What is the most common football score?
Published
The most common football score is 1-1, which happened in 12.2% of the 16,992 finished matches in our database — roughly one match in eight.
That is the short answer. The longer one is more useful, because the question hides an ambiguity that changes the result, and because the shape of the whole distribution explains more about football than the top line does.
The full distribution
Every finished match across the nine leagues we model, ranked by how often each exact scoreline occurred:
| Score | Matches | Share |
|---|---|---|
| 1-1 | 2,075 | 12.27% |
| 1-0 | 1,714 | 10.14% |
| 2-1 | 1,485 | 8.78% |
| 0-1 | 1,277 | 7.55% |
| 1-2 | 1,224 | 7.24% |
| 2-0 | 1,186 | 7.01% |
| 0-0 | 1,111 | 6.57% |
| 2-2 | 956 | 5.65% |
| 0-2 | 825 | 4.88% |
| 3-1 | 720 | 4.26% |
| 3-0 | 688 | 4.07% |
| 1-3 | 423 | 2.50% |
Those twelve scorelines account for about 81% of all matches. Everything else — every 4-3, every 5-0, every result you actually remember — shares the remaining fifth between dozens of possibilities.
The ambiguity: scoreline or margin?
Here is why published answers to this question disagree.
By exact scoreline, 1-1 wins, at 12.27%.
By result pattern, a one-goal win 1-0 is more common: 1-0 (10.14%) plus 0-1 (7.55%) means 17.69% of matches finish 1-0 to somebody. That is the single most common way for a football match to end, and it beats 1-1 comfortably.
Both answers are correct; they answer different questions. If you are betting a correct-score market, the exact scoreline matters and 1-1 is your answer. If you are asking how football matches tend to finish, “1-0 to someone” is the honest reply.
What the shape tells you
Three things fall out of that table.
Low scores dominate overwhelmingly. Every scoreline in the top seven involves two goals or fewer from at least one side, and nothing above three goals for a single team appears at all. Football’s distribution is compressed at the bottom, which is exactly why the sport is so hard to predict — there simply are not enough scoring events for the better team to reliably express itself.
Home advantage is visible in every pair. Compare the mirror images: 1-0 (10.14%) against 0-1 (7.55%); 2-0 (7.01%) against 0-2 (4.88%); 3-0 (4.07%) against 0-3. The home version is more common every time, which is home advantage showing up as an asymmetry in the scoreline table rather than as an abstract percentage. Across the whole dataset home teams average 1.52 goals to away teams’ 1.21.
Four scorelines carry more than a third of all football. 0-0, 1-0, 0-1 and 1-1 together make up 36.5% of matches. This is not a curiosity — it is precisely the region where a plain Poisson model misprices reality, and precisely what the Dixon-Coles correction exists to repair. A model that gets those four cells wrong is getting more than a third of football wrong.
Why “most likely” is still unlikely
The practical consequence catches people out constantly. 1-1 is the most common scoreline in football and it still only happens 12% of the time, which means backing it loses seven times in eight.
This is the defining feature of correct-score betting: the favourite outcome in that market is a heavy underdog in absolute terms, and the prices reflect it. A “most likely score” is a statement about ranking, not about probability being high — the full explanation is here, and it is the same reason our model’s modal scoreline is published as information rather than as a recommendation.
Does it vary by league?
Substantially. The Bundesliga averages 3.18 goals per match and the Brasileirão 2.43, and that difference reshapes the whole table — low-scoring competitions push weight into 0-0 and 1-0, high-scoring ones spread it across 2-1, 3-1 and beyond. In Brazil, those four low scorelines account for 43.0% of matches; in the Bundesliga, 28.2%.
This is why our model is fitted per league rather than globally. A single scoreline distribution estimated across all nine competitions would be wrong everywhere — too flat for Brazil, too concentrated for Germany.
About these numbers
Every figure comes from our own database of finished matches across the nine leagues we model, and it is a snapshot as this was written that will drift as more matches finish. The per-fixture scoreline grid our model produces for upcoming matches, and how it is graded afterwards, are described on the methodology page and published on the track record.
Nothing here is betting advice, and the most common score being 1-1 is not a system. Stake only what you can afford to lose.