Metz
Model pick
48%
Fair odds 2.07
Boulogne
26%
Fair odds 3.91
Share of the pitch
Who wins
| Metz | Draw | Boulogne | |
|---|---|---|---|
| Goals model | 48% | 26% | 26% |
| Elo model | 54% | 26% | 20% |
Why this prediction
-
Overall strength Favours Metz
Metz are rated 56 points above Boulogne (1377 to 1321), built from every result, weighted towards recent ones.
-
Home advantage Favours Metz
Playing at home is worth about 65 rating points, which adds 9 points to Metz's chance of winning and takes the same from Boulogne's.
-
Attack against defence Favours Metz
Expected goals 1.6 to 1.1. Metz score 1.0 a match against an average side (4th in Ligue 2); Boulogne concede 1.5 (9th best defence).
-
Last 5 matches Favours Boulogne
Metz LDWLD, lost 21 rating points; Boulogne LDWDL, gained 7 rating points. Already counted in the ratings above.
Rating, last 12 months
MetzBoulogneMetz
5/10 over 2.5
15 scored
16 conceded
08/14
08/22
08/29
09/05
09/14
09/19
10/12
10/17
10/23
10/30
Boulogne
1/10 over 2.5
7 scored
8 conceded
08/14
08/21
08/28
09/04
09/11
09/19
10/09
10/16
10/26
10/30
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
- 1-0
- 2-1
- 2-0
- 0-0
- 0-1
- 1-2
- 2-2
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 7 | 7 | 4 | 1 | ||
| 1 | 10 | 12 | 6 | 2 | 1 | |
| 2 | 9 | 9 | 5 | 2 | 1 | |
| 3 | 5 | 5 | 3 | 1 | ||
| 4 | 2 | 2 | 1 | |||
| 5 | 1 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Metz | Draw | Boulogne |
|---|---|---|---|
| Metz | 32%3.14 | 6%17.30 | 2%48.08 |
| Level | 15%6.63 | 15%6.47 | 11%9.00 |
| Boulogne | 2%52.91 | 4%25.51 | 13%7.78 |
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.