Automated Selection of Optimal Cricket Team Using Machine Learning

This paper presents an automated system using Machine Learning (ML) techniques to improve cricket team selection. Traditional methods, relying on expert opinions and manual analysis, are often time-consuming and subject to biases. The proposed system uses ML algorithms to analyze key performance indicators like batting averages, bowling averages, strike rates, and economy rates, creating a predictive model for optimal player selection. The model also integrates factors such as player endurance, injury history, and recovery times, providing a holistic view of each player’s contribution. This data-driven approach reduces human errors and biases, offering more objective insights. The system is adaptable to different cricket formats–Test, One-Day International (ODI), and Twenty20 (T20)–with tailored strategies for each format’s unique demands. By selecting players not only on current form but also on physical preparedness, the model ensures a well-rounded team. This approach has the potential to transform team selection processes and influence broader cricket strategies, including training and in-game tactics.