Pedro Tabacof - How I lost 1000€ betting on CS:GO with machine learning and Python

Having a good ML model alone is not enough - you need a complete end-to-end pipeline with proper risk management, backtesting, and financial planning

Start small with paper trades and shadow mode to validate your strategy before putting real money on the line. Gradually increase bet sizes only after proving the system works.

Model calibration is critical for financial decision making - probabilities need to match actual frequencies. Use techniques like isotonic regression to calibrate if needed.

Be very skeptical of perceived “edges” in betting markets. If there’s a discrepancy between your model’s probabilities and bookmaker odds, your model is likely wrong.

The Kelly criterion for bet sizing is often too aggressive - consider using half Kelly or fixed bet sizes to reduce risk of ruin.

Proper backtesting must be done with out-of-time evaluation on future data, not just random cross-validation. Monitor both ML and financial metrics.

Watch for feature degradation over time and model drift. Good test set performance doesn’t guarantee future performance.

Overfitting itself isn’t necessarily bad if the model performs well on unseen data. Focus more on removing harmful features.

Have clear exit criteria and risk management - don’t rely on “vibes” for when to quit. Set stop losses and validate strategies carefully.

For betting specifically, markets are likely rigged against you. Professional betting operations will limit/ban profitable players.