Chiba
Model pick
43%
Fair odds 2.32
Nagoya Grampus
31%
Fair odds 3.26
Share of the pitch
Who wins
| Chiba | Draw | Nagoya Grampus | |
|---|---|---|---|
| Goals model | 43% | 26% | 31% |
| Elo model | 45% | 27% | 27% |
Why this prediction
-
Overall strength Neutral
Chiba and Nagoya Grampus are rated almost level (1317 and 1319).
-
Home advantage Favours Chiba
Playing at home is worth about 65 rating points, which adds 9 points to Chiba's chance of winning and takes the same from Nagoya Grampus's.
-
Attack against defence Favours Chiba
Expected goals 1.5 to 1.3. Chiba score 0.8 a match against an average side (13th in J1 League); Nagoya Grampus concede 1.9 (10th best defence).
-
Last 5 matches Neutral
Chiba WDLDD, gained 8 rating points; Nagoya Grampus LDDWW, gained 23 rating points. Already counted in the ratings above.
Rating, last 12 months
ChibaNagoya GrampusChiba
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
Nagoya Grampus
6/10 over 2.5
11 scored
17 conceded
08/29
09/02
09/06
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
- 1-1
- 2-1
- 1-0
- 1-2
- 2-0
- 0-1
- 0-0
- 2-2
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 7 | 7 | 5 | 2 | 1 | |
| 1 | 9 | 12 | 7 | 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 → | Chiba | Draw | Nagoya Grampus |
|---|---|---|---|
| Chiba | 28%3.54 | 5%18.62 | 2%52.63 |
| Level | 13%7.55 | 15%6.63 | 11%9.26 |
| Nagoya Grampus | 2%48.31 | 5%20.75 | 18%5.41 |
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.