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2005-06 Superliga Season

198 games

Final

Champion

FC Copenhagen

73 points · 5th Title

Last Title: 2003-04

Relegated

Aarhus GF

22 pts

Sonderjyske · 26 pts

Biggest Overachiever

FC Copenhagen

12.26 points above expected

73 points · 60.74 expected points

Biggest Disappointment

Aarhus GF

13.28 points below expected

22 points · 35.28 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 FC Copenhagen Champion 33 22 7 4 73 62 27 +35 60.74 +12.26
2 Brondby 33 21 4 8 67 60 34 +26 61.09 +5.91
3 Odense 33 17 7 9 58 49 28 +21 51.51 +6.49
4 Viborg 33 15 9 9 54 62 43 +19 49.91 +4.09
5 Aalborg 33 11 12 10 45 48 44 +4 49.17 -4.17
6 Esbjerg 33 12 6 15 42 43 45 -2 47.17 -5.17
7 Midtjylland 33 10 11 12 41 42 52 -10 45.52 -4.52
8 Silkeborg 33 11 6 16 39 33 50 -17 38.42 +0.58
9 Nordsjaelland 33 9 11 13 38 49 55 -6 38.95 -0.95
10 Horsens 33 8 13 12 37 29 41 -12 36.81 +0.19
11 Sonderjyske Relegated 33 6 8 19 26 41 72 -31 29.07 -3.07
12 Aarhus GF Relegated 33 4 10 19 22 36 63 -27 35.28 -13.28

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
FC Copenhagen 1710 73 60.74 +12.26 96.3% 32 49 56 61 66 73 87
Brondby 1670 67 61.09 +5.91 81.3% 31 49 56 61 66 73 85
Odense 1649 58 51.51 +6.49 83.6% 24 39 47 51 56 63 81
Viborg 1624 54 49.91 +4.09 73.5% 24 38 45 50 55 62 75
Aalborg 1611 45 49.17 -4.17 30.7% 24 37 44 49 54 61 78
Esbjerg 1578 42 47.17 -5.17 26.1% 23 35 42 47 52 59 74
Midtjylland 1561 41 45.52 -4.52 28.7% 22 34 41 45 50 57 72
Silkeborg 1508 39 38.42 +0.58 56.5% 13 27 34 38 43 50 65
Horsens 1504 37 36.81 +0.19 54.4% 12 26 32 37 41 48 66
Nordsjaelland 1503 38 38.95 -0.95 47.9% 17 27 34 39 44 51 65
Aarhus GF 1449 22 35.28 -13.28 3.0% 14 24 30 35 40 47 62
Sonderjyske 1413 26 29.07 -3.07 35.7% 7 19 25 29 33 40 58

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 HOR MID NOR ODE SIL SON VIB
Aalborg —
1-2-0
5.01
1-0-2
2.77
2-0-1
4.45
0-0-3
3.56
1-2-0
5.03
1-2-0
4.42
1-1-1
5.18
1-1-1
3.63
2-1-0
5.26
1-2-0
6.16
0-1-2
3.64
Aarhus GF
0-2-1
3.20
 —
1-0-2
2.17
2-1-0
2.99
0-2-1
2.07
0-1-2
3.82
0-1-2
3.15
0-0-3
3.84
0-0-3
2.92
1-0-2
3.51
0-1-2
4.53
0-2-1
3.04
Brondby
2-0-1
5.47
2-0-1
6.17
 —
2-1-0
5.15
1-2-0
4.14
1-1-1
5.95
2-0-1
5.55
3-0-0
6.19
3-0-0
4.69
2-0-1
6.12
2-0-1
6.70
1-0-2
4.91
Esbjerg
1-0-2
3.73
0-1-2
5.26
0-1-2
3.07
 —
1-0-2
2.60
0-2-1
5.30
1-1-1
4.19
2-0-1
4.51
1-1-1
4.04
2-0-1
4.95
3-0-0
5.53
1-0-2
3.92
FC Copenhagen
3-0-0
4.63
1-2-0
6.29
0-2-1
4.05
2-0-1
5.66
 —
3-0-0
6.21
3-0-0
5.66
1-2-0
5.84
1-1-1
4.70
2-0-1
6.08
3-0-0
6.44
3-0-0
5.22
Horsens
0-2-1
3.18
2-1-0
4.35
1-1-1
2.35
1-2-0
2.93
0-0-3
2.14
 —
0-1-2
3.20
0-1-2
3.78
0-2-1
3.15
1-1-1
3.74
2-1-0
4.65
1-1-1
3.38
Midtjylland
0-2-1
3.75
2-1-0
5.06
1-0-2
2.70
1-1-1
3.99
0-0-3
2.60
2-1-0
5.01
 —
0-2-1
4.49
1-0-2
3.70
1-1-1
4.93
2-1-0
5.30
0-2-1
4.00
Nordsjaelland
1-1-1
3.05
3-0-0
4.35
0-0-3
2.17
1-0-2
3.68
0-2-1
2.46
2-1-0
4.39
1-2-0
3.69
 —
0-1-2
2.77
0-2-1
4.10
1-2-0
5.25
0-0-3
3.06
Odense
1-1-1
4.55
3-0-0
5.32
0-0-3
3.50
1-1-1
4.15
1-1-1
3.50
1-2-0
5.06
2-0-1
4.50
2-1-0
5.47
 —
2-0-1
5.48
2-0-1
6.16
2-1-0
3.80
Silkeborg
0-1-2
2.97
2-0-1
4.67
1-0-2
2.21
1-0-2
3.25
1-0-2
2.26
1-1-1
4.44
1-1-1
3.28
1-2-0
4.09
1-0-2
2.77
 —
1-0-2
5.33
1-1-1
3.24
Sonderjyske
0-2-1
2.18
2-1-0
3.66
1-0-2
1.74
0-0-3
2.73
0-0-3
1.97
0-1-2
3.56
0-1-2
2.93
0-2-1
2.97
1-0-2
2.18
2-0-1
2.90
 —
0-1-2
2.24
Viborg
2-1-0
4.54
1-2-0
5.21
2-0-1
3.28
2-0-1
4.27
0-0-3
3.00
1-1-1
4.83
1-2-0
4.19
3-0-0
5.16
0-1-2
4.37
1-1-1
4.98
2-1-0
6.08
 —

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 0.64 +11.1
Allowed 0.80 -10.2
Differential 0.95 +7.0

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%7.83%7.07%3.28%1.52%0.51%27.27%
17.83%11.62%4.80%5.30%1.77%0.25%31.57%
27.07%4.80%6.06%2.53%1.52%—21.97%
33.28%5.30%2.53%1.52%0.51%—13.13%
41.52%1.77%1.52%0.51%——5.30%
5+0.51%0.25%————0.76%
Total27.27%31.57%21.97%13.13%5.30%0.76%100%

Summary Statistics

Scored Allowed Difference
Mean 1.40 1.40 +0.00
SD 1.21 1.21 1.78
CV 0.86 0.86 —
Max 5 5 +5
Min 0 0 -5

Games Played: 198

↓ Scored | Allowed →012345+Total
06.06%6.06%9.09%———21.21%
112.12%18.18%6.06%3.03%——39.39%
26.06%—12.12%—3.03%—21.21%
33.03%—3.03%—3.03%—9.09%
4—3.03%6.06%———9.09%
5+———————
Total27.27%27.27%36.36%3.03%6.06%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.45 1.33 +0.12
SD 1.20 1.11 1.41
CV 0.83 0.83 —
Max 4 4 +3
Min 0 0 -2

Games Played: 33

↓ Scored | Allowed →012345+Total
0—12.12%9.09%—9.09%—30.30%
13.03%18.18%12.12%9.09%——42.42%
23.03%—6.06%3.03%3.03%—15.15%
33.03%3.03%—6.06%——12.12%
4———————
5+———————
Total9.09%33.33%27.27%18.18%12.12%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.09 1.91 -0.82
SD 0.98 1.18 1.59
CV 0.90 0.62 —
Max 3 4 +3
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
09.09%—9.09%6.06%——24.24%
112.12%3.03%3.03%3.03%3.03%—24.24%
26.06%6.06%————12.12%
312.12%12.12%3.03%———27.27%
43.03%3.03%—3.03%——9.09%
5+3.03%—————3.03%
Total45.45%24.24%15.15%12.12%3.03%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.82 1.03 +0.79
SD 1.47 1.19 2.10
CV 0.81 1.15 —
Max 5 4 +5
Min 0 0 -3

Games Played: 33

↓ Scored | Allowed →012345+Total
09.09%15.15%6.06%3.03%——33.33%
16.06%3.03%9.09%3.03%3.03%3.03%27.27%
29.09%3.03%6.06%3.03%——21.21%
3—6.06%6.06%———12.12%
46.06%—————6.06%
5+———————
Total30.30%27.27%27.27%9.09%3.03%3.03%100%

Summary Statistics

Scored Allowed Difference
Mean 1.30 1.36 -0.06
SD 1.24 1.27 1.87
CV 0.95 0.93 —
Max 4 5 +4
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
03.03%3.03%—3.03%——9.09%
118.18%15.15%—3.03%——36.36%
215.15%9.09%—3.03%——27.27%
33.03%9.09%—3.03%——15.15%
43.03%6.06%————9.09%
5+—3.03%————3.03%
Total42.42%45.45%—12.12%——100%

Summary Statistics

Scored Allowed Difference
Mean 1.88 0.82 +1.06
SD 1.24 0.95 1.56
CV 0.66 1.16 —
Max 5 3 +4
Min 0 0 -3

Games Played: 33

↓ Scored | Allowed →012345+Total
024.24%9.09%3.03%9.09%—3.03%48.48%
19.09%12.12%6.06%—3.03%—30.30%
23.03%3.03%3.03%———9.09%
3—6.06%——3.03%—9.09%
4—3.03%————3.03%
5+———————
Total36.36%33.33%12.12%9.09%6.06%3.03%100%

Summary Statistics

Scored Allowed Difference
Mean 0.88 1.24 -0.36
SD 1.11 1.37 1.69
CV 1.26 1.10 —
Max 4 5 +3
Min 0 0 -5

Games Played: 33

↓ Scored | Allowed →012345+Total
09.09%6.06%9.09%3.03%—3.03%30.30%
16.06%12.12%3.03%6.06%——27.27%
29.09%9.09%9.09%3.03%3.03%—33.33%
3———3.03%——3.03%
4——3.03%3.03%——6.06%
5+———————
Total24.24%27.27%24.24%18.18%3.03%3.03%100%

Summary Statistics

Scored Allowed Difference
Mean 1.27 1.58 -0.30
SD 1.13 1.30 1.59
CV 0.88 0.82 —
Max 4 5 +2
Min 0 0 -5

Games Played: 33

↓ Scored | Allowed →012345+Total
06.06%3.03%12.12%3.03%3.03%—27.27%
1—15.15%6.06%9.09%3.03%—33.33%
26.06%—9.09%———15.15%
33.03%6.06%3.03%3.03%——15.15%
4——6.06%———6.06%
5+3.03%—————3.03%
Total18.18%24.24%36.36%15.15%6.06%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.48 1.67 -0.18
SD 1.37 1.14 1.96
CV 0.92 0.68 —
Max 5 4 +5
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
012.12%15.15%6.06%———33.33%
19.09%3.03%—3.03%——15.15%
26.06%12.12%6.06%3.03%——27.27%
312.12%6.06%————18.18%
43.03%—3.03%———6.06%
5+———————
Total42.42%36.36%15.15%6.06%——100%

Summary Statistics

Scored Allowed Difference
Mean 1.48 0.85 +0.64
SD 1.30 0.91 1.62
CV 0.88 1.07 —
Max 4 3 +4
Min 0 0 -2

Games Played: 33

↓ Scored | Allowed →012345+Total
06.06%12.12%15.15%3.03%3.03%—39.39%
16.06%9.09%3.03%9.09%3.03%—30.30%
29.09%9.09%3.03%———21.21%
3——9.09%———9.09%
4———————
5+———————
Total21.21%30.30%30.30%12.12%6.06%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.00 1.52 -0.52
SD 1.00 1.15 1.60
CV 1.00 0.76 —
Max 3 4 +2
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
0—6.06%6.06%6.06%3.03%—21.21%
16.06%12.12%3.03%9.09%6.06%—36.36%
23.03%6.06%12.12%9.09%9.09%—39.39%
3——3.03%———3.03%
4———————
5+———————
Total9.09%24.24%24.24%24.24%18.18%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.24 2.18 -0.94
SD 0.83 1.26 1.48
CV 0.67 0.58 —
Max 3 4 +2
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
0—6.06%—3.03%——9.09%
16.06%18.18%6.06%6.06%——36.36%
29.09%—6.06%6.06%——21.21%
33.03%15.15%3.03%3.03%——24.24%
43.03%6.06%————9.09%
5+———————
Total21.21%45.45%15.15%18.18%——100%

Summary Statistics

Scored Allowed Difference
Mean 1.88 1.30 +0.58
SD 1.17 1.02 1.68
CV 0.62 0.78 —
Max 4 3 +4
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 FC Copenhagen 73 60.74 +12.26
2 Odense 58 51.51 +6.49
3 Brondby 67 61.09 +5.91
4 Viborg 54 49.91 +4.09
5 Silkeborg 39 38.42 +0.58

Biggest Disappointments

# Team Actual Sim vsSim
1 Aarhus GF 22 35.28 -13.28
2 Esbjerg 42 47.17 -5.17
3 Midtjylland 41 45.52 -4.52
4 Aalborg 45 49.17 -4.17
5 Sonderjyske 26 29.07 -3.07

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 Aalborg 4 Apr 30 – May 14 1 in 28
2 Viborg 4 Jul 20 – Aug 7 1 in 25
3 FC Copenhagen 6 Mar 29 – Apr 22 1 in 24
4 Aarhus GF 2 Aug 28 – Sep 11 1 in 22
5 Horsens 2 May 4 – May 7 1 in 14

Most Unlikely Losing Streaks

# Team Games Dates Probability
1 Aarhus GF 6 Mar 19 – Apr 13 1 in 60
2 Brondby 2 May 7 – May 14 1 in 51
3 Viborg 3 Dec 4 – Mar 19 1 in 37
4 Sonderjyske 5 Aug 28 – Sep 25 1 in 32
5 FC Copenhagen 2 May 7 – May 14 1 in 26

Most Unlikely Unbeaten Streaks

# Team Games Dates Probability
1 Horsens 8 Nov 27 – Apr 9 1 in 133
2 FC Copenhagen 16 Jul 20 – Oct 30 1 in 76
3 Nordsjaelland 9 Aug 28 – Oct 26 1 in 73
4 Viborg 9 Sep 11 – Oct 29 1 in 34
5 Sonderjyske 3 Mar 11 – Mar 26 1 in 14

Most Unlikely Winless Streaks

# Team Games Dates Probability
1 Aalborg 9 Mar 12 – Apr 23 1 in 299
2 Aarhus GF 12 Dec 4 – May 4 1 in 59
3 Silkeborg 10 Aug 7 – Oct 16 1 in 29
4 Horsens 8 Oct 2 – Nov 27 1 in 16
5 Midtjylland 6 Oct 2 – Nov 6 1 in 13

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
FC Copenhagen40.95%30.08%13.92%7.43%4.15%1.83%0.87%0.51%0.18%0.07%0.01%—
Brondby41.67%29.83%13.80%7.68%3.66%1.68%0.99%0.45%0.13%0.07%0.03%0.01%
Odense6.38%12.48%19.92%18.14%14.81%10.90%7.51%4.88%2.84%1.43%0.53%0.18%
Viborg4.46%9.21%16.08%17.70%16.08%13.49%9.68%6.00%3.94%2.20%0.89%0.27%
Aalborg3.31%8.27%13.94%16.13%16.20%14.48%11.20%7.53%4.82%2.52%1.22%0.38%
Esbjerg1.91%5.00%10.11%13.82%15.36%15.67%13.49%10.90%7.00%3.96%2.05%0.73%
Midtjylland1.12%3.61%7.69%10.43%13.25%15.54%15.74%12.76%9.01%6.02%3.61%1.22%
Silkeborg0.06%0.52%1.39%2.68%4.77%7.31%11.02%14.36%17.67%17.04%14.67%8.51%
Nordsjaelland0.09%0.54%1.81%3.10%5.43%8.31%12.86%15.14%16.62%16.50%12.78%6.82%
Horsens0.02%0.29%0.82%1.62%3.54%6.01%8.42%13.57%17.16%19.29%18.34%10.92%
Sonderjyske0.01%0.02%0.05%0.10%0.27%0.75%1.59%2.88%6.37%11.67%22.57%53.72%
Aarhus GF0.02%0.15%0.47%1.17%2.48%4.03%6.63%11.02%14.26%19.23%23.30%17.24%

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
+14.14%
Slight Edge
43.94%26.26%29.80%
Elo Value
Home Edge: 49.46 Elo pts.
167 Elo
0.006 goals per Elo point
0500
Scoring Tilt
Expected
+0.33 goals
Neutral
-2+0.30+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
2.9
Top-Heavy
124610
Champion Preseason Odds
41%
FC Copenhagen, 2nd of 12
LongshotFavorite
Title Margin
Expected
0.18/gm
Tight Race
00.180.5/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 5.69 * Some Luck: 5.69 to 8.54 * Lucky: 8.54 to 11.39 * Wild Swing: 11.39 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 1.31 * Close: 1.31 to 1.96 * Off: 1.96 to 2.62 * Way Off: 2.62 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 0.83 * A Surprise: 0.83 to 1.33 * Several Surprises: 1.33 to 1.83 * Many Surprises: 1.83 and up.
Luck Spread
Expected
6.43 points
Some Luck
07.1218
Average Finish Error
Expected
0.50
Pinpoint
01.644
Biggest Overachiever
Expected 95.83%
96.28%
FC Copenhagen
50100
Biggest Underachiever
Expected 4.17%
2.95%
Aarhus GF
050
Season Outliers
Expected
2 of 12
Minimal Outliers
01.26
Unexpected Relegations
Expected
0 of 2
As Expected
00.82

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
0.18
Top-Heavy
00.120.180.260.5
Noll-Scully
Coin-flip
1.78
Strong Separation
01.003
Interquartile Edge
67%
Slight Edge
50%60%70%80%100%
Best vs. Worst
Baseline
85%
Strong Edge
50%86%100%
Close Games
Expected
58%
Very Frequent
0%60%100%
Blowouts
Expected
15%
Frequent
0%16%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
0.60
Predictable
00.612
Matchup Imbalance
0.32
Lopsided
00.10.180.280.5
Strangeness
Expected
0.81
As Expected
01.002
Repeatability
0.83
Strong Carryover
00.30.60.851
Upset Rate
Expected
22%
Chalky
0%25%50%
Clear Favorite Upset Rate
Expected
16%
As Expected
0%21%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
0.62
Excellent
0.010.050.10.51
Calibration slope
Ideal
1.22
Underconfident
0.51.001.5
Calibration error (ECE)
Noise ceiling
0.092
Well Within Noise
00.1190.3

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
FC Copenhagen 99.99% 0.01%
Brondby 99.96% 0.04%
Odense 99.29% 0.71%
Viborg 98.84% 1.16%
Aalborg 98.40% 1.60%
Esbjerg 97.22% 2.78%
Midtjylland 95.17% 4.83%
Nordsjaelland 80.40% 19.60%
Silkeborg 76.82% 23.18%
Horsens 70.74% 29.26%
Aarhus GF 59.46% 40.54%
Sonderjyske 23.71% 76.29%

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
2005-07-19 Odense D 2-2 1596 1592 43.88% 27.31% 28.80% -0.3 1
2005-07-19 @ Esbjerg D 2-2 1592 1596 28.80% 27.31% 43.88% +0.3 1
2005-07-20 Aarhus GF W 2-1 1545 1524 46.20% 26.93% 26.87% +4.2 3
2005-07-20 @ Silkeborg L 1-2 1524 1545 26.87% 26.93% 46.20% -4.2 0
2005-07-20 FC Copenhagen L 0-1 1596 1626 39.05% 27.78% 33.17% -6.0 0
2005-07-20 @ Aalborg W 1-0 1626 1596 33.17% 27.78% 39.05% +6.0 3
2005-07-20 Midtjylland W 3-0 1660 1594 52.26% 25.47% 22.27% +10.4 3
2005-07-20 @ Brondby L 0-3 1594 1660 22.27% 25.47% 52.26% -10.4 0
2005-07-20 Sonderjyske D 1-1 1502 1462 48.81% 26.39% 24.80% -0.8 1
2005-07-20 @ Nordsjaelland D 1-1 1462 1502 24.80% 26.39% 48.81% +0.8 1
2005-07-20 Viborg L 0-3 1524 1558 38.53% 27.81% 33.66% -16.2 0
2005-07-20 @ Horsens W 3-0 1558 1524 33.66% 27.81% 38.53% +16.2 3
2005-07-23 Silkeborg W 2-0 1671 1549 59.07% 23.04% 17.89% +5.7 6
2005-07-23 @ Brondby L 0-2 1549 1671 17.89% 23.04% 59.07% -5.7 3
2005-07-24 Aalborg W 2-1 1593 1590 43.82% 27.32% 28.86% +4.5 4
2005-07-24 @ Odense L 1-2 1590 1593 28.86% 27.32% 43.82% -4.5 0
2005-07-24 Aarhus GF W 2-1 1463 1520 35.29% 27.85% 36.86% +5.4 4
2005-07-24 @ Sonderjyske L 1-2 1520 1463 36.86% 27.85% 35.29% -5.4 0
2005-07-24 Esbjerg W 2-1 1583 1596 41.63% 27.58% 30.79% +4.7 3
2005-07-24 @ Midtjylland L 1-2 1596 1583 30.79% 27.58% 41.63% -4.7 1
2005-07-24 Horsens W 2-0 1632 1507 59.45% 22.89% 17.67% +5.7 6
2005-07-24 @ FC Copenhagen L 0-2 1507 1632 17.67% 22.89% 59.45% -5.7 0
2005-07-24 Nordsjaelland W 3-1 1574 1502 53.05% 25.23% 21.72% +6.0 6
2005-07-24 @ Viborg L 1-3 1502 1574 21.72% 25.23% 53.05% -6.0 1
2005-07-30 Midtjylland D 1-1 1585 1588 42.96% 27.43% 29.61% -0.4 1
2005-07-30 @ Aalborg D 1-1 1588 1585 29.61% 27.43% 42.96% +0.4 4
2005-07-31 Brondby L 2-3 1591 1676 31.47% 27.65% 40.88% -4.6 1
2005-07-31 @ Esbjerg W 3-2 1676 1591 40.88% 27.65% 31.47% +4.6 9
2005-07-31 FC Copenhagen L 1-2 1496 1638 24.87% 26.41% 48.72% -4.0 1
2005-07-31 @ Nordsjaelland W 2-1 1638 1496 48.72% 26.41% 24.87% +4.0 9
2005-07-31 Odense D 0-0 1502 1597 30.17% 27.51% 42.32% +0.4 1
2005-07-31 @ Horsens D 0-0 1597 1502 42.32% 27.51% 30.17% -0.4 5
2005-07-31 Sonderjyske W 3-2 1543 1468 53.33% 25.14% 21.53% +3.3 6
2005-07-31 @ Silkeborg L 2-3 1468 1543 21.53% 25.14% 53.33% -3.3 4
2005-07-31 Viborg L 2-3 1515 1580 34.07% 27.82% 38.11% -4.8 0
2005-07-31 @ Aarhus GF W 3-2 1580 1515 38.11% 27.82% 34.07% +4.8 9
2005-08-06 Aalborg W 3-1 1681 1585 56.00% 24.23% 19.77% +5.5 12
2005-08-06 @ Brondby L 1-3 1585 1681 19.77% 24.23% 56.00% -5.5 1
2005-08-07 Aarhus GF D 1-1 1642 1510 60.32% 22.52% 17.17% -1.4 10
2005-08-07 @ FC Copenhagen D 1-1 1510 1642 17.17% 22.52% 60.32% +1.4 1
2005-08-07 Horsens W 2-1 1588 1502 54.76% 24.67% 20.57% +3.3 7
2005-08-07 @ Midtjylland L 1-2 1502 1588 20.57% 24.67% 54.76% -3.3 1
2005-08-07 Nordsjaelland D 0-0 1597 1492 57.12% 23.82% 19.07% -1.4 6
2005-08-07 @ Odense D 0-0 1492 1597 19.07% 23.82% 57.12% +1.4 2
2005-08-07 Silkeborg W 4-0 1587 1547 48.81% 26.39% 24.81% +15.0 4
2005-08-07 @ Esbjerg L 0-4 1547 1587 24.81% 26.39% 48.81% -15.0 6
2005-08-07 Sonderjyske W 3-1 1585 1465 58.89% 23.12% 18.00% +5.0 12
2005-08-07 @ Viborg L 1-3 1465 1585 18.00% 23.12% 58.89% -5.0 4
2005-08-13 Odense L 0-4 1511 1596 31.57% 27.66% 40.76% -18.3 1
2005-08-13 @ Aarhus GF W 4-0 1596 1511 40.76% 27.66% 31.57% +18.3 9
2005-08-14 Brondby D 0-0 1499 1686 20.72% 24.75% 54.54% +1.2 2
2005-08-14 @ Horsens D 0-0 1686 1499 54.54% 24.75% 20.72% -1.2 13
2005-08-14 Esbjerg W 2-0 1579 1602 40.24% 27.70% 32.06% +9.7 4
2005-08-14 @ Aalborg L 0-2 1602 1579 32.06% 27.70% 40.24% -9.7 4
2005-08-14 FC Copenhagen L 0-1 1460 1640 21.32% 25.04% 53.64% -3.6 4
2005-08-14 @ Sonderjyske W 1-0 1640 1460 53.64% 25.04% 21.32% +3.6 13
2005-08-14 Midtjylland W 3-2 1493 1592 29.75% 27.45% 42.79% +5.7 5
2005-08-14 @ Nordsjaelland L 2-3 1592 1493 42.79% 27.45% 29.75% -5.7 7
2005-08-14 Viborg D 1-1 1532 1590 35.07% 27.85% 37.08% +0.1 7
2005-08-14 @ Silkeborg D 1-1 1590 1532 37.08% 27.85% 35.07% -0.1 13
2005-08-20 Nordsjaelland W 2-1 1685 1499 66.45% 19.64% 13.91% +2.2 16
2005-08-20 @ Brondby L 1-2 1499 1685 13.91% 19.64% 66.45% -2.2 5
2005-08-21 Aarhus GF D 3-3 1586 1493 55.62% 24.37% 20.01% -0.7 8
2005-08-21 @ Midtjylland D 3-3 1493 1586 20.01% 24.37% 55.62% +0.7 2
2005-08-21 Horsens D 0-0 1592 1500 55.43% 24.44% 20.13% -1.3 5
2005-08-21 @ Esbjerg D 0-0 1500 1592 20.13% 24.44% 55.43% +1.3 3
2005-08-21 Silkeborg D 1-1 1589 1532 51.07% 25.81% 23.12% -0.9 5
2005-08-21 @ Aalborg D 1-1 1532 1589 23.12% 25.81% 51.07% +0.9 8
2005-08-21 Sonderjyske L 2-3 1614 1456 63.22% 21.21% 15.57% -7.8 9
2005-08-21 @ Odense W 3-2 1456 1614 15.57% 21.21% 63.22% +7.8 7
2005-08-21 Viborg W 2-1 1644 1590 50.65% 25.93% 23.43% +3.8 16
2005-08-21 @ FC Copenhagen L 1-2 1590 1644 23.43% 25.93% 50.65% -3.8 13
2005-08-27 Odense L 1-2 1586 1606 40.61% 27.68% 31.71% -5.8 13
2005-08-27 @ Viborg W 2-1 1606 1586 31.71% 27.68% 40.61% +5.8 12
2005-08-28 Aalborg D 0-0 1501 1588 31.29% 27.64% 41.08% +0.3 4
2005-08-28 @ Horsens D 0-0 1588 1501 41.08% 27.64% 31.29% -0.3 6
2005-08-28 Brondby W 3-0 1494 1687 20.20% 24.48% 55.32% +21.4 5
2005-08-28 @ Aarhus GF L 0-3 1687 1494 55.32% 24.48% 20.20% -21.4 16
2005-08-28 Esbjerg W 2-0 1496 1591 30.35% 27.53% 42.12% +11.9 8
2005-08-28 @ Nordsjaelland L 0-2 1591 1496 42.12% 27.53% 30.35% -11.9 5
2005-08-28 Midtjylland L 2-4 1464 1585 27.14% 27.00% 45.86% -6.6 7
2005-08-28 @ Sonderjyske W 4-2 1585 1464 45.86% 27.00% 27.14% +6.6 11
2005-08-28 Silkeborg W 2-0 1648 1533 58.32% 23.35% 18.33% +5.9 19
2005-08-28 @ FC Copenhagen L 0-2 1533 1648 18.33% 23.35% 58.32% -5.9 8
2005-09-10 Sonderjyske W 3-0 1666 1458 68.76% 18.45% 12.79% +5.6 19
2005-09-10 @ Brondby L 0-3 1458 1666 12.79% 18.45% 68.76% -5.6 7
2005-09-11 Aarhus GF L 0-1 1579 1515 51.89% 25.58% 22.53% -7.4 5
2005-09-11 @ Esbjerg W 1-0 1515 1579 22.53% 25.58% 51.89% +7.4 8
2005-09-11 FC Copenhagen L 0-2 1612 1654 37.46% 27.84% 34.70% -10.9 12
2005-09-11 @ Odense W 2-0 1654 1612 34.70% 27.84% 37.46% +10.9 22
2005-09-11 Horsens L 0-1 1527 1502 46.78% 26.82% 26.39% -6.9 8
2005-09-11 @ Silkeborg W 1-0 1502 1527 26.39% 26.82% 46.78% +6.9 7
2005-09-11 Nordsjaelland D 1-1 1588 1508 53.89% 24.96% 21.15% -1.0 7
2005-09-11 @ Aalborg D 1-1 1508 1588 21.15% 24.96% 53.89% +1.0 9
2005-09-11 Viborg D 1-1 1592 1581 44.95% 27.15% 27.90% -0.5 12
2005-09-11 @ Midtjylland D 1-1 1581 1592 27.90% 27.15% 44.95% +0.5 14
2005-09-17 Aalborg L 2-4 1522 1587 34.28% 27.83% 37.89% -7.9 8
2005-09-17 @ Aarhus GF W 4-2 1587 1522 37.89% 27.83% 34.28% +7.9 10
2005-09-18 Brondby W 3-1 1581 1672 30.79% 27.58% 41.62% +10.2 17
2005-09-18 @ Viborg L 1-3 1672 1581 41.62% 27.58% 30.79% -10.2 19
2005-09-18 Esbjerg L 0-1 1452 1571 27.36% 27.04% 45.60% -4.6 7
2005-09-18 @ Sonderjyske W 1-0 1571 1452 45.60% 27.04% 27.36% +4.5 8
2005-09-18 Horsens W 5-0 1509 1509 43.49% 27.37% 29.14% +21.1 12
2005-09-18 @ Nordsjaelland L 0-5 1509 1509 29.14% 27.37% 43.49% -21.1 7
2005-09-18 Midtjylland W 3-1 1665 1591 53.12% 25.21% 21.67% +6.0 25
2005-09-18 @ FC Copenhagen L 1-3 1591 1665 21.67% 25.21% 53.12% -6.0 12
2005-09-18 Silkeborg W 3-1 1601 1520 54.12% 24.88% 20.99% +5.8 15
2005-09-18 @ Odense L 1-3 1520 1601 20.99% 24.88% 54.12% -5.9 8
2005-09-21 Aarhus GF D 1-1 1487 1515 39.57% 27.75% 32.68% -0.2 8
2005-09-21 @ Horsens D 1-1 1515 1487 32.68% 27.75% 39.57% +0.2 9
2005-09-21 FC Copenhagen D 1-1 1661 1671 42.07% 27.54% 30.39% -0.3 20
2005-09-21 @ Brondby D 1-1 1671 1661 30.39% 27.54% 42.07% +0.3 26
2005-09-21 Nordsjaelland D 1-1 1514 1531 41.04% 27.64% 31.32% -0.3 9
2005-09-21 @ Silkeborg D 1-1 1531 1514 31.32% 27.64% 41.04% +0.3 13
2005-09-21 Odense L 0-1 1585 1607 40.38% 27.69% 31.93% -6.1 12
2005-09-21 @ Midtjylland W 1-0 1607 1585 31.93% 27.69% 40.38% +6.1 18
2005-09-21 Sonderjyske W 3-2 1595 1447 62.06% 21.75% 16.19% +2.4 13
2005-09-21 @ Aalborg L 2-3 1447 1595 16.19% 21.75% 62.06% -2.4 7
2005-09-21 Viborg L 1-4 1576 1591 41.19% 27.63% 31.18% -14.4 8
2005-09-21 @ Esbjerg W 4-1 1591 1576 31.18% 27.63% 41.19% +14.4 20
2005-09-24 Brondby L 1-3 1613 1661 36.57% 27.86% 35.58% -9.3 18
2005-09-24 @ Odense W 3-1 1661 1613 35.58% 27.86% 36.57% +9.3 23
2005-09-25 Aalborg W 2-0 1606 1597 44.57% 27.21% 28.22% +8.8 23
2005-09-25 @ Viborg L 0-2 1597 1606 28.22% 27.21% 44.57% -8.8 13
2005-09-25 Esbjerg W 5-1 1671 1561 57.64% 23.61% 18.74% +9.5 29
2005-09-25 @ FC Copenhagen L 1-5 1561 1671 18.74% 23.61% 57.64% -9.5 8
2005-09-25 Horsens L 1-3 1445 1487 37.40% 27.84% 34.76% -9.5 7
2005-09-25 @ Sonderjyske W 3-1 1487 1445 34.76% 27.84% 37.40% +9.5 11
2005-09-25 Nordsjaelland L 0-2 1515 1531 41.11% 27.63% 31.26% -11.7 9
2005-09-25 @ Aarhus GF W 2-0 1531 1515 31.26% 27.63% 41.11% +11.7 16
2005-09-25 Silkeborg W 1-0 1579 1514 52.15% 25.50% 22.35% +3.8 15
2005-09-25 @ Midtjylland L 0-1 1514 1579 22.35% 25.50% 52.15% -3.8 9
2005-10-01 Silkeborg D 2-2 1543 1510 47.85% 26.60% 25.55% -0.5 17
2005-10-01 @ Nordsjaelland D 2-2 1510 1543 25.55% 26.60% 47.85% +0.5 10
2005-10-02 Esbjerg D 2-2 1503 1552 36.46% 27.86% 35.68% -0.0 10
2005-10-02 @ Aarhus GF D 2-2 1552 1503 35.68% 27.86% 36.46% +0.0 9
2005-10-02 Horsens W 4-1 1670 1497 65.03% 20.34% 14.62% +5.6 26
2005-10-02 @ Brondby L 1-4 1497 1670 14.62% 20.34% 65.03% -5.6 11
2005-10-02 Midtjylland D 2-2 1588 1583 44.09% 27.28% 28.63% -0.3 14
2005-10-02 @ Aalborg D 2-2 1583 1588 28.63% 27.28% 44.09% +0.4 16
2005-10-02 Odense D 1-1 1681 1604 53.60% 25.06% 21.35% -1.0 30
2005-10-02 @ FC Copenhagen D 1-1 1604 1681 21.35% 25.06% 53.60% +1.0 19
2005-10-02 Sonderjyske D 1-1 1614 1436 65.63% 20.05% 14.32% -1.7 24
2005-10-02 @ Viborg D 1-1 1436 1614 14.32% 20.05% 65.63% +1.7 8
2005-10-15 Viborg L 0-1 1583 1613 39.24% 27.77% 32.99% -6.0 16
2005-10-15 @ Midtjylland W 1-0 1613 1583 32.99% 27.77% 39.24% +6.0 27
2005-10-16 Aalborg W 2-1 1552 1588 38.29% 27.82% 33.89% +5.1 12
2005-10-16 @ Esbjerg L 1-2 1588 1552 33.89% 27.82% 38.29% -5.1 14
2005-10-16 Aarhus GF W 3-1 1605 1503 56.66% 23.99% 19.35% +5.4 22
2005-10-16 @ Odense L 1-3 1503 1605 19.35% 23.99% 56.66% -5.4 10
2005-10-16 Brondby L 1-2 1437 1676 16.93% 22.34% 60.73% -2.7 8
2005-10-16 @ Sonderjyske W 2-1 1676 1437 60.73% 22.34% 16.93% +2.7 29
2005-10-16 FC Copenhagen L 0-3 1510 1680 22.30% 25.48% 52.22% -10.4 10
2005-10-16 @ Silkeborg W 3-0 1680 1510 52.22% 25.48% 22.30% +10.4 33
2005-10-16 Nordsjaelland D 0-0 1491 1542 36.15% 27.86% 36.00% -0.0 12
2005-10-16 @ Horsens D 0-0 1542 1491 36.00% 27.86% 36.15% +0.0 18
2005-10-22 Horsens W 1-0 1690 1491 67.77% 18.97% 13.27% +2.1 36
2005-10-22 @ FC Copenhagen L 0-1 1491 1690 13.27% 18.97% 67.77% -2.1 12
2005-10-23 Aarhus GF D 1-1 1583 1498 54.63% 24.71% 20.66% -1.1 15
2005-10-23 @ Aalborg D 1-1 1498 1583 20.66% 24.71% 54.63% +1.1 11
2005-10-23 Esbjerg W 1-0 1619 1557 51.67% 25.64% 22.69% +3.9 30
2005-10-23 @ Viborg L 0-1 1557 1619 22.69% 25.64% 51.67% -3.9 12
2005-10-23 Midtjylland W 5-0 1679 1577 56.60% 24.01% 19.39% +14.6 32
2005-10-23 @ Brondby L 0-5 1577 1679 19.39% 24.01% 56.60% -14.6 16
2005-10-23 Odense W 1-0 1500 1610 28.41% 27.24% 44.35% +6.6 13
2005-10-23 @ Silkeborg L 0-1 1610 1500 44.35% 27.24% 28.41% -6.6 22
2005-10-23 Sonderjyske W 4-2 1542 1434 57.39% 23.71% 18.90% +4.7 21
2005-10-23 @ Nordsjaelland L 2-4 1434 1542 18.90% 23.71% 57.39% -4.7 8
2005-10-26 Aalborg L 0-2 1603 1582 46.36% 26.90% 26.74% -12.8 22
2005-10-26 @ Odense W 2-0 1582 1603 26.74% 26.90% 46.36% +12.8 18
2005-10-26 Brondby D 0-0 1553 1693 25.10% 26.48% 48.42% +0.8 13
2005-10-26 @ Esbjerg D 0-0 1693 1553 48.42% 26.48% 25.10% -0.8 33
2005-10-26 FC Copenhagen L 1-4 1430 1692 15.45% 21.11% 63.44% -6.0 8
2005-10-26 @ Sonderjyske W 4-1 1692 1430 63.44% 21.11% 15.45% +6.0 39
2005-10-26 Nordsjaelland D 1-1 1563 1547 45.58% 27.04% 27.38% -0.5 17
2005-10-26 @ Midtjylland D 1-1 1547 1563 27.38% 27.04% 45.58% +0.6 22
2005-10-26 Silkeborg D 0-0 1489 1507 40.91% 27.65% 31.44% -0.3 13
2005-10-26 @ Horsens D 0-0 1507 1489 31.44% 27.65% 40.91% +0.3 14
2005-10-26 Viborg D 3-3 1499 1623 26.83% 26.93% 46.24% +0.4 12
2005-10-26 @ Aarhus GF D 3-3 1623 1499 46.24% 26.93% 26.83% -0.4 31
2005-10-29 Aalborg W 2-0 1622 1595 47.17% 26.75% 26.09% +8.2 34
2005-10-29 @ Viborg L 0-2 1595 1622 26.09% 26.75% 47.17% -8.2 18
2005-10-30 Aarhus GF W 4-0 1692 1499 67.18% 19.27% 13.55% +7.9 36
2005-10-30 @ Brondby L 0-4 1499 1692 13.55% 19.27% 67.18% -7.9 12
2005-10-30 Esbjerg L 0-2 1547 1554 42.47% 27.49% 30.04% -12.0 22
2005-10-30 @ Nordsjaelland W 2-0 1554 1547 30.04% 27.49% 42.47% +12.0 16
2005-10-30 Midtjylland W 2-0 1698 1562 60.74% 22.33% 16.92% +5.4 42
2005-10-30 @ FC Copenhagen L 0-2 1562 1698 16.92% 22.33% 60.74% -5.4 17
2005-10-30 Odense D 0-0 1489 1591 29.37% 27.40% 43.23% +0.5 14
2005-10-30 @ Horsens D 0-0 1591 1489 43.23% 27.40% 29.37% -0.5 23
2005-10-30 Sonderjyske L 0-1 1507 1424 54.36% 24.81% 20.84% -7.7 14
2005-10-30 @ Silkeborg W 1-0 1424 1507 20.84% 24.81% 54.36% +7.7 11
2005-11-05 FC Copenhagen W 3-1 1566 1703 25.37% 26.55% 48.08% +11.5 19
2005-11-05 @ Esbjerg L 1-3 1703 1566 48.08% 26.55% 25.37% -11.5 42
2005-11-06 Brondby W 3-0 1586 1700 27.96% 27.16% 44.88% +18.2 21
2005-11-06 @ Aalborg L 0-3 1700 1586 44.88% 27.16% 27.96% -18.2 36
2005-11-06 Horsens D 1-1 1432 1489 35.21% 27.85% 36.94% +0.0 12
2005-11-06 @ Sonderjyske D 1-1 1489 1432 36.94% 27.85% 35.21% -0.0 15
2005-11-06 Nordsjaelland L 1-3 1491 1535 37.12% 27.85% 35.03% -9.4 12
2005-11-06 @ Aarhus GF W 3-1 1535 1491 35.03% 27.85% 37.12% +9.4 25
2005-11-06 Silkeborg L 0-2 1557 1499 51.14% 25.79% 23.07% -13.9 17
2005-11-06 @ Midtjylland W 2-0 1499 1557 23.07% 25.79% 51.14% +13.9 17
2005-11-06 Viborg W 3-0 1590 1631 37.67% 27.84% 34.49% +14.9 26
2005-11-06 @ Odense L 0-3 1631 1590 34.49% 27.84% 37.67% -15.0 34
2005-11-19 Viborg W 1-0 1682 1616 52.26% 25.47% 22.27% +3.8 39
2005-11-19 @ Brondby L 0-1 1616 1682 22.27% 25.47% 52.26% -3.8 34
2005-11-20 Aalborg L 0-1 1545 1604 34.91% 27.85% 37.25% -5.5 25
2005-11-20 @ Nordsjaelland W 1-0 1604 1545 37.25% 27.85% 34.91% +5.5 24
2005-11-20 Aarhus GF D 1-1 1692 1482 68.97% 18.34% 12.69% -1.8 43
2005-11-20 @ FC Copenhagen D 1-1 1482 1692 12.69% 18.34% 68.97% +1.9 13
2005-11-20 Esbjerg W 2-1 1513 1577 34.26% 27.83% 37.91% +5.5 20
2005-11-20 @ Silkeborg L 1-2 1577 1513 37.91% 27.83% 34.26% -5.5 19
2005-11-20 Midtjylland L 3-4 1489 1543 35.72% 27.86% 36.42% -4.9 15
2005-11-20 @ Horsens W 4-3 1543 1489 36.42% 27.86% 35.72% +4.9 20
2005-11-20 Odense L 2-4 1432 1605 21.92% 25.32% 52.77% -5.5 12
2005-11-20 @ Sonderjyske W 4-2 1605 1432 52.77% 25.32% 21.92% +5.5 29
2005-11-26 Brondby L 0-1 1611 1686 32.76% 27.76% 39.48% -5.3 29
2005-11-26 @ Odense W 1-0 1686 1611 39.48% 27.76% 32.76% +5.2 42
2005-11-27 FC Copenhagen L 0-2 1610 1690 32.12% 27.71% 40.17% -9.8 24
2005-11-27 @ Aalborg W 2-0 1690 1610 40.17% 27.71% 32.12% +9.8 46
2005-11-27 Horsens D 1-1 1572 1484 54.94% 24.61% 20.45% -1.1 20
2005-11-27 @ Esbjerg D 1-1 1484 1572 20.45% 24.61% 54.94% +1.1 16
2005-11-27 Nordsjaelland W 4-1 1612 1539 53.03% 25.24% 21.74% +8.6 37
2005-11-27 @ Viborg L 1-4 1539 1612 21.74% 25.24% 53.03% -8.6 25
2005-11-27 Silkeborg W 2-0 1484 1518 38.45% 27.81% 33.74% +10.1 16
2005-11-27 @ Aarhus GF L 0-2 1518 1484 33.74% 27.81% 38.45% -10.1 20
2005-11-27 Sonderjyske D 2-2 1548 1426 59.09% 23.03% 17.88% -1.0 21
2005-11-27 @ Midtjylland D 2-2 1426 1548 17.88% 23.03% 59.09% +1.0 13
2005-12-03 Odense L 1-2 1547 1605 35.09% 27.85% 37.06% -5.2 21
2005-12-03 @ Midtjylland W 2-1 1605 1547 37.06% 27.85% 35.09% +5.2 32
2005-12-04 Aalborg L 1-4 1508 1600 30.62% 27.56% 41.81% -11.5 20
2005-12-04 @ Silkeborg W 4-1 1600 1508 41.81% 27.56% 30.62% +11.5 27
2005-12-04 Aarhus GF W 2-1 1485 1494 42.19% 27.52% 30.28% +4.7 19
2005-12-04 @ Horsens L 1-2 1494 1485 30.28% 27.52% 42.19% -4.7 16
2005-12-04 Brondby L 0-2 1531 1691 23.11% 25.81% 51.09% -7.4 25
2005-12-04 @ Nordsjaelland W 2-0 1691 1531 51.09% 25.81% 23.11% +7.4 45
2005-12-04 Esbjerg L 1-3 1427 1571 24.75% 26.37% 48.87% -6.8 13
2005-12-04 @ Sonderjyske W 3-1 1571 1427 48.87% 26.37% 24.75% +6.8 23
2005-12-04 Viborg W 3-1 1700 1620 53.93% 24.95% 21.12% +5.9 49
2005-12-04 @ FC Copenhagen L 1-3 1620 1700 21.12% 24.95% 53.93% -5.9 37
2006-03-11 Sonderjyske D 1-1 1489 1420 52.55% 25.38% 22.07% -1.0 17
2006-03-11 @ Aarhus GF D 1-1 1420 1489 22.07% 25.38% 52.55% +1.0 14
2006-03-12 FC Copenhagen W 3-0 1699 1706 42.36% 27.51% 30.14% +13.5 48
2006-03-12 @ Brondby L 0-3 1706 1699 30.14% 27.51% 42.36% -13.5 49
2006-03-12 Horsens D 2-2 1612 1490 59.10% 23.03% 17.87% -1.0 28
2006-03-12 @ Aalborg D 2-2 1490 1612 17.87% 23.03% 59.10% +1.0 20
2006-03-12 Midtjylland W 2-0 1578 1542 48.25% 26.51% 25.23% +8.0 26
2006-03-12 @ Esbjerg L 0-2 1542 1578 25.23% 26.51% 48.25% -8.0 21
2006-03-12 Nordsjaelland W 2-0 1611 1523 54.88% 24.63% 20.49% +6.6 35
2006-03-12 @ Odense L 0-2 1523 1611 20.49% 24.63% 54.88% -6.6 25
2006-03-12 Silkeborg L 2-3 1614 1497 58.59% 23.24% 18.17% -7.3 37
2006-03-12 @ Viborg W 3-2 1497 1614 18.17% 23.24% 58.59% +7.3 23
2006-03-18 Brondby W 2-0 1504 1712 19.11% 23.84% 57.05% +15.1 26
2006-03-18 @ Silkeborg L 0-2 1712 1504 57.05% 23.84% 19.11% -15.1 48
2006-03-19 Aalborg D 2-2 1421 1611 20.55% 24.66% 54.78% +0.8 15
2006-03-19 @ Sonderjyske D 2-2 1611 1421 54.78% 24.66% 20.55% -0.8 29
2006-03-19 Aarhus GF W 2-0 1534 1488 49.54% 26.21% 24.25% +7.7 24
2006-03-19 @ Midtjylland L 0-2 1488 1534 24.25% 26.21% 49.54% -7.7 17
2006-03-19 Esbjerg W 1-0 1617 1586 47.68% 26.64% 25.68% +4.3 38
2006-03-19 @ Odense L 0-1 1586 1617 25.68% 26.64% 47.68% -4.3 26
2006-03-19 Nordsjaelland D 3-3 1692 1517 65.26% 20.23% 14.51% -1.0 50
2006-03-19 @ FC Copenhagen D 3-3 1517 1692 14.51% 20.23% 65.26% +1.0 26
2006-03-19 Viborg W 3-1 1491 1607 27.70% 27.11% 45.19% +10.9 23
2006-03-19 @ Horsens L 1-3 1607 1491 45.19% 27.11% 27.70% -10.9 37
2006-03-25 Odense W 1-0 1581 1621 37.70% 27.84% 34.47% +5.5 29
2006-03-25 @ Esbjerg L 0-1 1621 1581 34.47% 27.84% 37.70% -5.5 38
2006-03-26 Horsens D 1-1 1596 1502 55.77% 24.32% 19.91% -1.2 38
2006-03-26 @ Viborg D 1-1 1502 1596 19.91% 24.32% 55.77% +1.2 24
2006-03-26 Midtjylland L 1-2 1480 1541 34.72% 27.84% 37.44% -5.2 17
2006-03-26 @ Aarhus GF W 2-1 1541 1480 37.44% 27.84% 34.72% +5.2 27
2006-03-26 Silkeborg W 3-1 1697 1519 65.48% 20.12% 14.40% +3.9 51
2006-03-26 @ Brondby L 1-3 1519 1697 14.40% 20.12% 65.48% -3.9 26
2006-03-26 Sonderjyske D 2-2 1610 1422 66.59% 19.57% 13.84% -1.3 30
2006-03-26 @ Aalborg D 2-2 1422 1610 13.84% 19.57% 66.59% +1.3 16
2006-03-29 Aalborg W 1-0 1691 1609 54.31% 24.82% 20.87% +3.6 53
2006-03-29 @ FC Copenhagen L 0-1 1609 1691 20.87% 24.82% 54.31% -3.6 30
2006-03-29 Aarhus GF W 1-0 1515 1475 48.84% 26.38% 24.78% +4.2 29
2006-03-29 @ Silkeborg L 0-1 1475 1515 24.78% 26.38% 48.84% -4.2 17
2006-03-29 Esbjerg W 1-0 1503 1587 31.67% 27.67% 40.66% +6.2 27
2006-03-29 @ Horsens L 0-1 1587 1503 40.66% 27.67% 31.67% -6.2 29
2006-03-29 Midtjylland L 0-2 1423 1547 26.92% 26.94% 46.14% -8.5 16
2006-03-29 @ Sonderjyske W 2-0 1547 1423 46.14% 26.94% 26.92% +8.5 30
2006-03-29 Odense W 1-0 1701 1616 54.58% 24.73% 20.69% +3.5 54
2006-03-29 @ Brondby L 0-1 1616 1701 20.69% 24.73% 54.58% -3.5 38
2006-03-29 Viborg L 1-3 1518 1595 32.50% 27.74% 39.76% -8.5 26
2006-03-29 @ Nordsjaelland W 3-1 1595 1518 39.76% 27.74% 32.50% +8.5 41
2006-04-01 FC Copenhagen L 0-4 1471 1695 17.94% 23.08% 58.98% -10.9 17
2006-04-01 @ Aarhus GF W 4-0 1695 1471 58.98% 23.08% 17.94% +10.9 56
2006-04-02 Brondby W 2-0 1604 1704 29.52% 27.42% 43.05% +12.1 44
2006-04-02 @ Viborg L 0-2 1704 1604 43.05% 27.42% 29.52% -12.1 54
2006-04-02 Horsens D 0-0 1555 1509 49.58% 26.20% 24.22% -0.9 31
2006-04-02 @ Midtjylland D 0-0 1509 1555 24.22% 26.20% 49.58% +0.9 28
2006-04-02 Nordsjaelland L 2-4 1605 1509 55.95% 24.25% 19.80% -11.5 30
2006-04-02 @ Aalborg W 4-2 1509 1605 19.80% 24.25% 55.95% +11.5 29
2006-04-02 Silkeborg W 2-0 1581 1520 51.54% 25.68% 22.78% +7.3 32
2006-04-02 @ Esbjerg L 0-2 1520 1581 22.78% 25.68% 51.54% -7.3 29
2006-04-02 Sonderjyske W 3-0 1612 1415 67.63% 19.04% 13.33% +6.0 41
2006-04-02 @ Odense L 0-3 1415 1612 13.33% 19.04% 67.63% -6.0 16
2006-04-08 Aalborg W 4-3 1692 1594 56.30% 24.12% 19.58% +2.9 57
2006-04-08 @ Brondby L 3-4 1594 1692 19.58% 24.12% 56.30% -2.9 30
2006-04-09 Aarhus GF W 3-1 1521 1460 51.51% 25.69% 22.80% +6.3 32
2006-04-09 @ Nordsjaelland L 1-3 1460 1521 22.80% 25.69% 51.51% -6.3 17
2006-04-09 Esbjerg W 2-1 1706 1588 58.64% 23.22% 18.14% +2.9 59
2006-04-09 @ FC Copenhagen L 1-2 1588 1706 18.14% 23.22% 58.64% -2.9 32
2006-04-09 Midtjylland D 0-0 1512 1554 37.46% 27.84% 34.70% -0.1 30
2006-04-09 @ Silkeborg D 0-0 1554 1512 34.70% 27.84% 37.46% +0.1 32
2006-04-09 Odense D 2-2 1616 1618 43.00% 27.43% 29.57% -0.3 45
2006-04-09 @ Viborg D 2-2 1618 1616 29.57% 27.43% 43.00% +0.3 42
2006-04-09 Sonderjyske W 2-0 1510 1409 56.59% 24.02% 19.39% +6.2 31
2006-04-09 @ Horsens L 0-2 1409 1510 19.39% 24.02% 56.59% -6.2 16
2006-04-13 Brondby L 0-1 1454 1695 16.76% 22.20% 61.04% -2.8 17
2006-04-13 @ Aarhus GF W 1-0 1695 1454 61.04% 22.20% 16.76% +2.8 60
2006-04-13 FC Copenhagen L 1-3 1554 1709 23.68% 26.02% 50.31% -6.6 32
2006-04-13 @ Midtjylland W 3-1 1709 1554 50.31% 26.02% 23.68% +6.6 62
2006-04-13 Horsens W 3-0 1619 1516 56.76% 23.95% 19.29% +9.0 45
2006-04-13 @ Odense L 0-3 1516 1619 19.29% 23.95% 56.76% -9.0 31
2006-04-13 Nordsjaelland W 4-0 1585 1527 51.14% 25.79% 23.07% +14.0 35
2006-04-13 @ Esbjerg L 0-4 1527 1585 23.07% 25.79% 51.14% -14.0 32
2006-04-13 Silkeborg W 2-0 1403 1512 28.48% 27.26% 44.26% +12.4 19
2006-04-13 @ Sonderjyske L 0-2 1512 1403 44.26% 27.26% 28.48% -12.4 30
2006-04-13 Viborg D 1-1 1591 1615 39.89% 27.73% 32.38% -0.2 31
2006-04-13 @ Aalborg D 1-1 1615 1591 32.38% 27.73% 39.89% +0.2 46
2006-04-16 Horsens W 2-1 1500 1507 42.33% 27.51% 30.16% +4.6 33
2006-04-16 @ Silkeborg L 1-2 1507 1500 30.16% 27.51% 42.33% -4.7 31
2006-04-17 Aarhus GF D 2-2 1616 1451 64.05% 20.82% 15.13% -1.2 47
2006-04-17 @ Viborg D 2-2 1451 1616 15.13% 20.82% 64.05% +1.2 18
2006-04-17 Esbjerg W 3-0 1698 1599 56.34% 24.11% 19.55% +9.1 63
2006-04-17 @ Brondby L 0-3 1599 1698 19.55% 24.11% 56.34% -9.1 35
2006-04-17 Midtjylland D 2-2 1513 1548 38.49% 27.81% 33.70% -0.1 33
2006-04-17 @ Nordsjaelland D 2-2 1548 1513 33.70% 27.81% 38.49% +0.1 33
2006-04-17 Odense D 0-0 1590 1628 38.11% 27.82% 34.07% -0.1 32
2006-04-17 @ Aalborg D 0-0 1628 1590 34.07% 27.82% 38.11% +0.1 46
2006-04-17 Sonderjyske W 4-1 1715 1415 77.55% 13.58% 8.87% +3.0 65
2006-04-17 @ FC Copenhagen L 1-4 1415 1715 8.87% 13.58% 77.55% -3.0 19
2006-04-22 FC Copenhagen L 0-1 1503 1718 18.53% 23.48% 58.00% -3.2 31
2006-04-22 @ Horsens W 1-0 1718 1503 58.00% 23.48% 18.53% +3.2 68
2006-04-23 Aalborg D 1-1 1452 1590 25.31% 26.54% 48.16% +0.7 19
2006-04-23 @ Aarhus GF D 1-1 1590 1452 48.16% 26.54% 25.31% -0.7 33
2006-04-23 Brondby W 2-0 1548 1707 23.21% 25.85% 50.94% +13.8 36
2006-04-23 @ Midtjylland L 0-2 1707 1548 50.94% 25.85% 23.21% -13.8 63
2006-04-23 Nordsjaelland D 2-2 1412 1513 29.51% 27.42% 43.07% +0.3 20
2006-04-23 @ Sonderjyske D 2-2 1513 1412 43.07% 27.42% 29.51% -0.3 34
2006-04-23 Silkeborg W 2-1 1628 1504 59.28% 22.95% 17.76% +2.8 49
2006-04-23 @ Odense L 1-2 1504 1628 17.76% 22.95% 59.28% -2.8 33
2006-04-23 Viborg W 3-2 1590 1614 39.91% 27.73% 32.36% +4.7 38
2006-04-23 @ Esbjerg L 2-3 1614 1590 32.36% 27.73% 39.91% -4.7 47
2006-04-26 FC Copenhagen D 1-1 1513 1721 19.04% 23.80% 57.17% +1.2 35
2006-04-26 @ Nordsjaelland D 1-1 1721 1513 57.17% 23.80% 19.04% -1.2 69
2006-04-29 Aarhus GF W 1-0 1412 1453 37.66% 27.84% 34.50% +5.5 23
2006-04-29 @ Sonderjyske L 0-1 1453 1412 34.50% 27.84% 37.66% -5.5 19
2006-04-30 Aalborg L 0-1 1499 1590 30.85% 27.59% 41.56% -5.0 31
2006-04-30 @ Horsens W 1-0 1590 1499 41.56% 27.59% 30.85% +5.0 36
2006-04-30 Brondby D 0-0 1720 1693 47.06% 26.77% 26.17% -0.7 70
2006-04-30 @ FC Copenhagen D 0-0 1693 1720 26.17% 26.77% 47.06% +0.7 64
2006-04-30 Esbjerg D 0-0 1562 1594 38.75% 27.80% 33.46% -0.2 37
2006-04-30 @ Midtjylland D 0-0 1594 1562 33.46% 27.80% 38.75% +0.2 39
2006-04-30 Odense L 0-3 1514 1631 27.63% 27.10% 45.27% -12.6 35
2006-04-30 @ Nordsjaelland W 3-0 1631 1514 45.27% 27.10% 27.63% +12.6 52
2006-04-30 Viborg L 1-3 1502 1610 28.63% 27.28% 44.09% -7.7 33
2006-04-30 @ Silkeborg W 3-1 1610 1502 44.09% 27.28% 28.63% +7.7 50
2006-05-04 FC Copenhagen L 0-1 1618 1719 29.37% 27.40% 43.23% -4.8 50
2006-05-04 @ Viborg W 1-0 1719 1618 43.23% 27.40% 29.37% +4.8 73
2006-05-04 Horsens L 0-1 1447 1494 36.71% 27.86% 35.44% -5.7 19
2006-05-04 @ Aarhus GF W 1-0 1494 1447 35.44% 27.86% 36.71% +5.7 34
2006-05-04 Midtjylland L 0-1 1643 1561 54.21% 24.86% 20.94% -7.7 52
2006-05-04 @ Odense W 1-0 1561 1643 20.94% 24.86% 54.21% +7.7 40
2006-05-04 Nordsjaelland W 3-1 1694 1501 67.11% 19.30% 13.59% +3.6 67
2006-05-04 @ Brondby L 1-3 1501 1694 13.59% 19.30% 67.11% -3.6 35
2006-05-04 Silkeborg W 1-0 1595 1494 56.55% 24.03% 19.42% +3.3 39
2006-05-04 @ Aalborg L 0-1 1494 1595 19.42% 24.03% 56.55% -3.3 33
2006-05-04 Sonderjyske W 3-2 1595 1418 65.38% 20.17% 14.44% +2.1 42
2006-05-04 @ Esbjerg L 2-3 1418 1595 14.44% 20.17% 65.38% -2.1 23
2006-05-07 Aalborg L 2-4 1569 1598 39.33% 27.76% 32.90% -8.8 40
2006-05-07 @ Midtjylland W 4-2 1598 1569 32.90% 27.76% 39.33% +8.8 42
2006-05-07 Aarhus GF L 1-3 1597 1442 62.97% 21.33% 15.70% -14.1 42
2006-05-07 @ Esbjerg W 3-1 1442 1597 15.70% 21.33% 62.97% +14.1 22
2006-05-07 Brondby W 4-1 1500 1698 19.91% 24.31% 55.78% +18.2 37
2006-05-07 @ Horsens L 1-4 1698 1500 55.78% 24.31% 19.91% -18.2 67
2006-05-07 FC Copenhagen W 1-0 1636 1724 31.01% 27.61% 41.38% +6.2 55
2006-05-07 @ Odense L 0-1 1724 1636 41.38% 27.61% 31.01% -6.2 73
2006-05-07 Nordsjaelland W 2-0 1491 1498 42.38% 27.50% 30.11% +9.3 36
2006-05-07 @ Silkeborg L 0-2 1498 1491 30.11% 27.50% 42.38% -9.3 35
2006-05-07 Viborg L 0-4 1416 1613 19.95% 24.34% 55.72% -12.2 23
2006-05-07 @ Sonderjyske W 4-0 1613 1416 55.72% 24.34% 19.95% +12.2 53
2006-05-14 Esbjerg W 1-0 1607 1583 46.67% 26.85% 26.49% +4.4 45
2006-05-14 @ Aalborg L 0-1 1583 1607 26.49% 26.85% 46.67% -4.4 42
2006-05-14 Horsens W 3-0 1488 1518 39.16% 27.77% 33.07% +14.5 38
2006-05-14 @ Nordsjaelland L 0-3 1518 1488 33.07% 27.77% 39.16% -14.5 37
2006-05-14 Midtjylland D 1-1 1625 1560 52.01% 25.54% 22.45% -0.9 54
2006-05-14 @ Viborg D 1-1 1560 1625 22.45% 25.54% 52.01% +0.9 41
2006-05-14 Odense L 0-2 1456 1642 20.84% 24.81% 54.35% -6.7 22
2006-05-14 @ Aarhus GF W 2-0 1642 1456 54.35% 24.81% 20.84% +6.7 58
2006-05-14 Silkeborg L 2-3 1718 1500 69.79% 17.90% 12.31% -8.3 73
2006-05-14 @ FC Copenhagen W 3-2 1500 1718 12.31% 17.90% 69.79% +8.3 39
2006-05-14 Sonderjyske L 1-2 1679 1404 75.39% 14.81% 9.79% -9.2 67
2006-05-14 @ Brondby W 2-1 1404 1679 9.79% 14.81% 75.39% +9.2 26

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 2006-05-14 9.79% Sonderjyske 1404 2 @ Brondby 1679 1
2 2006-05-14 12.31% Silkeborg 1500 3 @ FC Copenhagen 1718 2
3 2005-08-21 15.57% Sonderjyske 1456 3 @ Odense 1614 2
4 2006-05-07 15.70% Aarhus GF 1442 3 @ Esbjerg 1597 1
5 2006-03-12 18.17% Silkeborg 1497 3 @ Viborg 1614 2
6 2006-03-18 19.11% @ Silkeborg 1504 2 Brondby 1712 0
7 2006-04-02 19.80% Nordsjaelland 1509 4 @ Aalborg 1605 2
8 2006-05-07 19.91% @ Horsens 1500 4 Brondby 1698 1
9 2005-08-28 20.20% @ Aarhus GF 1494 3 Brondby 1687 0
10 2005-10-30 20.84% Sonderjyske 1424 1 @ Silkeborg 1507 0
11 2006-05-04 20.94% Midtjylland 1561 1 @ Odense 1643 0
12 2005-09-11 22.53% Aarhus GF 1515 1 @ Esbjerg 1579 0
13 2005-11-06 23.07% Silkeborg 1499 2 @ Midtjylland 1557 0
14 2006-04-23 23.21% @ Midtjylland 1548 2 Brondby 1707 0
15 2005-11-05 25.37% @ Esbjerg 1566 3 FC Copenhagen 1703 1
16 2005-09-11 26.39% Horsens 1502 1 @ Silkeborg 1527 0
17 2005-10-26 26.74% Aalborg 1582 2 @ Odense 1603 0
18 2006-03-19 27.70% @ Horsens 1491 3 Viborg 1607 1
19 2005-11-06 27.96% @ Aalborg 1586 3 Brondby 1700 0
20 2005-10-23 28.41% @ Silkeborg 1500 1 Odense 1610 0
21 2006-04-13 28.48% @ Sonderjyske 1403 2 Silkeborg 1512 0
22 2006-04-02 29.52% @ Viborg 1604 2 Brondby 1704 0
23 2005-08-14 29.75% @ Nordsjaelland 1493 3 Midtjylland 1592 2
24 2005-10-30 30.04% Esbjerg 1554 2 @ Nordsjaelland 1547 0
25 2005-08-28 30.35% @ Nordsjaelland 1496 2 Esbjerg 1591 0

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 2005-08-28 21.44 @ Aarhus GF 3 1494 20.20% Brondby 0 1687 55.32% 24.48%
2 2005-09-18 21.14 @ Nordsjaelland 5 1509 43.49% Horsens 0 1509 29.14% 27.37%
3 2005-08-13 18.27 Odense 4 1596 40.76% @ Aarhus GF 0 1511 31.57% 27.66%
4 2006-05-07 18.20 @ Horsens 4 1500 19.91% Brondby 1 1698 55.78% 24.31%
5 2005-11-06 18.19 @ Aalborg 3 1586 27.96% Brondby 0 1700 44.88% 27.16%
6 2005-07-20 16.21 Viborg 3 1558 33.66% @ Horsens 0 1524 38.53% 27.81%
7 2006-03-18 15.12 @ Silkeborg 2 1504 19.11% Brondby 0 1712 57.05% 23.84%
8 2005-08-07 14.99 @ Esbjerg 4 1587 48.81% Silkeborg 0 1547 24.81% 26.39%
9 2005-11-06 14.95 @ Odense 3 1590 37.67% Viborg 0 1631 34.49% 27.84%
10 2005-10-23 14.60 @ Brondby 5 1679 56.60% Midtjylland 0 1577 19.39% 24.01%
11 2006-05-14 14.49 @ Nordsjaelland 3 1488 39.16% Horsens 0 1518 33.07% 27.77%
12 2005-09-21 14.37 Viborg 4 1591 31.18% @ Esbjerg 1 1576 41.19% 27.63%
13 2006-05-07 14.13 Aarhus GF 3 1442 15.70% @ Esbjerg 1 1597 62.97% 21.33%
14 2006-04-13 14.03 @ Esbjerg 4 1585 51.14% Nordsjaelland 0 1527 23.07% 25.79%
15 2005-11-06 13.88 Silkeborg 2 1499 23.07% @ Midtjylland 0 1557 51.14% 25.79%
16 2006-04-23 13.83 @ Midtjylland 2 1548 23.21% Brondby 0 1707 50.94% 25.85%
17 2006-03-12 13.49 @ Brondby 3 1699 42.36% FC Copenhagen 0 1706 30.14% 27.51%
18 2005-10-26 12.85 Aalborg 2 1582 26.74% @ Odense 0 1603 46.36% 26.90%
19 2006-04-30 12.58 Odense 3 1631 45.27% @ Nordsjaelland 0 1514 27.63% 27.10%
20 2006-04-13 12.39 @ Sonderjyske 2 1403 28.48% Silkeborg 0 1512 44.26% 27.26%
21 2006-05-07 12.19 Viborg 4 1613 55.72% @ Sonderjyske 0 1416 19.95% 24.34%
22 2006-04-02 12.13 @ Viborg 2 1604 29.52% Brondby 0 1704 43.05% 27.42%
23 2005-10-30 12.00 Esbjerg 2 1554 30.04% @ Nordsjaelland 0 1547 42.47% 27.49%
24 2005-08-28 11.93 @ Nordsjaelland 2 1496 30.35% Esbjerg 0 1591 42.12% 27.53%
25 2005-09-25 11.71 Nordsjaelland 2 1531 31.26% @ Aarhus GF 0 1515 41.11% 27.63%