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Identifying the greatest team and captain—A complex network approach to cricket matches

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  • Mukherjee, Satyam
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    Abstract

    We consider all Test matches played between 1877 and 2010 and One Day International (ODI) matches played between 1971 and 2010. We form directed and weighted networks of teams and also of their captains. The success of a team (or captain) is determined by the ‘quality’ of the wins, not simply by the number of wins. We apply the diffusion-based PageRank algorithm to the networks to assess the importance of the wins, and rank the respective teams and captains. Our analysis identifies Australia as the best team in both forms of cricket, Test and ODI. Steve Waugh is identified as the best captain in Test cricket and Ricky Ponting is the best captain in the ODI format. We also compare our ranking scheme with an existing ranking scheme, the Reliance ICC ranking. Our method does not depend on ‘external’ criteria in the ranking of teams (captains). The purpose of this paper is to introduce a revised ranking of cricket teams and to quantify the success of the captains.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0378437112006127
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    Bibliographic Info

    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 391 (2012)
    Issue (Month): 23 ()
    Pages: 6066-6076

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    Handle: RePEc:eee:phsmap:v:391:y:2012:i:23:p:6066-6076

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    Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

    Related research

    Keywords: Social network analysis; Cricket; PageRank algorithm;

    References

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    1. C. Sire & S. Redner, 2009. "Understanding baseball team standings and streaks," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, vol. 67(3), pages 473-481, February.
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    3. E. Bittner & A. Nußbaumer & W. Janke & M. Weigel, 2009. "Football fever: goal distributions and non-Gaussian statistics," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, vol. 67(3), pages 459-471, February.
    4. de Saá Guerra, Y. & Martín González, J.M. & Sarmiento Montesdeoca, S. & Rodríguez Ruiz, D. & García-Rodríguez, A. & García-Manso, J.M., 2012. "A model for competitiveness level analysis in sports competitions: Application to basketball," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(10), pages 2997-3004.
    5. Skinner Brian, 2010. "The Price of Anarchy in Basketball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(1), pages 1-18, January.
    6. Borooah Vani K & Mangan John E, 2010. "The "Bradman Class": An Exploration of Some Issues in the Evaluation of Batsmen for Test Matches, 1877-2006," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(3), pages 1-21, July.
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    Cited by:
    1. Guo, Fangjian & Yang, Zimo & Zhou, Tao, 2013. "Predicting link directions via a recursive subgraph-based ranking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3402-3408.
    2. Yang, Chunxia & Chen, Yanhua & Niu, Lei & Li, Qian, 2014. "Cointegration analysis and influence rank—A network approach to global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 168-185.

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