Data pipeline, feature engineering with odds data, backtesting methodology, and live deployment. The full stack for quantitative sports betting.
1. What makes sports betting data different from other financial data
2. Setting up a data pipeline: ingestion, normalization, storage
3. Feature engineering: odds-based features that actually predict outcomes
4. Model selection: regression, classification, and market efficiency frameworks
5. Backtesting correctly: avoiding lookahead bias and overfitting
6. Going live: bet sizing, bankroll management, and monitoring
Start with the beginner resources below to build the foundation. The full modeling guide will be published when the BetFlux data API launches.
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