Multi-objective optimization and decision making approaches to cricket team selection

Selection of players for a sports team within a finite budget is a complex task which can be viewed as a constrained multi-objective optimization and a multiple criteria decision making problem. The task is specially challenging for the game of cricket where a team requires players who are efficient in multiple roles. In the formation of a good and successful cricket team, batting strength and bowling strength of a team are major factors affecting its performance and an optimum trade-off needs to be reached. We propose a novel gene representation scheme and a multi-objective approach using the NSGA-II algorithm to optimize the overall batting and bowling strength of a team with 11 players as variables. Fielding performance and a number of other cricketing criteria are also used in the optimization and decision-making process. Using the information from the trade-off front obtained, a multi-criteria decision making approach is then proposed for the final selection of team. Case studies using a set of players auctioned in Indian Premier League (IPL) 4th edition are illustrated and players’ current statistical data is used to define performance indicators. The proposed computational techniques are ready to be extended according to individualistic preferences of different franchises and league managers in order to form a preferred team within the budget constraints. It is also shown how such an analysis can help in dynamic auction environments, like selecting a team under player-by-player auction. The methodology is generic and can be easily extended to other sports like American football, baseball and other league games.

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► Based on statistics of performance of players in different aspects of the game of cricket, a bi-objective optimization problem is formulated.
► An evolutionary multi-objective optimization (EMO) method (NSGA-II) is employed to find multiple trade-off teams of 11 players.
► An analysis of trade-off teams has identified 29 key players from a set of 129 players considered in the study.
► A number of obtained trade-off teams have better scores than the winning Indian Premier League (IPL) team of 2011, thereby indicating the potential importance of this study.
► Finally, an auction-based bi-objective optimization method is suggested to form a competitive team following the IPL rules.
► Methods of this study can be extended to other games to construct a winning team and also to identify key players to choose in an auction-oriented team selection process.