How to read data properly — sample size, variance, regression to the mean, and the statistics traps that fool most bettors.
6 modules
Why short-term results are almost meaningless, and how many bets you actually need before drawing any conclusions.
Why extreme performances tend to be followed by more ordinary ones — and how to profit from bettors who do not understand this.
The mathematical distributions behind sports outcomes — Poisson, binomial, normal — and how to apply them to build better probability estimates.
A step-by-step guide to building your first sports prediction model — from data collection to outcome probabilities.
How to rigorously test whether your model actually works — backtesting, walk-forward testing, calibration curves, and proper scoring rules.
Regression analysis, machine learning basics, Elo rating systems, and ensemble modelling applied to sports prediction.
