Tutorials

Step-by-step, technically rigorous tutorials that explain how the Lottery Lab is built — from clean data foundations to advanced modeling.

7 of 9 tutorials complete

Foundations

Tutorial 1
Structural Breaks and Clean Data

Why historical lottery data must be segmented correctly before any statistical analysis or modeling.

beginner
Tutorial 2
Feature Engineering from Lottery Draws

How raw draws are transformed into a stable feature matrix that models can actually consume.

beginner
Tutorial 3
Exploratory Data Analysis & Distribution Validation

Systematic checks to verify the feature table behaves like random data should before formal testing.

beginner

Statistical Testing

Tutorial 4
Classical Probability Tests & Null Models

Using chi-squared tests to establish whether lottery data behaves randomly and set the baseline for pattern detection.

intermediate
Tutorial 5
Bayesian Inference with Beta-Binomial Models

From binary hypothesis tests to probability distributions over beliefs: quantifying uncertainty with conjugate priors.

intermediate
Tutorial 6
Multinomial Inference with Dirichlet Priors

Scaling Bayesian inference from 2 outcomes to 69 balls: testing which specific numbers deviate from uniform randomness.

intermediate
Tutorial 7
The Dimensionality Crisis

When manual analysis becomes impossible: discovering why 11 million pattern combinations require automated search.

intermediate

Advanced Topics

Tutorial 8
Deep Learning Model Zoo

Building neural architectures for high-dimensional pattern search: LSTMs for sequences, Transformers for interactions.

advanced
Tutorial 9
Causal Inference & Granger Causality

Testing whether past draws contain information that predicts future draws beyond random chance.

advanced