Wealdstone
Model pick
52%
Fair odds 1.93
Share of the pitch
Who wins
| Wealdstone | Draw | Hornchurch | |
|---|---|---|---|
| Goals model | 52% | 24% | 25% |
| Elo model | 44% | 28% | 28% |
Why this prediction
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Overall strength Favours Hornchurch
Hornchurch are rated 11 points above Wealdstone (1029 to 1018), built from every result, weighted towards recent ones.
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Home advantage Favours Wealdstone
Playing at home is worth about 65 rating points, which adds 9 points to Wealdstone's chance of winning and takes the same from Hornchurch's.
-
Attack against defence Favours Wealdstone
Expected goals 1.9 to 1.2. Wealdstone score 0.5 a match against an average side (14th in National League); Hornchurch concede 3.6 (22nd best defence).
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Last 5 matches Neutral
Wealdstone WLWLL, lost 18 rating points; Hornchurch LLLWL, lost 22 rating points. Already counted in the ratings above.
Rating, last 12 months
WealdstoneHornchurch
Wealdstone
7/10 over 2.5
16 scored
18 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
- 2-0
- 1-0
- 1-2
- 3-1
- 2-2
- 0-1
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 5 | 5 | 3 | 1 | ||
| 1 | 8 | 11 | 6 | 3 | 1 | |
| 2 | 8 | 10 | 6 | 2 | 1 | |
| 3 | 5 | 6 | 4 | 2 | ||
| 4 | 2 | 3 | 2 | 1 | ||
| 5 | 1 | 1 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Wealdstone | Draw | Hornchurch |
|---|---|---|---|
| Wealdstone | 29%3.40 | 4%25.06 | 1%74.63 |
| Level | 19%5.32 | 13%7.87 | 8%12.22 |
| Hornchurch | 4%23.98 | 6%16.89 | 15%6.46 |
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.