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In-play forecasting of win probability in One-Day International cricket: A dynamic logistic regression model

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  • Asif, Muhammad
  • McHale, Ian G.

Abstract

The paper presents a model for forecasting the outcomes of One-Day International cricket matches whilst the game is in progress. Our ‘in-play’ model is dynamic, in the sense that the parameters of the underlying logistic regression model are allowed to evolve smoothly as the match progresses. The use of this dynamic logistic regression approach reduces the number of parameters required dramatically, produces stable and intuitive forecast probabilities, and has a minimal effect on the explanatory power. Cross-validation techniques are used to identify the variables to be included in the model. We demonstrate the use of our model using two matches as examples, and compare the match result probabilities generated using our model with those from the betting market. The forecasts are similar quantitatively, a result that we take to be evidence that our modelling approach is appropriate.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:intfor:v:32:y:2016:i:1:p:34-43
    DOI: 10.1016/j.ijforecast.2015.02.005
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    References listed on IDEAS

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    1. McHale, Ian G. & Asif, Muhammad, 2013. "A modified Duckworth–Lewis method for adjusting targets in interrupted limited overs cricket," European Journal of Operational Research, Elsevier, vol. 225(2), pages 353-362.
    2. Raymond D. Sauer, 1998. "The Economics of Wagering Markets," Journal of Economic Literature, American Economic Association, vol. 36(4), pages 2021-2064, December.
    3. 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.
    4. F C Duckworth & A J Lewis, 2004. "A successful operational research intervention in one-day cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(7), pages 749-759, July.
    5. Akhtar, Sohail & Scarf, Philip, 2012. "Forecasting test cricket match outcomes in play," International Journal of Forecasting, Elsevier, vol. 28(3), pages 632-643.
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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. 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.
    3. J. James Reade & Carl Singleton & Alasdair Brown, 2021. "Evaluating strange forecasts: The curious case of football match scorelines," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(2), pages 261-285, May.
    4. Baboota, Rahul & Kaur, Harleen, 2019. "Predictive analysis and modelling football results using machine learning approach for English Premier League," International Journal of Forecasting, Elsevier, vol. 35(2), pages 741-755.
    5. Federico Fioravanti & Fernando Delbianco & Fernando Tohmé, 2023. "The relative importance of ability, luck and motivation in team sports: a Bayesian model of performance in the English Rugby Premiership," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 715-731, September.
    6. Marc Garnica-Caparrós & Daniel Memmert & Fabian Wunderlich, 2022. "Artificial data in sports forecasting: a simulation framework for analysing predictive models in sports," Information Systems and e-Business Management, Springer, vol. 20(3), pages 551-580, September.
    7. Asif, M. & McHale, I.G., 2019. "A generalized non-linear forecasting model for limited overs international cricket," International Journal of Forecasting, Elsevier, vol. 35(2), pages 634-640.

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