The definitional reference for correct score betting β how the market works, why it carries 15-20% margin, and the common scorelines by league. For the Poisson-driven workflow, see the Correct Score Strategy guide.
Bookmakers price correct score markets using Poisson distribution. Given expected goals (xG) for each team β typically 1.3β1.6 per match across both sides β the Poisson formula generates the probability of each scoreline from 0-0 to 5-0, 0-5, and beyond.
The formula for each scoreline probability: P(k goals) = (Ξ»^k Γ e^βΞ») Γ· k!, where Ξ» is the expected goals for that team. For two teams, multiply the independent home and away scoreline probabilities together.
Example: Home xG = 1.5, Away xG = 1.0
P(home scores 2) = (1.5Β² Γ e^β1.5) Γ· 2! = (2.25 Γ 0.2231) Γ· 2 = 0.251 β 25.1%
P(away scores 1) = (1.0ΒΉ Γ e^β1.0) Γ· 1! = 1.0 Γ 0.368 = 0.368 β 36.8%
P(2-1 scoreline) = 25.1% Γ 36.8% = 9.2% β fair odds: 10.9
Use the Poisson Calculator to model full scoreline distributions instantly for any pair of expected goals inputs.
Historical frequency data β probabilities vary significantly by match xG inputs.
| Score | Historical prob | Frequency | Typical match profile |
|---|---|---|---|
| 1-0 | 15β16% | ~1 in 6.5 matches | Most common PL result. 1.0 xG home match. |
| 1-1 | 13β14% | ~1 in 7 matches | Occurs most in even contests, xG 1.0β1.4 each. |
| 2-1 | 11β12% | ~1 in 8.5 matches | High home xG (2.0) + away resilience. |
| 2-0 | 9β10% | ~1 in 10 matches | Strong defensive home side. |
| 0-0 | 8β9% | ~1 in 11 matches | Low xG both teams (sub 1.0). |
| 0-1 | 7β8% | ~1 in 13 matches | Away team keeps clean sheet. |
| 2-2 | 5β6% | ~1 in 18 matches | Evenly matched, both attack. |
| 3-1 | 4β5% | ~1 in 22 matches | Dominant home performance. |
| 3-0 | 3β4% | ~1 in 28 matches | Very high home xG + clean sheet. |
| 1-2 | 7β8% | ~1 in 13 matches | Away win with single goal margin. |
Correct score markets carry a combined overround of 30β60% β far higher than 1X2 (typically 4β8%). This means the sum of all scoreline implied probabilities exceeds 100% by 30β60%. In practice, if you bet every listed scoreline equally, you would lose 30β60% of your stake in the long run.
Why the margin is so high
There are 30+ possible scorelines in most markets. Bookmakers apply margin across all of them. They squeeze prices on the most popular (bet-volume-heavy) scorelines like 1-0, 1-1, and 2-1 most aggressively, because these receive the most recreational bets. Obscure scorelines like 4-3 or 0-5 often carry less margin because they receive less volume β creating occasional value opportunities.
The only way to make correct score betting viable long-term is to generate Poisson probability estimates more accurate than the bookmaker's, identify specific scorelines where your probability significantly exceeds the implied probability (after stripping the margin from the offered price), and bet only those selections.
Build your own xG inputs
The bookmaker uses expected goals inputs based on historical averages. If you have more recent or more granular xG data β accounting for injuries, form, tactical changes, or specific opponent defensive metrics β your Poisson inputs will be more accurate. A 0.2 xG difference in expected goals shifts scoreline probabilities significantly.
Compare every scoreline systematically
Run the Poisson model for all scorelines up to 5-5. For each one, compare your estimated probability to the bookmaker implied probability (1 Γ· offered odds). Create a table of your model probability minus their implied probability. Only bet scorelines where your edge exceeds the margin.
Look for bookmaker clustering bias
Bookmakers often price low-scoring 0-0, 0-1, 1-0 scorelines shorter than Poisson suggests in matches where the public expects a goalfest. The reverse also occurs β when everyone expects a low-scoring game, higher-scoring scorelines may be underpriced. Contrarian Poisson analysis can find these systematic biases.
Manage bankroll conservatively
Even a +EV correct score bet has a win probability of 10β20%. You will have long losing sequences. A maximum stake of 0.5β1% of bankroll per correct score selection is appropriate. Do not increase stakes after losses β variance is built into the market by design.
Correct score betting means predicting the exact final scoreline of a football match. Common markets include any scoreline up to 5-0 / 0-5, plus an "any other score" option covering high-scoring matches. It is one of the highest-margin markets in football betting, with combined overrounds of 30β60% typical.
Bookmakers price correct score markets using Poisson distribution models. They estimate expected goals for each team (based on form, xG, home advantage), generate a Poisson probability for every scoreline, then apply a significant margin β typically 30β50% overround β because the market has many outcomes, low liquidity, and many recreational bettors seeking "big price" selections.
Historically in the Premier League, the most frequent scorelines are: 1-0 (around 15β16% of matches), 1-1 (around 13β14%), 2-1 (around 11β12%), 2-0 (around 9β10%), and 0-0 (around 8β9%). These five scores account for approximately 55β60% of all Premier League results. Scorelines involving 4+ goals are each less than 2% likely individually.
Yes, but it requires generating more accurate Poisson probability estimates than the bookmaker. The key is identifying matches where the bookmaker has mispriced specific scorelines β often because they use the same expected goals inputs as the market consensus but weight them differently. Using xG-adjusted expected goals and comparing systematically to bookmaker implied probabilities is the only viable approach.
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