Ascoli
Model pick
46%
Fair odds 2.15
Virtus Entella
26%
Fair odds 3.90
Share of the pitch
Who wins
| Ascoli | Draw | Virtus Entella | |
|---|---|---|---|
| Goals model | 46% | 28% | 26% |
| Elo model | 55% | 26% | 20% |
Why this prediction
-
Overall strength Favours Ascoli
Ascoli are rated 62 points above Virtus Entella (1382 to 1320), built from every result, weighted towards recent ones.
-
Home advantage Favours Ascoli
Playing at home is worth about 65 rating points, which adds 9 points to Ascoli's chance of winning and takes the same from Virtus Entella's.
-
Attack against defence Favours Ascoli
Expected goals 1.4 to 1.0. Ascoli score 0.9 a match against an average side (9th in Serie B); Virtus Entella concede 1.5 (8th best defence).
-
Last 5 matches Favours Ascoli
Ascoli DWWDD, gained 28 rating points; Virtus Entella LDLWD, lost 12 rating points. Already counted in the ratings above.
Rating, last 12 months
AscoliVirtus Entella
Ascoli
3/10 over 2.5
16 scored
6 conceded
Virtus Entella
4/10 over 2.5
11 scored
12 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
- 1-0
- 0-0
- 2-0
- 2-1
- 0-1
- 1-2
- 2-2
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 9 | 8 | 4 | 1 | ||
| 1 | 12 | 13 | 6 | 2 | 1 | |
| 2 | 9 | 9 | 4 | 1 | ||
| 3 | 4 | 4 | 2 | 1 | ||
| 4 | 2 | 2 | 1 | |||
| 5 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Ascoli | Draw | Virtus Entella |
|---|---|---|---|
| Ascoli | 27%3.66 | 4%22.83 | 1%80.65 |
| Level | 17%5.78 | 17%5.72 | 10%10.16 |
| Virtus Entella | 2%42.02 | 5%19.96 | 15%6.63 |
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.