An Individual-based Team Rating Method for T20 Cricket

Ankit Patel, Paul J Bracewell, Samuel J Rooney


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.  


Adaptive System, Product Weighted Measure, Analytical Hierarchical Process

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