Albacete
28%
Fair odds 3.63
Celta
Model pick
46%
Fair odds 2.18
Share of the pitch
Who wins
| Albacete | Draw | Celta | |
|---|---|---|---|
| Goals model | 28% | 27% | 46% |
| Elo model | 22% | 27% | 51% |
Why this prediction
-
Overall strength Favours Celta
Celta are rated 170 points above Albacete (1492 to 1322), built from every result, weighted towards recent ones.
-
Home advantage Favours Albacete
Playing at home is worth about 65 rating points, which adds 8 points to Albacete's chance of winning and takes the same from Celta's.
-
Attack against defence Favours Celta
Expected goals 1.1 to 1.5. Albacete score 0.9 a match against an average side (19th in La Liga 2); Celta concede 1.3 (10th best defence in La Liga).
-
Last 5 matches Neutral
Albacete WWLLL, lost 12 rating points; Celta LLDWW, barely moved. Already counted in the ratings above.
Rating, last 12 months
AlbaceteCeltaAlbacete
5/10 over 2.5
8 scored
19 conceded
08/29
09/06
09/12
09/18
09/25
10/03
10/12
10/17
10/25
10/31
Celta
6/10 over 2.5
12 scored
11 conceded
09/13
09/16
10/11
10/15
10/18
10/22
10/25
10/31
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
- 0-1
- 1-2
- 0-2
- 0-0
- 1-0
- 2-1
- 2-2
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 8 | 10 | 8 | 4 | 2 | |
| 1 | 7 | 13 | 9 | 5 | 2 | 1 |
| 2 | 4 | 7 | 5 | 3 | 1 | |
| 3 | 2 | 3 | 2 | 1 | ||
| 4 | 1 | 1 | ||||
| 5 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Albacete | Draw | Celta |
|---|---|---|---|
| Albacete | 17%5.91 | 6%18.08 | 3%34.84 |
| Level | 10%10.24 | 16%6.34 | 17%5.87 |
| Celta | 1%73.53 | 4%23.15 | 26%3.79 |
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.