Harrogate
Model pick
62%
Fair odds 1.61
Share of the pitch
Who wins
| Harrogate | Draw | Kidderminster | |
|---|---|---|---|
| Goals model | 62% | 23% | 15% |
| Elo model | 62% | 24% | 14% |
Why this prediction
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Overall strength Favours Harrogate
Harrogate are rated 113 points above Kidderminster (1153 to 1040), built from every result, weighted towards recent ones.
-
Home advantage Favours Harrogate
Playing at home is worth about 65 rating points, which adds 8 points to Harrogate's chance of winning and takes the same from Kidderminster's.
-
Attack against defence Favours Harrogate
Expected goals 1.9 to 0.8. Harrogate score 0.7 a match against an average side (4th in National League); Kidderminster concede 2.6 (8th best defence).
-
Last 5 matches Favours Harrogate
Harrogate DLWWW, gained 7 rating points; Kidderminster LDLDD, lost 10 rating points. Already counted in the ratings above.
Rating, last 12 months
HarrogateKidderminsterHarrogate
8/10 over 2.5
19 scored
14 conceded
09/15
09/19
09/26
09/29
10/10
10/24
10/28
10/31
09/11
09/16
09/19
09/26
09/29
10/03
10/10
10/27
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
- 1-0
- 2-0
- 1-1
- 2-1
- 0-0
- 3-0
- 3-1
- 0-1
Every score
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 8 | 5 | 2 | 1 | ||
| 1 | 13 | 11 | 4 | 1 | ||
| 2 | 12 | 10 | 4 | 1 | ||
| 3 | 8 | 6 | 2 | 1 | ||
| 4 | 4 | 3 | 1 | |||
| 5 | 1 | 1 |
Half-time result
First-half goals
Half-time / full-time
| Half-time ↓ Full-time → | Harrogate | Draw | Kidderminster |
|---|---|---|---|
| Harrogate | 44%2.27 | 4%22.99 | 1%— |
| Level | 17%5.98 | 14%6.96 | 6%16.47 |
| Kidderminster | 2%52.91 | 3%28.74 | 8%12.27 |
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.