Objective Mispricing Detection for Shortlisting Undervalued Football Players via Market Dynamics and News Signals

TL;DR

This paper develops a framework for detecting undervalued football players by combining market data and news signals, improving scouting decisions through objective valuation and NLP analysis.

Contribution

It introduces a reproducible method that integrates market dynamics and news signals for undervaluation detection, emphasizing ranking over classification.

Findings

Gradient-boosted regression explains significant variance in market value.

Market dynamics are the primary signal for undervaluation, with NLP providing secondary improvements.

News volatility cues enhance detection in high-uncertainty scenarios.

Abstract

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.