Statistical Modelling (Poisson)
Building your own probabilities from data instead of trusting the bookmaker's price.
Serious bettors build models to generate their own odds, then hunt for value against the market — and in football the Poisson distribution is a popular starting point.
How it works
Using each team's expected goals (attacking and defensive strength), the Poisson distribution estimates the probability of every scoreline, which you can convert into 1X2, over/under and correct-score probabilities. A model is only as good as its inputs and assumptions, so it needs constant testing against real results — but a well-built one gives you an independent price to compare against the bookmaker's.
Worked example
If Team A is expected to score 1.6 goals and Team B 1.1, Poisson gives the probability of 1-0, 2-1, 2-2 and every other scoreline — sum the relevant ones to price the 'over 2.5 goals' market and check it against the bookmaker.
Pros
- Generates independent, objective prices
- Scales across many matches quickly
- Removes gut-feel bias
Cons
- Only as good as its data and assumptions
- Ignores context (injuries, motivation) unless modelled
- Requires spreadsheet or coding skills
Tips
- Validate your model against historical results before betting.
- Use expected goals (xG) data as inputs where possible.
- Blend model output with context, don't follow it blindly.
FAQ
- Do I need to code to build a betting model?
- Not necessarily — a Poisson model can be built in a spreadsheet. Coding (e.g. Python) helps once you want to automate and test across large datasets.
Related reading
Related terms
Not sure on the jargon? Look up "Statistical Modelling (Poisson)" in our betting glossary for plain-English definitions of every betting term.
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