Forecasting test cricket match outcomes in play
AbstractThis 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.
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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 28 (2012)
Issue (Month): 3 ()
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Web page: http://www.elsevier.com/locate/ijforecast
Multinomial logistic regression; Strategy; Betting; Sport; Probability;
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- 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.
- 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.
- 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, vol. 62(11), pages 1931-1940, November.
- 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.
- 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.
- 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, 04.
- 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.
- 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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