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Player selection in the IPL requires a balance between roles, performance analytics, and match contexts. The work discussed here proposes a machine learning-driven framework for generating optimized playing XI squads using role-based clustering and performance-informed metrics. Primary roles include Batsmen, Bowlers, and All-Rounders. K-Means is used to subgroup them based on their traits, such as Fast Scoring Index, Consistency, and Bowling Economy. Two optimization techniques, Standard Genetic Algorithm (SGA) and Memetic Genetic Algorithm (MGA), are used to find the best team combinations based on a composite fitness function. The model proposes considering performance related to the home ground for each franchise. Additionally, it employs the Duckworth-Lewis-Stern (DLS) method, which favors aggressive hitters and economical bowlers. In addition, an Injury Optimization Mode is included to recommend injury-substitution strategies. It also presents AI-generated rationales for each selected player and provides visual representations of the role distribution as a heatmap.

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OptiXI: A Novel Hybrid Approach for Strategic Player Selection in IPL Using Machine Learning and Memetic Genetic Algorithms

  • 会议论文

  • 发表日期：

    2026年

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  • M. Meenalochani,
  • Rahul Ramesh,
  • Madhav Venkat Koduvayur

摘要

Player selection in the IPL requires a balance between roles, performance analytics, and match contexts. The work discussed here proposes a machine learning-driven framework for generating optimized playing XI squads using role-based clustering and performance-informed metrics. Primary roles include Batsmen, Bowlers, and All-Rounders. K-Means is used to subgroup them based on their traits, such as Fast Scoring Index, Consistency, and Bowling Economy. Two optimization techniques, Standard Genetic Algorithm (SGA) and Memetic Genetic Algorithm (MGA), are used to find the best team combinations based on a composite fitness function. The model proposes considering performance related to the home ground for each franchise. Additionally, it employs the Duckworth-Lewis-Stern (DLS) method, which favors aggressive hitters and economical bowlers. In addition, an Injury Optimization Mode is included to recommend injury-substitution strategies. It also presents AI-generated rationales for each selected player and provides visual representations of the role distribution as a heatmap.