Evaluation of teams in FIFA World Cup using a network of players transfers
DOI:
https://doi.org/10.5753/isys.2019.598Keywords:
Complex networks, Data mining, FootballAbstract
Football is the most popular sport in the world. The growth in the number of transactions of purchase and sale, marketing, sponsorships, sale of tickets, TV contracts, among other forms of monetization of football makes the flow of values increasingly higher. The majority of works related to this sport is associated with sociological analysis. This work proposes a study focused on the transactions occurred among the football teams classified to the World Cup 2018 using complex networks techniques for an analysis of the transfer of players among these countries. Also was also realized an analysis of the best placed countries in the World Cup, France, Croatia, Belgium and England. Through our analysis was possible to notice that the main countries in the generated rankings are European countries. Besides that, using community detection algorithms was possible to note that countries in the same cluster tend to be commercial partners
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Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding
of communities in large networks. Journal of Statistical Mechanics: Theory and
Experiment, 2008(10):P10008.
Clauset, A., Newman, M. E. J., and Moore, C. (2004). Finding community structure in
very large networks. Phys. Rev. E, 70:066111.
Deloitte (June 2016). Annual review of football finance.
Felix, L., Barbosa, C., Carvalho, I., Vieira, V., and Xavier, C. (2018a). Uma analise das ´
selec¸oes da copa utilizando uma rede de transfer ˜ encias de jogadores entre pa ˆ ´ıses. In
CSBC 2018 - VII BraSNAM ().
Felix, L., Barbosa, C., Vieira, V., and Xavier, C. (2018b). Analise do impacto das copas do ´
mundo no mercado de transac¸oes de jogadores de futebol e da globalizac¸ ˜ ao do futebol ˜
utilizando tecnicas de redes complexas. In ´ ENIAC 2018 - VII KdMIle ().
Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry,
pages 35–41.
Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social
Networks, page 215.
Frick, B. (2007). The football players’ labor market: Empirical evidence from the major
european leagues. Scottish Journal of Political Economy, 54(3):422–446.
Gonzalez-Badillo, J. J., Pareja-Blanco, F., Rodr ´ ´ıguez-Rosell, D., Abad-Herencia, J. L.,
del Ojo-Lopez, J. J., and S ´ anchez-Medina, L. (2015). Effects of velocity-based resis- ´
tance training on young soccer players of different ages. The Journal of Strength &
Conditioning Research, 29(5):1329–1338.
Iandoli, R. (2018). Quer ficar mais caro? marque gols em uma copa do mundo.
Jarvie, G. (2003). Sport, racism and british society: A sociological study of england’s elite
male afro/caribbean soccer and rugby union players. In Sport, racism and ethnicity,
pages 79–102. Routledge.
Lees, A., Asai, T., Andersen, T. B., Nunome, H., and Sterzing, T. (2010). The biomechanics
of kicking in soccer: A review. Journal of sports sciences, 28(8):805–817.
Liebig, J., Rhein, A. V., Kastner, C., Apel, S., Dorre, J., and Lengauer, C. (2012). Largescale
variability-aware type checking and dataflow analysis.
Magee, J. and Sugden, J. (2002). “the world at their feet” professional football and international
labor migration. Journal of sport and social issues, 26(4):421–437.
Martin, W. (2018). From 950, 000to220 million: The most valuable player for every team
at the 2018 world cup.
Newman, M. E. J. (2006). Finding community structure in networks using the eigenvectors
of matrices. Phys. Rev. E, 74:036104.
Newman, M. E. J. and Girvan, M. (2004). Finding and evaluating community structure in
networks. Phys. Rev. E, 69:026113.
Osgnach, C., Poser, S., Bernardini, R., Rinaldo, R., and Di Prampero, P. E. (2010). Energy
cost and metabolic power in elite soccer: a new match analysis approach. Med Sci
Sports Exerc, 42(1):170–178.
Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The pagerank citation ranking:
Bringing order to the web. Technical Report 1999-66, Stanford InfoLab. Previous
number = SIDL-WP-1999-0120.
Palacios-Huerta, I. (2004). Structural changes during a century of the world’s most popular
sport. Statistical Methods and Applications, 13(2):241–258.
Redhead, S. (2002). Post-fandom and the millennial blues: The transformation of soccer
culture. Routledge.
Taylor, I. (2014). 0n the sports violence question: soccer hooliganism revisited. Sport,
Culture and Ideology (RLE Sports Studies), page 152.
XF, L., Y-L, L., X-H, L., Q-X, W., and T-X, W. (2016). The anatomy of the global football
player transfer network: Club functionalities versus network properties. PLoS ONE,
11(6).
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Published
2019-09-18
How to Cite
Félix, L. G. da S., Barbosa, C. M., Carvalho, I. A., Vieira, V. da F., & Xavier, C. R. (2019). Evaluation of teams in FIFA World Cup using a network of players transfers. ISys - Brazilian Journal of Information Systems, 12(3), 73–93. https://doi.org/10.5753/isys.2019.598
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