Predictive Analytics in Football: An Ensemble-Based Approach for Player Rating

A proper assessment of football player performance is essential for modern sports analysis. It directly affects important decisions regarding scouting, recruitment, and strategy. Traditional player rating systems face criticism due to their subjectivity. They usually depend on opinion-based evaluations or simple statistical summaries that often fail to capture the complex ways players contribute on the field. This work explores machine learning (ML) regressors aimed at providing objective, data-driven player ratings. We evaluate five main regression models: Linear Regression, Decision Tree, Random Forest, XGBoost, and Support Vector Regression (SVR), along with a Weighted Ensemble method to predict overall player ratings using a publicly available dataset from sofifa.com. Our approach includes a careful process of data collection and preparation. This involves selecting features using correlation analysis and standardization methods. We set up our experiments with an 80:20 split between training and testing data. We evaluate model effectiveness using Mean Squared Error (MSE) and the R 2 measure. We observe that individual models such as XGBoost and Random Forest do well, but our weighted ensemble method consistently outperforms them all. It provides estimates that are more reliable and stable. The key innovation is that combining different predictive models through weighted averaging leads to better results than any single ML regressor. This advancement promotes progress in developing objective player evaluation in sports analysis as a whole.