ABSTRACT:
This paper introduces a queuing theory model to analyze One Day Internationals (ODIs) in cricket. In this model, the opening pair of batsmen is treated as a customer being served by a single server, which is the cricket pitch. The next batsman, waiting to bat, represents a customer in the queue. The server’s utilization factor is assessed by observing the duration and performance of a batting pair at the crease. By applying queuing theory concepts, the model helps determine the probabilities of various match outcomes, such as a tied game, no result (NR), or a match with a clear result. This approach provides a statistical framework to understand the impact of batting partnerships on match outcomes, offering new insights into ODI strategy and performance analysis.
Cite this article:
Dr. Manish Kumar Pandey, Seira Shinde, Dr. Raginee Pandey (2025), Analyzing Match Dynamics in ICC ODIs Using Queuing Theory. Spectrum of Emerging Sciences, 5 (3) 71-78., DOI: https://doi.org/10.55878/SES2025-5-3-16
References
1.
Akhtar, S., & Scarf, P. A. (2012). Forecasting Test Cricket Match
Outcomes in Play: The Application of Dynamic Linear Models. International Journal of Forecasting, 28(3),
632-643.
2.
Ancker, C. J., & Gafarian, A. V. (1962). Queuing Theory. Journal of Industrial Engineering, 13(4),
113–122.
3.
Anwar, M. N., & Khan, R. Z. (2017). Queuing Models in Cricket:
Applications and Analysis. International
Journal of Mathematics and Statistics Studies, 5(3), 1-14.
4. Ghosal, D., & Prakash, C. D. (2025).
International Cricket Council: rethinking growth strategy of cricket. Emerald Emerging Markets Case Studies, 15(2), 1-63.
5.
Philipson, P. M. (2024). A truncated
mean-parameterized Conway-Maxwell-Poisson model for the analysis of Test match
bowlers. Statistical Modelling, 24(5), 480-495.
6.
Halabi, A. K., Dickson, G., & Frost, L.
(2023). The Financial Impact of World Series Cricket on Australia’s State
Cricket Associations, 1974–1982. The
International Journal of the History of Sport, 40(1), 1-17.
7.
Kendall, D. G. (1953). Stochastic Processes Occurring in the Theory of
Queues and Their Analysis by the Method of the Imbedded Markov Chain. The Annals of Mathematical Statistics, 24(3),
338–354.
8.
Knott, G. (1980). A Queuing Theory Application in Sport. The Statistician, 29(2), 123-132.
9.
Skinner, G. K., & Freeman, G. H. (2009). Applying Queuing Theory to
the Analysis of Penalty Shootouts in Soccer. Journal of Quantitative Analysis in Sports, 5(1), Article 3.
10.
Katti, S. K., & Khare, A. P. (1992). Queuing Models in Sports. Mathematical Spectrum, 25(2), 59-63.
11.
Jackson, J. R. (1957). Networks of Waiting Lines. Operations Research, 5(4), 518–521.
12.
Sinha, S., & Chatterjee, S. (2010). Modeling Cricket as a Queuing
System. Journal of Sports Analytics, 3(2),
142-154.
13.
Bose, S., & Basu, S. (2008). A Study on Batting Strategy and
Application of Queuing Model. Journal
of Sports Science and Medicine, 7(3), 457-464.
14.
Pruyn, A. T. H., & Smidts, A. (1998). Queues and Their Effects on
the Experience of the Service: A Field Experiment in a Bank. Journal of Consumer Psychology, 7(1),
17-41.
15.
Kozar, K. A., & Holowczak, R. D. (1984). Queuing Theory and Sports
Scheduling. Management Science, 30(1),
141-150.
16.
Verma, T. L., & Gangeshwer, D. K. (2022). An Application of Queuing
Theory to Indian Premier League Based on M/M/1 Queuing Model. Specialusis Ugdymas, 1(43),
5173-5178.