Vissel Kobe
Model pick
74%
Fair odds 1.34
Mito
9%
Fair odds 11.59
Share of the pitch
Who wins
| Vissel Kobe | Draw | Mito | |
|---|---|---|---|
| Goals model | 74% | 17% | 9% |
| Elo model | 64% | 23% | 13% |
Why this prediction
-
Overall strength Favours Vissel Kobe
Vissel Kobe are rated 132 points above Mito (1441 to 1309), 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 Mito's.
-
Attack against defence Favours Vissel Kobe
Expected goals 2.4 to 0.7. Vissel Kobe score 1.0 a match against an average side (8th in J1 League); Mito concede 2.4 (22nd best defence).
-
Last 5 matches Favours Vissel Kobe
Vissel Kobe WDLWL, barely moved; Mito LLLLL, lost 49 rating points. Already counted in the ratings above.
Rating, last 12 months
Vissel KobeMitoVissel 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
Mito
4/10 over 2.5
7 scored
21 conceded
08/29
09/02
09/05
09/12
09/19
10/11
10/17
10/21
10/25
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
- 2-0
- 1-0
- 3-0
- 2-1
- 1-1
- 3-1
- 4-0
- 0-0
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 5 | 3 | 1 | |||
| 1 | 11 | 8 | 3 | 1 | ||
| 2 | 13 | 9 | 3 | 1 | ||
| 3 | 10 | 7 | 2 | 1 | ||
| 4 | 6 | 4 | 1 | |||
| 5 | 3 | 2 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Vissel Kobe | Draw | Mito |
|---|---|---|---|
| Vissel Kobe | 35%2.84 | 0%— | 0%— |
| Level | 30%3.31 | 8%11.98 | 1%— |
| Mito | 10%10.44 | 7%13.39 | 8%12.03 |
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.