Football predictions
Danish Superliga
predictions
Upcoming matches and the latest results in Danish Superliga. Open a match and the AI analysis gives you the win probabilities, the most likely score, expected goals (xG) and both teams' form on one page.
12 matches
Upcoming matches 12
AC Horsens–Randers FC
Analysis
Sonderjyske–Silkeborg
Analysis
Lyngby–FC Nordsjaelland
Analysis
Odense–Brondby
Analysis
FC Copenhagen–Aarhus
Analysis
Viborg–FC Midtjylland
AnalysisRanders FC–BrondbyAnalysisSilkeborg–FC NordsjaellandAnalysisFC Midtjylland–LyngbyAnalysisAC Horsens–SonderjyskeAnalysisAarhus–ViborgAnalysisFC Copenhagen–OdenseAnalysisLatest results 10
3–0Aarhus–SonderjyskeMatch report2–1FC Midtjylland–FC CopenhagenMatch report5–2Brondby–LyngbyMatch report3–3Silkeborg–AC HorsensMatch report0–2Randers FC–ViborgMatch report0–0FC Nordsjaelland–OdenseMatch report4–1Viborg–FC NordsjaellandMatch report2–2AC Horsens–AarhusMatch report4–2Sonderjyske–Randers FCMatch report0–1Brondby–FC CopenhagenMatch report
Aarhus–SonderjyskeMatch report2–1FC Midtjylland–FC CopenhagenMatch report5–2Brondby–LyngbyMatch report3–3Silkeborg–AC HorsensMatch report0–2Randers FC–ViborgMatch report0–0FC Nordsjaelland–OdenseMatch report4–1Viborg–FC NordsjaellandMatch report2–2AC Horsens–AarhusMatch report4–2Sonderjyske–Randers FCMatch report0–1Brondby–FC CopenhagenMatch reportDanish Superliga table 2026/27
| # | Team | P | W | D | L | GD | Pts |
|---|---|---|---|---|---|---|---|
| 1 | FC Copenhagen | 10 | 8 | 0 | 2 | +14 | 24 |
| 2 | FC Midtjylland | 10 | 6 | 4 | 0 | +9 | 22 |
| 3 | Viborg | 10 | 6 | 2 | 2 | +9 | 20 |
| 4 | FC Nordsjaelland | 10 | 5 | 3 | 2 | +4 | 18 |
| 5 | Brondby | 10 | 5 | 1 | 4 | 0 | 16 |
| 6 | AC Horsens | 10 | 3 | 3 | 4 | -1 | 12 |
| 7 | Silkeborg | 10 | 2 | 5 | 3 | 0 | 11 |
| 8 | Randers FC | 10 | 3 | 1 | 6 | -5 | 10 |
| 9 | Odense | 10 | 2 | 4 | 4 | -6 | 10 |
| 10 | Aarhus | 10 | 1 | 5 | 4 | -3 | 8 |
| 11 | Lyngby | 10 | 1 | 4 | 5 | -9 | 7 |
| 12 | Sonderjyske | 10 | 1 | 2 | 7 | -12 | 5 |
What does the analysis look at?
Before every match it reads both teams' recent form, expected goals (xG), injured and suspended players, the standings and their previous meetings.
From these the model works out the 1X2 probabilities, the most likely score and the over/under and both-teams-to-score chances; the written commentary rests on the same numbers.
Featured analyses are recorded before kick-off and kept with their results in a public track record.