XGBoosting Cricket: Enhancing Predictive Modeling for Twenty20 Match Results Using Machine Learning and Statistical Techniques

The emerging field of computer science research that combines Data Mining and Machine Learning in sports analytics, has revolutionized performance analysis and predictive modeling. This study presents a predictive model leveraging the XGBoost algorithm to forecast scores in Twenty20 (T20) cricket matches, with a comparative analysis against other popular regression algorithms such as Linear Regression, Decision Trees, and Random Forests. The model incorporates a comprehensive dataset of historical T20 match statistics, including player performance, match conditions, and venue changes. Rigorous preprocessing and feature selection techniques were employed to optimize model performance. The results demonstrate that the XGBoost algorithm outperforms other methods, achieving a Mean Absolute Error (MAE) of 1.73 on the test dataset. This margin of error highlights the model's precision in predicting T20 match outcomes, providing valuable insights for strategic decision-making in cricket analytics. The findings suggest that advanced machine learning techniques like XGBoost can significantly enhance predictive accuracy in sports analytics, paving the way for future research and practical applications in the field.