• Bhaskar S
• Nanjundeshwar
• V Padmapriya

The Indian national cricket team stands as a symbol of pride and unity for millions of fans. Renowned for its achievements and depth of talent, the team has played a pivotal role in shaping the global cricketing landscape. However, consistently selecting the optimal playing XI across Test, ODI, and T20I formats remains a persistent challenge. This paper presents a data-driven approach to identifying the best combination of batsmen and bowlers for India’s playing XI, leveraging recent player performance data and advanced machine learning algorithms. ESPN Cricinfo data sets, such as matches played, batting and bowling averages, strike rate, economy rate, runs, and wickets, form the basis of objective team selection. A range of supervised machine learning algorithms, like Random Forest, Gradient Boosting, AdaBoost, K-Nearest Neighbors, Support Vector Regressor, and XGBoost, are compared on the basis of standard regression evaluation metrics like Mean Absolute Error, Mean Squared Error, and R² Score. The suggested approach provides selectors and coaches with a useful tool to build a balanced and competitive XI, and to aid evidence-based decision-making. This paper underscores the potential of Machine Learning to enhance cricket analytics and offers a scalable approach for modernizing team management in international cricket.

Data analytics, ensemble models, Indian cricket team, Multi format, playing XI prediction, Sports analytics, Team selection

"Machine Learning Based Prediction of Optimal Batsmen and Bowlers for the Indian Cricket Team's Playing XI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.a287-a294, November-2025, Available :http://www.jetir.org/papers/JETIR2511035.pdf

"Machine Learning Based Prediction of Optimal Batsmen and Bowlers for the Indian Cricket Team's Playing XI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. ppa287-a294, November-2025, Available at : http://www.jetir.org/papers/JETIR2511035.pdf