The Risk Takers Podcast (Rebroadcast) How to Build a Simple Sports Betting Model | Ep 144
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Feb 25, 2026 A clear walkthrough of how to build a basic sports betting model, from napkin math to full distributions. They compare ground-up and hybrid approaches and explain when linear regression is enough. Feature engineering, gathering niche data, backtesting and spotting residual biases are covered. The show also reacts to recent industry news and answers practical modeling and time-management questions.
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Regression Struggles On Outliers And Tails
- Regressions perform best near the center of the data and can fail on outliers or tail events.
- Expect poorer performance on rare or extreme situations; use judgment or other tools there.
Bet Test And Inspect Large Residuals
- After building a model, compare projections to the market and actual outcomes to find consistent residuals or biases.
- Use large misses to identify missing variables (e.g., wind, serve speed) and iterate the model accordingly.
Start Betting Means Before Building Full Distributions
- Early on, you can bet on mean predictions without full distributions: bet when your projection is sufficiently different from the market.
- Use simple thresholds (e.g., X off) and recalibrate by betting until you build distributional confidence.