Sports Betting ML Framework

The Playbook for Building Your Own Gambling Algorithm.

$24,255 in profit. 179 bets. Three full seasons backtested. That's what the model we built using this framework produced — with tiered confidence sizing at $100 units. This guide shows you exactly how to build your own.

Written by an engineer with a Master's degree in Machine Learning and AI who spent weeks in the trenches building a real production sports betting system from scratch — and made every painful mistake so you don't have to. This isn't theory. It's a battle-tested blueprint.

What Makes This Different:

Most sports betting "guides" teach you to pick winners. That's the wrong game. Vegas already knows who's going to win. This framework teaches you to find structural edges — mathematical inefficiencies across spreads, moneylines, totals, parlays, teasers, and props that print money even when you lose individual bets.

What You'll Build:

• A complete ML pipeline: data ingestion, cleaning, feature engineering, model training, and automated daily pick generation • A margin prediction model using gradient boosting (XGBoost/LightGBM) — the proven winner for tabular sports data • Monte Carlo simulation engine that converts predictions into precise cover probabilities for ANY bet type • Multi-bet-type evaluation system that finds the best edge across spreads, moneylines, totals, alternative lines, parlays, and teasers for every game • Tiered confidence sizing that concentrates your bankroll in your highest-conviction plays

The Complete Journey — From Zero to Automated Picks:

• 48-Hour Quickstart — working prototype by Sunday night, even if you've never trained an ML model • Data pipeline architecture using free sources (Barttorvik, Sports Reference) plus smart odds API caching • Feature engineering mastery — EMA over rolling averages, season boundary resets, conference strength adjustments, and the forward selection process that took us from 190 features down to 16 with BETTER results • Walk-forward validation — why standard cross-validation lies for sports data, and the honest alternative • Edge calculation for every bet type — spreads, moneylines, totals, teasers, parlays, and props with worked Monte Carlo examples showing exact math • Bankroll management — position sizing, drawdown circuit breakers, and surviving the losing streaks that break most bettors • Model evaluation framework — calibration analysis, confidence tier validation, and the concrete decision framework for "is my model working or am I fooling myself?"

The Pitfalls Section Alone Is Worth the Price:

Seven critical mistakes that cost weeks or months to diagnose — data leakage from retroactive stats, score parsing bugs that silently corrupt every margin, team name mismatches that destroy backtests, the "52% ATS" illusion, and more. Each one documented with how to detect it, how to fix it, and how to prevent it.

Who This Is For:

Anyone serious about applying machine learning to sports betting — from complete beginners who've never trained a model to experienced data scientists entering the sports domain. No PhD required. Clear explanations throughout, with pseudocode you can adapt to any language.

Whether you're targeting college basketball, NFL, NBA, or any sport with reliable data and a betting market, this framework gives you the methodology and battle-tested process for building edge-detecting systems that actually work in production.

You're not buying picks. You're buying the ability to generate your own — forever.