Correct Score by Shots on Target: Likely Scores From 0 to 14+
Across 67,047 matches in 21 European divisions, I track how the most likely final score moves from 0-0 towards 2-2 as shots on target pile up.
- 38.0%0-0 rate with 0–3 total shots on target
- 0.8%0-0 rate with 14+ total shots on target
- 1-1Most common score at 8–9 shots on target
- 31.9Goals per 100 shots on target, 8–9 band
Correct Score forces you to picture the whole ninety minutes, and the cleanest way I know to test that picture is to ask how much a match will actually hit the target. Ten minutes of watching tells you whether a game is cagey or open. This study asks a simple question: which final scores go with low, medium and high totals of shots on target between both teams?
The answer is a clear staircase. With 0–3 shots on target, 38.0% of matches ended 0-0. At 8–9 the most common score became 1-1, and at 14+ it was 2-2, with 0-0 down to just 0.8%. That staircase sits behind much of the thinking on my today's correct score predictions page, and the four charts below show where each step falls.
Q1Which scores dominate at each level of shots on target?
The heatmap reads top to bottom as a match getting busier. In the 0–3 band the low scores take almost everything: 0-0 at 38.0%, 1-0 at 23.7% and 0-1 at 17.1%. Move down and that weight drains away. By 6–7 on target, 1-0 has dropped to 13.7%, and by 14+ it sits at 1.7%.
The scores with three or four goals do the opposite. 2-1 rises from 1.2% at 0–3 to 9.1% at 12–13, and 2-2 barely appears in quiet games but reaches 7.1% at 14+.
View the data: correct score share by total shots on target
| 0-0 | 1-0 | 0-1 | 1-1 | 2-1 | 1-2 | 2-0 | 2-2 | |
|---|---|---|---|---|---|---|---|---|
| 0–3 on target | 38.0% | 23.7% | 17.1% | 8.4% | 1.2% | 1.3% | 5.5% | 0.0% |
| 4–5 on target | 17.0% | 19.5% | 14.5% | 15.1% | 5.9% | 4.7% | 9.3% | 1.3% |
| 6–7 on target | 8.6% | 13.7% | 10.1% | 16.0% | 9.1% | 7.1% | 9.4% | 3.9% |
| 8–9 on target | 4.5% | 8.2% | 6.9% | 13.6% | 10.5% | 8.5% | 7.8% | 6.2% |
| 10–11 on target | 2.2% | 5.8% | 4.2% | 10.5% | 10.7% | 8.4% | 5.9% | 7.4% |
| 12–13 on target | 1.3% | 3.0% | 2.5% | 7.4% | 9.1% | 7.0% | 4.6% | 8.1% |
| 14+ on target | 0.8% | 1.7% | 1.2% | 5.1% | 6.9% | 5.3% | 2.6% | 7.1% |
What I take from this is that the score grid flattens as output rises. In quiet games one or two scores carry the market. In busy games no single score holds much share, so my confidence in any one line has to drop with it.
Q2How fast do goalless games vanish as shots on target rise?
This line chart uses exact totals rather than bands, and it shows how steep the change is. With exactly 4 shots on target, 21.1% of games ended 0-0. At 8 it was 5.1%, and at 12 only 1.5%. One-goal matches follow a similar slide, from 37.0% at 4 on target to 5.8% at 12.
The 4+ goals line is the one I watch most. Games with exactly 2 shots on target produced 4+ goals 0.1% of the time. With 16 it was 72.3%.
View the data: goalless, one-goal and 4+ goal games by shots on target
| 0-0 | Exactly one goal | 4+ goals | |
|---|---|---|---|
| 2 | 45.8% | 42.2% | 0.1% |
| 3 | 31.7% | 41.2% | 0.1% |
| 4 | 21.1% | 37.0% | 1.8% |
| 5 | 14.3% | 32.1% | 5.4% |
| 6 | 9.9% | 26.7% | 10.6% |
| 7 | 7.4% | 21.2% | 16.3% |
| 8 | 5.1% | 16.4% | 23.6% |
| 9 | 3.8% | 13.6% | 30.9% |
| 10 | 2.6% | 10.9% | 37.5% |
| 11 | 1.6% | 8.7% | 45.1% |
| 12 | 1.5% | 5.8% | 52.5% |
| 13 | 1.1% | 4.9% | 59.0% |
| 14 | 0.9% | 3.6% | 63.3% |
| 15 | 0.9% | 2.8% | 70.9% |
| 16 | 1.0% | 2.0% | 72.3% |
The lines cross in the middle, around the point where a match stops looking tight and starts looking loose. That crossover is where I see bettors most often stay attached to a 1-0 that the match has already moved past.
Q3What is the single most likely score in each band?
The table pulls out the top two scores in each band, with fair odds worked out from how often the leading score happened and no bookmaker margin added. At 0–3 on target, 0-0 at 38.0% carries fair odds of 2.63. At 8–9, 1-1 leads at 13.6% with fair odds of 7.37. At 14+, 2-2 tops the band at 7.1%, fair odds 13.99, with 2-1 just behind on 6.9%.
Look at how close first and second become. At 10–11 on target, 2-1 at 10.7% and 1-1 at 10.5% are almost level. The favourite in a busy band is barely a favourite at all.
| On target | Matches | Top score | How often | Fair odds | 2nd score | How often |
|---|---|---|---|---|---|---|
| 0–3 | 2,727 | 0-0 | 38.0% | 2.63 | 1-0 | 23.7% |
| 4–5 | 8,708 | 1-0 | 19.5% | 5.12 | 0-0 | 17.0% |
| 6–7 | 15,597 | 1-1 | 16.0% | 6.27 | 1-0 | 13.7% |
| 8–9 | 16,502 | 1-1 | 13.6% | 7.37 | 2-1 | 10.5% |
| 10–11 | 12,180 | 2-1 | 10.7% | 9.35 | 1-1 | 10.5% |
| 12–13 | 6,799 | 2-1 | 9.1% | 10.97 | 2-2 | 8.1% |
| 14+ | 4,534 | 2-2 | 7.1% | 13.99 | 2-1 | 6.9% |
The main caveat belongs here. Shots on target are only known once the match is over, and the data cannot say whether they came before or after the goals. This is a model of which scores go with each level of output, not a pre-match forecast, so the useful work is in estimating the band well before kick-off or reading it live.
Q4Do busier matches convert their shots on target less efficiently?
Conversion drifts down gently as output rises: 33.7 goals per 100 shots on target in the 0–3 band, 31.9 in the 8–9 band and 29.1 at 14+. The change is small, but it runs in one direction the whole way.
My reading is that very busy matches include plenty of speculative efforts and saves on an already stretched goalkeeper. Goals still rise with volume, just a little less than one for one.
View the data: goals per 100 shots on target
| Total goals ÷ total shots on target ×100, by band | |
|---|---|
| 0–3 on target | 33.7 |
| 4–5 on target | 33.6 |
| 6–7 on target | 32.6 |
| 8–9 on target | 31.9 |
| 10–11 on target | 31 |
| 12–13 on target | 30.6 |
| 14+ on target | 29.1 |
For a correct score bettor that matters at the top end. Expecting goals to follow shots on target in a straight line will nudge you towards scores slightly too big.
The playbook
How we would use these numbers before the next Correct Score bet.
- Estimate the band first. Before choosing a score, decide whether the match looks like 0–3, 8–9 or 14+ on target. That band alone moves 0-0 from 38.0% down to 0.8%.
- Drop 1-0 in open games. 1-0 was 23.7% in the quietest band but just 1.7% at 14+. If you expect a shootout, the classic narrow win is the wrong anchor.
- Cover 2-1 and 2-2 together. At 14+ on target, 2-2 at 7.1% and 2-1 at 6.9% are nearly level. In busy matches I would rather split stakes than pretend one score clearly leads.
Data & method
- Data: 21 European divisions, 2016/17–2025/26, 67,047 matches with full match statistics.
- Total shots on target = home + away shots on target over the full match.
- Bands chosen so each holds at least ~2,000 matches; the line chart uses exact counts from 2 to 16 (each 690+ matches).
- Conversion = total goals ÷ total shots on target ×100 (own goals count as goals).
- Fair odds = 1 ÷ observed frequency, with no bookmaker margin added.
- Caveat: Shots on target are only known after the match; this shows which scores go with each level of attacking output, not a pre-match forecast.
- Caveat: Shots and shots on target are full-match totals, so the data cannot say whether they came before or after any goal; links between them and the score are associations, not timelines.
Quick Q&A
- What is the most common correct score when there are few shots on target?
- With 0–3 total shots on target, 0-0 was the most common score at 38.0%, with fair odds of 2.63. 1-0 came second at 23.7%.
- What score is most likely in a match with lots of shots on target?
- At 14+ shots on target, 2-2 was most common at 7.1%, fair odds 13.99, followed closely by 2-1 at 6.9%. 0-0 happened in only 0.8% of those games.
- How many shots on target does it take to score a goal?
- Across the bands, conversion ran from 33.7 goals per 100 shots on target in quiet games to 29.1 in the busiest. The middle 8–9 band converted at 31.9.
I'm Maren Holloway, and I specialise in Correct Score markets, where you can't hide behind a result and actually have to picture how the match plays out.
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