用市场数据和新闻情绪找被低估的足球球员，提升球探决策效率。
Objective Mispricing Detection for Shortlisting Undervalued Football Players via Market Dynamics and News Signals
- 基于历史转会数据与合同信息估算球员合理价值，对比实际估值识别低估者。
- 市场动态是主要信号，新闻情感分析带来稳定辅助提升准确率。
- 适合球探团队做球员短名单，强调可复现性与决策透明度。
我们提出一种可复现的框架，用于客观识别被低估的足球运动员。不依赖主观专家标签，而是从结构化数据（历史市场动态、个人背景与合同特征、转会记录）中估算预期市场价值，并与实际估值对比以定义错估。进一步评估新闻文本的自然语言处理特征（如情感统计与语义嵌入）是否补充市场信号，用于筛选被低估球员。采用时间顺序的评估方法（防泄露），梯度提升回归模型解释了对数变换后市场价值的大部分方差。在低估筛选任务中，基于ROC-AUC的消融实验表明，市场动态为主要信号，而NLP特征提供持续且次要的增益，增强模型鲁棒性与可解释性。SHAP分析显示市场趋势与年龄占主导地位，新闻中的波动性线索在高不确定性环境下强化信号。该流程专为球探工作流设计，强调排序/短名单而非硬阈值分类，并包含简洁的可复现性与伦理声明。
原文摘要 · Abstract (English)
We present a practical, reproducible framework for identifying undervalued football players grounded in objective mispricing. Instead of relying on subjective expert labels, we estimate an expected market value from structured data (historical market dynamics, biographical and contract features, transfer history) and compare it to the observed valuation to define mispricing. We then assess whether news-derived Natural Language Processing (NLP) features (i.e., sentiment statistics and semantic embeddings from football articles) complement market signals for shortlisting undervalued players. Using a chronological (leakage-aware) evaluation, gradient-boosted regression explains a large share of the variance in log-transformed market value. For undervaluation shortlisting, ROC-AUC-based ablations show that market dynamics are the primary signal, while NLP features provide consistent, secondary gains that improve robustness and interpretability. SHAP analyses suggest the dominance of market trends and age, with news-derived volatility cues amplifying signals in high-uncertainty regimes. The proposed pipeline is designed for decision support in scouting workflows, emphasizing ranking/shortlisting over hard classification thresholds, and includes a concise reproducibility and ethics statement.
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