References
- Araujo, D., & Davids, K. (2016). Team synergies in sport: Theory and measures. Frontiers in Psychology, 7, 1449). https://doi.org/https://doi.org/10.3389/fpsyg.2016.01449
- Araujo, D., Davids, K., & Hristovski, R. (2006). The ecological dynamics of decision making in sport. Psychology of Sport and Exercise, 7(6), 653–676. https://doi.org/https://doi.org/10.1016/j.psychsport.2006.07.002
- Arthur, W., Jr., Day, E. A., Bennett, W., Jr., & Portrey, A. M. (Eds.). (2013). Applied psychology series. Individual and team skill decay: The science and implications for practice. Routledge/Taylor & Francis Group. https://doi.org/https://doi.org/10.4324/9780203576076
- Braham, C., & Small, M. (2018). Complex networks untangle competitive advantage in Australian football. Chaos, 28(5), 053105. https://doi.org/https://doi.org/10.1063/1.5006986
- Butterfill, K. G., & Sebanz, N. (2011). Psychological Research on Joint Action: Theory and Data. In B. Ross (Ed.), The Psychology of Learning and Motivation (Vol. 54, pp. 59–101). Academic Press.
- Cintia, P., Coscia, M., & Pappalardo, L. (2016). The Haka network: Evaluating rugby team performance with dynamic graph analysis. 2016 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM). p 1095–1102, San Francisco, CA, USA. https://doi.org/https://doi.org/10.1109/ASONAM.2016.7752377
- Croft, H., Lamb, P., & Middlemas, S. (2015). The application of self-organising maps to performance analysis data in rugby union. International Journal of Performance Analysis in Sport, 15(3), 1037–1046. https://doi.org/https://doi.org/10.1080/24748668.2015.11868849
- Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. Inter Journal: Complex Systems: 1695. https://igraph.org
- Deutsch, M. U., Kearney, G. A., & Rehrer, N. J. (2007). Time – Motion analysis of professional rugby union players during match-play. Journal of Sports Sciences, 25(4), 461–472. https://doi.org/https://doi.org/10.1080/02640410600631298
- Duch, J., Waitzman, J. S., Amaral, L. A. N., & Scalas, E. (2010). Quantifying the performance of individual players in a team activity. PLoS One, 5(6), e10937. https://doi.org/https://doi.org/10.1371/journal.pone.0010937
- Edelman, G. M., & Gally, J. A. (2018). Degeneracy and complexity in biological systems. Proceedings of the National Academy of Sciences Nov2001, 98(24): 13763–13768. https://doi.org/https://doi.org/10.1073/pnas.231499798
- Fewell, J. H., Armbruster, D., Ingraham, J., Petersen, A., Waters, J. S., & Boccaletti, S. (2012). Basketball teams as strategic networks. PLoS One, 7(11), e47445. https://doi.org/https://doi.org/10.1371/journal.pone.0047445
- George, T. M., Olsen, P. D., Kimber, N. E., Shearman, J. P., Hamilton, J. G., & Hamlin, M. J. (2015). The effect of altitude and travel on rugby union performance: Analysis of the 2012 Super Rugby competition. Journal of Strength and Conditioning Research, 29(12), 3360–3366. https://doi.org/https://doi.org/10.1519/JSC.0000000000001204
- Gibson, J. J. (1979). The ecological approach to visual perception. Houghton Mifflin Harcourt (HMH.
- Gonçalves, B., Coutinho, D., Santos, S., Lago-Penas, C., Jiménez, S., & Sampaio, J. (2017). Exploring team passing networks and player movement dynamics in youth association football. PLOS ONE 12(1): e0171156. https://doi.org/https://doi.org/10.1371/journal.pone.0171156
- Grandjean, M., & Mauro, A. (2016). A social network analysis of Twitter: Mapping the digital humanities community. Cogent Arts & Humanities, 3(1), 1171458. https://doi.org/https://doi.org/10.1080/23311983.2016.1171458
- Grund, T. U. (2012). Network structure and team performance: The case of English Premier League soccer teams. Social Networks,34(4): 682–690. https://doi.org/https://doi.org/10.1016/j.socnet.2012.08.004
- Himelboim, I., Smith, M. A., Rainie, L., Shneiderman, B., & Espina, C. (2017). Classifying Twitter topic-networks using social network analysis. Social Media + Society, 3(1), 205630511769154. https://doi.org/https://doi.org/10.1177/2056305117691545
- Hoaglin, D., & Iglewicz, B. (1987). Fine-tuning some resistant rules for outlier labeling. Journal of the American Statistical Association,82(400), 1147–1149. https://doi.org/https://doi.org/10.2307/2289392
- Hughes, M. D., & Bartlett, R. M. (2002). The use of performance indicators in performance analysis. Journal of Sports Sciences, 20(10), 739–754. https://doi.org/https://doi.org/10.1080/026404102320675602
- Korte, F., & Lames, M. (2018). Characterizing different team sports using network analysis. Current Issues in Sport Science 3,5. https://doi.org/https://doi.org/10.15203/CISS_2018.005
- Mendes, B., Clemente, F. & Maurício, N.(2018). Variance in prominence pevels and in patterns of passing sequences in elite and youth soccer players: A network approach. Journal of Human Kinetics, 61(1), 141-153. https://doi.org/https://doi.org/10.1515/hukin-2017-0117
- Oloritun, R. O., Madan, A., Pentland, A., & Khayal, I. (2013). Identifying close friendships in a sensed social network. Procedia - Social and Behavioral Sciences, 79(18), 18–26. https://doi.org/https://doi.org/10.1016/j.sbspro.2013.05.054
- R Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing. URL https://www.R-project.org/
- Ribeiro, J., Silva, P., Duarte, R., Davids, K., & Garganta, J. (2017). Team sports performance analysed through the lens of social network theory: Implications for research and practice. Sports Medicine, 47(9), 1689–1696. https://doi.org/https://doi.org/10.1007/s40279-017-0695-1
- Rienties, B., & Nolan, E.-M. (2014). Understanding friendship and learning networks of international and host students using longitudinal social network analysis. International Journal of Intercultural Relations, 41, 165–180. https://doi.org/https://doi.org/10.1016/j.ijintrel.2013.12.003
- RStudio Team. (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/
- Sheehan, W. B., Tribolet, R., Watsford, M. L., Novak, A. R., Rennie, M. J., & Fransen, J. (2020). Using cooperative networks to analyse behaviour in professional Australian football. Journal of Science and Medicine in Sport, 23(3), 291–296. https://doi.org/https://doi.org/10.1016/j.jsams.2019.09.012
- Silva, P., Garganta, J., Araújo, D., Davids, K., & Aguiar, P. (2013). Shared knowledge or shared affordances? Insights from an ecological dynamics approach to team coordination in sports. Sports Medicine,43(9): 765–772. https://doi.org/https://doi.org/10.1007/s40279-013-0070-9
- Silva, P., Vilar, L., Davids, K., Araujo, D., & Garganta, J. (2016). Sports teams as complex adaptive systems: Manipulating player numbers shapes behaviours during football small-sided games. SpringerPlus, 5, 191. https://doi.org/https://doi.org/10.1186/s40064-016-1813-5
- Watson, N., Durbach, I., Hendricks, S., & Stewart, T. (2017). On the validity of team performance indicators in rugby union. International Journal of Performance Analysis in Sport, 17(4), 609–621. https://doi.org/https://doi.org/10.1080/24748668.2017.1376998
- Zhao, S., Yu, D., Wang, J., & Meng, Z. (2020). Research on social network analysis method in cooperative innovation performance. Proceedings of the 4th International Symposium on Business Corporation and Development in South-East and South Asia under B&R Initiative,Kunming, China. https://doi.org/https://doi.org/10.2991/aebmr.k.200708.016