington.
71.
GyarmatiLAngueraX. Automatic extraction of the passing strategies of soccer teams. arXiv preprint arXiv:1508021712015.
72.
KorteFLinkDGrollJ, et al.Play-by-play network analysis in football. Front Psychol2019; 10: 1738.
73.
ClementeFMLourenco MartinsFMWongDP, et al.Midfielder as the prominent participant in the building attack: A network analysis of national teams in FIFA World Cup 2014. Int J Perform Anal Sport2015; 15: 704–722.
74.
ImmlerSRappelsbergerPBacaA, et al.Guardiola, Klopp, and Pochettino: The purveyors of what? The use of passing network analysis to identify and compare coaching styles in professional football. Front Sports Act Living2021; 3: 725554.
75.
FewellJHArmbrusterDIngrahamJ, et al.Basketball teams as strategic networks. PLoS One2012; 7: e47445.
76.
BuldúJMBusquetsJEchegoyenI, et al.Defining a historic football team: Using network science to analyze Guardiola's F.C. Barcelona. Sci Rep2019; 9: 13602.
77.
KroeckelPPiazzaANeuhoferK. Dynamic network analysis of the Euro2016 final preliminary results. In: SNAMS-2017 the fourth international symposium on social networks analysis, management and security, Prague, IEEE, 21-23 August 2017, pp.114–119.
78.
SchecterAPilnyALeungA, et al.Step by step: Capturing the dynamics of work team process through relational event sequences. J Organ Behav2018; 39: 1163–1181.
79.
LeendersRTAContractorNSDeChurchLA. Once upon a time: Understanding team processes as relational event networks. Organ Psychol Rev2016; 6: 92–115.
80.
SarmentoHClementeFMGonçalvesE, et al.Analysis of the offensive process of AS Monaco professional soccer team: A mixed-method approach. Chaos, Solitons Fractals2020; 133: 109676.
81.
Álvarez CelayaO. Análisis de las redes de comunicación motriz a través del pase entre los jugadores de la Selección uruguaya de fútbol. / An analysis of motor communication networks through the study of ball passes between the players of the Uruguayan football team. Rev Univ Educ Fís Deporte2012; 5: 56–61.
82.
AquinoRCarlingCPalucci VieiraLH, et al.Influence of situational variables, team formation, and playing position on match running performance and social network analysis in Brazilian professional soccer players. J Strength Cond Res2020; 34: 808–817.
83.
AquinoRMachadoJCManuel ClementeF, et al.Comparisons of ball possession, match running performance, player prominence and team network properties according to match outcome and playing formation during the 2018 FIFA World Cup. Int J Perform Anal Sport2019; 19: 1026–1037.
84.
ClementeFMCouceiroMSMartinsFML, et al.Using network metrics to investigate football team players’ connections: A pilot study. Mot Rev Educ Fis2014; 20: 262–271.
85.
ClementeFMMartinsFM. Study of sequences of passes between professional soccer player in home matches during a season: Applicability of social network analysis measures. Rev IberoamPsicol Ejercicio Deporte2017; 12: 195–202.
86.
ClementeFMMartinsFML. Network structure of UEFA Champions League teams: Association with classical notational variables and variance between different levels of success. Int J Comput Sci Sport2017; 16: 39–50.
87.
ClementeFMMartinsFML. Who are the prominent players in the UEFA champions league? An approach based on network analysis. Walailak J Sci Technol2017; 14: 627–636.
88.
ClementeFMLourenco MartinsFMKalamarasD, et al.General network analysis of national soccer teams in FIFA World Cup 2014. Int J Perform Anal Sport2015; 15: 80–96.
89.
ClementeFMMartinsFMLKalamarasD, et al.The social network analysis of Switzerland football team on FIFA World Cup 2014. Acta Kinesiol2015; 9: 25–30.
90.
ClementeFMMartinsFMLSilvaF. Development of sports network analysis: Methodological considerations. J Phys Educ Sport2016; 16: 940–944.
91.
ClementeFMLourenço MartinsFMMendesRS. Analysis of scored and conceded goals by a football team throughout a season: A network analysis. Kinesiology2016; 48: 103–114.
92.
ClementeFMMartinsFMLMendesRS. Technical accuracy it is associated with prominence levels in basketball?J Phys Educ Sport2015; 15: 400–406.
93.
ClementeFMSilvaFMartinsFML, et al.Performance analysis tool for network analysis on team sports: A case study of FIFA Soccer World Cup 2014. Proc IMechE, Part P: J Sports Engineering and Technology2016; 230: 158–170.
94.
ClementeFMSarmentoHPracaGM, et al.Variations of network centralities between playing positions in favorable and unfavorable close and unbalanced scores during the 2018 FIFA World Cup. Front Psychol2019; 10: 1802.
95.
GamaJDiasGCouceiroM, et al.Networks and centroid metrics for understanding football. S Afr J Res Sport Phys Educ Recreat2016; 38: 75–90.
96.
GamaJCouceiroMDiasG, et al.Small-world networks in professional football: Conceptual model and data. Eur J Hum Mov2015; 35: 85–113.
97.
GamaJPassosPDavidsK, et al.Network analysis and intra-team activity in attacking phases of professional football. Int J Perform Anal Sport2014; 14: 692–708.
98.
GonçalvesLGCClementeFVieiraLHP, et al.Effects of match location, quality of opposition, match outcome, and playing position on load parameters and players’ prominence during official matches in professional soccer players. Hum Mov2021; 22: 35–44.
99.
IevoliRGardiniAPalazzoL. The role of passing network indicators in modeling football outcomes: An application using Bayesian hierarchical models. Adv Stat Anal2021: 1–23.
100.
IevoliRPalazzoLRagoziniG. On the use of passing network indicators to predict football outcomes. Knowl Based Syst2021; 222: 106997.
101.
JiangZChenXDongB, et al.Trajectory-based community detection. IEEE Trans Circuits Syst II Express Briefs2019; 67: 1139–1143.
102.
KorteFLamesM. Characterizing different team sports using network analysis. Curr Issues Sport Sci2018; 3: 005–005.
103.
LiuTHohmannA. Apriori-based diagnostical analysis of passings in the football game. In: 2016 IEEE international conference on big data analysis (ICBDA), Hangzhou, China, 2016, pp.52–55.
104.
MartinsFGomesRLopesV, et al.Node and network entropy—a novel mathematical model for pattern analysis of team sports behavior. Mathematics2020; 8: 1543.
105.
MartinsFGomesRLopesV, et al.Mathematical models to measure the variability of nodes and networks in team sports. Entropy2021; 23: 1072.
106.
McHaleIGReltonSD. Identifying key players in soccer teams using network analysis and pass difficulty. Eur J Oper Res2018; 268: 339–347.
107.
MendesBClementeFMMauricioN. Variance in prominence levels and in patterns of passing sequences in elite and youth soccer players: A network approach. J Hum Kinet2018; 61: 141–153.
108.
MuhuriSChakrabortySSetuaSK. Differentiate the game maker in any soccer match based on social network approach. IEEE Trans Comput Soc Syst2020; 7: 1399–1408.
109.
NarizukaTYamamotoKYamazakiY. Degree distribution of position-dependent ball-passing networks in football games. J Phys Soc Japan2015; 84: 084003.
110.
NeumanYVilenchikD. Modeling small systems through the relative entropy lattice. IEEE Access2019; 7: 43591–43597.
111.
NeumanYIsraeliNVilenchikD, et al.The adaptive behavior of a soccer team: An entropy-based analysis. Entropy2018; 20: 758.
112.
PappalardoLCintiaPRossiA, et al.A public data set of spatio-temporal match events in soccer competitions. Sci Data2019; 6: 36.
113.
PeixotoDPraçaGMBredtS, et al.Comparison of network processes between successful and unsuccessful offensive sequences in elite soccer. Hum Mov Special Issues2017; 2017: 48–54.
114.
PraçaGDinizLClementeF, et al.The influence of playing position on the physical, technical, and network variables of sub-elite professional soccer athletes. Hum Mov2021; 22: 22–31.
115.
Moreira PracaGRochaelMFrancklinG, et al.The influence of age group and match period on tactical performance in youth soccer: A full season study. Proc IMechE, Part P: J Sports Engineering and Technology2022; 236: 360–367.
116.
Rojas-MoraJChávez-BustamanteFdel Río-AndradeJ, et al.A methodology for the analysis of soccer matches based on PageRank centrality. In: Sports management as an emerging economic activity: Trends and best practices.2017, pp.257–272.
117.
SargentJBedfordA. Evaluating Australian football league player contributions using interactive network simulation. J Sports Sci Med2013; 12: 116.
118.
SilvaFGMGomesAJPQuoc TrongN, et al.A new tool for network analysis on team sports the ultimate performance analysis tool. In: 2017 international conference on engineering, technology and innovation. 2017, pp.439–445.
119.
Uchoa Maia RodriguesDCMouraFACunhaSA, et al.Visualizing temporal graphs using visual rhythms a case study in soccer match analysis. Portugal: Visigrapp, 2017, p.96–107.
120.
WangQZhuHHuW, et al.Discerning tactical patterns for professional soccer teams: An enhanced topic model with applications. Sydney: KDD, 2015, p.2197–2206.
121.
WiigASHålandEMStålhaneM, et al.Analyzing passing networks in association football based on the difficulty, risk, and potential of passes. Int J Comput Sci Sport2019; 18: 44–68.
122.
XinLZhuMChipmanH. A continuous-time stochastic block model for basketball networks. Ann Appl Stat2017; 11: 553–597.
123.
YamamotoY. Scale-free property of the passing behaviour in a team sport. Int J Sport Health Sci2009; 7: 86–95.
124.
YamamotoYYokoyamaK. Common and unique network dynamics in football games. PLoS One2011; 6: e29638.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
