Always chasing
8/9Horsens conceded first in 8 of 9 recent matches with goal times (league 47%).
Superliga Predictions & Stats
Free AI predictions for every upcoming Superliga match — 1X2, Both Teams to Score, Over/Under goals and Correct Score probabilities.
Predictions updated on 11 October 2026.
Superliga Predictions by Market
Season 2026/2027 · Round 10 · Regular Season
Full-Time Result
54%
SUCCESS RATE AVG.
Both Teams To Score
60%
SUCCESS RATE AVG.
Over/Under 2.5 Goals
60%
SUCCESS RATE AVG.
Over/Under 3.5 Goals
60%
SUCCESS RATE AVG.
First Half Result
41%
SUCCESS RATE AVG.
Correct Score
9%
SUCCESS RATE AVG.
Superliga 2026/2027 prediction accuracy over the last 100 matches: 1X2 54%, BTTS 60%, Over/Under 2.5 60%, Over/Under 3.5 60%, correct score 9%.
Superliga stats: goals, BTTS, corners and cards vs other leagues
How this competition ranks among the leagues and cups we cover for goals, chances (xG), corners and cards.
How to read it
last 12 months · 182 matchesrank 1 = highest of 63 competitionsGoals galoreCorner-heavy
Goals a match
2.99
12th of 63
xG a match
2.98
12th of 62
Corners a match
10.25
11th of 62
Cards a match
3.48
58th of 63
Calculated by MyGameOdds · xG: our own model
How it works
We use the league's matches from the last 12 months, without qualifying rounds, and rank it against the 63 leagues and club competitions with 50 or more matches in that time (1 = highest). A label marks a league in the top fifth on goals, home or away wins, draws, cards or corners, or in the bottom fifth on goals. xG is our estimate of each match's chances from its shots.
Superliga: the stats that stand out
The Superliga teams most unusual in their last 10 games.
How to read it
the teamleague averageeach team's last 10 games8/9Fast starters
8/10Midtjylland scored first in 8 of 10 recent matches with goal times (league 47%).
Flop as favourites
1/7AGF won only 1 of 7 as favourites (league 51%).
Brick wall
5/10FC Copenhagen kept 5 clean sheets in their last 10 (league 25%).
Low-scoring
4/10OB games went over 2.5 goals in only 4 of their last 10 (league 58%).
Shot-shy
4/10Randers failed to score in 4 of their last 10 (league 25%).
Rarely both score
4/10Nordsjælland: both teams scored in only 4 of their last 10 (league 58%).
Calculated by MyGameOdds
How it works
For each stat we show the club that stands out most, one club per stat. A fact needs at least 6 games and a result far enough from the league average to be unusual (at least one standard deviation).
How we measure →Today's Betting Tips for Superliga Denmark
Free predictions for today's Superliga matches: 1X2, both teams to score, over/under and correct score.
Silkeborg3
Horsens3Winner
12 75%
BTTS*
Yes 60%
Correct Score
1-1 12%
Over/Under 2.5 goals
over 57%
Over/Under 3.5 goals
under 55%
Silkeborg3Horsens3Winner
12 75%
BTTS*
Yes 60%
Correct Score
1-1 12%
Over/Under 2.5 goals
over 57%
Over/Under 3.5 goals
under 55%
Full Time
135%
X25%
239%
Team Goals Over/Under
| <0.5 | >0.5 | <1.5 | >1.5 | |
|---|---|---|---|---|
 | 28% | 72% | 61% | 40% |
| 26% | 74% | 57% | 43% |
Goals Over/Under
| <2.5 | >2.5 | <3.5 | >3.5 | |
|---|---|---|---|---|
| FT | 43% | 57% | 55% | 45% |
Correct Score
1-1
11.7%
1-2
8.9%
2-1
8.0%
0-1
7.7%
1-0
7.2%
2-2
6.3%
Randers0
Viborg2Winner
X2 66%
BTTS*
Yes 59%
Correct Score
1-1 11%
Over/Under 2.5 goals
over 57%
Over/Under 3.5 goals
under 55%
Randers0Viborg2Winner
X2 66%
BTTS*
Yes 59%
Correct Score
1-1 11%
Over/Under 2.5 goals
over 57%
Over/Under 3.5 goals
under 55%
Brøndby5
Lyngby2Winner
1X 65%
BTTS*
Yes 60%
Correct Score
1-1 10%
Over/Under 2.5 goals
over 61%
Over/Under 3.5 goals
under 52%
Brøndby5Lyngby2Winner
1X 65%
BTTS*
Yes 60%
Correct Score
1-1 10%
Over/Under 2.5 goals
over 61%
Over/Under 3.5 goals
under 52%
Midtjylland2
FC Copenhagen1Winner
1X 72%
BTTS*
Yes 58%
Correct Score
1-1 11%
Over/Under 2.5 goals
over 57%
Over/Under 3.5 goals
under 55%
Midtjylland2FC Copenhagen1Winner
1X 72%
BTTS*
Yes 58%
Correct Score
1-1 11%
Over/Under 2.5 goals
over 57%
Over/Under 3.5 goals
under 55%
AGF–
SønderjyskE–Winner
1 58%
BTTS*
Yes 56%
Correct Score
1-1 10%
Over/Under 2.5 goals
over 57%
Over/Under 3.5 goals
under 55%
AGF–SønderjyskE–Winner
1 58%
BTTS*
Yes 56%
Correct Score
1-1 10%
Over/Under 2.5 goals
over 57%
Over/Under 3.5 goals
under 55%
Horsens–Randers–Winner
12 74%
BTTS*
Yes 57%
Correct Score
1-1 12%
Over/Under 2.5 goals
over 53%
Over/Under 3.5 goals
under 59%
Horsens–Randers–Winner
12 74%
BTTS*
Yes 57%
Correct Score
1-1 12%
Over/Under 2.5 goals
over 53%
Over/Under 3.5 goals
under 59%
Lyngby–
Nordsjælland–Winner
X2 65%
BTTS*
Yes 57%
Correct Score
1-1 10%
Over/Under 2.5 goals
over 56%
Over/Under 3.5 goals
under 55%
Lyngby–Nordsjælland–Winner
X2 65%
BTTS*
Yes 57%
Correct Score
1-1 10%
Over/Under 2.5 goals
over 56%
Over/Under 3.5 goals
under 55%
SønderjyskE–Silkeborg–Winner
12 73%
BTTS*
Yes 59%
Correct Score
1-1 12%
Over/Under 2.5 goals
over 55%
Over/Under 3.5 goals
under 56%
SønderjyskE–Silkeborg–Winner
12 73%
BTTS*
Yes 59%
Correct Score
1-1 12%
Over/Under 2.5 goals
over 55%
Over/Under 3.5 goals
under 56%
OB–Brøndby–Winner
12 75%
BTTS*
Yes 60%
Correct Score
1-1 12%
Over/Under 2.5 goals
over 56%
Over/Under 3.5 goals
under 56%
OB–Brøndby–Winner
12 75%
BTTS*
Yes 60%
Correct Score
1-1 12%
Over/Under 2.5 goals
over 56%
Over/Under 3.5 goals
under 56%
FC Copenhagen–AGF–Winner
1 62%
BTTS*
Yes 55%
Correct Score
2-1 11%
Over/Under 2.5 goals
over 58%
Over/Under 3.5 goals
under 54%
FC Copenhagen–AGF–Winner
1 62%
BTTS*
Yes 55%
Correct Score
2-1 11%
Over/Under 2.5 goals
over 58%
Over/Under 3.5 goals
under 54%
Viborg–Midtjylland–Winner
2 52%
BTTS*
Yes 56%
Correct Score
1-1 11%
Over/Under 2.5 goals
over 55%
Over/Under 3.5 goals
under 57%
Viborg–Midtjylland–Winner
2 52%
BTTS*
Yes 56%
Correct Score
1-1 11%
Over/Under 2.5 goals
over 55%
Over/Under 3.5 goals
under 57%
Randers–Brøndby–Winner
X2 69%
BTTS*
Yes 58%
Correct Score
1-1 11%
Over/Under 2.5 goals
over 55%
Over/Under 3.5 goals
under 57%
Randers–Brøndby–Winner
X2 69%
BTTS*
Yes 58%
Correct Score
1-1 11%
Over/Under 2.5 goals
over 55%
Over/Under 3.5 goals
under 57%
*Probability of each predicted outcome is expressed in percentage (%)
*BTTS - Both teams to score
Superliga xG: chances created and allowed
Every Superliga team's chances created and allowed a game, measured with our own xG.
How to read it
each logo is a team · tap one for detailseach logo is a team · hover one for detailsSuperliga average · 1.47 xG, 1.49 xGAlast 10 games eachTap a logo to see the team's numbers
↑ Fewer chances allowed (xGA a game)
More chances created (xG a game) →
Teams by box· 12
Good attack, good defence
Good attack, weak defence
Weak attack, good defence
Weak attack, weak defence
Calculated by MyGameOdds · xG: our own model
How it works
xG is our estimate of how many goals a team's chances were worth, from the shots in each match; xGA is the same for the chances it allowed. The dashed lines are the average of the teams shown. A team close to a line is about average. Teams with fewer than 5 games here aren't in the chart yet.
How we measure →Superliga Results vs the Odds
Season 2026/2027 and the last 30 days
How often each result has happened in Superliga this season (thick bar) and in the last 30 days (thin bar), next to what the bookmakers’ closing odds expected. The last 30 days cover fewer matches, so their normal range is wider and big swings there are still normal. This is not our prediction accuracy.
How to read it
what the closing odds expectednormal range: chance alone lands here 19 times in 20gap to the odds, in percentage pointsgap outside the normal rangethick bar: Season · thin bar: 30 days
Result · happened
◀ fewer −30◀ fewer than expected · −30 pts odds expected+30 more ▶+30 pts · more than expected ▶
Verdict
Home wins
55 matches
43.6%
Season (55) · 43.6% vs 43.0%
Season
+0.7
±12.2
30 days (13) · 38.5% vs 43.6%
30 days
−5.2
±25.4
Draws
55 matches
29.1%
Season (55) · 29.1% vs 23.0%
Season
+6.1
±11.1
30 days (13) · 38.5% vs 23.1%
30 days
+15.3
±22.9
Away wins
55 matches
27.3%
Season (55) · 27.3% vs 34.0%
Season
−6.7
±11.7
30 days (13) · 23.1% vs 33.2%
30 days
−10.1
±24.3
Over 2.5 goals
55 matches
52.7%
Season (55) · 52.7% vs 60.7%
Season
−8.0
±12.8
30 days (13) · 46.2% vs 61.2%
30 days
−15.1
±26.4
Both teams scored
55 matches
58.2%
Season (55) · 58.2% vs 59.7%
Season
−1.5
±12.9
30 days (13) · 61.5% vs 60.7%
30 days
+0.9
±26.5
Show more markets (6)Show fewer markets
Over 3.5 goals
55 matches
38.2%
Season (55) · 38.2% vs 39.1%
Season
−0.9
±12.8
30 days (13) · 38.5% vs 39.7%
30 days
−1.2
±26.5
Home side ahead at half-time
55 matches
30.9%
Season (55) · 30.9% vs 34.4%
Season
−3.5
±12.2
30 days (13) · 46.2% vs 34.5%
30 days
+11.7
±25.1
Level at half-time
55 matches
36.4%
Season (55) · 36.4% vs 37.7%
Season
−1.3
±12.8
30 days (13) · 30.8% vs 37.5%
30 days
−6.7
±26.3
Away side ahead at half-time
55 matches
32.7%
Season (55) · 32.7% vs 28.0%
Season
+4.8
±11.5
30 days (13) · 23.1% vs 28.0%
30 days
−4.9
±23.8
Home team scored 2+
55 matches
45.5%
Season (55) · 45.5% vs 49.5%
Season
−4.0
±12.7
30 days (13) · 46.2% vs 50.0%
30 days
−3.9
±26.3
Away team scored 2+
55 matches
40.0%
Season (55) · 40.0% vs 41.4%
Season
−1.4
±12.5
30 days (13) · 30.8% vs 42.0%
30 days
−11.3
±26.0
Shows what happened, not what comes next. Closing odds from one bookmaker per match, bookmaker margin removed. Updated daily.
Show as a tableHide the table
| Result | Period | Matches | Happened | Odds expected | Gap (pts) | Normal range (± pts) | Verdict |
|---|---|---|---|---|---|---|---|
| Home wins | Season | 55 | 43.6% | 43.0% | +0.7 | ±12.2 | Normal range |
| Home wins | 30 days | 13 | 38.5% | 43.6% | −5.2 | ±25.4 | Normal range |
| Draws | Season | 55 | 29.1% | 23.0% | +6.1 | ±11.1 | Normal range |
| Draws | 30 days | 13 | 38.5% | 23.1% | +15.3 | ±22.9 | Normal range |
| Away wins | Season | 55 | 27.3% | 34.0% | −6.7 | ±11.7 | Normal range |
| Away wins | 30 days | 13 | 23.1% | 33.2% | −10.1 | ±24.3 | Normal range |
| Over 2.5 goals | Season | 55 | 52.7% | 60.7% | −8.0 | ±12.8 | Normal range |
| Over 2.5 goals | 30 days | 13 | 46.2% | 61.2% | −15.1 | ±26.4 | Normal range |
| Both teams scored | Season | 55 | 58.2% | 59.7% | −1.5 | ±12.9 | Normal range |
| Both teams scored | 30 days | 13 | 61.5% | 60.7% | +0.9 | ±26.5 | Normal range |
| Over 3.5 goals | Season | 55 | 38.2% | 39.1% | −0.9 | ±12.8 | Normal range |
| Over 3.5 goals | 30 days | 13 | 38.5% | 39.7% | −1.2 | ±26.5 | Normal range |
| Home side ahead at half-time | Season | 55 | 30.9% | 34.4% | −3.5 | ±12.2 | Normal range |
| Home side ahead at half-time | 30 days | 13 | 46.2% | 34.5% | +11.7 | ±25.1 | Normal range |
| Level at half-time | Season | 55 | 36.4% | 37.7% | −1.3 | ±12.8 | Normal range |
| Level at half-time | 30 days | 13 | 30.8% | 37.5% | −6.7 | ±26.3 | Normal range |
| Away side ahead at half-time | Season | 55 | 32.7% | 28.0% | +4.8 | ±11.5 | Normal range |
| Away side ahead at half-time | 30 days | 13 | 23.1% | 28.0% | −4.9 | ±23.8 | Normal range |
| Home team scored 2+ | Season | 55 | 45.5% | 49.5% | −4.0 | ±12.7 | Normal range |
| Home team scored 2+ | 30 days | 13 | 46.2% | 50.0% | −3.9 | ±26.3 | Normal range |
| Away team scored 2+ | Season | 55 | 40.0% | 41.4% | −1.4 | ±12.5 | Normal range |
| Away team scored 2+ | 30 days | 13 | 30.8% | 42.0% | −11.3 | ±26.0 | Normal range |
Superliga luck check: the last 10 games
Superliga teams taking more or fewer points than their chances usually bring, measured with our own xG.
How to read it
what their chances usually bringnormal range (±4)outside it+7.2 = 7 points more than their chances usually bringRiding their luck1
- FC Copenhagen9 of 10 gamesClinical · +7 goals+7.2
Unlucky2
- AGF−7.5
Lyngby9 of 10 gamesWasteful · −5 goals−5.9
Points in line, but wasteful: OB (−6 goals), Nordsjælland (−5). Clinical: Horsens (+4, 9 games).
In line with their chances: Randers, Midtjylland, Brøndby, Viborg, Silkeborg and SønderjyskE.
Calculated by MyGameOdds · xG: our own model
How it works
For each of a team's last 10 games, we work out how many points a team usually gets from a game with those chances (xG) for and against, add them up and compare with the points it actually got. We do the same for goals. Strong teams often get a bit more than their chances suggest, so we compare each team with sides of similar strength in this league. That way nobody is flagged just for being good.
We flag a team when the gap is 4 points or more (or 3 goals or more). If some games have no xG data, we use the ones that do.
Does it mean anything? 85% of teams 4+ points ahead of their chances got fewer points in their next 10 games, and 83% of teams 4+ points behind got more. Bookmakers already expect these gaps to even out, so it's a fan's read, not a betting edge.
How we measure →Superliga last week: smash-and-grab wins and upsets
Smash-and-grab wins (the side with clearly fewer chances won) and upsets (results the closing odds gave under 20%) from the last 7 days.
How to read it
Oct 4–Oct 11 · 5 matches · odds: closing, margin removed- Midtjylland2-1FC Copenhagen
Smash-and-grabMidtjylland beat FC Copenhagen 2-1 despite fewer chances (xG 0.9 vs 1.8).
- Nordsjælland0-0OB
UpsetBookmakers gave the draw 19% at kick-off.
Calculated by MyGameOdds · xG: our own model
How it works
Smash-and-grab: the winner had at least 0.8 less xG than the loser (our xG, from shots). Upset: the result the closing odds gave under 20%, with the bookmaker margin removed. Across our leagues about 6% of matches are smash-and-grab wins and about 5% are upsets.
How we measure →Superliga Standings
Check the current league table, team positions, points, and form. Click to view complete standings with detailed statistics.
#TEAM | MP | PTS | W | D | L | G | GD | FORM |
|---|---|---|---|---|---|---|---|---|
1  FC Copenhagen | 10 | 24 | 8 | 0 | 2 | 24:10 | +14 | L W W W W |
2  Midtjylland | 10 | 22 | 6 | 4 | 0 | 19:10 | +9 | W D W D W |
3  Viborg | 10 | 20 | 6 | 2 | 2 | 17:8 | +9 | W W D L W |
4  Nordsjælland | 10 | 18 | 5 | 3 | 2 | 14:10 | +4 | D L W D L |
5  Brøndby | 10 | 16 | 5 | 1 | 4 | 17:17 | 0 | W L L L L |
Championship Round
Relegation Round
MP=Matches Played
PTS=Points
W=Wins
D=Draws
L=Losses
G=Goals (For:Against)
GD=Goal Difference
FORM=Last 5 Matches
About Superliga
The league profile compares Superliga with the 63 leagues and cups we rank over the last 12 months (rank 1 = highest). The stats that stand out pick the most unusual numbers from each team's last 10 games. The attack vs defence chart puts each team in one of four boxes: top right creates more and allows fewer chances than the league average. The luck check shows who got more or fewer points than their chances in the last 10 games, and Last week lists wins with fewer chances and results the bookmakers gave under 20%.
These statistics represent prediction accuracy based on the last 100 matches in the league. Accuracy reflects how often the model correctly predicted the outcome for each market. Our football predictions are generated using advanced AI techniques combined with historical match data, team form, player availability, tactical trends, and head-to-head records to deliver reliable forecasts. Explore our prediction results tracker for live accuracy across all completed matches, our league accuracy data for competition-level breakdowns, or prediction performance for team-level insights.
The Danish Superliga is the top professional football league in Denmark. It consists of 12 teams and operates on a system of promotion and relegation with the Danish 1st Division. The Danish Superliga is known for its competitive matches and emphasis on attacking football. FC Copenhagen and Brøndby IF are the most successful clubs in Danish football history, having won numerous league titles.