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A Statistical Analysis of Batting in Cricket

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  • Alan C. Kimber
  • Alan R. Hansford

Abstract

The batting average is ubiquitous in cricket. In this paper we show that the traditional batting average depends on an unrealistic parametric assumption. We propose a nonparametric approach based on runs scored for assessing batting performance. The methods have been applied to a large sample of players at various levels of cricket, examples of which are featured in this paper. The statistical methodology employed is akin to that used in reliability and survival analysis.

Suggested Citation

  • Alan C. Kimber & Alan R. Hansford, 1993. "A Statistical Analysis of Batting in Cricket," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 156(3), pages 443-455, May.
    • Handle: RePEc:bla:jorssa:v:156:y:1993:i:3:p:443-455
    DOI: 10.2307/2983068
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    Citations

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    Cited by:

    1. Manage Ananda B. W. & Mallawaarachchi Kumudu & Wijekularathna Kanchana, 2010. "Receiver Operating Characteristic (ROC) Curves for Measuring the Quality of Decisions in Cricket," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-15, April.
    2. Oliver G. Stevenson & Brendon J. Brewer, 2021. "Finding your feet: A Gaussian process model for estimating the abilities of batsmen in test cricket," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 481-506, March.
    3. G D I Barr & B S Kantor, 2004. "A criterion for comparing and selecting batsmen in limited overs cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1266-1274, December.
    4. Bracewell Paul J & Farhadieh Farinaz & Jowett Clint A & Forbes Don G. R. & Meyer Denny H, 2009. "Was Bradman Denied His Prime?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(4), pages 1-26, October.
    5. Ananda Bandulasiri & Tom Brown & Indika Wickramasinghe, 2016. "Factors affecting the result of matches in the one day format of cricket," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 26(4), pages 21-32.
    6. Sahadeb Sarkar & Subhasis Mishra & Sanjeev Kumar, 2022. "Development of a Comprehensive Multi-Factor Method for Comparing Batting Performances in One-Day International Cricket," IIM Kozhikode Society & Management Review, , vol. 11(1), pages 92-108, January.
    7. Sahadeb Sarkar & Anirban Banerjee, 2016. "Measuring batting consistency and comparing batting greats in test cricket: innovative applications of statistical tools," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 43(4), pages 365-400, December.
    8. Sargent Jonathan RT & Bedford Anthony, 2012. "Using Conditional Estimates to Simulate In-Play Outcomes in Limited Overs Cricket," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(2), pages 1-20, June.
    9. Stevenson Oliver George & Brewer Brendon J., 2017. "Bayesian survival analysis of batsmen in Test cricket," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(1), pages 25-36, March.
    10. Koulis Theodoro & Muthukumarana Saman & Briercliffe Creagh Dyson, 2014. "A Bayesian stochastic model for batting performance evaluation in one-day cricket," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(1), pages 1-13, January.
    11. J M Norman & S R Clarke, 2010. "Optimal batting orders in cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(6), pages 980-986, June.
    12. 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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