Brann
Model pick
48%
Fair odds 2.09
Aarhus
27%
Fair odds 3.75
Share of the pitch
Who wins
| Brann | Draw | Aarhus | |
|---|---|---|---|
| Goals model | 48% | 25% | 27% |
| Elo model | 50% | 27% | 23% |
Why this prediction
-
Overall strength Favours Brann
Brann are rated 31 points above Aarhus (1432 to 1402), built from every result, weighted towards recent ones.
-
Home advantage Favours Brann
Playing at home is worth about 65 rating points, which adds 9 points to Brann's chance of winning and takes the same from Aarhus's.
-
Attack against defence Favours Brann
Expected goals 1.7 to 1.2. Brann score 1.1 a match against an average side (6th in Eliteserien); Aarhus concede 1.6 (3rd best defence in Danish Superliga).
-
Last 5 matches Neutral
Brann DWLWL, barely moved; Aarhus DLWDD, barely moved. Already counted in the ratings above.
Rating, last 12 months
BrannAarhusBrann
7/10 over 2.5
16 scored
18 conceded
09/05
09/13
09/20
10/09
10/15
10/18
10/22
10/25
11/01
11/05
Aarhus
4/10 over 2.5
11 scored
12 conceded
09/05
09/13
09/20
10/12
10/15
10/18
10/22
10/25
11/01
11/05
Bars show total goals in each of the last 10 matches, oldest on the left: the team's goals in its colour, conceded in grey. The dashed line is 2.5 goals.
Total goals
Both teams to score & clean sheets
Most likely scores
- 1-1
- 2-1
- 1-0
- 2-0
- 1-2
- 0-0
- 0-1
- 2-2
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 6 | 6 | 4 | 2 | ||
| 1 | 9 | 12 | 7 | 3 | 1 | |
| 2 | 8 | 10 | 6 | 2 | 1 | |
| 3 | 4 | 5 | 3 | 1 | ||
| 4 | 2 | 2 | 1 | 1 | ||
| 5 | 1 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Brann | Draw | Aarhus |
|---|---|---|---|
| Brann | 30%3.37 | 4%22.37 | 1%72.99 |
| Level | 16%6.29 | 14%6.92 | 9%11.40 |
| Aarhus | 3%34.72 | 6%18.18 | 17%5.89 |
Corners
Most corners
Corner totals are hard to call: over 9.5 the model's log loss is 0.685 against 0.693 for a naive guess, across 30,937 past matches. Which side wins more corners is called much better.
Cards
Over 4.5 cards, the model's log loss is 0.653 against 0.682 for a naive guess, across 33,146 past matches. The referee matters a lot and isn't in the model.
They haven't met in the data.