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.
翻译:体育运动员的转移费用已成为天文。 这是因为让具有巨大未来价值的运动员加入俱乐部对于他们的生存至关重要。 我们根据国际足联的数据分析,对影响世界顶尖足球运动员转移费用的关键因素进行了案例研究。 为了预测每个运动员的市场价值,我们建议使用树结构Parzen Estimator(TPE)算法来优化其超参数,从而改进光GBM模型。我们确定了Shanapley Additive Explanation(SHAP)算法的突出特征。 拟议的方法已经与基线回归模型(如线性回归、拉索、弹性网、内核脊回归)和梯度加速模型进行了比较,而没有超分度优化。 优化的光GBM模型显示,与回归基线模型、GBDT和LightGBM模型相比,平均大约3.8、1.4和1.8倍的精准度。 我们的模型在决定足球俱乐部今后招聘球员时应考虑什么属性时提供了解释性。