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Superligaen
Referee: Sandi Putros
Silkeborg
T S T T S
2–1
Full-time
Horsens
S S T T S

Retrospective analysis — the model is trained only on matches played before this one.

🔒 Who wins? Pro

The model's probabilities for a home win, draw and away win — with reasoning and recommended markets.

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Team trends

Silkeborg Horsens
Last 6 — all competitions · 6/6 matches
Win Ø 42%
33%
50%
Points per game
1,0
1,5
Over 2.5 Ø 50%
50%
50%
Both score Ø 42%
33%
50%
Average goals
2,7
2,2
Scored
1,3
1,0
Conceded
1,3
1,2
Goal diff per game
+0,0
-0,2
This season — Superligaen · 10/10 matches
Win Ø 45%
50%
40%
Points per game
1,6
1,4
Over 2.5 Ø 40%
50%
30%
Both score Ø 40%
50%
30%
Average goals
2,8
1,9
Scored
1,6
0,9
Conceded
1,2
1,0
Goal diff per game
+0,4
-0,1
🔒 Goal markets Pro

Expected goals, over/under and both teams to score — priced by the model before kick-off.

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🔒 Most likely result Pro

The model's top final scores with the best bookmaker odds.

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Match stats

Half-time score: 1–0

63% Possession 37%
12 Shots 7
4 Shots on target 3
2 Blocked shots 0
2 Goalkeeper saves 2
2 Corners 6
6 Free kicks against 10
2 Yellow cards 1
0 Red cards 0
The table before the match Silkeborg 4. · Horsens 6.
# Team K V U T +/− P
1 Randers FC 10 6 4 0 +8 22
2 Nordsjaelland 10 6 2 2 +6 20
3 Viborg 10 6 1 3 +4 19
4 Silkeborg 10 5 1 4 +4 16
5 Aarhus 10 5 1 4 +4 16
6 Horsens 10 4 2 4 -1 14
7 Odense 11 4 2 5 -5 14
8 Midtjylland 10 3 4 3 +1 13
9 FC Copenhagen 10 4 0 6 +0 12
10 Brondby 10 3 2 5 -5 11
11 Aalborg 11 2 4 5 -5 10
12 Lyngby 10 0 3 7 -11 3

Calculated from our results data. “xG +/−” is expected goal difference — a positive number with a low league position suggests an undervalued team. Ties on points are sorted by goal difference; some leagues use other rules.

The analysis is a statistical estimate based on the teams' results and underlying performances (xG) — not a guarantee. See the model's results.