Santos
Model pick
53%
Fair odds 1.88
Club Leon
23%
Fair odds 4.33
Share of the pitch
Who wins
| Santos | Draw | Club Leon | |
|---|---|---|---|
| Goals model | 53% | 24% | 23% |
| Elo model | 50% | 27% | 23% |
Why this prediction
-
Overall strength Favours Santos
Santos are rated 29 points above Club Leon (1380 to 1350), built from every result, weighted towards recent ones.
-
Home advantage Favours Santos
Playing at home is worth about 65 rating points, which adds 9 points to Santos's chance of winning and takes the same from Club Leon's.
-
Attack against defence Favours Santos
Expected goals 1.9 to 1.2. Santos score 1.0 a match against an average side (4th in Brasileirão); Club Leon concede 1.8 (9th best defence in Liga MX).
-
Last 5 matches Neutral
Santos LDDWL, barely moved; Club Leon LDWLD, gained 6 rating points. Already counted in the ratings above.
Rating, last 12 months
SantosClub Leon
Santos
8/10 over 2.5
16 scored
13 conceded
Club Leon
4/10 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
- 1-1
- 2-1
- 1-0
- 2-0
- 1-2
- 3-1
- 2-2
- 0-0
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 5 | 5 | 3 | 1 | ||
| 1 | 9 | 11 | 6 | 2 | 1 | |
| 2 | 8 | 10 | 6 | 2 | 1 | |
| 3 | 5 | 6 | 4 | 1 | ||
| 4 | 2 | 3 | 2 | 1 | ||
| 5 | 1 | 1 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Santos | Draw | Club Leon |
|---|---|---|---|
| Santos | 31%3.25 | 4%26.95 | 1%93.46 |
| Level | 19%5.29 | 13%7.68 | 7%13.89 |
| Club Leon | 4%24.45 | 6%16.53 | 15%6.57 |
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.