Abstract
This paper introduces a novel ranking system for competitive sports based around the notion of subgraphs. Although the system is targeted specifically to professional tennis it could be applied to any dominance network due to its generality. The results of about 140,000 tennis matches played between Top-100 players are used to create a colored directed network where colors represent different surfaces and edge direction depends on head-to-read results between players. The main contribution of this work is a ranking system which relies on the occurrences of 4-node directed subgraphs and the positions (or orbits) where the players appear on them. Since the concept of orbit is intrinsically connected with node dominance, appearing frequently in dominant orbits indicates that the player himself is dominant. Even in a very sparse network and without any background knowledge on the tournaments or stages of the matches, our proposal is able to extract meaningful rankings which capture the intricate competitive relationships between players from different eras.
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References
AparÃcio, D., Ribeiro, P., Silva, F.: Network comparison using directed graphlets (2015). arXiv:1511.01964
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Seventh International World-Wide Web Conference (WWW 1998) (1998)
Dingle, N., Knottenbelt, W., Spanias, D.: On the (page) ranking of professional tennis players. In: Computer Performance Engineering, pp. 237–247. Springer (2013)
Hayes, W., Sun, K., Pržulj, N.: Graphlet-based measures are suitable for biological network comparison. Bioinformatics 29(4), 483–491 (2013)
London, A., Németh, J., Németh, T.: Time-dependent network algorithm for ranking in sports. Acta Cybernetica 21(3), 495–506 (2014)
Milenković, T., Ng, W.L., Hayes, W., Pržulj, N.: Optimal network alignment with graphlet degree vectors. Cancer Inform. 9, 121 (2010)
Motegi, S., Masuda, N.: A network-based dynamical ranking system for competitive sports. Sci. Rep. 2(904) (2012)
Pržulj, N.: Biological network comparison using graphlet degree distribution. Bioinformatics 23, 177–183 (2007)
Radicchi, F., Perc, M.: Who is the best player ever? a complex network analysis of the history of professional tennis. PloS ONE 6(2), e17249 (2011)
Shan, Z., Li, S., Dai, Y.: Gamerank: ranking and analyzing baseball network. In: Social Informatics. pp. 244–251. IEEE Computer Society (2012)
Stefani, R.: Survey of the major world sports rating systems. J. Appl. Stat. 24(6), 635–646 (1997)
Wang, P., L, J., Yu, X.: Identification of important nodes in directed biological networks: a network motif approach. PLoS ONE 9(8), e106132 (2014)
Acknowledgments
This work is partially funded by FCT (Portuguese Foundation for Science and Technology) within project UID/EEA/50014/2013. David AparÃcio is supported by a FCT/MAP-i PhD research grant (PD/BD/105801/2014).
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AparÃcio, D., Ribeiro, P., Silva, F. (2016). A Subgraph-Based Ranking System for Professional Tennis Players. In: Cherifi, H., Gonçalves, B., Menezes, R., Sinatra, R. (eds) Complex Networks VII. Studies in Computational Intelligence, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-319-30569-1_12
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DOI: https://doi.org/10.1007/978-3-319-30569-1_12
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