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Superliga 2023/2024

Superliga is a soccer competition played in Denmark. The 2023/2024 season features 12 teams competing for the title of winners of the Superliga rankings. The competition takes place over various stages such as championship or relegation rounds. Among soccer teams playing in Superliga are Lyngby, Viborg, OB, Vejle. Check below for the Superliga table rankings, match results and schedule, football trends, top scoring players and disciplinary, among other various statistics like over/under, clean sheets / fail to score, score average or scoring sequences.

🏆 League Tables

   P W D L GF GA Dif Pts Last 5 PPG % Pts
1 Midtjylland 17 11 3 3 34 20 +14 36
2.12 70.59
2 Brøndby 17 10 4 3 33 17 +16 34
2.00 66.67
3 FC Copenhagen 17 10 3 4 34 21 +13 33
1.94 64.71
4 AGF 17 7 7 3 21 17 +4 28
1.65 54.90
5 Nordsjælland 17 7 6 4 26 14 +12 27
1.59 52.94
6 Silkeborg 17 8 3 6 26 19 +7 27
1.59 52.94
7 Lyngby 17 5 5 7 21 27 -6 20
1.18 39.22
8 Viborg 17 5 4 8 17 29 -12 19
1.12 37.25
9 OB 17 4 5 8 21 25 -4 17
1.00 33.33
10 Randers 17 3 7 7 18 31 -13 16
0.94 31.37
11 Vejle 17 3 5 9 16 21 -5 14
0.82 27.45
12 Hvidovre 17 1 4 12 9 35 -26 7
0.41 13.73
   P W D L GF GA Dif Pts
1 Midtjylland 9 6 2 1 17 7 10 20
2 Silkeborg 9 5 2 2 15 8 7 17
3 Brøndby 8 5 1 2 17 8 9 16
4 FC Copenhagen 9 5 1 3 16 9 7 16
5 Nordsjælland 8 4 3 1 13 5 8 15
6 Viborg 8 4 3 1 12 10 2 15
7 AGF 8 4 2 2 10 10 0 14
8 Lyngby 9 3 4 2 15 12 3 13
9 Randers 9 2 4 3 9 15 -6 10
10 Vejle 8 2 2 4 9 10 -1 8
11 OB 9 0 3 6 8 17 -9 3
12 Hvidovre 8 0 1 7 4 20 -16 1
   P W D L GF GA Dif Pts
1 Brøndby 9 5 3 1 16 9 7 18
2 FC Copenhagen 8 5 2 1 18 12 6 17
3 Midtjylland 8 5 1 2 17 13 4 16
4 OB 8 4 2 2 13 8 5 14
5 AGF 9 3 5 1 11 7 4 14
6 Nordsjælland 9 3 3 3 13 9 4 12
7 Silkeborg 8 3 1 4 11 11 0 10
8 Lyngby 8 2 1 5 6 15 -9 7
9 Vejle 9 1 3 5 7 11 -4 6
10 Randers 8 1 3 4 9 16 -7 6
11 Hvidovre 9 1 3 5 5 15 -10 6
12 Viborg 9 1 1 7 5 19 -14 4

📈 Trends

⚽ Matches

 Round 17 
Monday04/12/2023Midtjylland
5 - 1
Viborg
Sunday03/12/2023FC Copenhagen
1 - 2
AGF
Sunday03/12/2023Brøndby
4 - 0
Hvidovre
Sunday03/12/2023Lyngby
2 - 0
Silkeborg
Sunday03/12/2023OB
1 - 1
Nordsjælland
Friday01/12/2023Randers
0 - 0
Vejle

Players

PlayerTeam
Denmark A. Lind Silkeborg 10 0 4 0.67
Denmark N. Vallys Brøndby 8 0 7 0.53
Denmark P. Mortensen AGF 7 1 5 0.47
Sweden R. Bardghji FC Copenhagen 7 0 2 0.47
Korea Republic Cho Gue-Sung Midtjylland 6 3 3 0.43
Russia G. Onugkha Vejle 6 0 4 0.40
Haiti L. Deedson OB 6 0 3 0.55
Denmark M. Ingvartsen Nordsjælland 6 2 1 0.46
Denmark T. Bech AGF 6 0 2 0.40
Iceland A. Guðjohnsen Lyngby 5 0 3 0.45
Portugal Diogo Gonçalves FC Copenhagen 5 3 1 0.42
Ghana E. Nuamah Nordsjælland 5 0 0 1.25
Denmark M. Kvistgaarden Brøndby 5 0 2 0.33
Denmark A. Ementa Viborg 4 0 2 0.29
Denmark B. Kadrii OB 4 1 1 0.27
PlayerTeam Pts
Spain Raúl Albentosa Vejle 7 0 0 7
Denmark N. Poulsen AGF 6 0 0 6
Denmark C. Winther Lyngby 2 0 1 5
Denmark F. Alves Brøndby 5 0 0 5
Denmark J. Gemmer Hvidovre 5 0 0 5
Iceland K. Finnsson Lyngby 3 1 0 5
Iran S. Ezatolahi Vejle 5 0 0 5
Germany S. Köhler OB 5 0 0 5
Denmark A. Ementa Viborg 4 0 0 4
Gambia A. Manneh OB 4 0 0 4
Northern Ireland B. Peacock-Farrell AGF 1 0 1 4
Croatia D. Kolinger Vejle 4 0 0 4
Tunisia E. Achouri FC Copenhagen 4 0 0 4
Nigeria I. Said Viborg 4 0 0 4
Denmark J. Rasmussen Brøndby 4 0 0 4
      
We have allocated points to each yellow (1 point), yellow-red (2 points) and red card (3 points) for ranking purposes. Please note that this does not represent any official rankings.

Over/Under

Matches of...Played Total match goals (team goals + opponent goals)
Avg. over 0.5over 1.5over 2.5over 3.5over 4.5
AGF 17 2.24 94.12% 82.35% 35.29% 11.76% 0.00%
Brøndby 17 2.94 94.12% 76.47% 70.59% 35.29% 11.76%
FC Copenhagen 17 3.24 94.12% 94.12% 70.59% 41.18% 17.65%
Hvidovre 17 2.59 88.24% 70.59% 47.06% 35.29% 11.76%
Lyngby 17 2.82 100.00% 76.47% 52.94% 29.41% 17.65%
Midtjylland 17 3.18 100.00% 88.24% 64.71% 35.29% 23.53%
Nordsjælland 17 2.35 82.35% 70.59% 41.18% 29.41% 11.76%
OB 17 2.71 94.12% 82.35% 64.71% 17.65% 5.88%
Randers 17 2.88 94.12% 70.59% 58.82% 47.06% 11.76%
Silkeborg 17 2.65 94.12% 82.35% 52.94% 23.53% 11.76%
Vejle 17 2.18 88.24% 64.71% 47.06% 11.76% 5.88%
Viborg 17 2.71 94.12% 82.35% 52.94% 23.53% 11.76%
League Avg.    93.14% 78.43% 54.90% 28.43% 11.76%
Matches of...Played Total match goals (team goals + opponent goals)
Avg. over 0.5over 1.5over 2.5over 3.5over 4.5
AGF 8 2.50 100.00% 75.00% 50.00% 25.00% 0.00%
Brøndby 8 3.12 100.00% 87.50% 75.00% 37.50% 12.50%
FC Copenhagen 9 2.78 88.89% 88.89% 66.67% 33.33% 0.00%
Hvidovre 8 3.00 100.00% 87.50% 50.00% 25.00% 25.00%
Lyngby 9 3.00 100.00% 88.89% 44.44% 33.33% 22.22%
Midtjylland 9 2.67 100.00% 77.78% 44.44% 22.22% 11.11%
Nordsjælland 8 2.25 75.00% 50.00% 50.00% 37.50% 12.50%
OB 9 2.78 100.00% 100.00% 66.67% 11.11% 0.00%
Randers 9 2.67 88.89% 55.56% 44.44% 44.44% 22.22%
Silkeborg 9 2.56 88.89% 77.78% 44.44% 22.22% 22.22%
Vejle 8 2.38 87.50% 62.50% 50.00% 25.00% 12.50%
Viborg 8 2.75 87.50% 87.50% 75.00% 25.00% 0.00%
League Avg.    93.06% 78.24% 55.09% 28.47% 11.69%
Matches of...Played Total match goals (team goals + opponent goals)
Avg. over 0.5over 1.5over 2.5over 3.5over 4.5
AGF 9 2.00 88.89% 88.89% 22.22% 0.00% 0.00%
Brøndby 9 2.78 88.89% 66.67% 66.67% 33.33% 11.11%
FC Copenhagen 8 3.75 100.00% 100.00% 75.00% 50.00% 37.50%
Hvidovre 9 2.22 77.78% 55.56% 44.44% 44.44% 0.00%
Lyngby 8 2.62 100.00% 62.50% 62.50% 25.00% 12.50%
Midtjylland 8 3.75 100.00% 100.00% 87.50% 50.00% 37.50%
Nordsjælland 9 2.44 88.89% 88.89% 33.33% 22.22% 11.11%
OB 8 2.62 87.50% 62.50% 62.50% 25.00% 12.50%
Randers 8 3.12 100.00% 87.50% 75.00% 50.00% 0.00%
Silkeborg 8 2.75 100.00% 87.50% 62.50% 25.00% 0.00%
Vejle 9 2.00 88.89% 66.67% 44.44% 0.00% 0.00%
Viborg 9 2.67 100.00% 77.78% 33.33% 22.22% 22.22%
League Avg.    93.40% 78.70% 55.79% 28.93% 12.04%

Clean Sheets / Fail to Score

Clean Sheets
  CS Pld Perc.
Brøndby 8 17 47.06%
Nordsjælland 7 17 41.18%
Silkeborg 6 17 35.29%
FC Copenhagen 5 17 29.41%
Midtjylland 4 17 23.53%
AGF 3 17 17.65%
Hvidovre 3 17 17.65%
Lyngby 3 17 17.65%
OB 3 17 17.65%
Randers 3 17 17.65%
Vejle 3 17 17.65%
Viborg 1 17 5.88%
Failed to score
  FS Pld Perc.
AGF 1 17 5.88%
Brøndby 1 17 5.88%
FC Copenhagen 2 17 11.76%
Midtjylland 2 17 11.76%
OB 3 17 17.65%
Silkeborg 4 17 23.53%
Lyngby 5 17 29.41%
Randers 5 17 29.41%
Vejle 5 17 29.41%
Nordsjælland 6 17 35.29%
Viborg 7 17 41.18%
Hvidovre 10 17 58.82%

Score Average

  Avg. goals overall Avg. goals home Avg. goals away Pld
Scored Conc.TotalScoredConc.TotalScoredConc.Total
AGF 1.24 1.00 2.24 1.25 1.25 2.50 1.22 0.78 2.00 17
Brøndby 1.94 1.00 2.94 2.13 1.00 3.13 1.78 1.00 2.78 17
FC Copenhagen 2.00 1.24 3.24 1.78 1.00 2.78 2.25 1.50 3.75 17
Hvidovre 0.53 2.06 2.59 0.50 2.50 3.00 0.56 1.67 2.23 17
Lyngby 1.24 1.59 2.83 1.67 1.33 3.00 0.75 1.88 2.63 17
Midtjylland 2.00 1.18 3.18 1.89 0.78 2.67 2.13 1.63 3.76 17
Nordsjælland 1.53 0.82 2.35 1.63 0.63 2.26 1.44 1.00 2.44 17
OB 1.24 1.47 2.71 0.89 1.89 2.78 1.63 1.00 2.63 17
Randers 1.06 1.82 2.88 1.00 1.67 2.67 1.13 2.00 3.13 17
Silkeborg 1.53 1.12 2.65 1.67 0.89 2.56 1.38 1.38 2.76 17
Vejle 0.94 1.24 2.18 1.13 1.25 2.38 0.78 1.22 2.00 17
Viborg 1.00 1.71 2.71 1.50 1.25 2.75 0.56 2.11 2.67 17

Form – Scoring Sequences

  Current Sequences Consecutive matches without...
W D L W D L
AGF 1 0 0 0 1 7
Brøndby 1 0 0 0 1 8
FC Copenhagen 0 0 2 3 2 0
Hvidovre 0 0 1 2 1 0
Lyngby 1 0 0 0 1 2
Midtjylland 6 0 0 0 6 11
Nordsjælland 0 2 0 3 0 2
OB 0 1 0 1 0 2
Randers 0 1 0 7 0 1
Silkeborg 0 0 2 5 2 0
Vejle 0 2 0 2 0 3
Viborg 0 0 1 1 4 0
  Current Sequences Consecutive matches without...
W D L W D L
AGF 2 0 0 0 2 3
Brøndby 1 0 0 0 1 4
FC Copenhagen 0 0 1 2 1 0
Hvidovre 0 1 0 8 0 1
Lyngby 1 0 0 0 1 3
Midtjylland 3 0 0 0 3 6
Nordsjælland 0 1 0 1 0 2
OB 0 1 0 9 0 1
Randers 0 1 0 4 0 1
Silkeborg 0 0 1 3 1 0
Vejle 1 0 0 0 1 4
Viborg 2 0 0 0 4 2
  Current Sequences Consecutive matches without...
W D L W D L
AGF 1 0 0 0 1 4
Brøndby 0 3 0 3 0 7
FC Copenhagen 0 0 1 1 2 0
Hvidovre 0 0 1 1 4 0
Lyngby 0 0 3 3 7 0
Midtjylland 4 0 0 0 4 5
Nordsjælland 0 1 0 3 0 1
OB 1 0 0 0 1 4
Randers 0 1 0 3 0 1
Silkeborg 0 0 2 2 6 0
Vejle 0 2 0 5 0 2
Viborg 0 0 2 7 2 0

The soccer statistics for Superliga are updated regularly, as matches are played and results are processed. The statistical data is presented in a manner that can be used easily to identify trends and probabilities for future football matches, which is what the SoccerStats247 system uses to produce predictions for Superliga. There are daily tips in the Predictions section, where our algorithm point to probable outcomes for different markets, and evolves as more matches and soccer statistics are feed in.

Seasons