Cluster Computing

, Volume 21, Issue 1, pp 523–537 | Cite as

Quantifying team precedence in the game of cricket

  • Haseeb AhmadEmail author
  • Ali Daud
  • Licheng Wang
  • Ibrar Ahmad
  • Muhammad Hafeez
  • Yixian Yang


Precedence of cricket teams depicts the authority of teams over the counter peers. The existing ad-hoc ranking systems either incorporate the count of won or lost matches, or just consider the winning margins. The batting and bowling productivity at team level along with the reward for each win and penalty against each lost never adopted for extracting the supremacy of teams over the others. The intuition of this paper is to address the aforementioned limitations while presenting an effective mechanism. With this aim, first of all, effective features are explicitly formulated for finding batting and bowling productivity precedence. Subsequently, these features are combined to devise the team productivity metric. Moreover, an efficient productivity precedence algorithm is presented that incorporates the defined features to retrieve the batting, bowling and team precedences in one day international matches. Extensive experiments are performed for this purpose, the results of which show that the presented method renders quite promising insights. Further, the batting, bowling and team evolution is also presented to depict the precedences of different spans. The presented method can be explicitly adopted for cricket team rankings.


Cricket Precedence Productivity Ranking 



This work was supported by the National Natural Science Foundation of China (NSFC) (Nos. 61370194, 61411146001, 61502048).


  1. 1.
    Swartz, T.B.: Research Directions in Cricket. Handbook of Statistical Methods and Analyses in Sports, p. 272. Chapman & Hall/CRC Handbooks of Modern Statistical Methods, Boca Raton (2016)Google Scholar
  2. 2.
    Mukherjee, S.: Quantifying individual performance in cricketa network analysis of batsmen and bowlers. Physica A 393, 624637 (2014)CrossRefGoogle Scholar
  3. 3.
    Duckworth, F., Lewis, A.: A successful operational research intervention in one-day cricket. J. Oper. Res. Soc. 55(7), 749759 (2004)CrossRefzbMATHGoogle Scholar
  4. 4.
    Perera, H., Davis, J., Swartz, T.B.: Optimal lineups in twenty20 cricket. J. Stat. Comput. Simul. 86(14), 28882900 (2016)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ahmad, H., Daud, A., Wang, L., Hong, H., Dawood, H., Yang, Y.: Prediction of rising stars in the game of cricket. IEEE Access, p. 99 (2017)Google Scholar
  6. 6.
    Daud, A., Muhammad, F., Dawood, H., Dawood, H.: Ranking cricket teams. Inform. Process. Manag. 51(2), 62 (2015)Google Scholar
  7. 7.
    Daud, A., Muhammad, F.: Ranking cricket teams through runs and wickets. In: International Conference on Active Media Technology, pp. 156–165. Springer International Publishing (2013)Google Scholar
  8. 8.
    Mukherjee, S.: Identifying the greatest team and captain a complex network approach to cricket matches. Physica A 391(23), 60666076 (2012)CrossRefGoogle Scholar
  9. 9.
    Borooah, V.K., Mangan, J.E., et al.: The bradman class: an exploration of some issues in the evaluation of batsmen for test matches, 18772006. J. Quant. Anal. Sports 6(3), 121 (2010)Google Scholar
  10. 10.
    Ali, R., Lee, S., Chung, T.C.: Accurate multi-criteria decision making methodology for recommending machine learning algorithm. Expert Syst. Appl. 71, 257278 (2017)CrossRefGoogle Scholar
  11. 11.
    Ahmed, I., Ali, R., Guan, D., Lee, Y.K., Lee, S., Chung, T.: Semi-supervised learning using frequent itemset and ensemble learning for sms classification. Expert Syst. Appl. 42(3), 10651073 (2015)CrossRefGoogle Scholar
  12. 12.
    Amin, G.R., Sharma, S.K.: Measuring batting parame- ters in cricket: a two-stage regression-owa method. Measurement 53, 5661 (2014)CrossRefGoogle Scholar
  13. 13.
    Bracewell, P.J., Ruggiero, K., et al.: A parametric control chart for monitoring individual batting performances in cricket. J. Quant. Anal. Sports 5(3), 119 (2009)MathSciNetGoogle Scholar
  14. 14.
    De Silva, B.M., Pond, G.R., Swartz, T.B.: Estimation of the magnitude of victory in one-day cricket. Australian N. Z. J.Stat. 43(3), 259268 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Allsopp, P., Clarke, S.R.: Rating teams and analysing outcomes in one-day and test cricket. J. R. Stat. Soc. 167(4), 657667 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Davis, J., Perera, H., Swartz, T.B.: A simulator for twenty20 cricket. Australian N. Z. J. Stat. 57(1), 5571 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The Pagerank Citation Ranking: Bringing Order to the Web. Tech. rep, Stanford InfoLab (1999)Google Scholar
  18. 18.
    Zhang, S., Ravana, S.D.: Estimating reliability of the retrieval systems effectiveness rank based on performance in multiple experiments. Clust. Comput. 1–16Google Scholar
  19. 19.
    Yang, L., Tian, Y., Li, J., Ma, J., Zhang, J.: Identifying opinion leaders in social networks with topic limitation. Clust. Comput. 1–11 (2017)Google Scholar
  20. 20.
    Cho, I., Park, M.: Technological-level evaluation using patent statistics: model and application in mobile communications. Clust. Comput. 18(1), 259268 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Haseeb Ahmad
    • 1
    Email author
  • Ali Daud
    • 2
    • 3
  • Licheng Wang
    • 1
  • Ibrar Ahmad
    • 1
  • Muhammad Hafeez
    • 1
  • Yixian Yang
    • 1
  1. 1.State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia
  3. 3.Department of Computer Science and Software EngineeringInternational Islamic UniversityIslamabadPakistan

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