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Quantifying the Current Form of Cricket Teams and Predicting the Match Winner

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  • Hemanta Saikia

Abstract

The study tries to predict the outcome of Twenty20 cricket matches based on two prime skills of the game: batting and bowling. The two different measures, namely batting performance (BP) and adjusted combined bowling rate (ACBR), developed by Lemmer (2011, European Journal of Sport Science , vol. 13, pp. 200–206) and Lemmer (2012, European Journal of Sports Science , vol. 14, pp. S191–S196), respectively, are used to quantify the overall batting and bowling performances of cricket teams. The rationale of choosing BP is that it takes into consideration the match situation against which the runs are scored, and ACBR considers the strength of a batsman using wicket weights and adjusted number of runs conceded by the bowler along with the match situation. Both the measures, BP and ACBR, are combined to get the overall strength of a cricket team prior to the knock-out or play-off stage of any given tournament. Thereafter, the overall strength of a team is used to quantify the team’s current form. If the current form of Team A (say) is considerably higher than that of Team B (say), then it is predicted that Team A will win the match and vice-versa .

Suggested Citation

  • Hemanta Saikia, 2020. "Quantifying the Current Form of Cricket Teams and Predicting the Match Winner," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 45(2), pages 151-158, May.
    • Handle: RePEc:sae:manlab:v:45:y:2020:i:2:p:151-158
    DOI: 10.1177/0258042X20912603
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    References listed on IDEAS

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    1. M. Jason Douglas & Nicholas Tam, 2010. "Analysis of team performances at the ICC World Twenty20 Cup 2009," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 10(1), pages 47-53, April.
    2. S R Clarke & P Allsopp, 2001. "Fair measures of performance: the World Cup of cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(4), pages 471-479, April.
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    5. 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.
    6. A J Lewis, 2005. "Towards fairer measures of player performance in one-day cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(7), pages 804-815, July.
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    Cited by:

    1. Praveen Puram & Soumya Roy & Deepak Srivastav & Anand Gurumurthy, 2023. "Understanding the effect of contextual factors and decision making on team performance in Twenty20 cricket: an interpretable machine learning approach," Annals of Operations Research, Springer, vol. 325(1), pages 261-288, June.
    2. Chitresh Kumar & Girish Balasubramanian, 2023. "Comparative Analysis of Pitch Ratings in All Formats of Cricket," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 48(3), pages 307-324, August.

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