Cricket player performance prediction: A machine learning

Cricket is a data-rich sport where accurate performance predictions can significantly impact strategic decision-making for teams, analysts, and coaches. This study leverages machine learning (ML), specifically Light Gradient Boosting Machine (LGBM), to enhance predictive accuracy by analyzing historical player statistics, pitch conditions, and real-time match factors. The proposed system follows a structured pipeline, including data preprocessing, feature engineering, and model optimization, ensuring scalability and reliability. Unlike traditional models, it integrates real-time adaptability, dynamically adjusting predictions based on live match updates such as pitch reports and player form. Performance metrics like RMSE, Precision, and F1-score validate the model’s efficiency across different cricket formats. A user-friendly interface using Streamlit enables interactive data visualization, making insights accessible to analysts and enthusiasts. By addressing data complexity and match-day variability, this research advances AI-driven sports analytics. Future enhancements will explore deep learning architectures and biomechanical data for further accuracy improvements. The study establishes a robust and scalable predictive framework, offering actionable insights to revolutionize cricket strategy and decision-making.