Prediction of Football Player Value using Bayesian Ensemble Approach
This work addresses the problem of high transfer fees for football clubs by providing a more accurate and interpretable prediction model, though it is incremental as it builds on existing gradient boosting methods.
The paper tackled predicting football player transfer values by proposing an improved LightGBM model with hyperparameter optimization using TPE and feature analysis via SHAP. The optimized model achieved RMSE improvements of approximately 3.8, 1.4, and 1.8 times compared to baseline regression models, GBDT, and standard LightGBM, respectively.
The transfer fees of sports players have become astronomical. This is because bringing players of great future value to the club is essential for their survival. We present a case study on the key factors affecting the world's top soccer players' transfer fees based on the FIFA data analysis. To predict each player's market value, we propose an improved LightGBM model by optimizing its hyperparameter using a Tree-structured Parzen Estimator (TPE) algorithm. We identify prominent features by the SHapley Additive exPlanations (SHAP) algorithm. The proposed method has been compared against the baseline regression models (e.g., linear regression, lasso, elastic net, kernel ridge regression) and gradient boosting model without hyperparameter optimization. The optimized LightGBM model showed an excellent accuracy of approximately 3.8, 1.4, and 1.8 times on average compared to the regression baseline models, GBDT, and LightGBM model in terms of RMSE. Our model offers interpretability in deciding what attributes football clubs should consider in recruiting players in the future.