Abstract
When to declare the third innings in a test cricket match is a crucial decision directly impacting the outcome of the match. The captain of the side batting in the third innings takes into account factors like lead runs, batting strength of the opposition, favorability of the pitch, approximate number of overs left in the game, etc. to make the decision. The objective of this study is to develop a decision support system for the captain using machine learning algorithms to predict the outcome of a test match at different stages of the match. This will aid the captain to decide when to declare. Several new crucial factors are identified that affect the match outcome. Declaration decisions of past test matches are analyzed using probability functions of win, loss, and draw derived using these models. Previous researches have used only simple regression based techniques to predict the match outcome with low accuracy. Data of 354 test matches from 2008 to 2017 has been used to train and test the algorithms. Support vector machine is found to be the most accurate with an accuracy of 88.8%.
Similar content being viewed by others
References
Adhikari, A., Majumdar, A., Gupta, G., & Bisi, A. (2018). An innovative super-efficiency data envelopment analysis, semi-variance, and Shannon-entropy-based methodology for player selection: Evidence from cricket. Annals of Operations Research, 284, 1–32.
Akhtar, S., & Scarf, P. (2012). Forecasting test cricket match outcomes in play. International Journal of Forecasting, 28(3), 632–643.
Akhtar, S., Scarf, P., & Rasool, Z. (2015). Rating players in test match cricket. Journal of the Operational Research Society, 66(4), 684–695.
Allsopp, P., Clarke, S. R., et al. (2002). Factors affecting outcomes in test match cricket. In Proceedings of the sixth Australian conference on mathematics and computers in sport (pp. 48–55).
Allsopp, P., & Clarke, S. R. (2004). Rating teams and analysing outcomes in one-day and test cricket. Journal of the Royal Statistical Society: Series A (Statistics in Society), 167(4), 657–667.
Böhning, D. (1992). Multinomial logistic regression algorithm. Annals of the Institute of Statistical Mathematics, 44(1), 197–200.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Brooks, R. D., Faff, R. W., & Sokulsky, D. (2002). An ordered response model of test cricket performance. Applied Economics, 34(18), 2353–2365.
Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785–794).
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Cricsheet. (2019). https://cricsheet.org/. Accessed on May 07.
De Silva, B. M., & Swartz, T. B. (1997). Winning the coin toss and the home team advantage in one-day international cricket matches. New Zealand Statistics, 32, 16–22.
Fung, G. M., & Mangasarian, O. L. (2005). Multicategory proximal support vector machine classifiers. Machine Learning, 59(1–2), 77–97.
Hagan, M. T., Demuth, H. B., Beale, M. H., & De Jesús, O. (1996). Neural network design. Boston: PWS Pub.
Haghighat, M., Rastegari, H., & Nourafza, N. (2013). A review of data mining techniques for result prediction in sports. Advances in Computer Science? An International Journal, 2(5), 7–12.
Hogg, R. V., McKean, J., & Craig, A. T. (2005). Introduction to mathematical statistics. London: Pearson Education.
Iyer, S. R., & Sharda, R. (2009). Prediction of athletes performance using neural networks: An application in cricket team selection. Expert Systems with Applications, 36(3), 5510–5522.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In IJCAI (pp. 1137–1145). Montreal: Canada.
Perera, H., Gill, P. S., & Swartz, T. B. (2014). Declaration guidelines in test cricket. Journal of Quantitative Analysis in Sports, 10(1), 15–26.
Scarf, P., & Akhtar, S. (2011). An analysis of strategy in the first three innings in test cricket: Declaration and the follow-on. Journal of the Operational Research Society, 62(11), 1931–1940.
Scarf, P., & Shi, X. (2005). Modelling match outcomes and decision support for setting a final innings target in test cricket. IMA Journal of Management Mathematics, 16(2), 161–178.
Scarf, P., Shi, X., & Akhtar, S. (2011). On the distribution of runs scored and batting strategy in test cricket. Journal of the Royal Statistical Society: Series A (Statistics in Society), 174(2), 471–497.
Schumaker, R. P., Solieman, O. K., & Chen, H. (2010). Predictive modeling for sports and gaming. In Sports data mining (pp. 55–63). Springer.
Stevenson, O. G., & Brewer, B. J. (2017). Bayesian survival analysis of batsmen in test cricket. Journal of Quantitative Analysis in Sports, 13(1), 25–36.
Acknowledgements
We are grateful to Zishan Muzeeb who carried out some preliminary analysis on this problem as part of a final year undergraduate project at Indian Institute of Technology Kanpur.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Deval, G., Hamid, F. & Goel, M. When to declare the third innings of a test cricket match?. Ann Oper Res 303, 81–99 (2021). https://doi.org/10.1007/s10479-021-04094-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-021-04094-0