  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.
