Ajax
Model pick
88%
Fair odds 1.14
Thun
4%
Fair odds 23.98
Share of the pitch
Who wins
| Ajax | Draw | Thun | |
|---|---|---|---|
| Goals model | 88% | 8% | 4% |
| Elo model | 78% | 17% | 5% |
Why this prediction
-
Overall strength Favours Ajax
Ajax are rated 254 points above Thun (1617 to 1363), built from every result, weighted towards recent ones.
-
Home advantage Favours Ajax
Playing at home is worth about 65 rating points, which adds 5 points to Ajax's chance of winning and takes the same from Thun's.
-
Attack against defence Favours Ajax
Expected goals 3.7 to 0.8. Ajax score 1.9 a match against an average side (1st in Eredivisie); Thun concede 2.2 (10th best defence in Swiss Super League).
-
Last 5 matches Favours Ajax
Ajax WWWWW, gained 50 rating points; Thun WWLLL, gained 9 rating points. Already counted in the ratings above.
Rating, last 12 months
AjaxThunAjax
7/10 over 2.5
30 scored
11 conceded
09/12
09/15
09/19
10/10
10/15
10/18
10/22
10/25
10/31
11/05
Thun
7/10 over 2.5
18 scored
23 conceded
09/06
09/12
09/16
10/10
10/15
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
- 3-0
- 4-0
- 3-1
- 2-0
- 4-1
- 5-0
- 2-1
- 5-1
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 1 | 1 | ||||
| 1 | 4 | 3 | 1 | |||
| 2 | 7 | 6 | 3 | 1 | ||
| 3 | 9 | 8 | 3 | 1 | ||
| 4 | 8 | 7 | 3 | 1 | ||
| 5 | 6 | 5 | 2 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Ajax | Draw | Thun |
|---|---|---|---|
| Ajax | 47%2.12 | 1%— | 0%— |
| Level | 30%3.31 | 4%27.78 | 1%— |
| Thun | 10%9.62 | 4%28.33 | 3%31.45 |
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.