The Bayesian blend is the step where the model tempers its raw statistical estimate with other evidence before finalising a probability. The earlier pipeline steps (the goal expectancies, the Poisson scoreline grid, the Dixon-Coles correction) produce a pure model probability for each market. That raw number is the model’s honest first estimate, and on any single fixture it can run hot or cold: small samples, unusual team situations, or noisy recent form can push it further from reality than the wider evidence supports.
Rather than publish the raw number, the model blends it with additional evidence. For match-result markets, it weighs its own estimate against the probabilities implied by the betting market and against long-run league tendencies. For goals markets, it leans harder on its own estimate, blended with league tendencies, because that is where its statistical machinery is strongest. The effect is the same in both cases: the model pulls an extreme raw estimate toward well-evidenced base rates and lets a well-supported estimate through intact.
For you, the payoff is stability. The fair odds and +EV figures on a results page are not one model’s unchecked opinion; they are that opinion weighed against the market and against years of league history. It is one of the reasons a Gecko Edge probability is a calculated, repeatable output rather than a generated guess. For a deeper look at the approach, read Bayesian football prediction models.