Danish Superliga: Predictions, Odds & Tips
Läs på svenskaModel-based predictions for the Danish Superliga — powered by the same match model behind PlaymakerAI. Ahead of every round the model computes 1X2 probabilities, a full score matrix, expected goals, over/under lines and both-teams-to-score, built from hundreds of data points per team: strength, form, playing style, weather and more.
We compare the predictions against live odds from multiple bookmakers and flag positive expected value (EV+) where the model’s probability beats the implied odds. Once matches are played, we grade every prediction openly — hit or miss. Everything updates continuously through the week until kickoff.
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League table2026/27
| Pos | Team | P | S | W–D–L | GF–GA | xP | xG | xGA | xGD | FT | Style |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 |  Midtjylland | 28 | 12 | 8–4–0 | 24–13 | 17,8 | 17,3 | 14,3 | +3,0 | 45 % | Possession control |
| 2 |  FC København | 25 | 11 | 8–1–2 | 25–11 | 18,5 | 18,7 | 12,6 | +6,1 | 53 % | Possession & high press |
| 3 |  Viborg | 20 | 12 | 6–2–4 | 19–12 | 19,1 | 18,8 | 15,2 | +3,6 | 45 % | Counter-attacks & crosses |
| 4 |  Nordsjælland | 20 | 12 | 5–5–2 | 18–14 | 18,8 | 24,1 | 15,1 | +9,0 | 57 % | Possession control |
| 5 |  Brøndby | 19 | 12 | 6–1–5 | 21–20 | 16,7 | 20,6 | 16,2 | +4,4 | 61 % | Possession & high press |
| 6 |  Horsens | 16 | 12 | 4–4–4 | 23–20 | 14,7 | 16,4 | 21,7 | −5,3 | 41 % | Possession control |
| 7 |  Odense | 13 | 11 | 3–4–4 | 10–15 | 13,4 | 16,3 | 18,4 | −2,1 | 54 % | Possession & high press |
| 8 |  AGF | 12 | 12 | 2–6–4 | 17–19 | 16,1 | 19,0 | 18,1 | +0,9 | 58 % | Possession & high press |
| 9 |  Silkeborg | 12 | 12 | 2–6–4 | 16–20 | 16,0 | 12,4 | 17,6 | −5,2 | 37 % | Counter-attacks & crosses |
| 10 |  Randers | 11 | 12 | 3–2–7 | 12–19 | 14,6 | 13,8 | 20,4 | −6,6 | 40 % | Low block & direct |
| 11 |  Lyngby | 8 | 12 | 1–5–6 | 17–27 | 16,7 | 19,4 | 18,4 | +1,0 | 64 % | Possession & high press |
| 12 |  Sønderjyske | 8 | 12 | 2–2–8 | 17–29 | 13,4 | 13,3 | 22,3 | −9,0 | 40 % | Counter-attacks & crosses |
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2026-10-15 · 2 min
**Weekly Pool Play 15/10: Locks and Upsets**
This week's pool betting offers several interesting matches, where Djurgården and Deportivo La Coruña are singled out as favorites thanks to strong pressing play and wing play respectively. Manchester City and Southampton appear as safe bankers, while Málaga against Real Sociedad is highlighted as this week's potential upset.
2026-10-08 · 3 min
**Pool play of the week 8/10: locks and upsets**
Saturday's pool betting offers a wide range with Stryktipset, Europatipset, Big 9, and several PoolX products, all based on the same match analysis. Eight matches recur in multiple games, with clear favorites like Shamrock Rovers and Fluminense, while Santos–Flamengo and Sunderland–Brighton point toward away wins.
2026-08-28 · 2 min
Today's banker 28/8: 1 in FC Nordsjaelland – Brondby IF
Nordsjaelland is expected to win the home match against Brondby thanks to better expected goals (1.9 vs 1.2), more corners and more effective pressing play, which makes the home win an attractive banker despite the odds of 2.20 seeming too high. The forwards Alexander Lucas Lind Rasmussen (35% goal chance) and Prince Amoako Junior (25%) are Nordsjaelland's biggest threats, while Brondby's goal threat is more spread out between Marcus Younis and Filip Bundgaard Kristensen.
Played matches
Prediction track recordAGFHorsensSilkeborgMidtjyllandRandersViborgFC KøbenhavnOdenseLyngbySønderjyskEHorsensAGFHow the predictions work
Every prediction comes from PlaymakerAI's match model — the same data platform professional clubs use for scouting and match analysis. The model is trained on event data from hundreds of thousands of matches and weighs over 200 factors per team: strength ratings, current form, playing style, squad player profiles, fixture congestion, travel and even matchday weather.
For each fixture the model produces a full goal distribution. From it we derive the 1X2 probabilities, the score matrix, expected goals, over/under lines and both-teams-to-score. Predictions are frozen before kickoff and never changed afterwards — once a match finishes we grade it openly, hit or miss.
Odds are collected continuously from several bookmakers. When the model's probability for an outcome exceeds what the odds imply, we flag it as value (EV+). That is the model's assessment, not a guarantee: even positive-EV bets lose often. Gamble responsibly.
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Hämta bonusDanish Superliga FAQ
Where do I find Danish Superliga predictions?
Right here: every match in the Danish Superliga has its own analysis with 1X2 probabilities, most likely scoreline, over/under and value against current odds. Pick a match from the upcoming list — predictions are refreshed before every round and graded openly afterwards.
How are the Danish Superliga predictions made?
Each match is replayed thousands of times in the model using hundreds of metrics per team — expected goals, pressing, expected threat, field tilt, form and squad. The output is a probability for every scoreline, which is then compared with bookmaker odds to find value.
What is the xP table?
xP means expected points: how many points each team should have taken given the quality of chances created and conceded. The gap between xP and the real table shows which the Danish Superliga teams are over- and underrated right now.
Which teams play in Danish Superliga 2026/27?
Danish Superliga 2026/27 has 12 teams: Midtjylland, FC København, Viborg, Nordsjælland, Brøndby, Horsens, Odense, AGF, Silkeborg, Randers, Lyngby, Sønderjyske.
Predictions come from PlaymakerAI's match model and reflect the model's assessment — not betting advice. Gamble responsibly.