Leicester
Model pick
54%
Fair odds 1.84
Mansfield
22%
Fair odds 4.65
Share of the pitch
Who wins
| Leicester | Draw | Mansfield | |
|---|---|---|---|
| Goals model | 54% | 24% | 22% |
| Elo model | 50% | 27% | 23% |
Why this prediction
-
Overall strength Favours Leicester
Leicester are rated 32 points above Mansfield (1401 to 1369), built from every result, weighted towards recent ones.
-
Home advantage Favours Leicester
Playing at home is worth about 65 rating points, which adds 9 points to Leicester's chance of winning and takes the same from Mansfield's.
-
Attack against defence Favours Leicester
Expected goals 1.8 to 1.1. Leicester score 1.1 a match against an average side (2nd in League One); Mansfield concede 1.6 (9th best defence).
-
Last 5 matches Neutral
Leicester DWLWW, gained 14 rating points; Mansfield DLWWD, gained 9 rating points. Already counted in the ratings above.
Rating, last 12 months
LeicesterMansfield
Leicester
8/10 over 2.5
20 scored
16 conceded
Mansfield
5/10 over 2.5
12 scored
13 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
- 0-0
- 3-1
- 1-2
- 3-0
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 6 | 5 | 3 | 1 | ||
| 1 | 10 | 11 | 6 | 2 | 1 | |
| 2 | 9 | 10 | 5 | 2 | 0 | |
| 3 | 6 | 6 | 3 | 1 | ||
| 4 | 3 | 3 | 1 | 1 | ||
| 5 | 1 | 1 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Leicester | Draw | Mansfield |
|---|---|---|---|
| Leicester | 36%2.78 | 5%18.80 | 2%55.56 |
| Level | 17%6.01 | 14%7.25 | 9%10.93 |
| Mansfield | 2%42.74 | 4%25.06 | 11%9.07 |
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.