How football prediction models work
A football prediction model is a small number of assumptions doing a lot of work. These posts open ours up: how attack and defence ratings are fitted, why a plain Poisson misprices the lowest scorelines and what Dixon-Coles does about it, how much weight old results should keep, and what happens at the edges — promoted clubs, cup ties, international breaks, a manager sacked on Tuesday. The limits get as much space as the machinery, because a model you cannot describe the failure modes of is not one you should trust.
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Monte Carlo simulation in football: predicting a whole season
Monte Carlo simulation in football, explained: how thousands of simulated seasons turn match probabilities into title and relegation odds — and their limits.
ModelCan you beat the bookmaker with maths?
Yes, in principle — and almost nobody does. What the closing market actually gets right, why apparent edges are usually model error, and what our own backtest found.
ModelThe Elo rating system in football: strengths and blind spots
How the Elo rating system in football turns results into ratings and ratings into probabilities — the formulas, the draw problem, and what Elo cannot price.
ModelHome advantage in football: what it's worth and why it exists
What home advantage in football is really worth, where it comes from, why it keeps shrinking, and how prediction models turn it into a number.
ModelWhy is football so hard to predict?
Not because the teams are unknowable. Because football is low-scoring, and a sport where one goal decides everything hands most of the outcome to variance.
ModelHow injuries affect football predictions: putting a number on absence
How injuries affect football predictions, quantified: what a key absence is worth in goals, why the market prices it first, and when team news really matters.
ModelWhy our model can't tell you if Arsenal are better than Flamengo
Team ratings look like they're on one scale. They aren't. Every league's ratings are anchored inside that league, which is why cross-league comparison needs data domestic fixtures never provide.
ModelDoes squad market value predict football results?
Does squad market value predict football results? It carries real signal, and quietly re-reads results you have. Where it helps, and where it misleads.
ModelWhy early-season football predictions are the least reliable
August is when every model is at its worst, and most of them don't say so. What actually breaks at the start of a season, and how long it takes to recover.
ModelWhy the Poisson model underrates draws in football
Independent Poisson misprices the scorelines football produces most. Where the assumption breaks, the correction that fixes it, and what our own data can and cannot show.
ModelMachine learning football predictions: does the algorithm matter?
Machine learning football predictions, assessed honestly: what swapping a Poisson model for gradient boosting actually buys — and what it cannot.
ModelInternational football predictions: why national teams break the model
Why international football predictions are harder than club ones: ten matches a year, a squad that reassembles each camp, and ratings that don't cross borders.
ModelThe Dixon-Coles model in football, explained with a worked example
How the Dixon-Coles model fixes the Poisson draw problem: the tau correction on four scorelines, time decay, and a worked example from score matrix to 1X2.
ModelCan ChatGPT predict football matches?
Can ChatGPT predict football matches? It will produce a confident answer for any fixture you name. Here is why that answer cannot be scored, and what can.
Model watchNew laws, old data: what a rule change does to a prediction model
Football's 2026/27 time-wasting laws are in force. Here's the honest answer to what they do to a model trained on 17,308 matches played under the old ones.
Model watch
Promoted clubs: what our model knows and when it guesses
Hull arrive with 230 matches in our dataset; Málaga arrived with one. Why promoted clubs are two different problems for a model, in four fixtures this weekend.
Model watch
Atlético 2–0 Málaga: what one result proves about a model–market gap
Our model said 58%, the market said above 70%, and Atlético won 2–0. What one result proves about who was right: almost nothing. Here's what would.
Model watch
Our model's five strongest calls of the 22–24 August weekend
Five home wins priced at 67% or better across five leagues — and the honest math on why even these probably won't all land.
ModelWhat is expected goals (xG)? The metric, its power and its limits
How xG turns shots into probabilities, what it genuinely fixes, the trap in reading an xG total — and why our model does not use it yet.
ModelHow football prediction algorithms actually work
Ratings, Poisson goals and probabilities — what is really inside a football prediction model, explained without hype by a team that publishes its own results.
ModelThe Poisson distribution in football, explained with real numbers
Why one 19th-century formula still powers most football prediction models — how it works, how well it actually fits 17,495 matches, and where it needs help.