Slavia Prague
38%
Fair odds 2.66
Villarreal
Model pick
39%
Fair odds 2.56
Share of the pitch
Who wins
| Slavia Prague | Draw | Villarreal | |
|---|---|---|---|
| Goals model | 38% | 23% | 39% |
| Elo model | 34% | 28% | 38% |
Why this prediction
-
Overall strength Favours Villarreal
Villarreal are rated 79 points above Slavia Prague (1524 to 1445), built from every result, weighted towards recent ones.
-
Home advantage Favours Slavia Prague
Playing at home is worth about 65 rating points, which adds 9 points to Slavia Prague's chance of winning and takes the same from Villarreal's.
-
Attack against defence Neutral
Expected goals 1.7 to 1.8. Slavia Prague score 1.1 a match against an average side; Villarreal concede 1.4 (17th best defence in La Liga).
-
Last 5 matches Neutral
Slavia Prague LDLLL, lost 17 rating points; Villarreal LLWLD, lost 23 rating points. Already counted in the ratings above.
Rating, last 12 months
Slavia PragueVillarreal
Slavia Prague
7/10 over 2.5
8 scored
23 conceded
Villarreal
7/10 over 2.5
15 scored
18 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
- 1-1
- 1-2
- 2-1
- 2-2
- 0-1
- 1-0
- 0-2
- 1-3
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 4 | 5 | 5 | 3 | 1 | |
| 1 | 5 | 10 | 8 | 5 | 2 | 1 |
| 2 | 5 | 8 | 7 | 4 | 2 | 1 |
| 3 | 3 | 5 | 4 | 2 | 1 | |
| 4 | 1 | 2 | 2 | 1 | ||
| 5 | 1 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Slavia Prague | Draw | Villarreal |
|---|---|---|---|
| Slavia Prague | 23%4.44 | 6%16.84 | 4%25.97 |
| Level | 13%7.79 | 12%8.33 | 15%6.59 |
| Villarreal | 3%38.61 | 5%21.74 | 20%4.88 |
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.