Slovan Bratislava
14%
Fair odds 7.00
Betis
Model pick
68%
Fair odds 1.47
Share of the pitch
Who wins
| Slovan Bratislava | Draw | Betis | |
|---|---|---|---|
| Goals model | 14% | 18% | 68% |
| Elo model | 20% | 26% | 54% |
Why this prediction
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Overall strength Favours Betis
Betis are rated 187 points above Slovan Bratislava (1583 to 1396), built from every result, weighted towards recent ones.
-
Home advantage Favours Slovan Bratislava
Playing at home is worth about 65 rating points, which adds 8 points to Slovan Bratislava's chance of winning and takes the same from Betis's.
-
Attack against defence Favours Betis
Expected goals 1.1 to 2.6. Slovan Bratislava score 0.8 a match against an average side; Betis concede 1.2 (5th best defence in La Liga).
-
Last 5 matches Neutral
Slovan Bratislava LLLLW, lost 5 rating points; Betis LWDWL, lost 6 rating points. Already counted in the ratings above.
Rating, last 12 months
Slovan BratislavaBetis
Slovan Bratislava
7/10 over 2.5
6 scored
26 conceded
Betis
6/10 over 2.5
18 scored
14 conceded
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
- 1-2
- 0-2
- 1-3
- 1-1
- 0-3
- 0-1
- 2-2
- 1-4
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 3 | 6 | 8 | 7 | 5 | 2 |
| 1 | 2 | 8 | 9 | 8 | 5 | 3 |
| 2 | 2 | 4 | 5 | 4 | 3 | 1 |
| 3 | 1 | 2 | 2 | 2 | 1 | 1 |
| 4 | 1 | |||||
| 5 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Slovan Bratislava | Draw | Betis |
|---|---|---|---|
| Slovan Bratislava | 12%8.40 | 9%11.68 | 14%7.29 |
| Level | 2%40.82 | 7%13.77 | 30%3.33 |
| Betis | 0%— | 1%96.15 | 25%4.03 |
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.