Wolves
Model pick
67%
Fair odds 1.48
Derby
13%
Fair odds 7.81
Share of the pitch
Who wins
| Wolves | Draw | Derby | |
|---|---|---|---|
| Goals model | 67% | 20% | 13% |
| Elo model | 57% | 25% | 18% |
Why this prediction
-
Overall strength Favours Wolves
Wolves are rated 80 points above Derby (1485 to 1406), built from every result, weighted towards recent ones.
-
Home advantage Favours Wolves
Playing at home is worth about 65 rating points, which adds 8 points to Wolves's chance of winning and takes the same from Derby's.
-
Attack against defence Favours Wolves
Expected goals 2.2 to 0.8. Wolves score 1.2 a match against an average side (6th in Championship); Derby concede 1.7 (23rd best defence).
-
Last 5 matches Favours Wolves
Wolves WWWDW, gained 28 rating points; Derby LLDLL, lost 41 rating points. Already counted in the ratings above.
Rating, last 12 months
WolvesDerbyWolves
5/10 over 2.5
14 scored
9 conceded
09/06
09/13
09/20
10/10
10/13
10/16
10/20
10/24
10/31
11/04
Derby
5/10 over 2.5
6 scored
22 conceded
09/05
09/09
09/12
09/19
10/10
10/14
10/17
10/24
10/31
11/03
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
- 2-0
- 1-0
- 2-1
- 1-1
- 3-0
- 3-1
- 0-0
- 4-0
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 5 | 4 | 2 | |||
| 1 | 10 | 9 | 4 | 1 | ||
| 2 | 12 | 10 | 4 | 1 | ||
| 3 | 8 | 7 | 3 | 1 | ||
| 4 | 5 | 4 | 2 | |||
| 5 | 2 | 2 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Wolves | Draw | Derby |
|---|---|---|---|
| Wolves | 39%2.59 | 3%38.46 | 1%— |
| Level | 24%4.09 | 11%8.86 | 4%23.26 |
| Derby | 5%20.79 | 5%19.76 | 8%12.00 |
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.