Vicenza
Model pick
42%
Fair odds 2.35
Cesena
31%
Fair odds 3.21
Share of the pitch
Who wins
| Vicenza | Draw | Cesena | |
|---|---|---|---|
| Goals model | 42% | 26% | 31% |
| Elo model | 43% | 28% | 30% |
Why this prediction
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Overall strength Favours Cesena
Cesena are rated 19 points above Vicenza (1318 to 1299), built from every result, weighted towards recent ones.
-
Home advantage Favours Vicenza
Playing at home is worth about 65 rating points, which adds 9 points to Vicenza's chance of winning and takes the same from Cesena's.
-
Attack against defence Favours Vicenza
Expected goals 1.5 to 1.3. Vicenza score 0.8 a match against an average side (20th in Serie B); Cesena concede 1.8 (17th best defence).
-
Last 5 matches Neutral
Vicenza LLWDL, lost 7 rating points; Cesena DWLDL, lost 10 rating points. Already counted in the ratings above.
Rating, last 12 months
VicenzaCesenaVicenza
8/10 over 2.5
13 scored
20 conceded
08/21
08/29
09/06
09/12
09/20
10/10
10/17
10/23
10/27
11/01
Cesena
4/10 over 2.5
11 scored
14 conceded
08/23
08/29
09/06
09/12
09/18
10/10
10/18
10/24
10/27
10/31
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
- 2-1
- 1-0
- 1-2
- 0-1
- 2-0
- 0-0
- 2-2
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 7 | 7 | 5 | 2 | 1 | |
| 1 | 9 | 13 | 8 | 3 | 1 | |
| 2 | 7 | 9 | 6 | 2 | 1 | |
| 3 | 4 | 5 | 3 | 1 | ||
| 4 | 1 | 2 | 1 | |||
| 5 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Vicenza | Draw | Cesena |
|---|---|---|---|
| Vicenza | 24%4.13 | 5%21.69 | 2%58.14 |
| Level | 16%6.26 | 15%6.57 | 11%8.95 |
| Cesena | 3%35.34 | 6%18.08 | 19%5.34 |
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.