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A Bayesian stochastic model for batting performance evaluation in one-day cricket

Author

Listed:
  • Koulis Theodoro
  • Muthukumarana Saman
  • Briercliffe Creagh Dyson

    (Department of Statistics, University of Manitoba, Winnipeg Manitoba, Canada)

Abstract

We consider the modeling of individual batting performance in one-day international (ODI) cricket by using a batsman-specific hidden Markov model (HMM). The batsman-specific number of hidden states allows us to account for the heterogeneous dynamics found in batting performance. Parallel sampling is used to choose the optimal number of hidden states. Using the batsman-specific HMM, we then introduce measures of performance to assess individual players via reliability analysis. By classifying states as either up or down, we compute the availability, reliability, failure rate and mean time to failure for each batsman. By choosing an appropriate classification of states, an overall prediction of batting performance of a batsman can be made. The classification of states can also be modified according to the type of game under consideration. One advantage of this batsman-specific HMM is that it does not require the consideration of unforeseen factors. This is important since cricket has gone through several rule changes in recent years that have further induced unforeseen dynamic factors to the game. We showcase the approach using data from 20 different batsmen having different underlying dynamics and representing different countries.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:jqsprt:v:10:y:2014:i:1:p:1-13:n:2
    DOI: 10.1515/jqas-2013-0057
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    References listed on IDEAS

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    1. Congdon, Peter, 2006. "Bayesian model choice based on Monte Carlo estimates of posterior model probabilities," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 346-357, January.
    2. 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.
    3. Scott S. L., 2002. "Bayesian Methods for Hidden Markov Models: Recursive Computing in the 21st Century," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 337-351, March.
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    Cited by:

    1. 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.
    2. Santos-Fernandez Edgar & Wu Paul & Mengersen Kerrie L., 2019. "Bayesian statistics meets sports: a comprehensive review," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 289-312, December.

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