Machine Learning
3 Open-Source AI Sports Betting Projects to Watch
Three trending open-source AI sports betting projects on GitHub — BettingAI, DGFantasy Optimizer, and AIFootballPredictions — and what builders can learn from them.
Read → Layer 4 — IntelligenceCalibration and Model Evaluation: How Agents Know Their Models Are Good
The mathematical framework for evaluating prediction model accuracy — calibration plots, Brier score decomposition, ECE, Hosmer-Lemeshow tests, and automated calibration audits for betting agents.
Read → Layer 4 — IntelligenceFeature Engineering for Sports Prediction Models: Building the Signal That Powers Agent Intelligence
How to build, select, and pipeline features for sports prediction models — raw stats, derived metrics, rolling windows, opponent adjustments, interaction terms, and LASSO selection with full Python implementation.
Read → Layer 4 — IntelligenceRegression Models for Sports Betting: From Linear to Logistic to Ridge
Build predictive sports models using linear, logistic, Poisson, and regularized regression. Full derivations, NFL worked examples, and production-ready Python code for autonomous betting agents.
Read →Builder Spotlight: From Data Scientist to Bot Seller — Building Sentiment Agents for Prediction Markets
Interview with Priya Sharma on building NLP sentiment trading agents for prediction markets. Model architecture, monetization, and lessons learned.
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