Vissel Kobe
Model pick
66%
Fair odds 1.51
Chiba
13%
Fair odds 7.67
Share of the pitch
Who wins
| Vissel Kobe | Draw | Chiba | |
|---|---|---|---|
| Goals model | 66% | 21% | 13% |
| Elo model | 63% | 24% | 14% |
Why this prediction
-
Overall strength Favours Vissel Kobe
Vissel Kobe are rated 123 points above Chiba (1441 to 1317), built from every result, weighted towards recent ones.
-
Home advantage Favours Vissel Kobe
Playing at home is worth about 65 rating points, which adds 8 points to Vissel Kobe's chance of winning and takes the same from Chiba's.
-
Attack against defence Favours Vissel Kobe
Expected goals 2.1 to 0.8. Vissel Kobe score 1.0 a match against an average side (8th in J1 League); Chiba concede 2.1 (17th best defence).
-
Last 5 matches Neutral
Vissel Kobe WDLWL, barely moved; Chiba WDLDD, gained 8 rating points. Already counted in the ratings above.
Rating, last 12 months
Vissel KobeChibaVissel Kobe
6/10 over 2.5
18 scored
9 conceded
08/29
09/02
09/06
09/11
09/20
10/09
10/18
10/21
10/24
10/31
Chiba
6/10 over 2.5
15 scored
15 conceded
08/29
09/02
09/06
09/13
09/19
10/11
10/17
10/21
10/25
11/01
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
- 1-1
- 2-1
- 3-0
- 3-1
- 0-0
- 4-0
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 6 | 4 | 2 | |||
| 1 | 11 | 10 | 4 | 1 | ||
| 2 | 12 | 10 | 4 | 1 | ||
| 3 | 8 | 7 | 3 | 1 | ||
| 4 | 4 | 4 | 1 | |||
| 5 | 2 | 1 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Vissel Kobe | Draw | Chiba |
|---|---|---|---|
| Vissel Kobe | 34%2.92 | 1%82.64 | 0%— |
| Level | 26%3.87 | 11%8.96 | 2%47.17 |
| Chiba | 7%14.58 | 7%13.57 | 11%8.98 |
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.