Sports Modeling
Elo Ratings and Power Rankings: Building Agent Rating Systems from Scratch
The complete math behind Elo ratings, Glicko-2, and margin-of-victory adjustments for building team and player rating systems that produce calibrated probabilities for sports betting agents.
Read → Layer 4 — IntelligenceMLB Run Expectancy and Win Expectancy: The Markov Chain Approach
Baseball as a Markov chain: derive run expectancy from the 24 base-out states, build a full win expectancy model, compute linear weights (wOBA, FIP), and apply the framework to MLB betting lines and F5 totals.
Read → Layer 4 — IntelligenceNBA Win Probability and Live Betting Models: Score Differential, Time, and Pace
Build a real-time NBA win probability model using score margin, time remaining, pace, and team quality. Deploy it as a live betting agent that detects mispriced in-game lines.
Read → Layer 4 — IntelligenceNFL Modeling: Point Spreads, Totals, and Player Prop Math
Build NFL point spread, totals, and player prop models from team efficiency metrics, key number analysis, teaser math, and same-game parlay correlation exploitation for autonomous betting agents.
Read → Layer 4 — IntelligencePoisson Distribution and Sports Modeling: Projecting Scores from First Principles
How to use the Poisson distribution to model goal-scoring in soccer, hockey, and baseball, build match probability matrices, and find +EV bets against sportsbook lines.
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 → Layer 4 — IntelligenceSoccer/Football Expected Goals (xG): Mathematical Framework for Betting
How to build and exploit expected goals (xG) models for soccer betting — from logistic regression shot models to Poisson match outcome predictions, with Python implementation for autonomous agents.
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