Cordoba
Model pick
47%
Fair odds 2.15
Lanus
26%
Fair odds 3.80
Share of the pitch
Who wins
| Cordoba | Draw | Lanus | |
|---|---|---|---|
| Goals model | 47% | 27% | 26% |
| Elo model | 38% | 28% | 34% |
Why this prediction
-
Overall strength Favours Lanus
Lanus are rated 49 points above Cordoba (1396 to 1347), built from every result, weighted towards recent ones.
-
Home advantage Favours Cordoba
Playing at home is worth about 65 rating points, which adds 9 points to Cordoba's chance of winning and takes the same from Lanus's.
-
Attack against defence Favours Cordoba
Expected goals 1.5 to 1.1. Cordoba score 1.1 a match against an average side (4th in La Liga 2); Lanus concede 1.3 (5th best defence in Liga Profesional).
-
Last 5 matches Neutral
Cordoba DWWLW, gained 22 rating points; Lanus DDWWD, gained 13 rating points. Already counted in the ratings above.
Rating, last 12 months
CordobaLanusCordoba
7/10 over 2.5
15 scored
18 conceded
08/30
09/07
09/12
09/18
09/27
10/05
10/10
10/16
10/24
Lanus
2/10 over 2.5
12 scored
5 conceded
08/25
08/29
09/06
09/14
09/22
10/03
10/12
10/17
10/24
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
- 1-1
- 1-0
- 2-1
- 2-0
- 0-0
- 0-1
- 1-2
- 2-2
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 8 | 8 | 4 | 2 | ||
| 1 | 11 | 13 | 7 | 2 | 1 | |
| 2 | 9 | 9 | 5 | 2 | ||
| 3 | 4 | 5 | 2 | 1 | ||
| 4 | 2 | 2 | 1 | |||
| 5 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Cordoba | Draw | Lanus |
|---|---|---|---|
| Cordoba | 24%4.20 | 2%42.74 | 0%— |
| Level | 19%5.27 | 16%6.33 | 5%18.42 |
| Lanus | 4%23.31 | 8%12.52 | 21%4.78 |
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.