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.

翻译：本研究提出了一种创新方法，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。基于从FIFA官网整理的数据集，我们采用集成机器学习方法结合Shapley Additive exPlanations (SHAP)，对模型预测结果提供详细解释。其中GBDT模型取得了最高的平均决定系数R-Squared (0.8780)和最低的均方根误差(3,221,632.175)，表明其在评估模型中性能最优。分析揭示了具体技能维度中球感、短传、终结能力、拦截、盘带和抢断等关键属性，体能维度中冲刺速度和加速度至关重要，而认知维度中反应能力尤为突出。本研究为市场价值估算提供了更准确、客观且一致的框架，为球员转会管理决策提供了重要洞见。