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.

通过FIFA数据分析，本文针对足球运动员转会费用的主要影响因素，提出了基于LightGBM模型的改进方法，采用树形Parzen算法进行超参优化，并通过SHapley Additive exPlanations算法进行特征筛选。与基线回归模型和梯度提升模型相比，优化后的LightGBM模型以RMSE为标准的表现约为回归基线模型、GBDT和LightGBM模型的3.8倍、1.4倍和1.8倍，具有很好的准确性。该模型提供了运动俱乐部在未来招募球员时所需考虑的属性解释。

贝叶斯集成方法预测足球运动员价值