Jablonec
24%
Fair odds 4.23
Lugano
Model pick
52%
Fair odds 1.92
Share of the pitch
Who wins
| Jablonec | Draw | Lugano | |
|---|---|---|---|
| Goals model | 24% | 24% | 52% |
| Elo model | 26% | 27% | 46% |
Why this prediction
-
Overall strength Favours Lugano
Lugano are rated 136 points above Jablonec (1520 to 1384), built from every result, weighted towards recent ones.
-
Home advantage Favours Jablonec
Playing at home is worth about 65 rating points, which adds 9 points to Jablonec's chance of winning and takes the same from Lugano's.
-
Attack against defence Favours Lugano
Expected goals 1.1 to 1.8. Jablonec score 1.0 a match against an average side; Lugano concede 1.2 (1st best defence in Swiss Super League).
-
Last 5 matches Neutral
Jablonec DDLLD, gained 30 rating points; Lugano WWWWW, gained 32 rating points. Already counted in the ratings above.
Rating, last 12 months
JablonecLuganoJablonec
3/9 over 2.5
9 scored
12 conceded
09/16
09/30
10/21
11/04
11/25
12/09
10/15
10/22
11/05
Lugano
10/10 over 2.5
33 scored
11 conceded
09/06
09/12
09/16
09/20
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
- 1-1
- 1-2
- 0-1
- 0-2
- 2-1
- 0-0
- 1-3
- 1-0
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 6 | 9 | 9 | 5 | 2 | 1 |
| 1 | 6 | 12 | 10 | 6 | 3 | 1 |
| 2 | 3 | 6 | 6 | 3 | 1 | 1 |
| 3 | 1 | 2 | 2 | 1 | 1 | |
| 4 | 1 | 1 | ||||
| 5 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Jablonec | Draw | Lugano |
|---|---|---|---|
| Jablonec | 19%5.14 | 9%10.96 | 7%14.37 |
| Level | 4%23.36 | 13%7.98 | 22%4.48 |
| Lugano | 0%— | 2%55.25 | 23%4.30 |
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.