A correct score bet wins only if you name the exact final score. That makes it one of the hardest football markets to win, and one of the easiest to price badly. The prices look big, but the chance of any one score is small.
The good news is that you can work out your own correct score odds with a simple grid. This guide shows you how to build one from two expected goals figures, turn it into fair prices, and test those prices against the market.
The short version
- A Poisson model gives each team’s chance of scoring 0, 1, 2 or more goals. Multiply the two chances to get the chance of a score.
- Fair odds = 1 ÷ chance. If 1-1 has an 11.68% chance, its fair price is 8.56.
- Correct score markets have many outcomes, and bookmakers’ margins there are usually bigger than on two-way markets.
- Even the likeliest score loses far more often than it wins, so losing runs are normal.
- The grid is an estimate. Low scores are its weakest point, and those are the scores that matter most.
How the correct score market works
You pick a final score, such as 2-1 to the home team. If the match ends that way, you win. Every other score loses, including one that is close.
Bookmakers list the common scores and may add an option for anything else. Exchanges often group the rarer results in a similar way. Read the rules for the market you are using, including how it treats unlisted scores and whether extra time counts. These details vary, and they change what you are betting on.
Step 1: each team’s goal chances
Start with an expected goals (xG) figure for each team in this match. We will use the same example as our BTTS odds guide: 1.6 for the home side and 1.2 for the away side.
The Poisson formula gives the chance of exactly k goals: P(k) = xGk × e−xG ÷ k! (e is about 2.718). For the home side, the chance of exactly 2 goals is 1.6² × 0.2019 ÷ 2 = 25.84%.
| Goals | Home (xG 1.6) | Away (xG 1.2) |
|---|---|---|
| 0 | 20.19% | 30.12% |
| 1 | 32.30% | 36.14% |
| 2 | 25.84% | 21.69% |
| 3 | 13.78% | 8.67% |
Step 2: build the grid
Multiply one figure from each column to get the chance of a score. For 2-1, that is 25.84% × 36.14% = 9.34%. This assumes the two teams’ goals are independent, which we come back to below.
Do it for every pair and you have a grid. Home goals run down the side and away goals run across the top:
| Home ↓ / Away → | 0 | 1 | 2 | 3 |
|---|---|---|---|---|
| 0 | 6.08% | 7.30% | 4.38% | 1.75% |
| 1 | 9.73% | 11.68% | 7.01% | 2.80% |
| 2 | 7.78% | 9.34% | 5.60% | 2.24% |
| 3 | 4.15% | 4.98% | 2.99% | 1.20% |
The likeliest single score is 1-1, yet it happens less than 12% of the time. That’s the nature of this market.
Step 3: turn chances into fair odds
Fair odds are 1 ÷ chance, worked from the unrounded figures:
| Score | Chance | Fair odds |
|---|---|---|
| 1-1 | 11.68% | 8.56 |
| 1-0 | 9.73% | 10.28 |
| 2-1 | 9.34% | 10.71 |
| 2-0 | 7.78% | 12.85 |
| 0-1 | 7.30% | 13.70 |
| 1-2 | 7.01% | 14.27 |
| 0-0 | 6.08% | 16.44 |
A useful check: one grid, every goals market
Because the grid covers every score, you can add up its cells to price other markets. Extended far enough to cover almost every score, this one gives:
- Home win 46.7%, draw 24.7%, away win 28.6%
- Over 2.5 goals 53.1%, the same as our over 2.5 goals lesson
- Both teams to score 55.8%
This is a quick test of your inputs. If the grid’s match result or over 2.5 figures sit a long way from the margin-free market, fix your expected goals before you trust any single score. Our fair odds lesson shows how to take the margin out.
Worked example: comparing prices
Say a bookmaker offers these prices (made up for this example). EV is expected value, and it uses the rounded chances shown:
| Score | Price | Working | EV per £10 staked |
|---|---|---|---|
| 1-1 | 7.50 | (0.1168 × 7.50 − 1) × £10 | −£1.24 (−12.4%) |
| 1-0 | 8.00 | (0.0973 × 8.00 − 1) × £10 | −£2.22 (−22.2%) |
| 2-1 | 8.50 | (0.0934 × 8.50 − 1) × £10 | −£2.06 (−20.6%) |
All three prices are shorter than fair, so all three lose money on average if the model is right. Those gaps come from the margin, which is spread across many scores.
Now say you find 1-1 at 9.50 somewhere else. That’s (0.1168 × 9.50 − 1) × £10 = +£1.10, or +11.0%. It looks like value, but check how much that depends on your inputs.
How fragile the edge is
Move the home side’s expected goals by 0.2 either way, and keep the away side at 1.2:
| Home xG | Chance of 1-1 | Fair odds | EV at 9.50 |
|---|---|---|---|
| 1.4 | 12.48% | 8.01 | +18.6% |
| 1.6 | 11.68% | 8.56 | +11.0% |
| 1.8 | 10.75% | 9.30 | +2.1% |
A small change in one input takes most of the apparent edge away. So treat a big gap with suspicion. If your price is far from what the market shows, your inputs are often the problem.
Why losing runs are normal here
Even at a true 11.68% chance, the bet loses 88.3% of the time. If every bet had that chance and they were all independent, you would lose 10 in a row about 28.9% of the time (0.883 to the power of 10) and 20 in a row about 8.3% of the time.
That’s why small stakes matter on long prices. Our variance lesson explains this in more detail. A good price says nothing about the next result, and every bet can lose.
Where the grid goes wrong
- Goals aren’t independent. A goal changes how both teams play, and the simple grid ignores this.
- Low scores. Plain Poisson models are known to misjudge how often 0-0, 1-0, 0-1 and 1-1 happen. These are among the biggest cells in the grid.
- Inputs drift. Team news changes expected goals, and a price from Monday can be out of date by Saturday.
- Rare scores are the hardest to judge. On a very small chance, a tiny error makes a big difference to the fair price.
Correct score odds FAQ
How do I work out the odds of a correct score?
Find each team’s chance of scoring that many goals, multiply them, then divide 1 by the result. A 9.34% chance gives fair odds of 10.71.
What is the most common correct score?
It depends on the teams. In our example it’s 1-1 at 11.68%, but a stronger favourite would shift the grid towards scores like 2-0 or 1-0.
Are big correct score prices good value?
Not because they’re big. A price is only value if it’s longer than your fair price, and these markets usually carry a large margin.
Can I do this in a spreadsheet?
Yes. In Excel or Google Sheets, POISSON.DIST(goals, xG, FALSE) gives the chance of exactly that many goals. Build one row and one column, then multiply them.
Does the bet include extra time?
It depends on the operator’s rules for that market, so check them before you bet.
Related lessons: Over 2.5 goals: how to price the market yourself, Fair odds, Expected value betting and Betting variance. Try the fair odds calculator or the expected value calculator.
18+ only. Betting should be fun, and every bet can lose. Set a budget you can afford to lose and stick to it. For free, confidential support, visit BeGambleAware.org or call the National Gambling Helpline on 0808 8020 133.