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Forecasting test cricket match outcomes in play

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  • Akhtar, Sohail
  • Scarf, Philip

Abstract

This paper forecasts match outcomes in test cricket in play, session by session. Match outcome probabilities at the start of each session are forecast using a sequence of multinomial logistic regression models. These probabilities can assist a team captain or management in considering a certain aggressive or defensive batting strategy for the coming session. We investigate how the outcome probabilities (of a win, draw, or loss) and covariate effects vary session by session. The covariates fall into two categories, pre-match effects (strengths of teams, a ground effect, home field advantage, outcome of the toss) and in-play effects (score or lead, overs-used, overs-remaining, run-rate, and wicket resources used). The results indicate that the lead has a small effect on the match outcome early on but is dominant later; pre-match team strengths, ground effect and home field advantage are important predictors of a win early on; and wicket resources used remains important throughout a match.

Suggested Citation

  • Akhtar, Sohail & Scarf, Philip, 2012. "Forecasting test cricket match outcomes in play," International Journal of Forecasting, Elsevier, vol. 28(3), pages 632-643.
  • Handle: RePEc:eee:intfor:v:28:y:2012:i:3:p:632-643
    DOI: 10.1016/j.ijforecast.2011.08.005
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    References listed on IDEAS

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    1. Liam J. A. Lenten, 2008. "Is The Decline In The Frequency Of Draws In Test Match Cricket Detrimental To The Long Form Of The Game?," Economic Papers, The Economic Society of Australia, vol. 27(4), pages 364-380, December.
    2. Trevor J. Ringrose, 2006. "Neutral umpires and leg before wicket decisions in test cricket," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 903-911.
    3. Dobson, Stephen & Goddard, John, 2003. "Persistence in sequences of football match results: A Monte Carlo analysis," European Journal of Operational Research, Elsevier, vol. 148(2), pages 247-256, July.
    4. Philip Scarf & Xin Shi & Sohail Akhtar, 2011. "On the distribution of runs scored and batting strategy in test cricket," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 471-497, April.
    5. P. E. Allsopp & Stephen R. Clarke, 2004. "Rating teams and analysing outcomes in one-day and test cricket," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(4), pages 657-667.
    6. 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.
    7. 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.
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    Cited by:

    1. 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.
    2. Zhang, Xinyu & Lu, Zudi & Zou, Guohua, 2013. "Adaptively combined forecasting for discrete response time series," Journal of Econometrics, Elsevier, vol. 176(1), pages 80-91.

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