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2004-05 Superliga Season

198 games

Final

Champion

Brondby

69 points · 10th Title

Last Title: 2001-02

Relegated

Randers

24 pts

Herfolge · 25 pts

Biggest Overachiever

Silkeborg

6.58 points above expected

47 points · 40.42 expected points

Biggest Disappointment

Herfolge

6.22 points below expected

25 points · 31.22 expected points

League Table

The final standings for the season. vsSim shows actual points minus the simulation's mean — positive means the team overachieved against the model, negative means they underperformed.

# Team GP W D L Pts GF GA GD SimPts vsSim
1 Brondby Champion 33 20 9 4 69 61 23 +38 62.47 +6.53
2 FC Copenhagen 33 16 9 8 57 53 39 +14 60.53 -3.53
3 Midtjylland 33 17 6 10 57 49 40 +9 50.74 +6.26
4 Aalborg 33 15 8 10 53 59 45 +14 51.26 +1.74
5 Esbjerg 33 13 10 10 49 61 47 +14 52.27 -3.27
6 Odense 33 13 9 11 48 61 41 +20 51.95 -3.95
7 Viborg 33 13 9 11 48 43 45 -2 46.94 +1.06
8 Silkeborg 33 13 8 12 47 50 52 -2 40.42 +6.58
9 Aarhus GF 33 11 6 16 39 47 53 -6 39.72 -0.72
10 Nordsjaelland 33 8 6 19 30 36 59 -23 34.12 -4.12
11 Herfolge Relegated 33 6 7 20 25 29 71 -42 31.22 -6.22
12 Randers Relegated 33 5 9 19 24 30 64 -34 26.92 -2.92

Going into Phase 2, the championship group halved its Phase 1 points (rounded up); the relegation group kept its full Phase 1 points. Each group then played its own round-robin to determine final standings.

Form

Each team's 5-game rolling points-per-game across the season. Hot streaks push above the dashed 1.5 PPG reference line; cold spells drop below. Each team gets a distinct color; the legend below the plot lets you read off which line is which. (First 4 games of each team have no rolling window, so the lines start at game 5.)

League Race

Cumulative points across the season for each team. Highlighted teams are drawn in color (top finishers for the Title Race, bottom finishers for the Relegation Race); the rest of the league appears in light gray as context. Switch views with the buttons below.

Season Summary

Every team's regular-season finish compared against 100,000 simulations. Click any column header to sort. Luck is the team's actual points minus the sim's mean — positive means the team beat the model. Percentile is where the actual result fell in the team's sim distribution (e.g. 90% = the team did this well or better in only 10% of sims).

Team Elo Points Avg Luck Percentile Min 5th Q1 Median Q3 95th Max
Brondby 1745 69 62.47 +6.53 84.1% 36 51 58 63 67 74 85
FC Copenhagen 1691 57 60.53 -3.53 33.0% 32 49 56 60 65 72 84
Midtjylland 1635 57 50.74 +6.26 82.4% 24 39 46 51 56 63 76
Aalborg 1634 53 51.26 +1.74 62.3% 24 39 46 51 56 63 80
Esbjerg 1633 49 52.27 -3.27 35.3% 24 40 47 52 57 64 82
Odense 1625 48 51.95 -3.95 31.8% 22 40 47 52 57 64 77
Viborg 1570 48 46.94 +1.06 58.3% 22 35 42 47 52 59 77
Silkeborg 1546 47 40.42 +6.58 84.1% 16 29 36 40 45 52 69
Aarhus GF 1505 39 39.72 -0.72 49.2% 15 28 35 40 44 51 65
Nordsjaelland 1464 30 34.12 -4.12 30.9% 10 23 29 34 39 46 61
Herfolge 1415 25 31.22 -6.22 19.7% 10 21 27 31 36 43 58
Randers 1381 24 26.92 -2.92 36.3% 7 17 22 27 31 38 50

Head-to-Head

Each cell shows a team's record in that matchup (row vs column, formatted W-D-L) with the model's expected points on the line below. Navy-tinted cells mean the team beat the model's expectations by more than one point in that matchup; gold-tinted cells mean they fell short by the same margin.

Beat expectations Fell short Within expectations
Team AAL AG BRO ESB FC HER MID NOR ODE RAN SIL VIB
Aalborg —
1-1-1
4.77
0-3-0
3.35
1-1-1
3.63
0-1-2
3.23
2-0-1
5.52
1-0-2
4.51
3-0-0
5.95
2-1-0
3.64
2-1-0
6.38
2-0-1
5.50
1-0-2
4.86
Aarhus GF
1-1-1
3.50
 —
0-1-2
2.43
1-0-2
2.92
1-0-2
2.31
2-0-1
5.28
2-1-0
3.47
1-1-1
4.32
1-1-1
2.82
2-0-1
4.95
0-1-2
3.77
0-0-3
3.86
Brondby
0-3-0
4.93
2-1-0
5.91
 —
1-2-0
4.89
2-0-1
3.87
3-0-0
6.51
1-0-2
5.57
2-1-0
6.70
1-1-1
5.36
3-0-0
7.13
2-1-0
6.13
3-0-0
5.60
Esbjerg
1-1-1
4.63
2-0-1
5.38
0-2-1
3.37
 —
0-2-1
3.18
2-1-0
6.10
1-1-1
4.49
2-0-1
5.31
2-0-1
4.43
1-2-0
6.17
1-0-2
4.92
1-1-1
4.32
FC Copenhagen
2-1-0
5.05
2-0-1
6.04
1-0-2
4.38
1-2-0
5.10
 —
2-1-0
6.65
1-0-2
4.60
3-0-0
5.93
1-2-0
5.16
2-1-0
6.70
0-1-2
5.68
1-1-1
5.13
Herfolge
1-0-2
2.79
1-0-2
3.01
0-0-3
1.93
0-1-2
2.28
0-1-2
1.82
 —
0-0-3
2.77
1-1-1
4.25
0-0-3
2.13
2-1-0
4.30
1-1-1
2.87
0-2-1
3.07
Midtjylland
2-0-1
3.74
0-1-2
4.80
2-0-1
2.74
1-1-1
3.77
2-0-1
3.65
3-0-0
5.55
 —
1-1-1
5.76
1-1-1
4.32
3-0-0
6.51
2-1-0
5.18
0-1-2
4.80
Nordsjaelland
0-0-3
2.39
1-1-1
3.94
0-1-2
1.76
1-0-2
2.99
0-0-3
2.43
1-1-1
4.01
1-1-1
2.56
 —
0-1-2
2.46
1-1-1
5.10
0-0-3
3.87
3-0-0
2.70
Odense
0-1-2
4.62
1-1-1
5.49
1-1-1
2.93
1-0-2
3.84
0-2-1
3.13
3-0-0
6.26
1-1-1
3.93
2-1-0
5.88
 —
2-1-0
6.44
2-0-1
4.66
0-1-2
4.83
Randers
0-1-2
2.04
1-0-2
3.32
0-0-3
1.41
0-2-1
2.20
0-1-2
1.76
0-1-2
3.96
0-0-3
1.91
1-1-1
3.19
0-1-2
1.98
 —
2-1-0
3.12
1-1-1
2.14
Silkeborg
1-0-2
2.80
2-1-0
4.51
0-1-2
2.24
2-0-1
3.35
2-1-0
2.64
1-1-1
5.42
0-1-2
3.12
3-0-0
4.37
1-0-2
3.59
0-1-2
5.16
 —
1-2-0
3.14
Viborg
2-0-1
3.42
3-0-0
4.39
0-0-3
2.71
1-1-1
3.92
1-1-1
3.14
1-2-0
5.23
2-1-0
3.48
0-0-3
5.61
2-1-0
3.44
1-1-1
6.26
0-2-1
5.16
 —

Points vs. Goals Scored, Allowed, and Differential

Points plotted against goals scored, allowed, and differential. Use the buttons to switch views; hover a team for exact values. The table gives each fit's R² and slope (the change in points per 10 goals).

Fit Metrics

R² Slope
Scored 0.72 +9.9
Allowed 0.93 -10.3
Differential 0.90 +5.6

Scoreline Distribution

Percentage of games ending with each combination of team-goals (rows) and opponent-goals (columns). The diagonal shows draws; cells below the diagonal are wins from the row team's perspective, cells above are losses. Marginal totals on the right and bottom show how often each goal count occurred regardless of opponent. Use the picker to switch between the league-wide view and any individual team.

↓ Scored | Allowed →012345+Total
07.07%7.32%5.05%2.27%2.53%0.76%25.00%
17.32%10.61%9.34%3.28%1.01%0.76%32.32%
25.05%9.34%5.56%3.54%1.01%0.25%24.75%
32.27%3.28%3.54%1.01%0.51%0.25%10.86%
42.53%1.01%1.01%0.51%——5.05%
5+0.76%0.76%0.25%0.25%——2.02%
Total25.00%32.32%24.75%10.86%5.05%2.02%100%

Summary Statistics

Scored Allowed Difference
Mean 1.46 1.46 +0.00
SD 1.29 1.29 1.89
CV 0.88 0.88 —
Max 7 7 +7
Min 0 0 -7

Games Played: 198

↓ Scored | Allowed →012345+Total
03.03%6.06%——3.03%—12.12%
16.06%15.15%15.15%—3.03%—39.39%
26.06%6.06%3.03%3.03%——18.18%
36.06%9.09%3.03%3.03%——21.21%
43.03%—3.03%———6.06%
5+———3.03%——3.03%
Total24.24%36.36%24.24%9.09%6.06%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.79 1.36 +0.42
SD 1.27 1.14 1.71
CV 0.71 0.84 —
Max 5 4 +4
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
03.03%9.09%—3.03%3.03%—18.18%
13.03%3.03%21.21%12.12%——39.39%
26.06%12.12%9.09%———27.27%
33.03%3.03%3.03%3.03%——12.12%
43.03%—————3.03%
5+———————
Total18.18%27.27%33.33%18.18%3.03%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.42 1.61 -0.18
SD 1.03 1.09 1.70
CV 0.72 0.68 —
Max 4 4 +4
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
012.12%3.03%—3.03%——18.18%
112.12%9.09%3.03%3.03%——27.27%
218.18%12.12%3.03%———33.33%
3—3.03%—3.03%——6.06%
46.06%3.03%————9.09%
5+6.06%—————6.06%
Total54.55%30.30%6.06%9.09%——100%

Summary Statistics

Scored Allowed Difference
Mean 1.85 0.70 +1.15
SD 1.58 0.95 1.95
CV 0.86 1.37 —
Max 7 3 +7
Min 0 0 -3

Games Played: 33

↓ Scored | Allowed →012345+Total
09.09%3.03%3.03%—3.03%—18.18%
112.12%9.09%12.12%———33.33%
2——12.12%3.03%3.03%—18.18%
33.03%6.06%6.06%—3.03%—18.18%
43.03%3.03%————6.06%
5+—3.03%3.03%———6.06%
Total27.27%24.24%36.36%3.03%9.09%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.85 1.42 +0.42
SD 1.60 1.20 1.89
CV 0.87 0.84 —
Max 7 4 +5
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
06.06%6.06%3.03%——3.03%18.18%
19.09%15.15%—3.03%3.03%—30.30%
29.09%12.12%6.06%6.06%——33.33%
33.03%—6.06%———9.09%
49.09%—————9.09%
5+———————
Total36.36%33.33%15.15%9.09%3.03%3.03%100%

Summary Statistics

Scored Allowed Difference
Mean 1.61 1.18 +0.42
SD 1.17 1.29 1.92
CV 0.73 1.09 —
Max 4 5 +4
Min 0 0 -5

Games Played: 33

↓ Scored | Allowed →012345+Total
06.06%9.09%12.12%6.06%—6.06%39.39%
19.09%15.15%9.09%3.03%3.03%3.03%42.42%
2—3.03%—3.03%3.03%3.03%12.12%
3——3.03%———3.03%
4——3.03%———3.03%
5+———————
Total15.15%27.27%27.27%12.12%6.06%12.12%100%

Summary Statistics

Scored Allowed Difference
Mean 0.88 2.15 -1.27
SD 0.96 1.84 2.00
CV 1.09 0.85 —
Max 4 7 +2
Min 0 0 -7

Games Played: 33

↓ Scored | Allowed →012345+Total
09.09%3.03%—3.03%3.03%—18.18%
115.15%6.06%12.12%6.06%——39.39%
26.06%12.12%3.03%3.03%——24.24%
33.03%9.09%————12.12%
4—3.03%—3.03%——6.06%
5+———————
Total33.33%33.33%15.15%15.15%3.03%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.48 1.21 +0.27
SD 1.12 1.17 1.63
CV 0.76 0.96 —
Max 4 4 +3
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
09.09%9.09%15.15%—3.03%—36.36%
13.03%3.03%12.12%3.03%—3.03%24.24%
26.06%9.09%6.06%12.12%——33.33%
3——6.06%———6.06%
4———————
5+———————
Total18.18%21.21%39.39%15.15%3.03%3.03%100%

Summary Statistics

Scored Allowed Difference
Mean 1.09 1.79 -0.70
SD 0.98 1.41 1.63
CV 0.90 0.79 —
Max 3 7 +2
Min 0 0 -6

Games Played: 33

↓ Scored | Allowed →012345+Total
09.09%6.06%6.06%———21.21%
13.03%15.15%12.12%3.03%——33.33%
23.03%6.06%—3.03%3.03%—15.15%
36.06%3.03%3.03%3.03%——15.15%
43.03%—3.03%———6.06%
5+3.03%6.06%————9.09%
Total27.27%36.36%24.24%9.09%3.03%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.85 1.24 +0.61
SD 1.72 1.06 2.08
CV 0.93 0.85 —
Max 7 4 +6
Min 0 0 -2

Games Played: 33

↓ Scored | Allowed →012345+Total
03.03%12.12%12.12%9.09%9.09%—45.45%
16.06%12.12%6.06%3.03%—3.03%30.30%
2—3.03%12.12%3.03%——18.18%
3———————
43.03%——3.03%——6.06%
5+———————
Total12.12%27.27%30.30%18.18%9.09%3.03%100%

Summary Statistics

Scored Allowed Difference
Mean 0.91 1.94 -1.03
SD 1.10 1.27 1.79
CV 1.21 0.66 —
Max 4 5 +4
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
06.06%12.12%—3.03%6.06%—27.27%
16.06%12.12%6.06%3.03%——27.27%
2—9.09%6.06%—3.03%—18.18%
33.03%6.06%9.09%—3.03%—21.21%
4—3.03%3.03%———6.06%
5+———————
Total15.15%42.42%24.24%6.06%12.12%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.52 1.58 -0.06
SD 1.28 1.20 1.71
CV 0.84 0.76 —
Max 4 4 +3
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
09.09%9.09%9.09%———27.27%
13.03%12.12%3.03%—3.03%—21.21%
26.06%27.27%6.06%6.06%——45.45%
3——3.03%——3.03%6.06%
4———————
5+———————
Total18.18%48.48%21.21%6.06%3.03%3.03%100%

Summary Statistics

Scored Allowed Difference
Mean 1.30 1.36 -0.06
SD 0.95 1.14 1.25
CV 0.73 0.84 —
Max 3 5 +2
Min 0 0 -3

Games Played: 33

Home-Field Advantage Edge

The simulation applies the same home-field boost to every game across the league, so the per-game home win probabilities bake in the model's idea of HFA. For each team, we sum expected points at home and compare to actual home points, and the same on the road. The bar shows (home points above expected) minus (away points above expected). A tall positive bar means the team's home/road split exceeded what the model predicted — a real fortress effect. A tall negative bar means the reverse: they played worse at home or better on the road than expected.

Top Overachievers & Disappointments

Teams that most beat — or most fell short of — their simulated point projections. A positive vsSim means the team accumulated more points than the model expected on average; a negative one means fewer.

Biggest Overachievers

# Team Actual Sim vsSim
1 Silkeborg 47 40.42 +6.58
2 Brondby 69 62.47 +6.53
3 Midtjylland 57 50.74 +6.26
4 Aalborg 53 51.26 +1.74
5 Viborg 48 46.94 +1.06

Biggest Disappointments

# Team Actual Sim vsSim
1 Herfolge 25 31.22 -6.22
2 Nordsjaelland 30 34.12 -4.12
3 Odense 48 51.95 -3.95
4 FC Copenhagen 57 60.53 -3.53
5 Esbjerg 49 52.27 -3.27

Top Streaks

Ranked by model unlikelihood — the product of pregame W/D/L probabilities over the games in each team's streak. A short streak by a poor team can outrank a longer one by a strong team since the poor team's per-game probabilities were lower going in. Unbeaten counts W or D consecutively; Winless counts L or D consecutively.

Most Unlikely Winning Streaks

# Team Games Dates Probability
1 FC Copenhagen 7 Mar 13 – May 1 1 in 87
2 Nordsjaelland 3 Aug 15 – Sep 12 1 in 67
3 Viborg 3 Sep 26 – Oct 17 1 in 24
4 Aalborg 5 Apr 24 – May 19 1 in 21
5 Odense 3 Sep 12 – Sep 22 1 in 18

Most Unlikely Losing Streaks

# Team Games Dates Probability
1 Silkeborg 6 Aug 29 – Oct 3 1 in 222
2 Midtjylland 3 Apr 24 – May 8 1 in 88
3 Esbjerg 3 Sep 22 – Oct 3 1 in 49
4 Odense 3 Sep 26 – Oct 16 1 in 33
5 Aalborg 3 Aug 8 – Aug 29 1 in 22

Most Unlikely Unbeaten Streaks

# Team Games Dates Probability
1 Brondby 19 Aug 1 – Mar 20 1 in 132
2 Silkeborg 7 Apr 2 – May 8 1 in 108
3 Viborg 7 Sep 22 – Nov 3 1 in 42
4 FC Copenhagen 10 Nov 14 – May 1 1 in 19
5 Randers 4 Apr 3 – Apr 17 1 in 14

Most Unlikely Winless Streaks

# Team Games Dates Probability
1 Odense 9 Nov 27 – May 8 1 in 162
2 Viborg 5 Nov 3 – Nov 28 1 in 26
3 Nordsjaelland 11 Sep 19 – Nov 25 1 in 26
4 FC Copenhagen 4 Jul 24 – Aug 15 1 in 18
5 Brondby 4 Mar 13 – Apr 10 1 in 17

Finish Position Heatmap

Each cell is the probability — across 100,000 simulations — that the team (row) finished at that position (column). Rows are sorted by actual finish (champion at top, bottom of the table at the bottom).

Team123456789101112
Brondby47.00%25.35%13.23%7.17%3.76%2.12%0.92%0.32%0.10%0.03%——
FC Copenhagen31.71%29.18%16.62%9.84%5.97%3.91%1.75%0.68%0.25%0.07%0.02%—
Midtjylland3.85%8.45%13.78%15.55%16.19%15.47%13.29%7.64%3.74%1.45%0.44%0.15%
Aalborg4.51%9.88%13.95%16.78%17.06%14.31%11.34%6.63%3.63%1.40%0.46%0.05%
Esbjerg6.13%11.31%16.46%16.67%16.33%13.40%9.56%6.18%2.58%1.01%0.33%0.04%
Odense5.37%11.38%16.31%17.45%15.28%14.21%9.95%5.69%2.88%1.13%0.27%0.08%
Viborg1.24%3.43%6.79%10.45%13.94%16.30%17.86%14.40%9.23%4.35%1.59%0.42%
Silkeborg0.12%0.48%1.37%3.13%4.97%8.96%14.24%21.33%21.40%13.81%7.36%2.83%
Aarhus GF0.06%0.47%1.16%2.29%4.59%7.87%12.76%20.03%22.32%16.18%9.35%2.92%
Nordsjaelland0.01%0.06%0.27%0.49%1.29%2.33%4.96%9.68%17.67%27.21%23.23%12.80%
Herfolge—0.01%0.06%0.15%0.56%0.88%2.60%5.41%11.25%20.94%31.75%26.39%
Randers———0.03%0.06%0.24%0.77%2.01%4.95%12.42%25.20%54.32%

Points Required Per Position

The empirical CDF of simulated point totals per finishing position. Each curve shows, for one position (1st, 2nd, ..., last), the spread of point totals teams accumulated across simulations. Reading the curve at the 50% mark gives the median points typically needed to finish at that position. Steep curves mean the position is tightly clustered around a particular point range; shallow curves mean the position came with a wide variety of point totals.

Points Totals in Context

How did each team's actual results compare to their simulated points, Elo rating, average opponent Elo, and percentile within simulated outcomes? Use the buttons to switch between views. In each chart, dashed crosshairs at the league means split the plot into four quadrants: pastel green (team did well, model agreed), pastel red (team did poorly, model agreed), and pastel yellow (model and reality disagreed).

Season Trends

How each metric evolved across the season - Elo rating, actual and projected points, title/promotion probability, and relegation probability - day by day.

Edges & Scoring

How these are measured

The navy dot is the actual value, the gold line and key are the model's reference where one applies, and the slate ticks are the cutoffs between read labels listed under each metric.

Home Edge
Share of matches won by the home team minus the share won by the away team.
No Edge: under 5% * Slight Edge: 5% to 15% * Clear Edge: 15% to 25% * Strong Edge: 25% and up.
Elo Value
Number of Elo rating points one goal is worth. A team this many Elo points better than another is expected to win by one goal on a neutral field.
Scoring Tilt
Average home goals minus average away goals per match. The gold line is the home goals edge implied by the scoring value of an Elo point and the home-field Elo bonus.
Road-Tilted: under -0.2 * Neutral: -0.2 to 0.5 * Home-Tilted: 0.5 to 1.2 * Strong Home: 1.2 and up.
Home Edge
HomeDrawAway
+21.21%
Clear Edge
48.48%24.24%27.27%
Elo Value
Home Edge
154 Elo
0.006 goals per Elo point
074.83400
Scoring Tilt
Expected
+0.61 goals
Home-Tilted
-2+0.49+2

Title Race

How these are measured

The navy dot is the actual value, the gold line and key are the model's reference where one applies, and the slate ticks are the cutoffs between read labels listed under each metric.

Title-Race Openness
Effective number of teams with a live shot at finishing 1st, from the simulation. 1 means the favorite is a near-lock; larger means a wide-open race. Equal to 1 divided by the sum of squared title odds.
One-Team Race: under 2 * Top-Heavy: 2 to 4 * Open: 4 to 6 * Wide Open: 6 and up.
Champion Preseason Odds
Preseason probability that the eventual champion would finish 1st, from the simulation. The dot marks their rank across all teams, from longshot to favorite.
Preseason Favorite: 1st * Among the Favorites: 2nd * Middle of the Pack: 3rd to 6th * Longshot: 7th or lower.
Title Margin
Points-per-game gap between the champion and the runner-up. Shown per game so it reads the same across long and short seasons. The gold line is the winning margin the model expected, so a dot to the right means a more one-sided race than projected. A title won on goal difference shows 0.00.
Photo Finish: under 0.15 * Tight Race: 0.15 to 0.4 * Comfortable: 0.4 to 0.75 * Runaway: 0.75 and up.
Title-Race Openness
3.0
Top-Heavy
124610
Champion Preseason Odds
47%
Brondby, 1st of 12
LongshotFavorite
Title Margin
Expected
0.36/gm
Tight Race
00.170.5/gm

Simulation-Based Surprises

How these are measured

The navy dot is the actual value, the gold line and key are the model's reference where one applies, and the slate ticks are the cutoffs between read labels listed under each metric.

Luck Spread
Standard deviation of the gap between each team's actual points and their simulated average points. The gold line is the spread the model expected from chance alone.
As Expected: under 5.62 * Some Luck: 5.62 to 8.43 * Lucky: 8.43 to 11.24 * Wild Swing: 11.24 and up.
Average Finish Error
Average gap between where each team was projected to finish and where they actually finished in the table.
Pinpoint: under 1.17 * Close: 1.17 to 1.76 * Off: 1.76 to 2.35 * Way Off: 2.35 and up.
Biggest Overachiever
The team that finished highest inside its own range of simulated outcomes. The gold line is where the top team in a league of 12 typically lands.
Biggest Underachiever
The team that finished lowest inside its own range of simulated outcomes. The gold line is where the bottom team in a league of 12 typically lands.
Season Outliers
Number of teams that finished above the 95th or below the 5th percentile of their own simulated range. Even a well-calibrated model expects about 10% of teams in the extremes.
Minimal Outliers: under 12 * As Expected: 12 to 19.2 * Several Outliers: 19.2 to 26.4 * Many Outliers: 26.4 and up.
Unexpected Relegations
Number of teams that were actually relegated but were not in the model's projected bottom field of the same size. The gold line is how many the model expected to miss on average.
As Expected: under 0.62 * A Surprise: 0.62 to 1 * Several Surprises: 1 to 1.37 * Many Surprises: 1.37 and up.
Luck Spread
Expected
4.40 points
As Expected
07.0218
Average Finish Error
Expected
0.67
Pinpoint
01.473
Biggest Overachiever
Expected 95.83%
84.14%
Silkeborg
50100
Biggest Underachiever
Expected 4.17%
19.70%
Herfolge
050
Season Outliers
Expected
0 of 12
Minimal Outliers
01.26
Unexpected Relegations
Expected
0 of 2
As Expected
00.62

Parity

How these are measured

The navy dot is the actual value, the gold line and key are the model's reference where one applies, and the slate ticks are the cutoffs between read labels listed under each metric.

Gini Index
How evenly points were spread across the league. Lower means a tight, balanced table; higher means a few teams ran off with most of the points.
Balanced: under 0.12 * Even: 0.12 to 0.18 * Top-Heavy: 0.18 to 0.26 * Lopsided: 0.26 and up.
Noll-Scully
How much more spread out the table was than a league where every match is a coin flip. The gold line at 1 is that coin-flip baseline. Above it, real talent gaps stretched the table; below it, the league was tighter than luck alone would produce.
Coin-Flip Parity: under 1 * Moderate Separation: 1 to 1.6 * Strong Separation: 1.6 to 2.2 * Wide Separation: 2.2 and up.
Interquartile Edge
Chance the team at the 75th percentile of Elo would beat the team at the 25th percentile on a neutral field. Higher means a bigger gap between the upper and lower half of the table.
Even: under 60% * Slight Edge: 60% to 70% * Clear Edge: 70% to 80% * Wide Edge: 80% and up.
Best vs. Worst
Chance the top-rated team would beat the bottom-rated team on a neutral field. The gold line is how large that gap tends to be in a league of this size; a dot to the right flags an unusually dominant or unusually weak team.
Even: under 70% * Clear Edge: 70% to 82% * Strong Edge: 82% to 92% * Dominant: 92% and up.
Close Games
Share of matches decided by 1 goal or fewer, draws included. The gold line is how many close games the matchups and the scoring value of an Elo point predict.
Few: under 30% * Some Drama: 30% to 40% * Frequent: 40% to 50% * Very Frequent: 50% and up.
Blowouts
Share of matches decided by 3 goals or more. The gold line is how many routs the matchups and the scoring model predict.
Rare: under 8% * Occasional: 8% to 14% * Frequent: 14% to 22% * Very Frequent: 22% and up.
Gini Index
0.16
Even
00.120.180.260.5
Noll-Scully
Coin-flip
1.59
Moderate Separation
01.003
Interquartile Edge
69%
Slight Edge
50%60%70%80%100%
Best vs. Worst
Baseline
89%
Strong Edge
50%90%100%
Close Games
Expected
66%
Very Frequent
0%57%100%
Blowouts
Expected
15%
Frequent
0%19%100%

Predictability

How these are measured

The navy dot is the actual value, the gold line and key are the model's reference where one applies, and the slate ticks are the cutoffs between read labels listed under each metric.

Brier Score
How close the pregame probabilities landed to the actual result, averaged over the season. Confident, correct calls are rewarded most; confident misses are punished most. Lower is better; since draws are possible, a score under 0.66 beats guessing the base rate.
Highly Predictable: under 0.56 * Predictable: 0.56 to 0.62 * Hard to Predict: 0.62 to 0.66 * Coin-Flip: 0.66 and up.
Matchup Imbalance
How lopsided the matchups were on paper, averaging the gap between the two win probabilities over their sum. 0 means every match was a toss-up; 1 means every match was a heavy favorite against a big underdog.
Very Even: under 0.1 * Slight Separation: 0.1 to 0.18 * Notable Separation: 0.18 to 0.28 * Lopsided: 0.28 and up.
Strangeness
How wild the final table was versus what the model expected. A value of 1 means teams landed about one standard deviation from their projections on average. Above 1 is a stranger season; below 1 hugged the projections.
Very Predictable: under 0.8 * As Expected: 0.8 to 1.1 * Wilder Than Modeled: 1.1 to 1.4 * Chaotic: 1.4 and up.
Repeatability
How closely the final table order matched the preseason Elo order, by Spearman rank correlation. Higher means last season's ratings strongly predicted this season's finish. Shows N/A for an inaugural season.
Weak Carryover: under 0.3 * Some Carryover: 0.3 to 0.6 * Strong Carryover: 0.6 to 0.85 * Near-Lock: 0.85 and up.
Upset Rate
Share of matches the underdog won. The gold line is how often the model expected underdogs to win; a dot to the right means upsets ran hotter than expected.
Chalky: under 25% * As Expected: 25% to 33% * Upset-Prone: 33% to 42% * Very Upset-Prone: 42% and up.
Clear Favorite Upset Rate
Share of matches the underdog won, counting only games with a clear favorite (at least 60% likely to win once a draw is set aside). The gold line is how often the model expected these favorites to slip.
Solid Favorites: under 15% * As Expected: 15% to 25% * Shaky Favorites: 25% to 35% * Very Shaky: 35% and up.
Brier Score
Expected
0.58
Predictable
00.592
Matchup Imbalance
0.38
Lopsided
00.10.180.280.5
Strangeness
Expected
0.40
Very Predictable
01.002
Repeatability
0.92
Near-Lock
00.30.60.851
Upset Rate
Expected
25%
Chalky
0%24%50%
Clear Favorite Upset Rate
Expected
17%
As Expected
0%20%50%

Calibration

How these are measured

The navy dot is the actual value, the gold line and key are the model's reference where one applies, and the slate ticks are the cutoffs between read labels listed under each metric.

Probability calibration
An all-in-one chi-square test of the model's probabilities. Matches are grouped by how confident the model was, and within each group the predicted and actual counts of home wins, draws, and away wins are compared. The p-value is plotted; above 0.05 means well-calibrated.
Miscalibrated: under 0.05 * Borderline: 0.05 to 0.1 * Well Calibrated: 0.1 to 0.5 * Excellent: 0.5 and up.
Calibration slope
Checks whether the spread of the probabilities is right. Each probability is turned into log-odds and a line is fit predicting the actual results. A slope of 1.00 is perfect; below 1 is overconfidence (favorites lost more than their odds implied); above 1 is under-confidence.
Overconfident: under 0.85 * Calibrated: 0.85 to 1.15 * Underconfident: 1.15 to 1.3 * Very Underconfident: 1.3 and up.
Calibration error (ECE)
The average gap between the model's stated chances and how often the predicted result actually happened. Smaller is better. The gold line is the noise ceiling, the error luck alone can produce even with perfect probabilities; below it, the model's error is no larger than chance.
Well Within Noise: under 0.12 * Near Noise Ceiling: 0.12 to 0.18 * Above Noise: 0.18 to 0.24 * Well Above Noise: 0.24 and up.
Probability calibration
0.17
Well Calibrated
0.010.050.10.51
Calibration slope
Ideal
1.13
Calibrated
0.51.001.5
Calibration error (ECE)
Noise ceiling
0.099
Well Within Noise
00.1220.3

Next-Season Status

Across 100,000 regular-season simulations, the probability of each team's next-season status. Columns appear in best-to-worst outcome order: promotion-positive on the left, relegation-positive on the right. Cells with darker shading indicate higher likelihood.

Team Same level Direct relegation
Brondby 100% —
FC Copenhagen 99.98% 0.02%
Odense 99.65% 0.35%
Esbjerg 99.63% 0.37%
Aalborg 99.49% 0.51%
Midtjylland 99.41% 0.59%
Viborg 97.99% 2.01%
Silkeborg 89.81% 10.19%
Aarhus GF 87.73% 12.27%
Nordsjaelland 63.97% 36.03%
Herfolge 41.86% 58.14%
Randers 20.48% 79.52%

Overall Game Log

Summary of every completed game this season. Sort any column by clicking its header.

Date Opponent Score Pre Elo Opp Elo Win % Tie % Loss % Elo Δ Points
2004-07-24 FC Copenhagen D 2-2 1534 1693 26.62% 24.74% 48.64% +0.5 1
2004-07-24 @ Silkeborg D 2-2 1693 1534 48.64% 24.74% 26.62% -0.5 1
2004-07-25 Aarhus GF D 2-2 1496 1501 47.17% 24.98% 27.85% -0.4 1
2004-07-25 @ Nordsjaelland D 2-2 1501 1496 27.85% 24.98% 47.17% +0.4 1
2004-07-25 Midtjylland L 1-2 1610 1610 47.87% 24.87% 27.26% -6.4 0
2004-07-25 @ Aalborg W 2-1 1610 1610 27.26% 24.87% 47.87% +6.4 3
2004-07-25 Odense L 1-2 1675 1615 55.55% 23.18% 21.27% -7.3 0
2004-07-25 @ Brondby W 2-1 1615 1675 21.27% 23.18% 55.55% +7.3 3
2004-07-25 Viborg D 0-0 1488 1570 36.45% 25.70% 37.85% +0.0 1
2004-07-25 @ Herfolge D 0-0 1570 1488 37.85% 25.70% 36.45% -0.0 1
2004-07-31 Odense W 2-1 1616 1622 47.09% 24.99% 27.92% +4.2 6
2004-07-31 @ Midtjylland L 1-2 1622 1616 27.92% 24.99% 47.09% -4.2 3
2004-08-01 Aarhus GF W 3-1 1534 1502 52.11% 24.05% 23.84% +6.4 4
2004-08-01 @ Silkeborg L 1-3 1502 1534 23.84% 24.05% 52.11% -6.4 1
2004-08-01 Esbjerg D 2-2 1693 1624 56.64% 22.86% 20.50% -0.9 2
2004-08-01 @ FC Copenhagen D 2-2 1624 1693 20.50% 22.86% 56.64% +0.9 1
2004-08-01 Herfolge L 0-1 1428 1488 39.47% 25.67% 34.86% -5.9 0
2004-08-01 @ Randers W 1-0 1488 1428 34.86% 25.67% 39.47% +5.9 4
2004-08-01 Nordsjaelland W 2-0 1603 1495 61.16% 21.34% 17.50% +5.4 3
2004-08-01 @ Aalborg L 0-2 1495 1603 17.50% 21.34% 61.16% -5.4 1
2004-08-01 Viborg W 2-0 1668 1570 59.97% 21.77% 18.26% +5.7 3
2004-08-01 @ Brondby L 0-2 1570 1668 18.26% 21.77% 59.97% -5.7 1
2004-08-07 FC Copenhagen W 3-2 1565 1692 30.40% 25.34% 44.25% +5.7 4
2004-08-07 @ Viborg L 2-3 1692 1565 44.25% 25.34% 30.40% -5.7 2
2004-08-08 Aalborg W 2-1 1494 1609 32.03% 25.50% 42.46% +5.8 7
2004-08-08 @ Herfolge L 1-2 1609 1494 42.46% 25.50% 32.03% -5.9 3
2004-08-08 Brondby D 0-0 1495 1673 24.68% 24.29% 51.03% +0.9 2
2004-08-08 @ Aarhus GF D 0-0 1673 1495 51.03% 24.29% 24.68% -0.9 4
2004-08-08 Midtjylland W 3-0 1625 1621 48.44% 24.78% 26.79% +11.8 4
2004-08-08 @ Esbjerg L 0-3 1621 1625 26.79% 24.78% 48.44% -11.8 6
2004-08-08 Randers W 5-1 1618 1422 70.41% 17.36% 12.23% +5.7 6
2004-08-08 @ Odense L 1-5 1422 1618 12.23% 17.36% 70.41% -5.7 0
2004-08-08 Silkeborg L 2-3 1490 1540 40.95% 25.60% 33.45% -5.4 1
2004-08-08 @ Nordsjaelland W 3-2 1540 1490 33.45% 25.60% 40.95% +5.4 7
2004-08-14 Aarhus GF L 0-3 1416 1496 36.70% 25.70% 37.60% -15.2 0
2004-08-14 @ Randers W 3-0 1496 1416 37.60% 25.70% 36.70% +15.2 5
2004-08-15 Esbjerg W 2-1 1546 1636 35.22% 25.68% 39.10% +5.5 10
2004-08-15 @ Silkeborg L 1-2 1636 1546 39.10% 25.68% 35.22% -5.5 4
2004-08-15 Midtjylland W 2-1 1672 1609 56.00% 23.05% 20.95% +3.2 7
2004-08-15 @ Brondby L 1-2 1609 1672 20.95% 23.05% 56.00% -3.2 6
2004-08-15 Nordsjaelland L 0-2 1500 1485 49.94% 24.51% 25.56% -13.3 7
2004-08-15 @ Herfolge W 2-0 1485 1500 25.56% 24.51% 49.94% +13.3 4
2004-08-15 Odense D 1-1 1686 1624 55.84% 23.09% 21.06% -1.1 3
2004-08-15 @ FC Copenhagen D 1-1 1624 1686 21.06% 23.09% 55.84% +1.1 7
2004-08-15 Viborg L 1-2 1603 1570 52.13% 24.05% 23.82% -6.9 3
2004-08-15 @ Aalborg W 2-1 1570 1603 23.82% 24.05% 52.13% +6.9 7
2004-08-29 Aalborg W 3-2 1685 1596 59.01% 22.10% 18.89% +2.8 6
2004-08-29 @ FC Copenhagen L 2-3 1596 1685 18.89% 22.10% 59.01% -2.8 3
2004-08-29 Aarhus GF L 0-2 1625 1511 61.78% 21.11% 17.11% -15.8 7
2004-08-29 @ Odense W 2-0 1511 1625 17.11% 21.11% 61.78% +15.8 8
2004-08-29 Herfolge W 7-2 1631 1487 65.12% 19.77% 15.11% +7.5 7
2004-08-29 @ Esbjerg L 2-7 1487 1631 15.11% 19.77% 65.12% -7.5 7
2004-08-29 Nordsjaelland L 0-1 1577 1498 57.86% 22.48% 19.66% -8.0 7
2004-08-29 @ Viborg W 1-0 1498 1577 19.66% 22.48% 57.86% +8.0 7
2004-08-29 Randers W 3-1 1605 1401 71.21% 16.97% 11.82% +3.0 9
2004-08-29 @ Midtjylland L 1-3 1401 1605 11.82% 16.97% 71.21% -3.0 0
2004-08-29 Silkeborg W 1-0 1676 1551 63.00% 20.64% 16.36% +2.7 10
2004-08-29 @ Brondby L 0-1 1551 1676 16.36% 20.64% 63.00% -2.7 10
2004-09-11 FC Copenhagen D 0-0 1398 1688 15.61% 20.13% 64.25% +1.8 1
2004-09-11 @ Randers D 0-0 1688 1398 64.25% 20.13% 15.61% -1.8 7
2004-09-12 Brondby D 1-1 1593 1678 35.99% 25.70% 38.32% +0.1 4
2004-09-12 @ Aalborg D 1-1 1678 1593 38.32% 25.70% 35.99% -0.1 11
2004-09-12 Esbjerg W 2-1 1506 1638 29.76% 25.27% 44.97% +6.1 10
2004-09-12 @ Nordsjaelland L 1-2 1638 1506 44.97% 25.27% 29.76% -6.1 7
2004-09-12 Midtjylland L 0-1 1479 1608 30.18% 25.32% 44.50% -4.8 7
2004-09-12 @ Herfolge W 1-0 1608 1479 44.50% 25.32% 30.18% +4.8 12
2004-09-12 Odense L 1-2 1549 1609 39.53% 25.67% 34.80% -5.5 10
2004-09-12 @ Silkeborg W 2-1 1609 1549 34.80% 25.67% 39.53% +5.5 10
2004-09-12 Viborg L 1-2 1527 1569 42.13% 25.53% 32.34% -5.8 8
2004-09-12 @ Aarhus GF W 2-1 1569 1527 32.34% 25.53% 42.13% +5.8 10
2004-09-18 Silkeborg W 2-1 1613 1543 56.77% 22.82% 20.41% +3.2 15
2004-09-18 @ Midtjylland L 1-2 1543 1613 20.41% 22.82% 56.77% -3.2 10
2004-09-19 Aalborg W 1-0 1632 1593 52.95% 23.86% 23.19% +3.8 10
2004-09-19 @ Esbjerg L 0-1 1593 1632 23.19% 23.86% 52.95% -3.8 4
2004-09-19 Brondby L 1-3 1686 1678 48.92% 24.70% 26.39% -11.3 7
2004-09-19 @ FC Copenhagen W 3-1 1678 1686 26.39% 24.70% 48.92% +11.3 14
2004-09-19 Herfolge W 2-0 1521 1475 53.93% 23.61% 22.46% +6.9 11
2004-09-19 @ Aarhus GF L 0-2 1475 1521 22.46% 23.61% 53.93% -7.0 7
2004-09-19 Nordsjaelland W 4-0 1615 1512 60.55% 21.56% 17.88% +10.5 13
2004-09-19 @ Odense L 0-4 1512 1615 17.88% 21.56% 60.55% -10.5 10
2004-09-19 Randers L 0-1 1575 1399 68.37% 18.33% 13.30% -9.1 10
2004-09-19 @ Viborg W 1-0 1399 1575 13.30% 18.33% 68.37% +9.1 4
2004-09-22 Aarhus GF W 2-1 1675 1528 65.36% 19.67% 14.97% +2.3 10
2004-09-22 @ FC Copenhagen L 1-2 1528 1675 14.97% 19.67% 65.36% -2.3 11
2004-09-22 Herfolge L 2-4 1540 1468 57.03% 22.74% 20.23% -11.5 10
2004-09-22 @ Silkeborg W 4-2 1468 1540 20.23% 22.74% 57.03% +11.5 10
2004-09-22 Nordsjaelland D 0-0 1690 1501 69.62% 17.74% 12.64% -2.1 15
2004-09-22 @ Brondby D 0-0 1501 1690 12.64% 17.74% 69.62% +2.1 11
2004-09-22 Odense L 2-4 1636 1625 49.35% 24.62% 26.03% -10.2 10
2004-09-22 @ Esbjerg W 4-2 1625 1636 26.03% 24.62% 49.35% +10.2 16
2004-09-22 Randers W 3-0 1589 1409 68.90% 18.08% 13.02% +5.7 7
2004-09-22 @ Aalborg L 0-3 1409 1589 13.02% 18.08% 68.90% -5.7 4
2004-09-22 Viborg D 1-1 1616 1566 54.37% 23.50% 22.13% -1.0 16
2004-09-22 @ Midtjylland D 1-1 1566 1616 22.13% 23.50% 54.37% +1.0 11
2004-09-25 Brondby L 1-4 1479 1688 21.78% 23.37% 54.86% -8.3 10
2004-09-25 @ Herfolge W 4-1 1688 1479 54.86% 23.37% 21.78% +8.3 18
2004-09-26 Aalborg L 1-3 1635 1595 53.09% 23.82% 23.09% -12.1 16
2004-09-26 @ Odense W 3-1 1595 1635 23.09% 23.82% 53.09% +12.1 10
2004-09-26 Esbjerg W 2-0 1567 1626 39.75% 25.66% 34.59% +10.0 14
2004-09-26 @ Viborg L 0-2 1626 1567 34.59% 25.66% 39.75% -10.0 10
2004-09-26 FC Copenhagen L 1-2 1504 1677 25.15% 24.41% 50.44% -3.8 11
2004-09-26 @ Nordsjaelland W 2-1 1677 1504 50.44% 24.41% 25.15% +3.9 13
2004-09-26 Midtjylland W 3-1 1526 1615 35.41% 25.68% 38.90% +9.5 14
2004-09-26 @ Aarhus GF L 1-3 1615 1526 38.90% 25.68% 35.41% -9.5 16
2004-09-26 Silkeborg W 4-3 1403 1528 30.64% 25.37% 43.99% +5.6 7
2004-09-26 @ Randers L 3-4 1528 1403 43.99% 25.37% 30.64% -5.6 10
2004-10-02 Herfolge W 2-0 1681 1471 71.73% 16.72% 11.56% +3.4 16
2004-10-02 @ FC Copenhagen L 0-2 1471 1681 11.56% 16.72% 71.73% -3.4 10
2004-10-03 Aarhus GF L 1-2 1616 1536 57.98% 22.44% 19.58% -7.5 10
2004-10-03 @ Esbjerg W 2-1 1536 1616 19.58% 22.44% 57.98% +7.5 17
2004-10-03 Nordsjaelland D 1-1 1606 1500 60.97% 21.41% 17.62% -1.4 17
2004-10-03 @ Midtjylland D 1-1 1500 1606 17.62% 21.41% 60.97% +1.4 12
2004-10-03 Randers W 2-0 1696 1408 78.45% 13.19% 8.36% +2.3 21
2004-10-03 @ Brondby L 0-2 1408 1696 8.36% 13.19% 78.45% -2.3 7
2004-10-03 Silkeborg W 1-0 1607 1523 58.46% 22.29% 19.25% +3.2 13
2004-10-03 @ Aalborg L 0-1 1523 1607 19.25% 22.29% 58.46% -3.2 10
2004-10-03 Viborg L 0-2 1623 1577 53.84% 23.64% 22.53% -14.2 16
2004-10-03 @ Odense W 2-0 1577 1623 22.53% 23.64% 53.84% +14.2 17
2004-10-16 Odense W 2-1 1698 1609 59.02% 22.10% 18.88% +2.9 24
2004-10-16 @ Brondby L 1-2 1609 1698 18.88% 22.10% 59.02% -2.9 16
2004-10-17 Aalborg D 2-2 1543 1610 38.54% 25.69% 35.77% -0.1 18
2004-10-17 @ Aarhus GF D 2-2 1610 1543 35.77% 25.69% 38.54% +0.1 14
2004-10-17 Esbjerg L 2-3 1501 1608 32.97% 25.57% 41.45% -4.6 12
2004-10-17 @ Nordsjaelland W 3-2 1608 1501 41.45% 25.57% 32.97% +4.6 13
2004-10-17 FC Copenhagen W 4-1 1520 1684 26.09% 24.63% 49.28% +16.1 13
2004-10-17 @ Silkeborg L 1-4 1684 1520 49.28% 24.63% 26.09% -16.1 16
2004-10-17 Midtjylland W 2-1 1591 1604 46.09% 25.13% 28.78% +4.3 20
2004-10-17 @ Viborg L 1-2 1604 1591 28.78% 25.13% 46.09% -4.3 17
2004-10-17 Randers W 3-2 1467 1406 55.70% 23.13% 21.17% +3.1 13
2004-10-17 @ Herfolge L 2-3 1406 1467 21.17% 23.13% 55.70% -3.1 7
2004-10-20 Esbjerg D 1-1 1403 1613 21.65% 23.32% 55.02% +1.0 8
2004-10-20 @ Randers D 1-1 1613 1403 55.02% 23.32% 21.65% -1.0 14
2004-10-23 Brondby L 0-2 1404 1701 15.19% 19.83% 64.98% -4.6 8
2004-10-23 @ Randers W 2-0 1701 1404 64.98% 19.83% 15.19% +4.6 27
2004-10-24 Aarhus GF W 3-1 1612 1543 56.62% 22.87% 20.52% +5.5 17
2004-10-24 @ Esbjerg L 1-3 1543 1612 20.52% 22.87% 56.62% -5.5 18
2004-10-24 Herfolge W 3-0 1610 1471 64.67% 19.96% 15.37% +6.8 17
2004-10-24 @ Aalborg L 0-3 1471 1610 15.37% 19.96% 64.67% -6.8 13
2004-10-24 Nordsjaelland W 2-0 1600 1496 60.68% 21.52% 17.80% +5.5 20
2004-10-24 @ Midtjylland L 0-2 1496 1600 17.80% 21.52% 60.68% -5.5 12
2004-10-24 Silkeborg W 3-0 1606 1536 56.78% 22.82% 20.40% +9.2 19
2004-10-24 @ Odense L 0-3 1536 1606 20.40% 22.82% 56.78% -9.2 13
2004-10-24 Viborg D 0-0 1668 1595 57.07% 22.73% 20.20% -1.3 17
2004-10-24 @ FC Copenhagen D 0-0 1595 1668 20.20% 22.73% 57.07% +1.3 21
2004-10-30 FC Copenhagen L 2-3 1491 1667 24.91% 24.35% 50.74% -3.6 12
2004-10-30 @ Nordsjaelland W 3-2 1667 1491 50.74% 24.35% 24.91% +3.6 20
2004-10-30 Midtjylland D 2-2 1537 1606 38.42% 25.69% 35.89% -0.1 19
2004-10-30 @ Aarhus GF D 2-2 1606 1537 35.89% 25.69% 38.42% +0.1 21
2004-10-31 Brondby L 0-1 1527 1706 24.57% 24.26% 51.17% -4.0 13
2004-10-31 @ Silkeborg W 1-0 1706 1527 51.17% 24.26% 24.57% +4.0 30
2004-10-31 Esbjerg D 1-1 1464 1617 27.29% 24.88% 47.83% +0.6 14
2004-10-31 @ Herfolge D 1-1 1617 1464 47.83% 24.88% 27.29% -0.6 18
2004-10-31 Odense W 1-0 1597 1615 45.40% 25.22% 29.39% +4.7 24
2004-10-31 @ Viborg L 0-1 1615 1597 29.39% 25.22% 45.40% -4.7 19
2004-10-31 Randers D 1-1 1617 1399 72.45% 16.35% 11.20% -2.0 18
2004-10-31 @ Aalborg D 1-1 1399 1617 11.20% 16.35% 72.45% +2.0 9
2004-11-03 Aalborg W 2-1 1537 1615 37.03% 25.70% 37.27% +5.3 22
2004-11-03 @ Aarhus GF L 1-2 1615 1537 37.27% 25.70% 37.03% -5.3 18
2004-11-03 Brondby D 2-2 1617 1710 34.90% 25.67% 39.43% +0.1 19
2004-11-03 @ Esbjerg D 2-2 1710 1617 39.43% 25.67% 34.90% -0.1 31
2004-11-03 FC Copenhagen W 2-0 1606 1670 38.92% 25.68% 35.40% +10.2 24
2004-11-03 @ Midtjylland L 0-2 1670 1606 35.40% 25.68% 38.92% -10.2 20
2004-11-03 Herfolge W 5-0 1611 1464 65.34% 19.68% 14.98% +10.7 22
2004-11-03 @ Odense L 0-5 1464 1611 14.98% 19.68% 65.34% -10.7 14
2004-11-03 Randers D 2-2 1487 1401 58.63% 22.23% 19.14% -0.9 13
2004-11-03 @ Nordsjaelland D 2-2 1401 1487 19.14% 22.23% 58.63% +1.0 10
2004-11-03 Silkeborg D 1-1 1601 1523 57.81% 22.50% 19.69% -1.2 25
2004-11-03 @ Viborg D 1-1 1523 1601 19.69% 22.50% 57.81% +1.2 14
2004-11-06 Aalborg L 2-3 1617 1610 48.80% 24.72% 26.49% -6.2 19
2004-11-06 @ Esbjerg W 3-2 1610 1617 26.49% 24.72% 48.80% +6.2 21
2004-11-06 Viborg W 1-0 1710 1600 61.33% 21.28% 17.39% +2.8 34
2004-11-06 @ Brondby L 0-1 1600 1710 17.39% 21.28% 61.33% -2.8 25
2004-11-07 Aarhus GF L 2-3 1660 1543 62.24% 20.94% 16.82% -7.5 20
2004-11-07 @ FC Copenhagen W 3-2 1543 1660 16.82% 20.94% 62.24% +7.6 25
2004-11-07 Herfolge W 1-0 1616 1454 67.02% 18.94% 14.04% +2.2 27
2004-11-07 @ Midtjylland L 0-1 1454 1616 14.04% 18.94% 67.02% -2.3 14
2004-11-07 Nordsjaelland W 7-1 1621 1486 64.13% 20.18% 15.69% +11.0 25
2004-11-07 @ Odense L 1-7 1486 1621 15.69% 20.18% 64.13% -11.0 13
2004-11-07 Silkeborg W 4-0 1402 1524 31.15% 25.42% 43.43% +22.6 13
2004-11-07 @ Randers L 0-4 1524 1402 43.43% 25.42% 31.15% -22.6 14
2004-11-13 Midtjylland W 2-1 1616 1618 47.63% 24.91% 27.47% +4.2 24
2004-11-13 @ Aalborg L 1-2 1618 1616 27.47% 24.91% 47.63% -4.2 27
2004-11-13 Silkeborg D 1-1 1597 1501 59.82% 21.82% 18.35% -1.3 26
2004-11-13 @ Viborg D 1-1 1501 1597 18.35% 21.82% 59.82% +1.3 15
2004-11-14 Brondby L 0-1 1475 1713 19.37% 22.34% 58.29% -3.2 13
2004-11-14 @ Nordsjaelland W 1-0 1713 1475 58.29% 22.34% 19.37% +3.2 37
2004-11-14 FC Copenhagen L 0-1 1451 1653 22.43% 23.60% 53.97% -3.7 14
2004-11-14 @ Herfolge W 1-0 1653 1451 53.97% 23.60% 22.43% +3.7 23
2004-11-14 Odense D 3-3 1550 1632 36.44% 25.70% 37.86% +0.0 26
2004-11-14 @ Aarhus GF D 3-3 1632 1550 37.86% 25.70% 36.44% -0.0 26
2004-11-14 Randers W 1-0 1611 1425 69.37% 17.86% 12.77% +2.0 22
2004-11-14 @ Esbjerg L 0-1 1425 1611 12.77% 17.86% 69.37% -2.0 13
2004-11-20 Esbjerg W 4-3 1614 1613 48.07% 24.84% 27.09% +3.8 30
2004-11-20 @ Midtjylland L 3-4 1613 1614 27.09% 24.84% 48.07% -3.8 22
2004-11-20 Herfolge W 2-0 1632 1448 69.27% 17.91% 12.83% +3.8 29
2004-11-20 @ Odense L 0-2 1448 1632 12.83% 17.91% 69.27% -3.8 14
2004-11-21 Aalborg W 4-0 1656 1620 52.57% 23.95% 23.48% +13.7 26
2004-11-21 @ FC Copenhagen L 0-4 1620 1656 23.48% 23.95% 52.57% -13.8 24
2004-11-21 Aarhus GF W 4-0 1716 1550 67.33% 18.80% 13.86% +8.0 40
2004-11-21 @ Brondby L 0-4 1550 1716 13.86% 18.80% 67.33% -8.0 26
2004-11-25 Nordsjaelland W 2-1 1503 1472 51.85% 24.11% 24.04% +3.7 18
2004-11-25 @ Silkeborg L 1-2 1472 1503 24.04% 24.11% 51.85% -3.7 13
2004-11-25 Viborg D 2-2 1423 1596 25.20% 24.42% 50.38% +0.6 14
2004-11-25 @ Randers D 2-2 1596 1423 50.38% 24.42% 25.20% -0.6 27
2004-11-27 FC Copenhagen D 1-1 1609 1670 39.41% 25.67% 34.92% -0.1 23
2004-11-27 @ Esbjerg D 1-1 1670 1609 34.92% 25.67% 39.41% +0.1 27
2004-11-27 Odense D 1-1 1607 1636 43.88% 25.38% 30.74% -0.4 25
2004-11-27 @ Aalborg D 1-1 1636 1607 30.74% 25.38% 43.88% +0.4 30
2004-11-28 Brondby L 1-2 1444 1724 16.28% 20.59% 63.13% -2.5 14
2004-11-28 @ Herfolge W 2-1 1724 1444 63.13% 20.59% 16.28% +2.5 43
2004-11-28 Randers W 2-1 1618 1424 70.21% 17.46% 12.33% +1.8 33
2004-11-28 @ Midtjylland L 1-2 1424 1618 12.33% 17.46% 70.21% -1.8 14
2004-11-28 Silkeborg L 1-3 1542 1506 52.58% 23.95% 23.48% -12.0 26
2004-11-28 @ Aarhus GF W 3-1 1506 1542 23.48% 23.95% 52.58% +12.0 21
2004-11-28 Viborg W 3-2 1468 1595 30.45% 25.35% 44.20% +5.7 16
2004-11-28 @ Nordsjaelland L 2-3 1595 1468 44.20% 25.35% 30.45% -5.7 27
2005-03-12 Aarhus GF W 2-1 1590 1530 55.48% 23.20% 21.33% +3.3 30
2005-03-12 @ Viborg L 1-2 1530 1590 21.33% 23.20% 55.48% -3.3 26
2005-03-13 Aalborg D 1-1 1726 1606 62.50% 20.84% 16.67% -1.5 44
2005-03-13 @ Brondby D 1-1 1606 1726 16.67% 20.84% 62.50% +1.5 26
2005-03-13 Esbjerg L 0-1 1637 1609 51.52% 24.18% 24.30% -7.2 30
2005-03-13 @ Odense W 1-0 1609 1637 24.30% 24.18% 51.52% +7.2 26
2005-03-13 Midtjylland W 4-0 1670 1620 54.39% 23.49% 22.11% +13.0 30
2005-03-13 @ FC Copenhagen L 0-4 1620 1670 22.11% 23.49% 54.39% -13.0 33
2005-03-19 Silkeborg W 3-1 1608 1518 59.05% 22.09% 18.86% +5.1 29
2005-03-19 @ Aalborg L 1-3 1518 1608 18.86% 22.09% 59.05% -5.1 21
2005-03-19 Viborg D 1-1 1441 1593 27.52% 24.92% 47.56% +0.6 15
2005-03-19 @ Herfolge D 1-1 1593 1441 47.56% 24.92% 27.52% -0.6 31
2005-03-20 Brondby D 0-0 1616 1725 32.79% 25.56% 41.65% +0.3 27
2005-03-20 @ Esbjerg D 0-0 1725 1616 41.65% 25.56% 32.79% -0.3 45
2005-03-20 Nordsjaelland W 2-1 1527 1474 54.66% 23.42% 21.92% +3.4 29
2005-03-20 @ Aarhus GF L 1-2 1474 1527 21.92% 23.42% 54.66% -3.4 16
2005-03-20 Odense D 0-0 1607 1629 44.83% 25.28% 29.89% -0.5 34
2005-03-20 @ Midtjylland D 0-0 1629 1607 29.89% 25.28% 44.83% +0.5 31
2005-03-20 Randers W 4-0 1683 1422 76.33% 14.34% 9.33% +5.0 33
2005-03-20 @ FC Copenhagen L 0-4 1422 1683 9.33% 14.34% 76.33% -5.0 14
2005-04-02 Esbjerg W 2-1 1513 1616 33.53% 25.61% 40.86% +5.7 24
2005-04-02 @ Silkeborg L 1-2 1616 1513 40.86% 25.61% 33.53% -5.7 27
2005-04-02 Midtjylland L 0-1 1724 1606 62.31% 20.91% 16.78% -8.5 45
2005-04-02 @ Brondby W 1-0 1606 1724 16.78% 20.91% 62.31% +8.5 37
2005-04-03 Aalborg W 2-1 1592 1613 45.14% 25.25% 29.62% +4.4 34
2005-04-03 @ Viborg L 1-2 1613 1592 29.62% 25.25% 45.14% -4.4 29
2005-04-03 Aarhus GF W 1-0 1417 1530 32.13% 25.51% 42.35% +6.2 17
2005-04-03 @ Randers L 0-1 1530 1417 42.35% 25.51% 32.13% -6.2 29
2005-04-03 FC Copenhagen L 1-2 1630 1688 39.81% 25.66% 34.54% -5.6 31
2005-04-03 @ Odense W 2-1 1688 1630 34.54% 25.66% 39.81% +5.6 36
2005-04-03 Herfolge D 0-0 1471 1442 51.66% 24.15% 24.19% -1.0 17
2005-04-03 @ Nordsjaelland D 0-0 1442 1471 24.19% 24.15% 51.66% +1.0 16
2005-04-06 Herfolge W 3-0 1519 1443 57.46% 22.61% 19.93% +9.0 27
2005-04-06 @ Silkeborg L 0-3 1443 1519 19.93% 22.61% 57.46% -9.0 16
2005-04-09 Nordsjaelland W 2-0 1608 1470 64.52% 20.02% 15.46% +4.7 32
2005-04-09 @ Aalborg L 0-2 1470 1608 15.46% 20.02% 64.52% -4.7 17
2005-04-09 Silkeborg D 0-0 1615 1528 58.73% 22.20% 19.07% -1.4 38
2005-04-09 @ Midtjylland D 0-0 1528 1615 19.07% 22.20% 58.73% +1.4 28
2005-04-10 Aarhus GF W 1-0 1434 1524 35.28% 25.68% 39.04% +5.8 19
2005-04-10 @ Herfolge L 0-1 1524 1434 39.04% 25.68% 35.28% -5.8 29
2005-04-10 Brondby W 3-0 1694 1716 44.90% 25.27% 29.83% +12.9 39
2005-04-10 @ FC Copenhagen L 0-3 1716 1694 29.83% 25.27% 44.90% -12.9 45
2005-04-10 Odense D 1-1 1423 1624 22.43% 23.60% 53.96% +1.0 18
2005-04-10 @ Randers D 1-1 1624 1423 53.96% 23.60% 22.43% -1.0 32
2005-04-10 Viborg W 4-1 1611 1597 49.70% 24.55% 25.75% +9.6 30
2005-04-10 @ Esbjerg L 1-4 1597 1611 25.75% 24.55% 49.70% -9.6 34
2005-04-13 Nordsjaelland W 2-1 1424 1465 42.25% 25.52% 32.23% +4.7 21
2005-04-13 @ Randers L 1-2 1465 1424 32.23% 25.52% 42.25% -4.7 17
2005-04-16 Brondby L 0-2 1587 1703 31.87% 25.49% 42.64% -9.4 34
2005-04-16 @ Viborg W 2-0 1703 1587 42.64% 25.49% 31.87% +9.4 48
2005-04-16 Esbjerg D 0-0 1613 1620 46.93% 25.01% 28.05% -0.6 33
2005-04-16 @ Aalborg D 0-0 1620 1613 28.05% 25.01% 46.93% +0.7 31
2005-04-17 FC Copenhagen L 1-2 1518 1707 23.66% 24.00% 52.34% -3.7 29
2005-04-17 @ Aarhus GF W 2-1 1707 1518 52.34% 24.00% 23.66% +3.6 42
2005-04-17 Midtjylland L 1-3 1440 1613 25.16% 24.41% 50.43% -6.7 19
2005-04-17 @ Herfolge W 3-1 1613 1440 50.43% 24.41% 25.16% +6.7 41
2005-04-17 Odense D 0-0 1460 1623 26.29% 24.67% 49.04% +0.8 18
2005-04-17 @ Nordsjaelland D 0-0 1623 1460 49.04% 24.67% 26.29% -0.8 33
2005-04-17 Randers D 2-2 1529 1429 60.33% 21.64% 18.02% -1.0 29
2005-04-17 @ Silkeborg D 2-2 1429 1529 18.02% 21.64% 60.33% +1.0 22
2005-04-23 Nordsjaelland W 2-0 1710 1461 75.27% 14.90% 9.83% +2.8 45
2005-04-23 @ FC Copenhagen L 0-2 1461 1710 9.83% 14.90% 75.27% -2.8 18
2005-04-24 Aalborg L 0-4 1430 1612 24.21% 24.16% 51.63% -14.1 22
2005-04-24 @ Randers W 4-0 1612 1430 51.63% 24.16% 24.21% +14.1 36
2005-04-24 Aarhus GF L 0-1 1620 1515 60.84% 21.46% 17.70% -8.3 41
2005-04-24 @ Midtjylland W 1-0 1515 1620 17.70% 21.46% 60.84% +8.3 32
2005-04-24 Herfolge W 5-1 1621 1433 69.58% 17.76% 12.66% +6.0 34
2005-04-24 @ Esbjerg L 1-5 1433 1621 12.66% 17.76% 69.58% -6.0 19
2005-04-24 Silkeborg D 0-0 1712 1528 69.22% 17.93% 12.85% -2.1 49
2005-04-24 @ Brondby D 0-0 1528 1712 12.85% 17.93% 69.22% +2.1 30
2005-04-24 Viborg D 0-0 1622 1578 53.64% 23.69% 22.67% -1.1 34
2005-04-24 @ Odense D 0-0 1578 1622 22.67% 23.69% 53.64% +1.1 35
2005-04-30 Randers W 2-0 1710 1416 79.03% 12.87% 8.10% +2.2 52
2005-04-30 @ Brondby L 0-2 1416 1710 8.10% 12.87% 79.03% -2.2 22
2005-05-01 Aalborg L 2-4 1427 1627 22.60% 23.66% 53.74% -5.4 19
2005-05-01 @ Herfolge W 4-2 1627 1427 53.74% 23.66% 22.60% +5.4 39
2005-05-01 Esbjerg L 0-1 1523 1627 33.43% 25.60% 40.97% -5.2 32
2005-05-01 @ Aarhus GF W 1-0 1627 1523 40.97% 25.60% 33.43% +5.2 37
2005-05-01 FC Copenhagen L 0-2 1579 1713 29.56% 25.24% 45.20% -8.8 35
2005-05-01 @ Viborg W 2-0 1713 1579 45.20% 25.24% 29.56% +8.8 48
2005-05-01 Midtjylland W 3-2 1458 1612 27.35% 24.89% 47.76% +6.1 21
2005-05-01 @ Nordsjaelland L 2-3 1612 1458 47.76% 24.89% 27.35% -6.1 41
2005-05-01 Odense W 4-2 1530 1621 35.17% 25.68% 39.15% +8.5 33
2005-05-01 @ Silkeborg L 2-4 1621 1530 39.15% 25.68% 35.17% -8.5 34
2005-05-07 Aarhus GF W 3-1 1632 1518 61.86% 21.08% 17.06% +4.6 42
2005-05-07 @ Aalborg L 1-3 1518 1632 17.06% 21.08% 61.86% -4.6 32
2005-05-08 Brondby D 1-1 1613 1713 33.99% 25.63% 40.38% +0.2 35
2005-05-08 @ Odense D 1-1 1713 1613 40.38% 25.63% 33.99% -0.2 53
2005-05-08 Herfolge D 1-1 1413 1422 46.76% 25.04% 28.20% -0.6 23
2005-05-08 @ Randers D 1-1 1422 1413 28.20% 25.04% 46.76% +0.6 20
2005-05-08 Nordsjaelland W 3-1 1632 1464 67.56% 18.70% 13.74% +3.6 40
2005-05-08 @ Esbjerg L 1-3 1464 1632 13.74% 18.70% 67.56% -3.6 21
2005-05-08 Silkeborg L 0-1 1722 1539 69.13% 17.97% 12.90% -9.2 48
2005-05-08 @ FC Copenhagen W 1-0 1539 1722 12.90% 17.97% 69.13% +9.2 36
2005-05-08 Viborg L 1-2 1606 1570 52.49% 23.97% 23.55% -6.9 41
2005-05-08 @ Midtjylland W 2-1 1570 1606 23.55% 23.97% 52.49% +6.9 38
2005-05-15 Midtjylland L 0-1 1548 1599 40.93% 25.60% 33.46% -6.0 36
2005-05-15 @ Silkeborg W 1-0 1599 1548 33.46% 25.60% 40.93% +6.0 44
2005-05-16 Aalborg L 0-1 1461 1637 24.93% 24.35% 50.72% -4.1 21
2005-05-16 @ Nordsjaelland W 1-0 1637 1461 50.72% 24.35% 24.93% +4.1 45
2005-05-16 Esbjerg D 2-2 1577 1636 39.79% 25.66% 34.55% -0.1 39
2005-05-16 @ Viborg D 2-2 1636 1577 34.55% 25.66% 39.79% +0.1 41
2005-05-16 FC Copenhagen W 5-0 1712 1713 47.82% 24.88% 27.31% +19.4 56
2005-05-16 @ Brondby L 0-5 1713 1712 27.31% 24.88% 47.82% -19.4 48
2005-05-16 Herfolge W 2-1 1513 1422 59.23% 22.03% 18.74% +2.9 35
2005-05-16 @ Aarhus GF L 1-2 1422 1513 18.74% 22.03% 59.23% -2.9 20
2005-05-16 Randers W 1-0 1613 1413 70.79% 17.18% 12.03% +1.9 38
2005-05-16 @ Odense L 0-1 1413 1613 12.03% 17.18% 70.79% -1.9 23
2005-05-19 Brondby W 3-1 1605 1732 30.43% 25.35% 44.22% +10.5 47
2005-05-19 @ Midtjylland L 1-3 1732 1605 44.22% 25.35% 30.43% -10.5 56
2005-05-19 Nordsjaelland W 1-0 1419 1457 42.77% 25.48% 31.75% +5.0 23
2005-05-19 @ Herfolge L 0-1 1457 1419 31.75% 25.48% 42.77% -5.0 21
2005-05-19 Odense D 1-1 1694 1615 57.78% 22.51% 19.71% -1.2 49
2005-05-19 @ FC Copenhagen D 1-1 1615 1694 19.71% 22.51% 57.78% +1.2 39
2005-05-19 Randers W 4-0 1516 1411 60.87% 21.45% 17.68% +10.4 38
2005-05-19 @ Aarhus GF L 0-4 1411 1516 17.68% 21.45% 60.87% -10.4 23
2005-05-19 Silkeborg W 4-0 1636 1542 59.55% 21.92% 18.53% +10.9 44
2005-05-19 @ Esbjerg L 0-4 1542 1636 18.53% 21.92% 59.55% -10.9 36
2005-05-19 Viborg W 5-3 1641 1577 55.99% 23.05% 20.96% +4.8 48
2005-05-19 @ Aalborg L 3-5 1577 1641 20.96% 23.05% 55.99% -4.8 39
2005-05-22 Aalborg W 1-0 1531 1645 32.05% 25.51% 42.44% +6.2 39
2005-05-22 @ Silkeborg L 0-1 1645 1531 42.44% 25.51% 32.05% -6.2 48
2005-05-22 Aarhus GF W 2-1 1452 1527 37.48% 25.70% 36.82% +5.2 24
2005-05-22 @ Nordsjaelland L 1-2 1527 1452 36.82% 25.70% 37.48% -5.2 38
2005-05-22 Esbjerg W 4-0 1721 1647 57.30% 22.66% 20.04% +11.8 59
2005-05-22 @ Brondby L 0-4 1647 1721 20.04% 22.66% 57.30% -11.8 44
2005-05-22 FC Copenhagen L 0-1 1400 1692 15.51% 20.06% 64.44% -2.5 23
2005-05-22 @ Randers W 1-0 1692 1400 64.44% 20.06% 15.51% +2.5 52
2005-05-22 Herfolge W 2-1 1572 1424 65.51% 19.60% 14.88% +2.3 42
2005-05-22 @ Viborg L 1-2 1424 1572 14.88% 19.60% 65.51% -2.3 23
2005-05-22 Midtjylland W 3-1 1616 1615 48.03% 24.84% 27.12% +7.1 42
2005-05-22 @ Odense L 1-3 1615 1616 27.12% 24.84% 48.03% -7.1 47
2005-05-28 Brondby D 3-3 1639 1733 34.78% 25.66% 39.55% +0.1 49
2005-05-28 @ Aalborg D 3-3 1733 1639 39.55% 25.66% 34.78% -0.1 60
2005-05-29 FC Copenhagen W 1-0 1608 1695 35.75% 25.69% 38.56% +5.8 50
2005-05-29 @ Midtjylland L 0-1 1695 1608 38.56% 25.69% 35.75% -5.8 52
2005-05-29 Odense W 3-2 1635 1623 49.42% 24.61% 25.97% +3.8 47
2005-05-29 @ Esbjerg L 2-3 1623 1635 25.97% 24.61% 49.42% -3.8 42
2005-05-29 Randers W 2-0 1457 1398 55.44% 23.21% 21.35% +6.6 27
2005-05-29 @ Nordsjaelland L 0-2 1398 1457 21.35% 23.21% 55.44% -6.6 23
2005-05-29 Silkeborg D 1-1 1422 1537 31.94% 25.50% 42.57% +0.3 24
2005-05-29 @ Herfolge D 1-1 1537 1422 42.57% 25.50% 31.94% -0.3 40
2005-05-29 Viborg L 1-2 1521 1574 40.58% 25.62% 33.80% -5.6 38
2005-05-29 @ Aarhus GF W 2-1 1574 1521 33.80% 25.62% 40.58% +5.6 45
2005-06-11 Herfolge W 7-0 1733 1422 80.24% 12.20% 7.56% +6.6 63
2005-06-11 @ Brondby L 0-7 1422 1733 7.56% 12.20% 80.24% -6.6 24
2005-06-12 Aalborg L 1-2 1620 1639 45.22% 25.24% 29.55% -6.1 42
2005-06-12 @ Odense W 2-1 1639 1620 29.55% 25.24% 45.22% +6.2 52
2005-06-12 Aarhus GF D 1-1 1537 1516 50.68% 24.36% 24.96% -0.8 41
2005-06-12 @ Silkeborg D 1-1 1516 1537 24.96% 24.36% 50.68% +0.8 39
2005-06-12 Esbjerg W 1-0 1689 1639 54.38% 23.50% 22.13% +3.6 55
2005-06-12 @ FC Copenhagen L 0-1 1639 1689 22.13% 23.50% 54.38% -3.6 47
2005-06-12 Midtjylland L 0-3 1391 1614 20.57% 22.89% 56.54% -9.3 23
2005-06-12 @ Randers W 3-0 1614 1391 56.54% 22.89% 20.57% +9.3 53
2005-06-12 Nordsjaelland L 1-2 1580 1464 62.10% 20.99% 16.91% -7.9 45
2005-06-12 @ Viborg W 2-1 1464 1580 16.91% 20.99% 62.10% +8.0 30
2005-06-15 Brondby L 1-2 1516 1740 20.52% 22.86% 56.62% -3.2 39
2005-06-15 @ Aarhus GF W 2-1 1740 1516 56.62% 22.86% 20.52% +3.2 66
2005-06-15 FC Copenhagen D 1-1 1645 1693 41.39% 25.58% 33.03% -0.2 53
2005-06-15 @ Aalborg D 1-1 1693 1645 33.03% 25.58% 41.39% +0.2 56
2005-06-15 Midtjylland D 0-0 1635 1623 49.48% 24.60% 25.93% -0.8 48
2005-06-15 @ Esbjerg D 0-0 1623 1635 25.93% 24.60% 49.48% +0.8 54
2005-06-15 Odense L 2-3 1416 1613 22.78% 23.72% 53.50% -3.3 24
2005-06-15 @ Herfolge W 3-2 1613 1416 53.50% 23.72% 22.78% +3.3 45
2005-06-15 Randers W 2-1 1572 1382 69.80% 17.65% 12.54% +1.9 48
2005-06-15 @ Viborg L 1-2 1382 1572 12.54% 17.65% 69.80% -1.9 23
2005-06-15 Silkeborg L 2-3 1472 1536 38.95% 25.68% 35.36% -5.2 30
2005-06-15 @ Nordsjaelland W 3-2 1536 1472 35.36% 25.68% 38.95% +5.2 44
2005-06-19 Aalborg W 4-1 1624 1645 44.99% 25.26% 29.74% +10.9 57
2005-06-19 @ Midtjylland L 1-4 1645 1624 29.74% 25.26% 44.99% -10.9 53
2005-06-19 Aarhus GF W 3-0 1617 1513 60.64% 21.53% 17.82% +8.0 48
2005-06-19 @ Odense L 0-3 1513 1617 17.82% 21.53% 60.64% -8.0 39
2005-06-19 Esbjerg D 2-2 1380 1634 18.08% 21.68% 60.24% +1.0 24
2005-06-19 @ Randers D 2-2 1634 1380 60.24% 21.68% 18.08% -1.0 49
2005-06-19 Herfolge D 1-1 1693 1412 77.90% 13.49% 8.61% -2.2 57
2005-06-19 @ FC Copenhagen D 1-1 1412 1693 8.61% 13.49% 77.90% +2.2 25
2005-06-19 Nordsjaelland W 2-0 1743 1466 77.55% 13.68% 8.77% +2.4 69
2005-06-19 @ Brondby L 0-2 1466 1743 8.77% 13.68% 77.55% -2.5 30
2005-06-19 Viborg W 3-2 1541 1574 43.44% 25.42% 31.14% +4.4 47
2005-06-19 @ Silkeborg L 2-3 1574 1541 31.14% 25.42% 43.44% -4.4 48

Biggest Upsets

The 25 games where the underdog won despite the lowest pregame win probability. Underdog Win % is the winner's pregame chance of winning the game (lower = bigger upset). @ before a team name indicates the away side. An asterisk (*) after the date marks a playoff game.

# Date Underdog Win % Winning Team Losing Team
Team Elo Score Team Elo Score
1 2005-05-08 12.90% Silkeborg 1539 1 @ FC Copenhagen 1722 0
2 2004-09-19 13.30% Randers 1399 1 @ Viborg 1575 0
3 2005-04-02 16.78% Midtjylland 1606 1 @ Brondby 1724 0
4 2004-11-07 16.82% Aarhus GF 1543 3 @ FC Copenhagen 1660 2
5 2005-06-12 16.91% Nordsjaelland 1464 2 @ Viborg 1580 1
6 2004-08-29 17.11% Aarhus GF 1511 2 @ Odense 1625 0
7 2005-04-24 17.70% Aarhus GF 1515 1 @ Midtjylland 1620 0
8 2004-10-03 19.58% Aarhus GF 1536 2 @ Esbjerg 1616 1
9 2004-08-29 19.66% Nordsjaelland 1498 1 @ Viborg 1577 0
10 2004-09-22 20.23% Herfolge 1468 4 @ Silkeborg 1540 2
11 2004-07-25 21.27% Odense 1615 2 @ Brondby 1675 1
12 2004-10-03 22.53% Viborg 1577 2 @ Odense 1623 0
13 2004-09-26 23.09% Aalborg 1595 3 @ Odense 1635 1
14 2004-11-28 23.48% Silkeborg 1506 3 @ Aarhus GF 1542 1
15 2005-05-08 23.55% Viborg 1570 2 @ Midtjylland 1606 1
16 2004-08-15 23.82% Viborg 1570 2 @ Aalborg 1603 1
17 2005-03-13 24.30% Esbjerg 1609 1 @ Odense 1637 0
18 2004-08-15 25.56% Nordsjaelland 1485 2 @ Herfolge 1500 0
19 2004-09-22 26.03% Odense 1625 4 @ Esbjerg 1636 2
20 2004-10-17 26.09% @ Silkeborg 1520 4 FC Copenhagen 1684 1
21 2004-09-19 26.39% Brondby 1678 3 @ FC Copenhagen 1686 1
22 2004-11-06 26.49% Aalborg 1610 3 @ Esbjerg 1617 2
23 2004-07-25 27.26% Midtjylland 1610 2 @ Aalborg 1610 1
24 2005-05-01 27.35% @ Nordsjaelland 1458 3 Midtjylland 1612 2
25 2005-06-12 29.55% Aalborg 1639 2 @ Odense 1620 1

Biggest Elo Changes

The 25 games that resulted in the largest shift in Elo rating.

# Date Elo Δ Winning Team Losing Team Tie %
Team Score Elo Win % Team Score Elo Win %
1 2004-11-07 22.59 @ Randers 4 1402 31.15% Silkeborg 0 1524 43.43% 25.42%
2 2005-05-16 19.35 @ Brondby 5 1712 47.82% FC Copenhagen 0 1713 27.31% 24.88%
3 2004-10-17 16.13 @ Silkeborg 4 1520 26.09% FC Copenhagen 1 1684 49.28% 24.63%
4 2004-08-29 15.84 Aarhus GF 2 1511 17.11% @ Odense 0 1625 61.78% 21.11%
5 2004-08-14 15.21 Aarhus GF 3 1496 37.60% @ Randers 0 1416 36.70% 25.70%
6 2004-10-03 14.17 Viborg 2 1577 22.53% @ Odense 0 1623 53.84% 23.64%
7 2005-04-24 14.13 Aalborg 4 1612 51.63% @ Randers 0 1430 24.21% 24.16%
8 2004-11-21 13.75 @ FC Copenhagen 4 1656 52.57% Aalborg 0 1620 23.48% 23.95%
9 2004-08-15 13.31 Nordsjaelland 2 1485 25.56% @ Herfolge 0 1500 49.94% 24.51%
10 2005-03-13 12.99 @ FC Copenhagen 4 1670 54.39% Midtjylland 0 1620 22.11% 23.49%
11 2005-04-10 12.94 @ FC Copenhagen 3 1694 44.90% Brondby 0 1716 29.83% 25.27%
12 2004-09-26 12.12 Aalborg 3 1595 23.09% @ Odense 1 1635 53.09% 23.82%
13 2004-11-28 12.03 Silkeborg 3 1506 23.48% @ Aarhus GF 1 1542 52.58% 23.95%
14 2004-08-08 11.83 @ Esbjerg 3 1625 48.44% Midtjylland 0 1621 26.79% 24.78%
15 2005-05-22 11.81 @ Brondby 4 1721 57.30% Esbjerg 0 1647 20.04% 22.66%
16 2004-09-22 11.51 Herfolge 4 1468 20.23% @ Silkeborg 2 1540 57.03% 22.74%
17 2004-09-19 11.34 Brondby 3 1678 26.39% @ FC Copenhagen 1 1686 48.92% 24.70%
18 2004-11-07 11.03 @ Odense 7 1621 64.13% Nordsjaelland 1 1486 15.69% 20.18%
19 2005-05-19 10.91 @ Esbjerg 4 1636 59.55% Silkeborg 0 1542 18.53% 21.92%
20 2005-06-19 10.89 @ Midtjylland 4 1624 44.99% Aalborg 1 1645 29.74% 25.26%
21 2004-11-03 10.70 @ Odense 5 1611 65.34% Herfolge 0 1464 14.98% 19.68%
22 2004-09-19 10.52 @ Odense 4 1615 60.55% Nordsjaelland 0 1512 17.88% 21.56%
23 2005-05-19 10.46 @ Midtjylland 3 1605 30.43% Brondby 1 1732 44.22% 25.35%
24 2005-05-19 10.39 @ Aarhus GF 4 1516 60.87% Randers 0 1411 17.68% 21.45%
25 2004-09-22 10.22 Odense 4 1625 26.03% @ Esbjerg 2 1636 49.35% 24.62%