Complex Network Characteristics and Team Performance in the Game of Cricket

  • Rudra M. Tripathy
  • Amitabha Bagchi
  • Mona Jain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8302)


In this paper a complex network model is used to analyze the game of cricket. The nodes of this network are individual players and edges are placed between players who have scored runs in partnership. Results of these complex network models based on partnership are compared with performance of teams. Our study examines Test cricket, One Day Internationals (ODIs) and T20 cricket matches of the Indian Premier League (IPL). We find that complex network properties: average degree, average strength and average clustering coefficient are directly related to the performance (win over loss ratio) of the teams, i.e., teams having higher connectivity and well-interconnected groups perform better in Test matches but not in ODIs and IPL. For our purpose, the basic difference between different forms of the game is duration of the game: Test cricket is played for 5-days, One day cricket is played only for a single day and T20 is played only for 20 overs in an inning. In this regard, we make a clear distinction in social network properties between the Test, One day, and T20 cricket networks by finding relationships between average weight with their end point’s degrees. We know that performance of teams varies with time - for example West Indies, who had established themselves as the best team during 1970s now is one of the worst teams in terms of results. So we have looked at evolution of team’s performances with respect to their network properties for every decade. We have observed that, the average degree and average clustering coefficient follow similar trends as the performance of the team in Test cricket but not in One day cricket and T20. So partnership actually plays a more significant role in team performance in Test cricket as compared to One day cricket and T20 cricket.


Complex Networks Social Networks Cricket Partnership 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Albert, R., Jeong, H., Barabasi, A.L.: The diameter of the world wide web. Nature 401, 130 (1999)CrossRefGoogle Scholar
  2. 2.
    Ahn, Y., Han, S., Kwak, H., Moon, S., Jeong, H.: Analysis of topological characteristics of huge online social networking services. In: WWW 2007: Proceedings of the 16th International Conference on World Wide Web, pp. 835–844. ACM, New York (2007)CrossRefGoogle Scholar
  3. 3.
    Newman, M.E.: The structure and function of complex networks. SIAM Review 45, 167 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Newman, M.E.: The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences 98(2), 404–409 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: SIGCOMM 1999: Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, pp. 251–262. ACM, New York (1999)Google Scholar
  6. 6.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar
  7. 7.
    Milgram, S.: The small world problem. Psychology Today 2, 60–67 (1967)Google Scholar
  8. 8.
    ESPNCricinfo: Cricket website, (accessed 2013)
  9. 9.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: Structure and dynamics. Physics Reports 424(4-5), 175–308 (2006)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Newman, M.E.: Analysis of weighted networks. Physical Review E 70, 56–131 (2004)Google Scholar
  11. 11.
    Barrat, A., Barthelemy, M., Pastor-Satorras, R., Vespignani, A.: The architecture of complex weighted networks. Proceedings of the National Academy of Sciences 101, 37–47 (2004)CrossRefGoogle Scholar
  12. 12.
    De Melo, P.O.V., Almeida, V.A., Loureiro, A.A.: Can complex network metrics predict the behavior of NBA teams? In: KDD 2008: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 695–703. ACM, New York (2008)CrossRefGoogle Scholar
  13. 13.
    Onody, R.N., De Castro, P.A.: Complex network study of Brazilian soccer players. Physical Review E 70, 37–103 (2004)CrossRefGoogle Scholar
  14. 14.
    Damodaran, U.: Stochastic dominance and analysis of ODI batting performance: The Indian cricket team, 1989-2005. Journal of Sports Science and Medicine 5(4), 503–508 (2006)Google Scholar
  15. 15.
    Allsopp, P.E., Clarke, S.R.: Rating teams and analysing outcomes in One-day and Test cricket. Journal of The Royal Statistical Society Series A 167(4), 657–667 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    NetworkX: Networkx documentation, (accessed 2013)
  17. 17.
    Clauset, A., Shalizi, C.R., Newman, M.E.: Power-law distributions in empirical data. SIAM Review 51(4) (November 2009)Google Scholar
  18. 18.
    Leung, C.C., Chau, H.F.: Weighted assortative and disassortative networks model (2006)Google Scholar
  19. 19.
    Chang, H., Su, B., Zhou, Y., He, D.: Assortativity and act degree distribution of some collaboration networks. Physica A: Statistical Mechanics and its Applications 383(2), 687–702 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Rudra M. Tripathy
    • 1
  • Amitabha Bagchi
    • 2
  • Mona Jain
    • 2
  1. 1.Silicon Institute of TechnologyBhubaneshwarIndia
  2. 2.Indian Institute of Technology, DelhiIndia

Personalised recommendations