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An ordered response model of test cricket performance

Author

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  • Robert Brooks
  • Robert Faff
  • David Sokulsky

Abstract

The paper analyses the prediction of test cricket outcomes using an ordered response model. The results, based on data over the period 1994 to 1999, suggest that the ordered categorized production outcome of test cricket (win, draw, loss) can be explained by simple measures of the batting and bowling labour inputs. For example, across all countries the model correctly predicts 71% of test cricket outcomes. Further, it is found that losses are correctly predicted most often at 81% of the sample but that the model faces its biggest challenge predicting test match draws-only getting 57% of these cases correct. Also analysed are the circumstances in which the model produces incorrect predictions and it is found that the most common events are unsuccessful last innings runs chases; successful last innings runs chases and rain-affected matches. An analysis of failed model predictions in terms of country factors suggests that (relative to all other countries) Pakistan has a higher tendency to be involved in such matches, whereas Sri Lanka has a higher tendency to be involved in matches that are 'predictable'. A 'style' analysis using this model suggests that five test cricket styles are evident. Style I is that of 'Bowling and Batting Performance' and describes Pakistan, the West Indies and (perhaps to a lesser extent) Zimbabwe. Style II is that of 'Batting Performance' and describes England, New Zealand and (perhaps to a lesser extent) India. Style III is 'Bowling Performance' and describes Australia. Style IV is 'Bowling Performance/Batting Strike Rate' and describes South Africa. Finally, Style V is 'Bowling Performance and Strike Rate' and describes Sri Lanka. Finally, the model is used to analyse which country can claim to be the world champions of test cricket over the sample period. In an initial analysis based on average performance over this period, South Africa has the best claim. However, in a 'heavyweight title' contest between South Africa and Australia, Australia has the superior claim.

Suggested Citation

  • Robert Brooks & Robert Faff & David Sokulsky, 2002. "An ordered response model of test cricket performance," Applied Economics, Taylor & Francis Journals, vol. 34(18), pages 2353-2365.
    • Handle: RePEc:taf:applec:v:34:y:2002:i:18:p:2353-2365
    DOI: 10.1080/00036840210148085
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    References listed on IDEAS

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    1. Walter C. Neale, 1964. "The Peculiar Economics of Professional Sports," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 78(1), pages 1-14.
    2. Zak, Thomas A & Huang, Cliff J & Siegfried, John J, 1979. "Production Efficiency: The Case of Professional Basketball," The Journal of Business, University of Chicago Press, vol. 52(3), pages 379-392, July.
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    Cited by:

    1. Lenten, Liam J.A. & Geerling, Wayne & Kónya, László, 2012. "A hedonic model of player wage determination from the Indian Premier League auction: Further evidence," Sport Management Review, Elsevier, vol. 15(1), pages 60-71.
    2. P Scarf & S Akhtar, 2011. "An analysis of strategy in the first three innings in test cricket: declaration and the follow-on," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(11), pages 1931-1940, November.
    3. Bissoondoyal-Bheenick, Emawtee & Brooks, Robert & Yip, Angela Y.N., 2006. "Determinants of sovereign ratings: A comparison of case-based reasoning and ordered probit approaches," Global Finance Journal, Elsevier, vol. 17(1), pages 136-154, September.
    4. Akhtar, Sohail & Scarf, Philip, 2012. "Forecasting test cricket match outcomes in play," International Journal of Forecasting, Elsevier, vol. 28(3), pages 632-643.
    5. Ajit Karnik, 2010. "Valuing Cricketers Using Hedonic Price Models," Journal of Sports Economics, , vol. 11(4), pages 456-469, August.
    6. Colin Cannonier & Bibhudutta Panda & Sudipta Sarangi, 2015. "20-Over Versus 50-Over Cricket," Journal of Sports Economics, , vol. 16(7), pages 760-783, October.
    7. 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.
    8. Asif, Muhammad & McHale, Ian G., 2016. "In-play forecasting of win probability in One-Day International cricket: A dynamic logistic regression model," International Journal of Forecasting, Elsevier, vol. 32(1), pages 34-43.
    9. 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.
    10. Sudipta Sarangi & Emre Unlu, 2011. "Key Players and Key Groups in Teams," Departmental Working Papers 2011-10, Department of Economics, Louisiana State University.
    11. Gaurav Deval & Faiz Hamid & Mayank Goel, 2021. "When to declare the third innings of a test cricket match?," Annals of Operations Research, Springer, vol. 303(1), pages 81-99, August.
    12. Liam J A Lenten & Wayne Geerling & László Kónya, 2010. "A Hedonic Model of Player Wage Determination from the Indian Premier League Auction#," Working Papers 2010.04, School of Economics, La Trobe University.
    13. Emawtee Bissoondoyal-Bheenick & Sirimon Treepongkaruna, 2011. "An analysis of the determinants of bank ratings: comparison across ratings agencies," Australian Journal of Management, Australian School of Business, vol. 36(3), pages 405-424, December.
    14. Bissoondoyal-Bheenick, Emawtee, 2005. "An analysis of the determinants of sovereign ratings," Global Finance Journal, Elsevier, vol. 15(3), pages 251-280, February.

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