How models learn from past matches to predict the next one: features and targets, training and testing, and the traps in between.
8 parts Beginner to Intermediate
Start at part 1Every machine learning model starts with two decisions, what to predict and what to tell the model. The first is the target, the second the features, and real SPFL results show why the features matter as much as the model.
Beginner Part 1
A model that has seen the answers will always look brilliant. Holding matches back to test it, and choosing which ones, is how you find out whether it can predict a match it hasn't seen. Real SPFL seasons show the difference.
Beginner Part 2
A model can learn its training matches too well, rules and all, and then fall apart on matches it hasn't seen. Real SPFL seasons show how it happens, how to spot it, and what to do about it.
Beginner Part 3
A model can fail by being too simple to see the patterns or too complicated to ignore the noise. Real SPFL seasons, and the bookmakers, show what each looks like and where the sweet spot sits.
Beginner Part 4
A model can be too rigid to learn, or so sensitive that one result rewrites everything it believes. Those are bias and variance, and twenty-five years of SPFL results show what each costs.
Beginner Part 5
A single test season can flatter a model or bury it. Cross-validation tests it again and again on different slices of the data; for football, that means walking forward through the seasons. 23 SPFL seasons show why it matters.
Beginner Part 6
Accuracy counts how many results a model called right, and hides almost everything else. A confusion matrix shows where the mistakes are, probability scores show how confident it was, and five SPFL seasons show why draws break accuracy.
Beginner Part 7
Three numbers known before kick-off, one model trained by gradient descent, and a probability for every result. Tested on five SPFL seasons it beats every model in the series so far and gets close to the bookmakers.
Intermediate Part 8