OPTIMIZING SELECTION OF INDIAN CRICKET TEAM USING INTEGER PROGRAMMING

Abstract

The selection of the Indian ODI team has always been a heavily debated issue. This dissertation attempts to present a structured data driven approach for selection of ODI team through evaluation of players using match data from January 2022 to December 2025. This data covers a very important phase for Indian white ball cricket including the ICC Cricket World Cup 2023, the ICC Champions Trophy 2025, dominance of the Indians in the ODI format under Rohit Sharma and later Shubman Gill. Four broad categories are used for player assessment: Batsmen, Bowlers, All Rounders and Wicketkeepers. Four sets of role-based metrics combined with a measure of player consistency (using Coefficient of Variation over match-by-match data) to evaluate players. Filter-out low consistency players such that team focuses on reliable rather than merely exceptional performers, who have only exceptional performances once in a while. The team is then selected using Integer Linear Programming (ILP), with a binary variable denoting selection, the objective function being to maximize the combined weighted player score, while adhering to reasonable constraints like minimum bats men, bowlers, all rounders and wicketkeepers in the team. The resulting recommended team resembles a highly dominant Indian ODI unit of the period. This provides an objective, repeatable and transparent way to form an ODI team, and is replicable to future ODIs, as well as future tournament preparations.