Evolving Fantasy Cricket Teams: Applying Genetic Algorithms for Optimal Player Selection

Fantasy cricket has emerged as a popular platform where users create virtual teams based on reallife player performances. Traditional methods of team formation, such as random sampling and systematic replacements, often fail to effectively explore large solution spaces, limiting their optimization potential. This paper introduces the use of Genetic Algorithms (GA) to enhance fantasy cricket team selection by iteratively improving team configurations through evolutionary techniques like selection, crossover, and mutation. The GA approach ensures compliance with credit and role constraints while maximizing predicted team performance. We compare the performance of GA to Random Sampling, Systematic Replacements, and KMeans Clustering, demonstrating that GA consistently produces higher-performing teams. Unlike traditional methods, GA adapts dynamically to changing player performance data and offers a more flexible and efficient solution to the team-building problem. Our results show that the Genetic Algorithm outperforms previous approaches in balancing performance metrics with resource constraints. This study highlights the potential of GA to revolutionize team selection in fantasy sports by providing a data-driven, strategic, and adaptive method for optimizing team formation.