Kashiwa Reysol
Model pick
75%
Fair odds 1.33
Chiba
9%
Fair odds 10.67
Share of the pitch
Who wins
| Kashiwa Reysol | Draw | Chiba | |
|---|---|---|---|
| Goals model | 75% | 16% | 9% |
| Elo model | 65% | 23% | 12% |
Why this prediction
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Overall strength Favours Kashiwa Reysol
Kashiwa Reysol are rated 136 points above Chiba (1453 to 1317), built from every result, weighted towards recent ones.
-
Home advantage Favours Kashiwa Reysol
Playing at home is worth about 65 rating points, which adds 7 points to Kashiwa Reysol's chance of winning and takes the same from Chiba's.
-
Attack against defence Favours Kashiwa Reysol
Expected goals 2.6 to 0.9. Kashiwa Reysol score 1.2 a match against an average side (2nd in J1 League); Chiba concede 2.1 (17th best defence).
-
Last 5 matches Neutral
Kashiwa Reysol WWDLW, gained 10 rating points; Chiba WDLDD, gained 8 rating points. Already counted in the ratings above.
Rating, last 12 months
Kashiwa ReysolChiba
Kashiwa Reysol
6/10 over 2.5
20 scored
15 conceded
Chiba
6/10 over 2.5
15 scored
15 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
- 2-0
- 3-0
- 2-1
- 3-1
- 1-0
- 1-1
- 4-0
- 4-1
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 3 | 2 | 1 | |||
| 1 | 8 | 7 | 3 | 1 | ||
| 2 | 11 | 9 | 4 | 1 | ||
| 3 | 9 | 8 | 3 | 1 | ||
| 4 | 6 | 5 | 2 | 1 | ||
| 5 | 3 | 3 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Kashiwa Reysol | Draw | Chiba |
|---|---|---|---|
| Kashiwa Reysol | 35%2.90 | 1%— | 0%— |
| Level | 30%3.29 | 7%13.95 | 2%63.29 |
| Chiba | 11%9.39 | 7%14.77 | 8%12.59 |
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.