The objective of this research was to develop a roster-based optimisation system for limited overs cricket by deriving a meaningful, overall team rating using a combination of individual ratings from a playing eleven. The research hypothesis was that an adaptive rating system accounting for individual player abilities, outperforms systems that only consider macro variables such as home advantage, opposition strength and past team performances. The assessment of performance is observed through the prediction accuracy of future match outcomes. The expectation is that in elite sport, better teams are expected to win more often. To test the hypothesis, an adaptive rating system was developed. This framework was a combination of an optimisation system and an individual rating system. The adaptive rating system was selected due to its ability to update player and team ratings based on past performances. A Binary Integer Programming model was the optimisation method of choice, while a modified product weighted measure (PWM) with an embedded exponentially weighted moving average (EWMA) functionality was the adopted individual rating system. The weights for this system were created using a combination of a Random Forest and Analytical Hierarchical Process. The model constraints were objectively obtained by identifying the player’s role and performance outcomes a limited over cricket team must obtain in order to increase their chances of winning. Utilising a random forest technique, it was found that players with strong scoring consistency, scoring efficiency, runs restricting abilities and wicket-taking efficiency are preferred for limited over cricket due to the positive impact those performance metrics have on a team’s chance of winning. To define pertinent individual player ratings, performance metrics that significantly affect match outcomes were identified. Random Forests proved to be an effective means of optimal variable selection. The important performance metrics were derived in terms of contribution to winning, and were input into the modified PWM and EWMA method to generate a player rating. The underlying framework of this system was validated by demonstrating an increase in the accuracy of predicted match outcomes compared to other established rating methods for cricket teams. Applying the Bradley-Terry method to the team ratings, generated through the adaptive system, we calculated the probability of teami beating teamj. The adaptive rating system was applied to the Caribbean Premier League 2015 and the Cricket World Cup 2015, and the systems predictive accuracy was benchmarked against the New Zealand T.A.B (Totalisator Agency Board) and the CricHQ algorithm. The results revealed that the developed rating system outperformed the T.A.B by 9% and the commercial algorithm by 6% for the Cricket World Cup (2015), respectively, and outperformed the T.A.B and CricHQ algorithm by 25% and 12%, for the Caribbean Premier League (2015), respectively. These results demonstrate that cricket team ratings based on the aggregation of individual player ratings are superior to ratings based on summaries of team performances and match outcomes; validating the research hypothesis. The insights derived from this research also inform interested parties of the key attributes to win limited over cricket matches and can be used for team selection
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