Description
Comprehensive quantitative and statistical analysis of the 19th round of the Danish Superliga, season 2025-2026
This report is a detailed study of the dynamics in the Danish Superliga at the critical moment of the 2025-2026 season, immediately after the end of the winter break in February 2026. The analysis is based on a precise mathematical protocol that integrates historical performance data, performance indicators in attack and defense, as well as probabilistic models to predict the results of upcoming matches. Danish top-flight football this season is characterized by unusual intensity, with the average number of goals per match reaching 3.22, which is an indication of a strong offensive orientation of the leading teams. The focus is on the leader AGF Aarhus, who has demonstrated remarkable stability, as well as Midtjylland, who has the most powerful attack in the championship, led by top scorer Franculino Djú with his 16 goals.
Analysis of the current state of the league and distribution of forces
Ahead of the start of the 19th round, the Super League is divided into clear clusters, but the points differences remain minimal, which increases the severity of any mistake. Leaders AGF Aarhus occupy first place with 40 points after 18 matches played, closely followed by Midtjylland with 36 points. Traditional giants FC Copenhagen occupy an uncharacteristic fifth place, which adds additional pressure to their upcoming derby with Midtjylland.
Table 1: Super League standings after the 18th round (Season 2025/2026)
| Pos | Team | Matches | Wins | Equal | Losses | Goals | DG | Points |
| 1 | AGF Aarhus | 18 | 12 | 4 | 2 | 36:18 | +18 | 40 |
| 2 | Central Denmark Region | 18 | 10 | 6 | 2 | 48:21 | +27 | 36 |
| 3 | Brondby | 18 | 10 | 1 | 7 | 31:21 | +10 | 31 |
| 4 | Sonderjyske | 18 | 8 | 5 | 5 | 30:25 | +5 | 29 |
| 5 | FC Copenhagen | 18 | 8 | 4 | 6 | 30:26 | +4 | 28 |
| 6 | OB (Odense) | 18 | 7 | 5 | 6 | 32:37 | -5 | 26 |
| 7 | Viborg | 18 | 7 | 3 | 8 | 31:29 | +2 | 24 |
| 8 | North Zealand | 18 | 8 | 0 | 10 | 29:32 | -3 | 24 |
| 9 | Randers | 18 | 5 | 4 | 9 | 17:24 | -7 | 19 |
| 10 | Silkeborg | 18 | 5 | 4 | 9 | 22:36 | -14 | 19 |
| 11 | Fredericia | 18 | 4 | 2 | 12 | 22:44 | -22 | 14 |
| 12 | Vejle | 18 | 3 | 4 | 11 | 20:35 | -15 | 13 |
Statistics from Wikipedia confirm that the season was marked by a number of managerial changes that directly affected the stability of the teams’ formations. For example, Jakob Poulsen, who took over at AGF Aarhus after a successful move from Viborg, managed to lead the team to a 12-match unbeaten run. At the same time, teams like Fredericia and Vejle struggled with long winless periods, which negatively affected their defensive indexes.
Methodological framework of calculations
The analysis is performed by applying a rigorous mathematical protocol that defines the strength of each team as a function of its historical results and current form. The calculations go through several key phases:
- Initial calculation: Determining the percentages of wins ( W % ), draws ( D % ) and losses ( L % ) for each team, as well as the average goals scored and conceded.
- Attacking Power ( AP ): Formulated as the sum of win percentage, loss percentage, and average goals scored, this metric allows you to assess a team’s potential to penetrate opposing defenses.
AP = W %+ L %+Average goals scored
- Defensive Strength ( DP ): Calculated as the reciprocal of the balanced performance, including the difference between wins and losses, adjusted for the average number of goals conceded.
DP = W %− L %+Average goals scored1
- Expected goals ( xG ): For each match, xG is calculated for the home and away teams as the arithmetic average of the attacking strength of one team and the defensive strength of the other.
xG Home = 2 AP Home + DP Away
xG Away = 2 AP Away + DP Home
- Probabilities (Poisson): The Poisson distribution is used to determine the odds of winning (1), drawing (X), and losing (2) based on the xG values .
- Stability ( K ): This metric measures the dispersion of probabilities and serves as a filter for the reliability of the model. The value is limited to 0.99.
K = ( μ ( 1, X ,2) σ (1, X ,2) ) ×1.67
- Similarity Index ( L ): Reflects the structural similarity between the two teams.
L = ∣∣ AP Home − AP Guest ∣ − ∣ DP Home − DP Guest ∣∣
- Harmony Index ( HI ): The final score for the quality of the forecast.
HI = K 2 + 1− L 1
The process ends with the generation of Verdict V3 , which is based on the difference between the home and away win probabilities ( V 3= P 1 − P 2 ) .
Detailed analysis of the matches from the 19th round
FC Nordsjaelland vs Sonderjyske
The match at Right to Dream Park in Farum pits eight-time winners Nordsjaelland against Sonderjyske, who are in excellent form before the winter break. What is specific to Nordsjaelland is that they have not recorded a single draw this season so far – 8 wins and 10 losses, which makes their matches extremely unpredictable in terms of points distribution.
Statistical profiles and calculations
FC Nordsjaelland (Home):
- W %=0.44 , L %=0.56
- Average goals: 1.61 (Scored), 1.78 (Conceded)
- Attack Power ( AP H ) :44+0.56+1.61=2.61
- Protection Strength ( DP H ) :1 /( 0.44−0.56+1.78)=1/1.66=0.60
Sonderjyske (Guest):
- W %=0.44 , L %=0.28 (Based on 18 matches, 8 wins, 5 draws, 5 losses)
- Average goals: 1.67 (Scored), 1.39 (Conceded)
- Attack Power ( AP A ) :44+0.28+1.67=2.39
- Defense Power ( DP A ) :1 /( 0.44−0.28+1.39)=1/1.55=0.65
Predicted results and probabilities
- xG (Home):(2.61+0.65)/2=1.63
- xG (Guest):(2.39+0.60)/2=1.50
- Poisson probabilities: Win (1) – 42%, Draw (X) – 24%, Win (2) – 34%
- V3 Verdict:42−0.34=0.08 . According to the formula, this results in a prediction of “1X”.
- Harmony Index: Stability ( K ) is calculated at 0.46 and the evenness index ( L ) is calculated at 0.17. The final HI is 5.56, which categorizes the match as High Risk .
Nordsjaelland dominate possession (57% on average) but suffer from low intensity in the finishing phase. Sonderjyske, on the other hand, are a dangerous away team with 1.56 goals scored on average on away matches. The lack of draws at home suggests that “1X” is a cautious choice, given the 24% probability of a hick that the model identifies despite the historical trend.
Silkeborg vs Viborg
Silkeborg is in a difficult position, occupying 10th place with a negative goal difference of -14. Their rival Viborg is seventh and has a more balanced squad.
Statistical profiles and calculations
Silkeborg (Host):
- W %=0.28 , L %=0.50
- Average goals: 1.22 (Scored), 2.00 (Conceded)
- AP_H:28+0.50+1.22=2.00
- DP_H:1 /( 0.28−0.50+2.00)=1/1.78=0.56
Viborg (Guest):
- W %=0.39 , L %=0.44
- Average goals: 1.72 (Scored), 1.61 (Conceded)
- AP_A:39+0.44+1.72=2.55
- DP_A:1 /( 0.39−0.44+1.61)=1/1.56=0.64
Predicted results and probabilities
- xG (Home):(2.00+0.64)/2=1.32
- xG (Guest):(2.55+0.56)/2=1.56
- Probabilities: Win (1) – 31%, Draw (X) – 25%, Win (2) – 44%
- V3 Verdict:31−0.44=−0.13 . Prediction: “X2”.
- Harmony index:K =0.39 , L =0.47 . HI =7.02 ( High risk ).
Viborg shows a higher offensive power, with Silkeborg having conceded 36 goals so far – the second weakest defence in the top half of the table. The model strongly supports the performance of the visitors, who have won three of their last four matches in all competitions.
The derby of the round: Midtjylland vs. FC Copenhagen
This is the clash of the titans in Danish football. Midtjylland are second and chasing the leaders, while Copenhagen are only fifth and desperately need points to get back into the title race. The hosts are unbeaten in 21 of their last 23 Superliga matches, making them favourites by almost every mathematical measure.
Statistical profiles and calculations
Midtjylland (Home):
- W %=0.56 , L %=0.11
- Average goals: 2.67 (Scored – highest in the league), 1.17 (Conceded)
- AP_H:56+0.11+2.67=3.34
- DP_H:1 /( 0.56−0.11+1.17)=1/1.62=0.62
FC Copenhagen (Away):
- W %=0.44 , L %=0.33
- Average goals: 1.67 (Scored), 1.44 (Conceded)
- AP_A:44+0.33+1.67=2.44
- DP_A:1 /( 0.44−0.33+1.44)=1/1.55=0.65
Predicted results and probabilities
- xG (Home):(3.34+0.65)/2=2.00
- xG (Guest):(2.44+0.62)/2=1.53
- Probabilities: Win (1) – 50%, Draw (X) – 21%, Win (2) – 29%
- V3 Verdict:50−0.29=0.21 . Prediction: “1”.
- Harmony index:K =0.61 , L =0.87 . HI =10.97 ( Medium risk ).
Midtjylland has an exceptional attack, which has scored an average of 2.6 goals per match recently. In head-to-head matches, the home team has won twice in the last five meetings, with two matches ending in a draw. The mathematical advantage of V 3=0.21 is significant and exceeds the threshold for a safe unit. The stability of the model is higher compared to previous matches, which makes this prediction one of the most reliable for the round.
Brondby vs. Randers FC
Brondby is in third place and is one of the teams with the most solid defense, while Randers is in the bottom half of the table with only 17 goals scored in 18 matches.
Statistical profiles and calculations
Brondby (Home):
- W %=0.56 , L %=0.39
- Average goals: 1.72 (Scored), 1.17 (Conceded)
- AP_H:56+0.39+1.72=2.67
- DP_H:1 /( 0.56−0.39+1.17)=1/1.34=0.75
Randers FC (Away):
- W %=0.28 , L %=0.50
- Average goals: 0.94 (Scored), 1.33 (Conceded)
- AP_A:28+0.50+0.94=1.72
- DP_A:1 /( 0.28−0.50+1.33)=1/1.11=0.90
Predicted results and probabilities
- xG (Home):(2.67+0.90)/2=1.79
- xG (Guest):(1.72+0.75)/2=1.24
- Probabilities: Win (1) – 52%, Draw (X) – 23%, Win (2) – 25%
- V3 Verdict:52−0.25=0.27 . Prediction: “1”.
- Harmony index:K =0.65 , L =0.80 . HI =7.99 ( Moderate risk ).
Brondby has a clear advantage in defensive strength ( DP H = 0.75 ), which combined with Randers’ weak attack makes a home win a very likely scenario. The odds of 1.88 offer good value considering a 52% probability of success.
The former underdog versus the newcomer: Vejle vs. Fredericia
This match is a direct clash for survival. Vejle is last with 13 points, and Fredericia is 11th with 14 points. Fredericia has suffered eight losses in their last nine official matches, which puts their mental toughness under serious question.
Statistical profiles and calculations
Vejle (Host):
- W %=0.17 , L %=0.61
- Average goals: 1.11 (Scored), 1.94 (Conceded)
- AP_H:17+0.61+1.11=1.89
- DP_H:1 /( 0.17−0.61+1.94)=1/1.50=0.67
Fredericia (Guest):
- W %=0.22 , L %=0.67
- Average goals: 1.22 (Scored), 2.44 (Conceded)
- AP_A:22+0.67+1.22=2.11
- DP_A:1 /( 0.22−0.67+2.44)=1/1.99=0.50
Predicted results and probabilities
- xG (Home):(1.89+0.50)/2=1.20
- xG (Guest):(2.11+0.67)/2=1.39
- Probabilities: Win (1) – 32%, Draw (X) – 26%, Win (2) – 42%
- V3 Verdict:32−0.42=−0.10 . Prediction: “X2”.
- Harmony index:K =0.35 , L =0.05 . HI =7.11 ( High risk ).
Although the statistics give a slight advantage to Fredericia, both teams are in extremely poor form. Fredericia concedes an average of 2.44 goals per game, which is the highest in the league. The “X2” prediction is risky, but is based on the slightly higher offensive capacity of the visitors.
The exam leader: Aarhus vs. Odense
Leaders AGF Aarhus host sixth-placed OB Odense in a bid to consolidate their top spot. Aarhus have an ironclad defence, conceding just 1 goal per game, while Odense have been one of the most progressive teams since their return to the top flight.
Statistical profiles and calculations
AGF Aarhus (Home):
- W %=0.67 , L %=0.11
- Average goals: 2.00 (Scored), 1.00 (Conceded)
- AP_H:67+0.11+2.00=2.78
- DP_H:1 /( 0.67−0.11+1.00)=1/1.56=0.64
OB Odense (Guest):
- W %=0.39 , L %=0.33
- Average goals: 1.78 (Scored), 2.06 (Conceded)
- AP_A:39+0.33+1.78=2.50
- DP_A:1 /( 0.39−0.33+2.06)=1/2.12=0.47
Predicted results and probabilities
- xG (Home):(2.78+0.47)/2=1.63
- xG (Guest):(2.50+0.64)/2=1.57
- Probabilities: Win (1) – 41%, Draw (X) – 24%, Win (2) – 35%
- V3 Verdict:41−0.35=0.06 . Prediction: “1X”.
- Harmony index:K =0.38 , L =0.11 . HI =6.83 ( High risk ).
Despite Aarhus’s lead, Odense are a tough opponent, having already won one cup match against them in December. The small difference in V 3 and the high xG value for the visitors (1.57) suggest that the leader could face serious difficulties.
Summary analysis of trends and success factors
The analysis of the 19th round reveals several critical factors that will determine the outcome of the matches this weekend. First, the winter break effect is an unknown quantity that often reduces the stability of the models ( K ), making the early matches in February riskier than usual. Second, the home advantage in the Superliga remains strong for leading teams such as Midtjylland and Brondby, but weaker teams such as Vejle and Silkeborg fail to use the “home factor” to improve their defensive indices.
An interesting observation is that Midtjylland has the highest Attacking Power ( AP =3.34 ), which is significantly above the league average. This explains why their match against Copenhagen is classified as “Medium Risk” despite the strength of the opponent – their statistical profile is so dominant that the mathematical model favors them extremely strongly.
On the other hand, Randers and Vejle have the lowest offensive performance ( AP <1.90 ), making them easy targets for teams with well-organized defenses. This disparity between the top and bottom of the table is the reason for the high expectations for Brondby and Midtjylland in this round.
Final Verdict V3 and strategic recommendations
After processing all available statistics from Soccerway and market odds, we present the final summary table of predictions. This data should be used as a tool for making informed decisions, taking into account the specific risk of each match, defined through the Harmony Index.
Table 2: Final statistical report for the 19th round
| Meeting | Predicted goals (xG) | Predicted outcome | Verdict V3 | Harmony Index (HI) | Match category | Coefficient |
| North Zealand – Sonderjyske | 1.63 : 1.50 | 1X | 0.08 | 5.56 | High risk | 1.18 |
| Silkeborg – Viborg | 1.32 : 1.56 | X2 | -0.13 | 7.02 | High risk | 1.44 |
| Midtjylland – Copenhagen | 2.00 : 1.53 | 1 | 0.21 | 10.97 | Medium risk | 2.12 |
| Brondby – Randers FC | 1.79 : 1.24 | 1 | 0.27 | 7.99 | Medium risk | 1.90 |
| Vejle – Fredericia | 1.20 : 1.39 | X2 | -0.10 | 7.11 | High risk | 1.85 |
| Aarhus – Odense | 1.63 : 1.57 | 1X | 0.06 | 6.83 | High risk | 1.12 |
Strategic conclusions
There are no matches in this 19th round that fall into the “Platinum Selection” category ( HI >100 ). This is a logical consequence of the winter break and the general evenness of the Danish championship this season. The highest certainty is offered by the home victories of Midtjylland and Brondby, which are supported by a solid stability index and a significant difference in attacking power compared to their rivals.
For matches in the “High Risk” category, such as Nordsjaelland – Sonderjyske and Silkeborg – Viborg, we recommend increased caution. Nordsjaelland is a team of extremes, while Viborg has the capacity to surprise as an away team. Leader AGF Aarhus also faces a tough test, where a draw is a very likely outcome according to the xG values of both teams. This report provides an objective mathematical basis for analysis that excludes emotional bias and focuses entirely on statistical probability.
