FIFAWorldCup

This World Cup forecasting dashboard represents a sophisticated application of quantitative finance techniques to prediction markets. The ensemble ML approach suggests multiple model architectures (likely gradient boosting, neural networks, and statistical models) combined through weighted averaging to produce more robust predictions. The Monte Carlo simulation component enables probabilistic outcome distribution modeling, which is particularly valuable for tournament structures with complex elimination scenarios. The system tracks multiple performance metrics including log-loss, Brier score, calibration loss, and ROI, representing a comprehensive backtesting framework. The 21MB codebase suggests significant data, trained model artifacts, and visualization components. This tool is especially timely given the July 2026 World Cup timing, making it immediately applicable. The paper-trading integration with Polymarket and Kalshi suggests the system can operate without risking capital. Developers interested in prediction market forecasting should clone this for the ensemble methodology and Monte Carlo implementation alone. The comprehensive metric tracking demonstrates proper quant discipline. The project appears to be genuine academic-grade work, likely from a quant or data scientist background. This is one of the most sophisticated forecasting tools in the database and warrants serious attention from prediction market researchers.