This study presents an innovative method for predicting the market value of
professional soccer players using explainable machine learning models. Using a
dataset curated from the FIFA website, we employ an ensemble machine learning
approach coupled with Shapley Additive exPlanations (SHAP) to provide detailed
explanations of the models' predictions. The GBDT model achieves the highest
mean R-Squared (0.8780) and the lowest mean Root Mean Squared Error
(3,221,632.175), indicating its superior performance among the evaluated
models. Our analysis reveals that specific skills such as ball control, short
passing, finishing, interceptions, dribbling, and tackling are paramount within
the skill dimension, whereas sprint speed and acceleration are critical in the
fitness dimension, and reactions are preeminent in the cognitive dimension. Our
results offer a more accurate, objective, and consistent framework for market
value estimation, presenting useful insights for managerial decisions in player
transfers.

本文提出了一种创新的方法，利用可解释机器学习模型预测职业足球运动员的市场价值。使用从 FIFA 网站整理的数据集，我们采用集成机器学习方法结合 Shapley Additive exPlanations (SHAP) 来提供模型预测的详细解释。GBDT 模型在评估模型中表现出最高的平均 R-Squared（0.8780）和最低的平均均方根误差（3,221,632.175），表明其在性能上卓越。我们的分析发现，球控、短传、射门、拦截、带球和抢断等特定技能在技能维度上很重要，而冲刺速度和加速度在健身维度上至关重要，反应在认知维度上占主导地位。我们的结果为市场价值估计提供了更准确、客观和一致的框架，并为球员转会的管理决策提供了有用的见解。