Optimizing Rugby Match Predictions Using Advanced Machine Learning Techniques

This study explores the use of machine learning algorithms to predict rugby match outcomes by analyzing a dataset that includes factors such as scores, teams, competition type, and location. It evaluates the effectiveness of several algorithms, including Gradient Boosting (XGBoost), Random Forest, Logistic Regression, and Support Vector Machines (SVM). Among these, XGBoost emerges as the most accurate predictor, which delivered the highest accuracy at 92.4%, thanks to its ability to process structured data, capture complex non-linear relationships, and highlight the importance of various features. The research includes comprehensive data preprocessing, feature engineering, and hyperparameter tuning to optimize model performance. The results emphasize the growing potential of machine learning in sports analytics, providing valuable insights into the key factors that influence match outcomes and offering a solid foundation for strategic decision-making in rugby.