Objective Mispricing Detection for Shortlisting Undervalued Football Players via Market Dynamics and News Signals
#football players #mispricing detection #market dynamics #news signals #undervalued #valuation models #transfer market
📌 Key Takeaways
- Researchers propose a method to detect mispricing in football player valuations using market dynamics and news signals.
- The approach aims to identify undervalued players by analyzing discrepancies between market prices and performance indicators.
- News sentiment and media coverage are integrated as signals to enhance the accuracy of valuation models.
- The model provides a data-driven tool for clubs and investors to shortlist potential transfer targets efficiently.
📖 Full Retelling
arXiv:2603.17687v1 Announce Type: cross
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) f
🏷️ Themes
Sports Analytics, Market Valuation
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Original Source
arXiv:2603.17687v1 Announce Type: cross
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) f
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