Moneyball Scout ML
> Statistical analysis to identify undervalued football players using ML. Identifies undervalued players by analyzing performance metrics vs market value.
> Overview
Inspired by the Moneyball philosophy, this project applies statistical analysis to football (soccer). By leveraging machine learning, we identify undervalued players whose on-field performance metrics exceed their current market valuation. This tool supports decision-making in football management, particularly in player acquisition and team building.
> Key Features
- Uncover Value: Identify players whose market values do not reflect their true contributions
- Data-Driven Decisions: Statistical models for player acquisition
- Comprehensive Analysis: Performance, physical attributes, and historical trends
- Market Value Prediction: ML-based valuation estimation
> Tech Stack
[Python]
[Pandas]
[Scikit-Learn]
[Jupyter Notebooks]
Links & Resources
Website
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