An Individual-based Team Rating Method for T20 Cricket

Ankit Patel, Paul J Bracewell, Samuel J Rooney

Abstract


Cricket is an ideal sport to isolate individual team member contribution with respect to winning.   This is due to the volume of digital data available, combined with the relatively isolated nature of the batsman versus bowler contest observed per ball.

     Like many other sports, Cricket is reliant on the contribution of interacting individuals causing fluctuations in match outcomes. Understanding the quantifiable causes of this variation can help interested parties derive insight into team success and potential strategies for optimising performance.

     Understanding the individual dynamic within the team setting can lead to improved team ratings.  The objective of this research was to develop a roster-based system for limited overs cricket by deriving a team rating as a combination of individual ratings. The intent was to build an adaptive optimisation system that selects a cricket team of 11 players from a list of available players, such that the optimal team produces the greatest team rating. 

     The attributes used to define the individual ratings are based on the statistical significance and practical contribution to winning. An adaptive system was used to create the individual ratings using a modified version of a Product Weighted Measure. The weights for this system were created using a combination of a Random Forest and Analytical Hierarchical Process.

     The underlying framework of this system was validated by demonstrating an increase in the accuracy of predicted match outcomes compared to other established ranking methods for cricket teams. For the 2015/16 Big Bash, this approach outperformed the results outlined by Patel et al. (2016) by 12.3%.

    The results confirm that cricket team ratings based on the aggregation of individual playing ratings with attributes weighted towards winning limited over matches are superior to ratings based on summaries of team performances and match outcomes. This impact is highlighted by visualizing the variability of the ratings of Perth Scorchers during the 2015/2016 Australian Big Bash.  


Keywords


Adaptive System, Product Weighted Measure, Analytical Hierarchical Process

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References


Annis, D. H., and Craig, B. A. (2005). Hybrid paired comparison analysis, with applications to the ranking of college football teams. Journal of Quantitative Analysis in Sports, 1(1).

Ghosh, P. (2014). Billions of dollars at stake: Why is the international cricket council changing its revenue sharing model? http://www.ibtimes.com/billions-dollars-stake-why-international-cricket-council-changing-its-revenue-sharing-1554078. Accessed: 2016-02-05.

Breiman, L. (2001). Random forests. Machine learning, 45(1): 5-32.

Bracewell, P. J., Downs, M. C. F., & Sewell, J. W. (2014). The development of a performance based rating system for limited overs cricket. In MathSport 2014: 40-47.

Bracewell, P. J., and Ruggiero, K. (2009). A parametric control chart for monitoring individual batting performances in cricket. Journal of Quantitative Analysis in Sports, 5 (3).

Patel, A.K., Bracewell, P.J., & Rooney, S. J. (2016, July 12). Team Rating Optimisation For T20 Cricket. Paper presented at The Proceedings of the 13th Australian Conference on Mathematics and Computers in Sports. (91-96). Melbourne, Victoria, Australia: ANZIAM Mathsport. ISBN: 978-0- 646-95741- 8

Clarke, S. R. (2011). Rating non-elite tennis players using team doubles competition results. Journal of the Operational Research Society, 62(7): 1385–1390.

Fenez. M, & Clark, J.D. (2011). PWC outlook for the global sports market to 2015. Report P-25, PriceWaterhouseCoopers.

Croucher, J. (2000). Player ratings in one-day cricket. In Proceedings of the fifth Australian conference on mathematics and computers in sport. Sydney University of Technology, Sydney, NSW, 95–106.

Damodaran, U. (2006). Stochastic dominance and analysis of ODI batting performance: The Indian cricket team. Journal of Sports Science and Medicine 5: 503–508.

Daniyal, M., Nawaz, T., Mubeen, I., and Aleem, M. (2012). Analysis of batting performance in cricket using individual and moving range (MR) control charts. International Journal of Sports Science and Engineering 6(4): 195–202.

Di Salvo, V., Baron, R., Gonzalez´-Haro, C., Gormasz, C., Pigozzi, F., and Bachl, N. (2010). Sprinting analysis of elite soccer players during European Champions league and UEFA cup matches. Journal of Sports Sciences 28(14): 1489–1494.

Harville, D. A. (2003). The selection or seeding of college basketball or football teams for postseason competition. Journal of the American Statistical Association, 98(461): 17–27.

Gerber, H., & Sharp, G. (2006). Selecting a limited overs cricket squad using an integer programming model. Journal of the Operations Research Society, 5.

Leitner, C., Zeileis, A., & Hornik, K. (2010). Forecasting sports tournaments by ratings of (prob) abilities: A comparison for the euro 2008. International Journal of Forecasting, 26(3): 471–481.

Manage, A. B., & Scariano, S. M. (2013). An introductory application of principal components to cricket data. Journal of Statistics Education, 21: 3.

Saaty, T. (1987). The analytic hierarchy process. Math Modelling, 9: 161–176.

Sargent, J. (2013). Player ratings in continuous and discrete team sports. PhD thesis, RMIT University.

Schumaker, R. P., Solieman, O. K., & Chen, H (2010). Predictive modeling for sports and gaming. Springer, 2010.

Sharp, G., Brettenny, W., Gonsalves, J., Lourens, M., & Stretch, R. (2011). Integer optimisation for the selection of a Twenty20 cricket team. Journal of the Operational Research Society, 62(9): 1688–1694.

Sinuany-Stern, Z. (1988). Ranking of sports teams via the AHP. Journal of the Operational Research Society: 661–667.

Stefani, R. (2011). The methodology of officially recognized international sports rating systems. Journal of Quantitative Analysis in Sports, 7(4).

Steiner, S. H. (1999). EWMA control charts with time-varying control limits and fast initial response. Journal of Quality Technology, 31(1): 75.

Vrooman, J. (2012). The economic structure of the NFL. In The Economics of the National Football League. Springer, 7: 31.

Winston, W. L., & Goldberg, J. B. (2004).Operations research: applications and algorithms (Vol. 3). Belmont, CA: Duxbury press.




DOI: https://doi.org/10.12922/jshp.v5i1.94

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