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2012-13 Superliga Season

198 games

Final

Champion

FC Copenhagen

65 points · 10th Title

Last Title: 2010-11

Relegated

Silkeborg

31 pts

Horsens · 34 pts

Biggest Overachiever

Randers

15.56 points above expected

52 points · 36.44 expected points

Biggest Disappointment

Horsens

10.33 points below expected

34 points · 44.33 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 18 11 4 65 62 32 +30 61.67 +3.33
2 Nordsjaelland 33 17 9 7 60 60 37 +23 59.15 +0.85
3 Randers 33 15 7 11 52 36 42 -6 36.44 +15.56
4 Esbjerg 33 13 8 12 47 38 32 +6 44.39 +2.61
5 Aalborg 33 13 8 12 47 51 46 +5 44.52 +2.48
6 Midtjylland 33 12 11 10 47 51 47 +4 48.14 -1.14
7 Aarhus GF 33 11 8 14 41 50 49 +1 47.32 -6.32
8 Sonderjyske 33 12 5 16 41 53 57 -4 40.07 +0.93
9 Brondby 33 9 12 12 39 39 45 -6 36.53 +2.47
10 Odense 33 10 8 15 38 52 59 -7 42.45 -4.45
11 Horsens Relegated 33 8 10 15 34 31 49 -18 44.33 -10.33
12 Silkeborg Relegated 33 8 7 18 31 38 66 -28 37.09 -6.09

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 1733 65 61.67 +3.33 69.7% 36 50 57 62 67 73 89
Nordsjaelland 1706 60 59.15 +0.85 57.7% 35 47 54 59 64 71 87
Midtjylland 1644 47 48.14 -1.14 46.7% 19 36 43 48 53 60 76
Esbjerg 1636 47 44.39 +2.61 67.0% 18 33 39 44 49 57 71
Aarhus GF 1602 41 47.32 -6.32 21.9% 23 35 42 47 52 59 78
Aalborg 1589 47 44.52 +2.48 66.2% 14 33 40 44 49 57 73
Sonderjyske 1584 41 40.07 +0.93 58.5% 13 28 35 40 45 52 66
Brondby 1573 39 36.53 +2.47 66.8% 13 25 32 36 41 48 66
Odense 1565 38 42.45 -4.45 29.8% 20 31 37 42 47 54 75
Horsens 1559 34 44.33 -10.33 8.5% 18 32 39 44 49 56 70
Randers 1554 52 36.44 +15.56 98.8% 12 25 32 36 41 48 64
Silkeborg 1513 31 37.09 -6.09 21.9% 12 26 32 37 42 49 63

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 RAN SIL SON
Aalborg —
0-1-2
3.81
2-1-0
4.86
0-1-2
4.50
0-1-2
2.59
2-1-0
3.66
2-0-1
3.98
0-1-2
3.00
2-1-0
3.81
2-1-0
5.07
1-0-2
4.88
2-0-1
4.41
Aarhus GF
2-1-0
4.37
 —
1-0-2
5.16
0-1-2
4.51
0-1-2
2.83
1-1-1
4.06
1-1-1
4.17
0-0-3
3.20
3-0-0
4.54
0-1-2
4.87
1-1-1
4.86
2-1-0
4.78
Brondby
0-1-2
3.33
2-0-1
3.06
 —
0-2-1
3.56
0-2-1
2.09
2-1-0
3.61
0-3-0
3.11
1-1-1
2.19
1-0-2
3.80
1-0-2
3.86
2-1-0
4.04
0-1-2
3.97
Esbjerg
2-1-0
3.69
2-1-0
3.67
1-2-0
4.62
 —
1-1-1
2.92
1-2-0
4.24
0-1-2
3.69
1-0-2
2.91
2-0-1
4.49
1-0-2
4.55
2-0-1
4.85
0-0-3
4.69
FC Copenhagen
2-1-0
5.68
2-1-0
5.43
1-2-0
6.27
1-1-1
5.32
 —
1-1-1
5.47
2-1-0
5.46
3-0-0
4.45
1-2-0
5.55
2-0-1
6.02
2-0-1
6.44
1-2-0
5.57
Horsens
0-1-2
4.53
1-1-1
4.12
0-1-2
4.56
0-2-1
3.94
1-1-1
2.79
 —
0-1-2
3.98
0-0-3
3.06
1-1-1
4.21
2-1-0
4.57
2-1-0
4.39
1-0-2
4.23
Midtjylland
1-0-2
4.20
1-1-1
4.01
0-3-0
5.11
2-1-0
4.49
0-1-2
2.78
2-1-0
4.20
 —
1-1-1
3.22
1-1-1
4.46
2-0-1
5.28
0-2-1
5.32
2-0-1
4.99
Nordsjaelland
2-1-0
5.21
3-0-0
5.00
1-1-1
6.15
2-0-1
5.32
0-0-3
3.72
3-0-0
5.17
1-1-1
4.99
 —
1-1-1
5.78
0-3-0
6.14
2-1-0
5.92
2-1-0
5.72
Odense
0-1-2
4.37
0-0-3
3.64
2-0-1
4.38
1-0-2
3.70
0-2-1
2.71
1-1-1
3.97
1-1-1
3.71
1-1-1
2.50
 —
0-1-2
4.90
2-1-0
4.36
2-0-1
4.14
Randers
0-1-2
3.14
2-1-0
3.34
2-0-1
4.31
2-0-1
3.62
1-0-2
2.31
0-1-2
3.60
1-0-2
2.95
0-3-0
2.20
2-1-0
3.29
 —
3-0-0
3.83
2-0-1
3.80
Silkeborg
2-0-1
3.32
1-1-1
3.34
0-1-2
4.14
1-0-2
3.34
1-0-2
1.95
0-1-2
3.79
1-2-0
2.91
0-1-2
2.38
0-1-2
3.82
0-0-3
4.35
 —
2-0-1
3.79
Sonderjyske
1-0-2
3.77
0-1-2
3.41
2-1-0
4.20
3-0-0
3.49
0-2-1
2.71
2-0-1
3.96
1-0-2
3.22
0-1-2
2.56
1-0-2
4.04
1-0-2
4.39
1-0-2
4.39
 —

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 0.39 +6.3
Allowed 0.64 -7.8
Differential 0.85 +5.9

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
06.06%8.84%5.30%4.04%2.02%0.76%27.02%
18.84%11.11%6.31%2.78%1.52%0.51%31.06%
25.30%6.31%8.08%3.03%1.01%0.51%24.24%
34.04%2.78%3.03%1.01%0.25%—11.11%
42.02%1.52%1.01%0.25%——4.80%
5+0.76%0.51%0.51%———1.77%
Total27.02%31.06%24.24%11.11%4.80%1.77%100%

Summary Statistics

Scored Allowed Difference
Mean 1.42 1.42 +0.00
SD 1.25 1.25 1.87
CV 0.88 0.88 —
Max 6 6 +5
Min 0 0 -5

Games Played: 198

↓ Scored | Allowed →012345+Total
03.03%12.12%3.03%9.09%3.03%—30.30%
16.06%12.12%3.03%3.03%——24.24%
23.03%6.06%9.09%3.03%——21.21%
33.03%3.03%3.03%———9.09%
49.09%3.03%—3.03%——15.15%
5+———————
Total24.24%36.36%18.18%18.18%3.03%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.55 1.39 +0.15
SD 1.42 1.14 2.06
CV 0.92 0.82 —
Max 4 4 +4
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
09.09%9.09%9.09%6.06%——33.33%
1—9.09%6.06%3.03%——18.18%
2—9.09%3.03%6.06%3.03%—21.21%
39.09%3.03%3.03%3.03%——18.18%
43.03%3.03%3.03%———9.09%
5+———————
Total21.21%33.33%24.24%18.18%3.03%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.52 1.48 +0.03
SD 1.37 1.12 1.85
CV 0.91 0.76 —
Max 4 4 +4
Min 0 0 -3

Games Played: 33

↓ Scored | Allowed →012345+Total
06.06%12.12%3.03%9.09%——30.30%
16.06%18.18%3.03%6.06%——33.33%
23.03%9.09%12.12%3.03%——27.27%
33.03%—3.03%———6.06%
43.03%—————3.03%
5+———————
Total21.21%39.39%21.21%18.18%——100%

Summary Statistics

Scored Allowed Difference
Mean 1.18 1.36 -0.18
SD 1.04 1.03 1.57
CV 0.88 0.75 —
Max 4 3 +4
Min 0 0 -3

Games Played: 33

↓ Scored | Allowed →012345+Total
015.15%12.12%—6.06%——33.33%
121.21%3.03%12.12%3.03%——39.39%
26.06%3.03%6.06%3.03%——18.18%
33.03%—————3.03%
43.03%—————3.03%
5+——3.03%———3.03%
Total48.48%18.18%21.21%12.12%——100%

Summary Statistics

Scored Allowed Difference
Mean 1.15 0.97 +0.18
SD 1.30 1.10 1.65
CV 1.13 1.14 —
Max 6 3 +4
Min 0 0 -3

Games Played: 33

↓ Scored | Allowed →012345+Total
06.06%9.09%3.03%———18.18%
13.03%18.18%————21.21%
26.06%15.15%9.09%———30.30%
36.06%3.03%9.09%———18.18%
43.03%3.03%3.03%———9.09%
5+3.03%—————3.03%
Total27.27%48.48%24.24%———100%

Summary Statistics

Scored Allowed Difference
Mean 1.88 0.97 +0.91
SD 1.34 0.73 1.51
CV 0.71 0.75 —
Max 5 2 +5
Min 0 0 -2

Games Played: 33

↓ Scored | Allowed →012345+Total
012.12%9.09%15.15%—3.03%—39.39%
19.09%6.06%3.03%3.03%6.06%—27.27%
215.15%—12.12%—3.03%3.03%33.33%
3———————
4———————
5+———————
Total36.36%15.15%30.30%3.03%12.12%3.03%100%

Summary Statistics

Scored Allowed Difference
Mean 0.94 1.48 -0.55
SD 0.86 1.48 1.66
CV 0.92 1.00 —
Max 2 5 +2
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
03.03%——3.03%——6.06%
112.12%24.24%9.09%9.09%——54.55%
26.06%3.03%6.06%6.06%3.03%—24.24%
33.03%6.06%3.03%———12.12%
4———————
5+——3.03%———3.03%
Total24.24%33.33%21.21%18.18%3.03%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.55 1.42 +0.12
SD 1.00 1.15 1.47
CV 0.65 0.80 —
Max 5 4 +3
Min 0 0 -3

Games Played: 33

↓ Scored | Allowed →012345+Total
06.06%3.03%—3.03%3.03%—15.15%
115.15%12.12%3.03%3.03%3.03%—36.36%
26.06%3.03%9.09%3.03%——21.21%
39.09%3.03%————12.12%
43.03%6.06%3.03%———12.12%
5+—3.03%————3.03%
Total39.39%30.30%15.15%9.09%6.06%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.82 1.12 +0.70
SD 1.45 1.22 2.04
CV 0.80 1.09 —
Max 6 4 +5
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
03.03%6.06%3.03%3.03%3.03%—18.18%
16.06%9.09%9.09%—6.06%—30.30%
26.06%6.06%9.09%6.06%3.03%3.03%33.33%
39.09%——3.03%3.03%—15.15%
4———————
5+3.03%—————3.03%
Total27.27%21.21%21.21%12.12%15.15%3.03%100%

Summary Statistics

Scored Allowed Difference
Mean 1.58 1.79 -0.21
SD 1.15 1.60 2.07
CV 0.73 0.89 —
Max 5 6 +5
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
09.09%12.12%3.03%3.03%6.06%—33.33%
118.18%6.06%3.03%——3.03%30.30%
29.09%12.12%6.06%3.03%——30.30%
3——6.06%———6.06%
4———————
5+———————
Total36.36%30.30%18.18%6.06%6.06%3.03%100%

Summary Statistics

Scored Allowed Difference
Mean 1.09 1.27 -0.18
SD 0.95 1.44 1.76
CV 0.87 1.13 —
Max 3 6 +2
Min 0 0 -5

Games Played: 33

↓ Scored | Allowed →012345+Total
0—18.18%9.09%3.03%3.03%6.06%39.39%
13.03%9.09%9.09%3.03%—3.03%27.27%
23.03%3.03%6.06%———12.12%
3—6.06%9.09%6.06%——21.21%
4———————
5+———————
Total6.06%36.36%33.33%12.12%3.03%9.09%100%

Summary Statistics

Scored Allowed Difference
Mean 1.15 2.00 -0.85
SD 1.18 1.39 1.92
CV 1.02 0.70 —
Max 3 6 +2
Min 0 0 -5

Games Played: 33

↓ Scored | Allowed →012345+Total
0—3.03%15.15%3.03%3.03%3.03%27.27%
16.06%6.06%15.15%—3.03%—30.30%
2—6.06%9.09%3.03%——18.18%
33.03%9.09%————12.12%
4—3.03%3.03%———6.06%
5+3.03%3.03%————6.06%
Total12.12%30.30%42.42%6.06%6.06%3.03%100%

Summary Statistics

Scored Allowed Difference
Mean 1.61 1.73 -0.12
SD 1.56 1.15 2.33
CV 0.97 0.67 —
Max 6 5 +5
Min 0 0 -5

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 Randers 52 36.44 +15.56
2 FC Copenhagen 65 61.67 +3.33
3 Esbjerg 47 44.39 +2.61
4 Aalborg 47 44.52 +2.48
5 Brondby 39 36.53 +2.47

Biggest Disappointments

# Team Actual Sim vsSim
1 Horsens 34 44.33 -10.33
2 Aarhus GF 41 47.32 -6.32
3 Silkeborg 31 37.09 -6.09
4 Odense 38 42.45 -4.45
5 Midtjylland 47 48.14 -1.14

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 5 Aug 12 – Sep 16 1 in 244
2 FC Copenhagen 9 Nov 4 – Mar 15 1 in 198
3 Esbjerg 4 Apr 26 – May 16 1 in 57
4 Randers 4 Dec 8 – Mar 17 1 in 48
5 Odense 3 Aug 3 – Aug 20 1 in 36

Most Unlikely Losing Streaks

# Team Games Dates Probability
1 Odense 6 Apr 14 – May 16 1 in 381
2 Horsens 5 Nov 18 – Mar 3 1 in 206
3 Silkeborg 5 Jul 20 – Aug 18 1 in 199
4 Aarhus GF 4 Mar 29 – Apr 12 1 in 69
5 Sonderjyske 4 Aug 20 – Sep 15 1 in 69

Most Unlikely Unbeaten Streaks

# Team Games Dates Probability
1 Randers 8 Sep 22 – Nov 19 1 in 241
2 Brondby 6 Apr 21 – May 20 1 in 57
3 Horsens 9 Sep 2 – Nov 9 1 in 49
4 FC Copenhagen 13 Jul 15 – Oct 21 1 in 33
5 Aalborg 6 Aug 4 – Sep 16 1 in 24

Most Unlikely Winless Streaks

# Team Games Dates Probability
1 FC Copenhagen 6 Apr 21 – May 20 1 in 217
2 Brondby 12 Aug 5 – Nov 4 1 in 45
3 Midtjylland 6 Sep 17 – Oct 28 1 in 39
4 Esbjerg 8 Jul 15 – Sep 2 1 in 34
5 Odense 8 Apr 7 – May 20 1 in 33

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 Copenhagen54.34%27.51%9.65%4.30%2.12%1.12%0.48%0.31%0.12%0.04%—0.01%
Nordsjaelland35.09%36.93%13.98%6.39%3.51%2.01%0.95%0.68%0.26%0.10%0.09%0.01%
Randers0.09%0.36%1.41%2.44%4.04%5.43%7.56%9.77%12.06%14.76%18.35%23.73%
Esbjerg1.21%4.22%10.39%11.65%12.11%12.03%11.78%10.67%9.14%7.76%5.39%3.65%
Aalborg1.14%4.50%9.90%12.13%12.72%12.24%11.71%10.68%8.73%7.25%5.69%3.31%
Midtjylland3.47%9.50%17.23%16.21%13.39%11.30%8.69%7.54%5.08%3.89%2.37%1.33%
Aarhus GF2.62%8.08%15.68%15.45%13.72%11.56%9.47%7.74%6.36%4.79%2.84%1.69%
Sonderjyske0.27%1.42%3.73%6.09%7.70%9.23%11.04%10.89%12.70%12.97%12.61%11.35%
Brondby0.08%0.50%1.52%2.96%3.70%5.73%7.09%9.68%12.27%14.84%18.90%22.73%
Odense0.56%2.57%6.14%8.66%10.32%11.28%12.25%11.60%10.95%10.36%8.99%6.32%
Horsens1.05%3.87%8.78%10.88%12.41%12.05%11.67%10.51%10.08%8.05%6.58%4.07%
Silkeborg0.08%0.54%1.59%2.84%4.26%6.02%7.31%9.93%12.25%15.19%18.19%21.80%

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
+10.10%
Slight Edge
41.92%26.26%31.82%
Elo Value
Home Edge: 35.21 Elo pts.
232 Elo
0.004 goals per Elo point
0600
Scoring Tilt
Expected
+0.26 goals
Neutral
-2+0.15+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
2.4
Top-Heavy
124610
Champion Preseason Odds
54%
FC Copenhagen, 1st of 12
LongshotFavorite
Title Margin
Expected
0.15/gm
Tight Race
00.200.5/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 5.76 * Some Luck: 5.76 to 8.64 * Lucky: 8.64 to 11.52 * Wild Swing: 11.52 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 1.65 * Close: 1.65 to 2.48 * Off: 2.48 to 3.31 * Way Off: 3.31 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 1.16 * A Surprise: 1.16 to 1.86 * Several Surprises: 1.86 to 2.56 * Many Surprises: 2.56 and up.
Luck Spread
Expected
6.32 points
Some Luck
07.2018
Average Finish Error
Expected
2.33
Close
02.075
Biggest Overachiever
Expected 95.83%
98.84%
Randers
50100
Biggest Underachiever
Expected 4.17%
8.54%
Horsens
050
Season Outliers
Expected
1 of 12
Minimal Outliers
01.26
Unexpected Relegations
Expected
2 of 2
Several Surprises
01.22

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
0.12
Balanced
00.120.180.260.5
Noll-Scully
Coin-flip
1.17
Moderate Separation
01.003
Interquartile Edge
61%
Slight Edge
50%60%70%80%100%
Best vs. Worst
Baseline
78%
Clear Edge
50%78%100%
Close Games
Expected
63%
Very Frequent
0%58%100%
Blowouts
Expected
19%
Frequent
0%18%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
0.65
Hard to Predict
00.632
Matchup Imbalance
0.27
Notable Separation
00.10.180.280.5
Strangeness
Expected
0.79
Very Predictable
01.002
Repeatability
0.32
Some Carryover
00.30.60.851
Upset Rate
Expected
33%
As Expected
0%27%50%
Clear Favorite Upset Rate
Expected
22%
As Expected
0%23%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 0.23 * Well Above Noise: 0.23 and up.
Probability calibration
0.19
Well Calibrated
0.010.050.10.51
Calibration slope
Ideal
0.71
Overconfident
0.51.001.5
Calibration error (ECE)
Noise ceiling
0.109
Well Within Noise
00.1170.3

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%
Nordsjaelland 99.90% 0.10%
Midtjylland 96.30% 3.70%
Aarhus GF 95.47% 4.53%
Aalborg 91.00% 9.00%
Esbjerg 90.96% 9.04%
Horsens 89.35% 10.65%
Odense 84.69% 15.31%
Sonderjyske 76.04% 23.96%
Silkeborg 60.01% 39.99%
Brondby 58.37% 41.63%
Randers 57.92% 42.08%

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
2012-07-13 Aalborg D 1-1 1606 1561 47.58% 26.59% 25.83% -0.6 1
2012-07-13 @ Aarhus GF D 1-1 1561 1606 25.83% 26.59% 47.58% +0.6 1
2012-07-14 Randers W 6-1 1593 1531 49.84% 26.05% 24.12% +14.0 3
2012-07-14 @ Sonderjyske L 1-6 1531 1593 24.12% 26.05% 49.84% -14.0 0
2012-07-15 Midtjylland W 4-2 1677 1637 46.95% 26.73% 26.32% +6.0 3
2012-07-15 @ FC Copenhagen L 2-4 1637 1677 26.32% 26.73% 46.95% -6.0 0
2012-07-15 Odense L 0-1 1560 1576 39.02% 27.78% 33.21% -5.6 0
2012-07-15 @ Brondby W 1-0 1576 1560 33.21% 27.78% 39.02% +5.6 3
2012-07-15 Silkeborg L 2-3 1608 1598 42.79% 27.43% 29.78% -5.4 0
2012-07-15 @ Esbjerg W 3-2 1598 1608 29.78% 27.43% 42.79% +5.4 3
2012-07-16 Nordsjaelland L 0-4 1632 1680 34.49% 27.83% 37.68% -18.3 0
2012-07-16 @ Horsens W 4-0 1680 1632 37.68% 27.83% 34.49% +18.4 3
2012-07-20 Horsens L 0-2 1603 1614 39.90% 27.72% 32.38% -10.8 3
2012-07-20 @ Silkeborg W 2-0 1614 1603 32.38% 27.72% 39.90% +10.8 3
2012-07-21 Nordsjaelland W 3-1 1631 1699 31.83% 27.67% 40.49% +9.4 3
2012-07-21 @ Midtjylland L 1-3 1699 1631 40.49% 27.67% 31.83% -9.4 3
2012-07-22 Brondby W 2-1 1561 1554 42.40% 27.48% 30.12% +4.4 4
2012-07-22 @ Aalborg L 1-2 1554 1561 30.12% 27.48% 42.40% -4.4 0
2012-07-22 FC Copenhagen D 1-1 1607 1683 30.84% 27.57% 41.59% +0.3 4
2012-07-22 @ Sonderjyske D 1-1 1683 1607 41.59% 27.57% 30.84% -0.3 4
2012-07-22 Randers L 0-1 1582 1517 50.21% 25.94% 23.84% -6.8 3
2012-07-22 @ Odense W 1-0 1517 1582 23.84% 25.94% 50.21% +6.8 3
2012-07-23 Esbjerg D 0-0 1605 1603 41.71% 27.56% 30.73% -0.3 2
2012-07-23 @ Aarhus GF D 0-0 1603 1605 30.73% 27.56% 41.71% +0.3 1
2012-07-27 Midtjylland D 2-2 1624 1640 39.16% 27.77% 33.07% -0.1 4
2012-07-27 @ Horsens D 2-2 1640 1624 33.07% 27.77% 39.16% +0.1 4
2012-07-28 Aalborg W 3-0 1683 1566 56.91% 23.72% 19.37% +8.5 7
2012-07-28 @ FC Copenhagen L 0-3 1566 1683 19.37% 23.72% 56.91% -8.5 4
2012-07-28 Sonderjyske L 1-2 1603 1607 40.75% 27.65% 31.60% -5.5 1
2012-07-28 @ Esbjerg W 2-1 1607 1603 31.60% 27.65% 40.75% +5.5 7
2012-07-29 Aarhus GF W 2-1 1524 1605 30.27% 27.50% 42.23% +5.6 6
2012-07-29 @ Randers L 1-2 1605 1524 42.23% 27.50% 30.27% -5.6 2
2012-07-29 Odense D 1-1 1689 1575 56.58% 23.85% 19.58% -1.1 4
2012-07-29 @ Nordsjaelland D 1-1 1575 1689 19.58% 23.85% 56.58% +1.1 4
2012-07-30 Silkeborg W 2-1 1550 1592 35.27% 27.85% 36.88% +5.1 3
2012-07-30 @ Brondby L 1-2 1592 1550 36.88% 27.85% 35.27% -5.1 3
2012-08-03 Odense L 0-1 1587 1576 42.95% 27.41% 29.64% -6.0 3
2012-08-03 @ Silkeborg W 1-0 1576 1587 29.64% 27.41% 42.95% +6.0 7
2012-08-04 FC Copenhagen L 1-2 1597 1691 28.67% 27.25% 44.08% -4.2 1
2012-08-04 @ Esbjerg W 2-1 1691 1597 44.08% 27.25% 28.67% +4.2 10
2012-08-04 Nordsjaelland D 1-1 1557 1688 24.62% 26.22% 49.16% +0.7 5
2012-08-04 @ Aalborg D 1-1 1688 1557 49.16% 26.22% 24.62% -0.7 5
2012-08-05 Brondby W 3-1 1599 1555 47.49% 26.61% 25.90% +6.7 5
2012-08-05 @ Aarhus GF L 1-3 1555 1599 25.90% 26.61% 47.49% -6.7 3
2012-08-05 Randers W 2-1 1640 1530 56.10% 24.03% 19.87% +3.0 7
2012-08-05 @ Midtjylland L 1-2 1530 1640 19.87% 24.03% 56.10% -3.0 6
2012-08-06 Horsens L 0-2 1613 1624 39.74% 27.73% 32.52% -10.7 7
2012-08-06 @ Sonderjyske W 2-0 1624 1613 32.52% 27.73% 39.74% +10.7 7
2012-08-10 Midtjylland W 2-1 1582 1643 32.78% 27.75% 39.47% +5.3 10
2012-08-10 @ Odense L 1-2 1643 1582 39.47% 27.75% 32.78% -5.3 7
2012-08-11 Silkeborg W 6-1 1687 1581 55.56% 24.23% 20.21% +11.8 8
2012-08-11 @ Nordsjaelland L 1-6 1581 1687 20.21% 24.23% 55.56% -11.8 3
2012-08-12 Aalborg L 1-4 1635 1558 51.86% 25.47% 22.67% -16.1 7
2012-08-12 @ Horsens W 4-1 1558 1635 22.67% 25.47% 51.86% +16.1 8
2012-08-12 Aarhus GF W 3-0 1695 1606 53.48% 24.96% 21.56% +9.4 13
2012-08-12 @ FC Copenhagen L 0-3 1606 1695 21.56% 24.96% 53.48% -9.4 5
2012-08-12 Sonderjyske L 0-1 1548 1602 33.72% 27.80% 38.48% -5.0 3
2012-08-12 @ Brondby W 1-0 1602 1548 38.48% 27.80% 33.72% +5.0 10
2012-08-13 Esbjerg W 1-0 1527 1593 32.05% 27.69% 40.26% +5.7 9
2012-08-13 @ Randers L 0-1 1593 1527 40.26% 27.69% 32.05% -5.7 1
2012-08-17 Midtjylland W 3-0 1574 1638 32.40% 27.72% 39.88% +15.6 11
2012-08-17 @ Aalborg L 0-3 1638 1574 39.88% 27.72% 32.40% -15.6 7
2012-08-18 Brondby D 1-1 1705 1543 62.28% 21.41% 16.31% -1.4 14
2012-08-18 @ FC Copenhagen D 1-1 1543 1705 16.31% 21.41% 62.28% +1.4 4
2012-08-18 Randers L 0-1 1570 1532 46.52% 26.82% 26.66% -6.4 3
2012-08-18 @ Silkeborg W 1-0 1532 1570 26.66% 26.82% 46.52% +6.4 12
2012-08-19 Horsens D 0-0 1587 1619 36.90% 27.85% 35.25% -0.0 2
2012-08-19 @ Esbjerg D 0-0 1619 1587 35.25% 27.85% 36.90% +0.0 8
2012-08-19 Nordsjaelland L 0-1 1596 1699 27.58% 27.03% 45.38% -4.3 5
2012-08-19 @ Aarhus GF W 1-0 1699 1596 45.38% 27.03% 27.58% +4.3 11
2012-08-20 Odense L 1-2 1607 1588 44.11% 27.24% 28.65% -5.8 10
2012-08-20 @ Sonderjyske W 2-1 1588 1607 28.65% 27.24% 44.11% +5.8 13
2012-08-24 Sonderjyske W 4-1 1704 1601 55.08% 24.40% 20.52% +7.6 14
2012-08-24 @ Nordsjaelland L 1-4 1601 1704 20.52% 24.40% 55.08% -7.6 10
2012-08-25 FC Copenhagen L 2-3 1539 1703 21.53% 24.94% 53.52% -3.1 12
2012-08-25 @ Randers W 3-2 1703 1539 53.52% 24.94% 21.53% +3.1 17
2012-08-26 Aalborg L 0-4 1593 1590 41.90% 27.54% 30.56% -21.2 13
2012-08-26 @ Odense W 4-0 1590 1593 30.56% 27.54% 41.90% +21.2 14
2012-08-26 Esbjerg D 1-1 1544 1587 35.25% 27.85% 36.90% +0.0 5
2012-08-26 @ Brondby D 1-1 1587 1544 36.90% 27.85% 35.25% -0.1 3
2012-08-26 Silkeborg D 1-1 1622 1563 49.48% 26.14% 24.38% -0.7 8
2012-08-26 @ Midtjylland D 1-1 1563 1622 24.38% 26.14% 49.48% +0.7 4
2012-08-27 Aarhus GF L 1-4 1619 1592 45.16% 27.07% 27.77% -14.5 8
2012-08-27 @ Horsens W 4-1 1592 1619 27.77% 27.07% 45.16% +14.5 8
2012-08-31 Aalborg L 0-4 1594 1611 38.95% 27.78% 33.27% -20.0 10
2012-08-31 @ Sonderjyske W 4-0 1611 1594 33.27% 27.78% 38.95% +20.0 17
2012-09-01 Aarhus GF L 0-4 1564 1606 35.32% 27.85% 36.82% -18.7 4
2012-09-01 @ Silkeborg W 4-0 1606 1564 36.82% 27.85% 35.32% +18.7 11
2012-09-02 Brondby D 0-0 1711 1545 62.86% 21.14% 16.00% -1.6 15
2012-09-02 @ Nordsjaelland D 0-0 1545 1711 16.00% 21.14% 62.86% +1.6 6
2012-09-02 Esbjerg W 1-0 1621 1587 46.12% 26.90% 26.99% +4.2 11
2012-09-02 @ Midtjylland L 0-1 1587 1621 26.99% 26.90% 46.12% -4.2 3
2012-09-02 FC Copenhagen D 2-2 1572 1706 24.31% 26.11% 49.58% +0.6 14
2012-09-02 @ Odense D 2-2 1706 1572 49.58% 26.11% 24.31% -0.5 18
2012-09-02 Horsens L 0-1 1536 1604 31.76% 27.67% 40.57% -4.8 12
2012-09-02 @ Randers W 1-0 1604 1536 40.57% 27.67% 31.76% +4.8 11
2012-09-14 Odense W 3-0 1583 1573 42.84% 27.42% 29.74% +12.5 6
2012-09-14 @ Esbjerg L 0-3 1573 1583 29.74% 27.42% 42.84% -12.5 14
2012-09-15 Nordsjaelland W 2-1 1706 1710 40.85% 27.64% 31.51% +4.5 21
2012-09-15 @ FC Copenhagen L 1-2 1710 1706 31.51% 27.64% 40.85% -4.5 15
2012-09-15 Silkeborg L 0-2 1574 1545 45.34% 27.04% 27.62% -11.9 10
2012-09-15 @ Sonderjyske W 2-0 1545 1574 27.62% 27.04% 45.34% +11.8 7
2012-09-16 Horsens D 2-2 1546 1609 32.48% 27.73% 39.79% +0.2 7
2012-09-16 @ Brondby D 2-2 1609 1546 39.79% 27.73% 32.48% -0.1 12
2012-09-16 Randers W 4-0 1631 1531 54.78% 24.51% 20.71% +11.8 20
2012-09-16 @ Aalborg L 0-4 1531 1631 20.71% 24.51% 54.78% -11.8 12
2012-09-17 Midtjylland W 3-2 1625 1626 41.28% 27.60% 31.12% +4.2 14
2012-09-17 @ Aarhus GF L 2-3 1626 1625 31.12% 27.60% 41.28% -4.2 11
2012-09-21 Sonderjyske D 2-2 1629 1562 50.59% 25.84% 23.57% -0.6 15
2012-09-21 @ Aarhus GF D 2-2 1562 1629 23.57% 25.84% 50.59% +0.6 11
2012-09-22 Randers D 1-1 1705 1519 65.06% 20.07% 14.87% -1.5 16
2012-09-22 @ Nordsjaelland D 1-1 1519 1705 14.87% 20.07% 65.06% +1.5 13
2012-09-23 Brondby D 1-1 1621 1546 51.60% 25.55% 22.85% -0.9 12
2012-09-23 @ Midtjylland D 1-1 1546 1621 22.85% 25.55% 51.60% +0.9 8
2012-09-23 Odense D 2-2 1609 1560 48.13% 26.47% 25.40% -0.5 13
2012-09-23 @ Horsens D 2-2 1560 1609 25.40% 26.47% 48.13% +0.5 15
2012-09-23 Silkeborg W 5-0 1710 1557 61.30% 21.86% 16.84% +11.7 24
2012-09-23 @ FC Copenhagen L 0-5 1557 1710 16.84% 21.86% 61.30% -11.7 7
2012-09-24 Esbjerg L 0-2 1643 1596 47.87% 26.53% 25.60% -12.4 20
2012-09-24 @ Aalborg W 2-0 1596 1643 25.60% 26.53% 47.87% +12.4 9
2012-09-28 Esbjerg W 3-0 1703 1608 54.21% 24.71% 21.08% +9.2 19
2012-09-28 @ Nordsjaelland L 0-3 1608 1703 21.08% 24.71% 54.21% -9.2 9
2012-09-29 FC Copenhagen D 1-1 1609 1722 26.45% 26.76% 46.78% +0.6 14
2012-09-29 @ Horsens D 1-1 1722 1609 46.78% 26.76% 26.45% -0.6 25
2012-09-30 Aalborg W 2-1 1545 1630 29.71% 27.42% 42.87% +5.7 10
2012-09-30 @ Silkeborg L 1-2 1630 1545 42.87% 27.42% 29.71% -5.7 20
2012-09-30 Aarhus GF L 1-2 1561 1629 31.86% 27.68% 40.46% -4.5 15
2012-09-30 @ Odense W 2-1 1629 1561 40.46% 27.68% 31.86% +4.6 18
2012-09-30 Brondby W 3-2 1521 1547 37.62% 27.84% 34.55% +4.6 16
2012-09-30 @ Randers L 2-3 1547 1521 34.55% 27.84% 37.62% -4.6 8
2012-10-01 Sonderjyske L 1-3 1621 1562 49.37% 26.17% 24.47% -11.0 12
2012-10-01 @ Midtjylland W 3-1 1562 1621 24.47% 26.17% 49.37% +11.0 14
2012-10-05 Nordsjaelland W 3-0 1556 1713 22.22% 25.27% 52.51% +19.3 18
2012-10-05 @ Odense L 0-3 1713 1556 52.51% 25.27% 22.22% -19.3 19
2012-10-06 Aarhus GF L 0-3 1573 1633 32.93% 27.76% 39.31% -13.6 14
2012-10-06 @ Sonderjyske W 3-0 1633 1573 39.31% 27.76% 32.93% +13.6 21
2012-10-07 Aalborg L 1-3 1543 1625 30.06% 27.47% 42.47% -7.5 8
2012-10-07 @ Brondby W 3-1 1625 1543 42.47% 27.47% 30.06% +7.5 23
2012-10-07 FC Copenhagen D 2-2 1599 1721 25.47% 26.49% 48.03% +0.5 10
2012-10-07 @ Esbjerg D 2-2 1721 1599 48.03% 26.49% 25.47% -0.5 26
2012-10-07 Horsens D 1-1 1551 1609 33.14% 27.77% 39.08% +0.2 11
2012-10-07 @ Silkeborg D 1-1 1609 1551 39.08% 27.77% 33.14% -0.2 15
2012-10-07 Midtjylland W 2-1 1525 1610 29.79% 27.43% 42.78% +5.7 19
2012-10-07 @ Randers L 1-2 1610 1525 42.78% 27.43% 29.79% -5.7 12
2012-10-19 Silkeborg W 3-0 1693 1551 59.98% 22.45% 17.57% +7.6 22
2012-10-19 @ Nordsjaelland L 0-3 1551 1693 17.57% 22.45% 59.98% -7.6 11
2012-10-20 Sonderjyske W 2-1 1632 1560 51.24% 25.65% 23.10% +3.5 26
2012-10-20 @ Aalborg L 1-2 1560 1632 23.10% 25.65% 51.24% -3.5 14
2012-10-21 Brondby W 1-0 1721 1535 65.06% 20.07% 14.87% +2.3 29
2012-10-21 @ FC Copenhagen L 0-1 1535 1721 14.87% 20.07% 65.06% -2.3 8
2012-10-21 Esbjerg D 0-0 1609 1599 42.75% 27.44% 29.81% -0.4 16
2012-10-21 @ Horsens D 0-0 1599 1609 29.81% 27.44% 42.75% +0.4 11
2012-10-21 Odense D 1-1 1604 1575 45.35% 27.04% 27.61% -0.5 13
2012-10-21 @ Midtjylland D 1-1 1575 1604 27.61% 27.04% 45.35% +0.5 19
2012-10-22 Randers D 1-1 1647 1531 56.77% 23.77% 19.46% -1.1 22
2012-10-22 @ Aarhus GF D 1-1 1531 1647 19.46% 23.77% 56.77% +1.1 20
2012-10-26 Aalborg W 1-0 1701 1636 50.31% 25.92% 23.77% +3.8 25
2012-10-26 @ Nordsjaelland L 0-1 1636 1701 23.77% 25.92% 50.31% -3.8 26
2012-10-27 Randers L 1-2 1544 1532 42.99% 27.40% 29.60% -5.7 11
2012-10-27 @ Silkeborg W 2-1 1532 1544 29.60% 27.40% 42.99% +5.7 23
2012-10-28 Aarhus GF L 2-4 1576 1646 31.64% 27.66% 40.71% -7.0 19
2012-10-28 @ Odense W 4-2 1646 1576 40.71% 27.66% 31.64% +7.0 25
2012-10-28 Brondby D 1-1 1603 1533 51.03% 25.72% 23.26% -0.8 14
2012-10-28 @ Midtjylland D 1-1 1533 1603 23.26% 25.72% 51.03% +0.8 9
2012-10-28 Sonderjyske L 1-2 1600 1556 47.38% 26.64% 25.98% -6.1 11
2012-10-28 @ Esbjerg W 2-1 1556 1600 25.98% 26.64% 47.38% +6.1 17
2012-10-29 FC Copenhagen W 1-0 1609 1723 26.32% 26.73% 46.95% +6.4 19
2012-10-29 @ Horsens L 0-1 1723 1609 46.95% 26.73% 26.32% -6.5 29
2012-11-02 Silkeborg W 1-0 1594 1538 49.03% 26.25% 24.72% +3.9 14
2012-11-02 @ Esbjerg L 0-1 1538 1594 24.72% 26.25% 49.03% -3.9 11
2012-11-03 Nordsjaelland L 1-2 1562 1705 23.52% 25.82% 50.66% -3.5 17
2012-11-03 @ Sonderjyske W 2-1 1705 1562 50.66% 25.82% 23.52% +3.5 28
2012-11-04 FC Copenhagen L 0-2 1653 1717 32.35% 27.72% 39.93% -9.2 25
2012-11-04 @ Aarhus GF W 2-0 1717 1653 39.93% 27.72% 32.35% +9.2 32
2012-11-04 Horsens D 0-0 1538 1615 30.65% 27.55% 41.80% +0.4 24
2012-11-04 @ Randers D 0-0 1615 1538 41.80% 27.55% 30.65% -0.3 20
2012-11-04 Odense L 0-3 1534 1569 36.32% 27.86% 35.82% -14.6 9
2012-11-04 @ Brondby W 3-0 1569 1534 35.82% 27.86% 36.32% +14.6 22
2012-11-05 Midtjylland L 1-3 1632 1603 45.46% 27.02% 27.52% -10.3 26
2012-11-05 @ Aalborg W 3-1 1603 1632 27.52% 27.02% 45.46% +10.3 17
2012-11-09 Aarhus GF W 2-0 1615 1643 37.30% 27.84% 34.86% +9.7 23
2012-11-09 @ Horsens L 0-2 1643 1615 34.86% 27.84% 37.30% -9.7 25
2012-11-10 Randers D 2-2 1708 1538 63.27% 20.94% 15.79% -1.1 29
2012-11-10 @ Nordsjaelland D 2-2 1538 1708 15.79% 20.94% 63.27% +1.1 25
2012-11-11 Aalborg W 4-0 1726 1622 55.34% 24.31% 20.35% +11.6 35
2012-11-11 @ FC Copenhagen L 0-4 1622 1726 20.35% 24.31% 55.34% -11.6 26
2012-11-11 Brondby L 1-2 1534 1519 43.49% 27.34% 29.18% -5.7 11
2012-11-11 @ Silkeborg W 2-1 1519 1534 29.18% 27.34% 43.49% +5.7 12
2012-11-11 Sonderjyske W 5-0 1584 1559 44.81% 27.13% 28.06% +19.2 25
2012-11-11 @ Odense L 0-5 1559 1584 28.06% 27.13% 44.81% -19.2 17
2012-11-12 Esbjerg D 0-0 1613 1597 43.54% 27.33% 29.13% -0.5 18
2012-11-12 @ Midtjylland D 0-0 1597 1613 29.13% 27.33% 43.54% +0.5 15
2012-11-16 Nordsjaelland L 0-2 1634 1707 31.14% 27.60% 41.26% -9.0 25
2012-11-16 @ Aarhus GF W 2-0 1707 1634 41.26% 27.60% 31.14% +9.0 32
2012-11-17 Silkeborg L 2-3 1540 1528 42.98% 27.41% 29.61% -5.4 17
2012-11-17 @ Sonderjyske W 3-2 1528 1540 29.61% 27.41% 42.98% +5.4 14
2012-11-18 Esbjerg D 2-2 1525 1598 31.17% 27.61% 41.22% +0.2 13
2012-11-18 @ Brondby D 2-2 1598 1525 41.22% 27.61% 31.17% -0.2 16
2012-11-18 Horsens W 2-0 1610 1625 39.31% 27.76% 32.93% +9.3 29
2012-11-18 @ Aalborg L 0-2 1625 1610 32.93% 27.76% 39.31% -9.3 23
2012-11-18 Midtjylland W 2-1 1738 1612 57.92% 23.32% 18.76% +2.8 38
2012-11-18 @ FC Copenhagen L 1-2 1612 1738 18.76% 23.32% 57.92% -2.8 18
2012-11-19 Odense W 3-2 1539 1603 32.43% 27.72% 39.85% +5.1 28
2012-11-19 @ Randers L 2-3 1603 1539 39.85% 27.72% 32.43% -5.1 25
2012-11-23 Aalborg L 0-1 1544 1619 30.95% 27.58% 41.47% -4.7 28
2012-11-23 @ Randers W 1-0 1619 1544 41.47% 27.58% 30.95% +4.7 32
2012-11-24 Odense L 0-2 1534 1598 32.37% 27.72% 39.92% -9.2 14
2012-11-24 @ Silkeborg W 2-0 1598 1534 39.92% 27.72% 32.37% +9.2 28
2012-11-25 FC Copenhagen L 1-2 1534 1740 18.29% 22.98% 58.73% -2.7 17
2012-11-25 @ Sonderjyske W 2-1 1740 1534 58.73% 22.98% 18.29% +2.8 41
2012-11-25 Horsens W 2-0 1525 1615 29.07% 27.32% 43.61% +11.5 16
2012-11-25 @ Brondby L 0-2 1615 1525 43.61% 27.32% 29.07% -11.5 23
2012-11-25 Nordsjaelland W 1-0 1598 1716 25.91% 26.62% 47.47% +6.5 19
2012-11-25 @ Esbjerg L 0-1 1716 1598 47.47% 26.62% 25.91% -6.5 32
2012-11-26 Midtjylland D 1-1 1625 1610 43.49% 27.34% 29.17% -0.4 26
2012-11-26 @ Aarhus GF D 1-1 1610 1625 29.17% 27.34% 43.49% +0.4 19
2012-11-30 Sonderjyske L 1-3 1604 1532 51.21% 25.67% 23.13% -11.3 23
2012-11-30 @ Horsens W 3-1 1532 1604 23.13% 25.67% 51.21% +11.3 20
2012-12-01 Esbjerg W 3-0 1607 1604 41.75% 27.55% 30.70% +12.8 31
2012-12-01 @ Odense L 0-3 1604 1607 30.70% 27.55% 41.75% -12.9 19
2012-12-02 Brondby W 3-0 1710 1536 63.62% 20.77% 15.60% +6.6 35
2012-12-02 @ Nordsjaelland L 0-3 1536 1710 15.60% 20.77% 63.62% -6.7 16
2012-12-02 Randers W 2-0 1743 1540 67.02% 19.07% 13.90% +4.0 44
2012-12-02 @ FC Copenhagen L 0-2 1540 1743 13.90% 19.07% 67.02% -4.0 28
2012-12-02 Silkeborg L 1-3 1610 1524 52.96% 25.13% 21.91% -11.6 19
2012-12-02 @ Midtjylland W 3-1 1524 1610 21.91% 25.13% 52.96% +11.6 17
2012-12-03 Aarhus GF L 0-3 1624 1624 41.34% 27.60% 31.06% -16.0 32
2012-12-03 @ Aalborg W 3-0 1624 1624 31.06% 27.60% 41.34% +16.0 29
2012-12-07 Brondby D 2-2 1543 1530 43.22% 27.37% 29.41% -0.3 21
2012-12-07 @ Sonderjyske D 2-2 1530 1543 29.41% 27.37% 43.22% +0.3 17
2012-12-08 Esbjerg W 2-1 1536 1591 33.47% 27.79% 38.74% +5.3 31
2012-12-08 @ Randers L 1-2 1591 1536 38.74% 27.79% 33.47% -5.3 19
2012-12-09 Midtjylland L 0-2 1592 1598 40.52% 27.67% 31.81% -10.9 23
2012-12-09 @ Horsens W 2-0 1598 1592 31.81% 27.67% 40.52% +10.9 22
2012-12-09 Nordsjaelland W 4-1 1747 1716 45.67% 26.98% 27.35% +9.9 47
2012-12-09 @ FC Copenhagen L 1-4 1716 1747 27.35% 26.98% 45.67% -9.9 35
2012-12-10 Silkeborg D 3-3 1640 1536 55.34% 24.31% 20.35% -0.7 30
2012-12-10 @ Aarhus GF D 3-3 1536 1640 20.35% 24.31% 55.34% +0.7 18
2013-03-01 Aalborg W 3-2 1537 1608 31.42% 27.63% 40.95% +5.2 21
2013-03-01 @ Silkeborg L 2-3 1608 1537 40.95% 27.63% 31.42% -5.2 32
2013-03-02 Sonderjyske W 1-0 1609 1543 50.51% 25.86% 23.63% +3.8 25
2013-03-02 @ Midtjylland L 0-1 1543 1609 23.63% 25.86% 50.51% -3.8 21
2013-03-03 FC Copenhagen L 2-3 1620 1757 24.00% 26.00% 49.99% -3.4 31
2013-03-03 @ Odense W 3-2 1757 1620 49.99% 26.00% 24.00% +3.4 50
2013-03-03 Horsens W 1-0 1706 1582 57.90% 23.33% 18.78% +3.0 38
2013-03-03 @ Nordsjaelland L 0-1 1582 1706 18.78% 23.33% 57.90% -3.0 23
2013-03-03 Randers L 0-2 1530 1541 39.84% 27.72% 32.43% -10.7 17
2013-03-03 @ Brondby W 2-0 1541 1530 32.43% 27.72% 39.84% +10.7 34
2013-03-04 Aarhus GF W 2-1 1586 1640 33.77% 27.81% 38.42% +5.2 22
2013-03-04 @ Esbjerg L 1-2 1640 1586 38.42% 27.81% 33.77% -5.2 30
2013-03-06 Odense D 2-2 1603 1616 39.46% 27.75% 32.79% -0.1 33
2013-03-06 @ Aalborg D 2-2 1616 1603 32.79% 27.75% 39.46% +0.1 32
2013-03-08 Sonderjyske W 2-0 1552 1539 43.18% 27.38% 29.44% +8.6 37
2013-03-08 @ Randers L 0-2 1539 1552 29.44% 27.38% 43.18% -8.6 21
2013-03-09 Esbjerg D 0-0 1603 1591 42.96% 27.41% 29.63% -0.4 34
2013-03-09 @ Aalborg D 0-0 1591 1603 29.63% 27.41% 42.96% +0.4 23
2013-03-10 Brondby L 0-3 1634 1519 56.69% 23.80% 19.50% -20.5 30
2013-03-10 @ Aarhus GF W 3-0 1519 1634 19.50% 23.80% 56.69% +20.5 20
2013-03-10 Odense W 2-0 1579 1616 35.96% 27.86% 36.18% +10.0 26
2013-03-10 @ Horsens L 0-2 1616 1579 36.18% 27.86% 35.96% -10.0 32
2013-03-10 Silkeborg W 3-1 1760 1542 68.64% 18.23% 13.13% +3.2 53
2013-03-10 @ FC Copenhagen L 1-3 1542 1760 13.13% 18.23% 68.64% -3.2 21
2013-03-11 Nordsjaelland D 1-1 1613 1709 28.36% 27.19% 44.45% +0.5 26
2013-03-11 @ Midtjylland D 1-1 1709 1613 44.45% 27.19% 28.36% -0.5 39
2013-03-15 Horsens W 2-1 1764 1589 63.84% 20.67% 15.49% +2.3 56
2013-03-15 @ FC Copenhagen L 1-2 1589 1764 15.49% 20.67% 63.84% -2.3 26
2013-03-17 Midtjylland D 1-1 1540 1614 31.11% 27.60% 41.29% +0.3 21
2013-03-17 @ Brondby D 1-1 1614 1540 41.29% 27.60% 31.11% -0.3 27
2013-03-17 Odense W 2-1 1614 1606 42.43% 27.48% 30.09% +4.4 33
2013-03-17 @ Aarhus GF L 1-2 1606 1614 30.09% 27.48% 42.43% -4.4 32
2013-03-17 Silkeborg W 1-0 1560 1539 44.39% 27.20% 28.41% +4.4 40
2013-03-17 @ Randers L 0-1 1539 1560 28.41% 27.20% 44.39% -4.4 21
2013-03-28 Aalborg D 2-2 1586 1602 39.11% 27.77% 33.12% -0.1 27
2013-03-28 @ Horsens D 2-2 1602 1586 33.12% 27.77% 39.11% +0.1 35
2013-03-28 Randers D 0-0 1602 1565 46.59% 26.80% 26.61% -0.6 33
2013-03-28 @ Odense D 0-0 1565 1602 26.61% 26.80% 46.59% +0.7 41
2013-03-28 Sonderjyske L 0-5 1534 1530 41.94% 27.53% 30.53% -26.1 21
2013-03-28 @ Silkeborg W 5-0 1530 1534 30.53% 27.53% 41.94% +26.1 24
2013-03-29 Aarhus GF W 4-2 1709 1618 53.60% 24.92% 21.48% +5.0 42
2013-03-29 @ Nordsjaelland L 2-4 1618 1709 21.48% 24.92% 53.60% -5.0 33
2013-03-29 Brondby W 1-0 1592 1540 48.51% 26.38% 25.11% +4.0 26
2013-03-29 @ Esbjerg L 0-1 1540 1592 25.11% 26.38% 48.51% -4.0 21
2013-03-29 FC Copenhagen D 2-2 1613 1766 22.56% 25.43% 52.01% +0.7 28
2013-03-29 @ Midtjylland D 2-2 1766 1613 52.01% 25.43% 22.56% -0.7 57
2013-03-31 Horsens W 2-0 1601 1586 43.53% 27.33% 29.14% +8.5 36
2013-03-31 @ Odense L 0-2 1586 1601 29.14% 27.33% 43.53% -8.5 27
2013-03-31 Randers L 0-2 1556 1565 40.12% 27.70% 32.18% -10.8 24
2013-03-31 @ Sonderjyske W 2-0 1565 1556 32.18% 27.70% 40.12% +10.8 44
2013-04-01 Aalborg W 1-0 1596 1602 40.45% 27.68% 31.88% +4.8 29
2013-04-01 @ Esbjerg L 0-1 1602 1596 31.88% 27.68% 40.45% -4.8 35
2013-04-01 Aarhus GF W 3-2 1536 1613 30.67% 27.55% 41.78% +5.3 24
2013-04-01 @ Brondby L 2-3 1613 1536 41.78% 27.55% 30.67% -5.3 33
2013-04-01 FC Copenhagen W 1-0 1508 1765 15.05% 20.24% 64.71% +8.3 24
2013-04-01 @ Silkeborg L 0-1 1765 1508 64.71% 20.24% 15.05% -8.3 57
2013-04-01 Midtjylland W 3-1 1714 1614 54.80% 24.50% 20.69% +5.4 45
2013-04-01 @ Nordsjaelland L 1-3 1614 1714 20.69% 24.50% 54.80% -5.4 28
2013-04-04 Nordsjaelland L 0-1 1598 1719 25.55% 26.51% 47.93% -4.0 35
2013-04-04 @ Aalborg W 1-0 1719 1598 47.93% 26.51% 25.55% +4.0 48
2013-04-05 Esbjerg L 0-1 1608 1601 42.42% 27.48% 30.10% -6.0 33
2013-04-05 @ Aarhus GF W 1-0 1601 1608 30.10% 27.48% 42.42% +6.0 32
2013-04-06 Midtjylland L 0-2 1546 1609 32.51% 27.73% 39.76% -9.2 24
2013-04-06 @ Sonderjyske W 2-0 1609 1546 39.76% 27.73% 32.51% +9.2 31
2013-04-07 Brondby L 0-1 1576 1541 46.19% 26.88% 26.92% -6.4 44
2013-04-07 @ Randers W 1-0 1541 1576 26.92% 26.88% 46.19% +6.4 27
2013-04-07 Nordsjaelland L 0-2 1578 1723 23.20% 25.69% 51.11% -7.0 27
2013-04-07 @ Horsens W 2-0 1723 1578 51.11% 25.69% 23.20% +7.0 51
2013-04-07 Odense D 1-1 1757 1610 60.57% 22.19% 17.24% -1.3 58
2013-04-07 @ FC Copenhagen D 1-1 1610 1757 17.24% 22.19% 60.57% +1.3 37
2013-04-08 Silkeborg W 1-0 1593 1516 51.86% 25.47% 22.67% +3.6 38
2013-04-08 @ Aalborg L 0-1 1516 1593 22.67% 25.47% 51.86% -3.6 24
2013-04-09 Esbjerg W 3-1 1536 1607 31.55% 27.65% 40.80% +9.5 27
2013-04-09 @ Sonderjyske L 1-3 1607 1536 40.80% 27.65% 31.55% -9.5 32
2013-04-12 Aarhus GF W 3-1 1513 1602 29.19% 27.34% 43.47% +9.9 27
2013-04-12 @ Silkeborg L 1-3 1602 1513 43.47% 27.34% 29.19% -9.9 33
2013-04-13 Horsens W 5-2 1618 1571 47.88% 26.53% 25.59% +8.1 34
2013-04-13 @ Midtjylland L 2-5 1571 1618 25.59% 26.53% 47.88% -8.1 27
2013-04-14 Aalborg L 3-4 1611 1597 43.37% 27.35% 29.28% -5.3 37
2013-04-14 @ Odense W 4-3 1597 1611 29.28% 27.35% 43.37% +5.3 41
2013-04-14 Randers W 4-0 1597 1570 45.21% 27.06% 27.73% +15.5 35
2013-04-14 @ Esbjerg L 0-4 1570 1597 27.73% 27.06% 45.21% -15.5 44
2013-04-14 Sonderjyske L 0-3 1548 1546 41.66% 27.56% 30.78% -16.1 27
2013-04-14 @ Brondby W 3-0 1546 1548 30.78% 27.56% 41.66% +16.1 30
2013-04-15 FC Copenhagen L 2-3 1730 1756 37.77% 27.83% 34.39% -4.9 51
2013-04-15 @ Nordsjaelland W 3-2 1756 1730 34.39% 27.83% 37.77% +4.9 61
2013-04-20 Horsens D 0-0 1592 1563 45.49% 27.01% 27.50% -0.6 34
2013-04-20 @ Aarhus GF D 0-0 1563 1592 27.50% 27.01% 45.49% +0.6 28
2013-04-21 FC Copenhagen D 1-1 1602 1761 22.07% 25.20% 52.73% +0.9 42
2013-04-21 @ Aalborg D 1-1 1761 1602 52.73% 25.20% 22.07% -0.9 62
2013-04-21 Nordsjaelland D 0-0 1554 1725 20.95% 24.64% 54.40% +1.1 45
2013-04-21 @ Randers D 0-0 1725 1554 54.40% 24.64% 20.95% -1.1 52
2013-04-21 Odense W 4-1 1562 1606 35.09% 27.85% 37.06% +12.5 33
2013-04-21 @ Sonderjyske L 1-4 1606 1562 37.06% 27.85% 35.09% -12.5 37
2013-04-21 Silkeborg D 2-2 1532 1523 42.63% 27.45% 29.92% -0.3 28
2013-04-21 @ Brondby D 2-2 1523 1532 29.92% 27.45% 42.63% +0.3 28
2013-04-22 Midtjylland L 0-1 1613 1626 39.50% 27.75% 32.76% -5.7 35
2013-04-22 @ Esbjerg W 1-0 1626 1613 32.76% 27.75% 39.50% +5.7 37
2013-04-26 Esbjerg L 0-1 1523 1607 29.84% 27.44% 42.72% -4.6 28
2013-04-26 @ Silkeborg W 1-0 1607 1523 42.72% 27.44% 29.84% +4.6 38
2013-04-27 Sonderjyske D 2-2 1724 1574 60.91% 22.04% 17.05% -1.0 53
2013-04-27 @ Nordsjaelland D 2-2 1574 1724 17.05% 22.04% 60.91% +1.0 34
2013-04-28 Aarhus GF D 0-0 1760 1592 63.04% 21.05% 15.91% -1.6 63
2013-04-28 @ FC Copenhagen D 0-0 1592 1760 15.91% 21.05% 63.04% +1.6 35
2013-04-28 Brondby L 1-2 1594 1531 49.90% 26.03% 24.07% -6.4 37
2013-04-28 @ Odense W 2-1 1531 1594 24.07% 26.03% 49.90% +6.4 31
2013-04-28 Randers W 1-0 1563 1555 42.48% 27.47% 30.05% +4.6 31
2013-04-28 @ Horsens L 0-1 1555 1563 30.05% 27.47% 42.48% -4.6 45
2013-04-29 Aalborg L 2-3 1631 1603 45.30% 27.05% 27.65% -5.6 37
2013-04-29 @ Midtjylland W 3-2 1603 1631 27.65% 27.05% 45.30% +5.6 45
2013-05-03 Nordsjaelland D 2-2 1518 1723 18.37% 23.04% 58.59% +0.9 29
2013-05-03 @ Silkeborg D 2-2 1723 1518 58.59% 23.04% 18.37% -0.9 54
2013-05-04 Aalborg W 1-0 1575 1609 36.59% 27.86% 35.55% +5.2 37
2013-05-04 @ Sonderjyske L 0-1 1609 1575 35.55% 27.86% 36.59% -5.2 45
2013-05-05 Aarhus GF W 1-0 1551 1593 35.32% 27.85% 36.83% +5.4 48
2013-05-05 @ Randers L 0-1 1593 1551 36.83% 27.85% 35.32% -5.4 35
2013-05-05 FC Copenhagen D 0-0 1538 1758 17.32% 22.25% 60.44% +1.5 32
2013-05-05 @ Brondby D 0-0 1758 1538 60.44% 22.25% 17.32% -1.5 64
2013-05-05 Horsens W 1-0 1611 1568 47.42% 26.63% 25.95% +4.1 41
2013-05-05 @ Esbjerg L 0-1 1568 1611 25.95% 26.63% 47.42% -4.1 31
2013-05-06 Midtjylland L 0-1 1587 1626 35.84% 27.86% 36.30% -5.3 37
2013-05-06 @ Odense W 1-0 1626 1587 36.30% 27.86% 35.84% +5.3 40
2013-05-10 Odense W 4-1 1722 1582 59.79% 22.53% 17.68% +6.5 57
2013-05-10 @ Nordsjaelland L 1-4 1582 1722 17.68% 22.53% 59.79% -6.5 37
2013-05-11 Sonderjyske W 2-1 1588 1581 42.38% 27.48% 30.13% +4.4 38
2013-05-11 @ Aarhus GF L 1-2 1581 1588 30.13% 27.48% 42.38% -4.4 37
2013-05-12 Brondby D 1-1 1604 1539 50.20% 25.95% 23.85% -0.8 46
2013-05-12 @ Aalborg D 1-1 1539 1604 23.85% 25.95% 50.20% +0.8 33
2013-05-12 Esbjerg L 0-2 1757 1616 59.84% 22.51% 17.65% -14.7 64
2013-05-12 @ FC Copenhagen W 2-0 1616 1757 17.65% 22.51% 59.84% +14.7 44
2013-05-12 Randers W 3-0 1631 1556 51.59% 25.55% 22.86% +10.0 43
2013-05-12 @ Midtjylland L 0-3 1556 1631 22.86% 25.55% 51.59% -10.0 48
2013-05-13 Silkeborg W 2-0 1564 1519 47.52% 26.61% 25.87% +7.7 34
2013-05-13 @ Horsens L 0-2 1519 1564 25.87% 26.61% 47.52% -7.7 29
2013-05-16 Aalborg W 3-0 1592 1603 39.85% 27.72% 32.42% +13.4 41
2013-05-16 @ Aarhus GF L 0-3 1603 1592 32.42% 27.72% 39.85% -13.4 46
2013-05-16 FC Copenhagen W 1-0 1546 1742 19.04% 23.50% 57.46% +7.6 51
2013-05-16 @ Randers L 0-1 1742 1546 57.46% 23.50% 19.04% -7.6 64
2013-05-16 Horsens W 4-2 1576 1571 42.06% 27.52% 30.42% +6.8 40
2013-05-16 @ Sonderjyske L 2-4 1571 1576 30.42% 27.52% 42.06% -6.8 34
2013-05-16 Midtjylland D 1-1 1512 1641 24.77% 26.27% 48.96% +0.7 30
2013-05-16 @ Silkeborg D 1-1 1641 1512 48.96% 26.27% 24.77% -0.7 44
2013-05-16 Nordsjaelland W 4-0 1540 1729 19.55% 23.83% 56.62% +26.7 36
2013-05-16 @ Brondby L 0-4 1729 1540 56.62% 23.83% 19.55% -26.7 57
2013-05-16 Odense W 6-2 1630 1575 48.93% 26.28% 24.79% +9.9 47
2013-05-16 @ Esbjerg L 2-6 1575 1630 24.79% 26.28% 48.93% -9.9 37
2013-05-20 Aarhus GF W 3-2 1640 1606 46.22% 26.88% 26.90% +3.8 47
2013-05-20 @ Midtjylland L 2-3 1606 1640 26.90% 26.88% 46.22% -3.8 41
2013-05-20 Brondby L 0-1 1565 1567 41.08% 27.62% 31.30% -5.8 34
2013-05-20 @ Horsens W 1-0 1567 1565 31.30% 27.62% 41.08% +5.8 39
2013-05-20 Esbjerg W 1-0 1702 1640 49.87% 26.04% 24.10% +3.8 60
2013-05-20 @ Nordsjaelland L 0-1 1640 1702 24.10% 26.04% 49.87% -3.8 47
2013-05-20 Randers D 2-2 1590 1554 46.36% 26.85% 26.79% -0.4 47
2013-05-20 @ Aalborg D 2-2 1554 1590 26.79% 26.85% 46.36% +0.4 52
2013-05-20 Silkeborg D 3-3 1565 1512 48.69% 26.34% 24.97% -0.4 38
2013-05-20 @ Odense D 3-3 1512 1565 24.97% 26.34% 48.69% +0.4 31
2013-05-20 Sonderjyske D 1-1 1734 1583 61.06% 21.97% 16.97% -1.3 65
2013-05-20 @ FC Copenhagen D 1-1 1583 1734 16.97% 21.97% 61.06% +1.4 41

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 2013-04-01 15.05% @ Silkeborg 1508 1 FC Copenhagen 1765 0
2 2013-05-12 17.65% Esbjerg 1616 2 @ FC Copenhagen 1757 0
3 2013-05-16 19.04% @ Randers 1546 1 FC Copenhagen 1742 0
4 2013-03-10 19.50% Brondby 1519 3 @ Aarhus GF 1634 0
5 2013-05-16 19.55% @ Brondby 1540 4 Nordsjaelland 1729 0
6 2012-12-02 21.91% Silkeborg 1524 3 @ Midtjylland 1610 1
7 2012-10-05 22.22% @ Odense 1556 3 Nordsjaelland 1713 0
8 2012-08-12 22.67% Aalborg 1558 4 @ Horsens 1635 1
9 2012-11-30 23.13% Sonderjyske 1532 3 @ Horsens 1604 1
10 2012-07-22 23.84% Randers 1517 1 @ Odense 1582 0
11 2013-04-28 24.07% Brondby 1531 2 @ Odense 1594 1
12 2012-10-01 24.47% Sonderjyske 1562 3 @ Midtjylland 1621 1
13 2012-09-24 25.60% Esbjerg 1596 2 @ Aalborg 1643 0
14 2012-11-25 25.91% @ Esbjerg 1598 1 Nordsjaelland 1716 0
15 2012-10-28 25.98% Sonderjyske 1556 2 @ Esbjerg 1600 1
16 2012-10-29 26.32% @ Horsens 1609 1 FC Copenhagen 1723 0
17 2012-08-18 26.66% Randers 1532 1 @ Silkeborg 1570 0
18 2013-04-07 26.92% Brondby 1541 1 @ Randers 1576 0
19 2012-11-05 27.52% Midtjylland 1603 3 @ Aalborg 1632 1
20 2012-09-15 27.62% Silkeborg 1545 2 @ Sonderjyske 1574 0
21 2013-04-29 27.65% Aalborg 1603 3 @ Midtjylland 1631 2
22 2012-08-27 27.77% Aarhus GF 1592 4 @ Horsens 1619 1
23 2012-08-20 28.65% Odense 1588 2 @ Sonderjyske 1607 1
24 2012-11-25 29.07% @ Brondby 1525 2 Horsens 1615 0
25 2012-11-11 29.18% Brondby 1519 2 @ Silkeborg 1534 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 2013-05-16 26.74 @ Brondby 4 1540 19.55% Nordsjaelland 0 1729 56.62% 23.83%
2 2013-03-28 26.11 Sonderjyske 5 1530 30.53% @ Silkeborg 0 1534 41.94% 27.53%
3 2012-08-26 21.18 Aalborg 4 1590 30.56% @ Odense 0 1593 41.90% 27.54%
4 2013-03-10 20.49 Brondby 3 1519 19.50% @ Aarhus GF 0 1634 56.69% 23.80%
5 2012-08-31 20.05 Aalborg 4 1611 33.27% @ Sonderjyske 0 1594 38.95% 27.78%
6 2012-10-05 19.30 @ Odense 3 1556 22.22% Nordsjaelland 0 1713 52.51% 25.27%
7 2012-11-11 19.25 @ Odense 5 1584 44.81% Sonderjyske 0 1559 28.06% 27.13%
8 2012-09-01 18.67 Aarhus GF 4 1606 36.82% @ Silkeborg 0 1564 35.32% 27.85%
9 2012-07-16 18.35 Nordsjaelland 4 1680 37.68% @ Horsens 0 1632 34.49% 27.83%
10 2013-04-14 16.13 Sonderjyske 3 1546 30.78% @ Brondby 0 1548 41.66% 27.56%
11 2012-08-12 16.12 Aalborg 4 1558 22.67% @ Horsens 1 1635 51.86% 25.47%
12 2012-12-03 16.05 Aarhus GF 3 1624 31.06% @ Aalborg 0 1624 41.34% 27.60%
13 2012-08-17 15.62 @ Aalborg 3 1574 32.40% Midtjylland 0 1638 39.88% 27.72%
14 2013-04-14 15.47 @ Esbjerg 4 1597 45.21% Randers 0 1570 27.73% 27.06%
15 2013-05-12 14.71 Esbjerg 2 1616 17.65% @ FC Copenhagen 0 1757 59.84% 22.51%
16 2012-11-04 14.58 Odense 3 1569 35.82% @ Brondby 0 1534 36.32% 27.86%
17 2012-08-27 14.47 Aarhus GF 4 1592 27.77% @ Horsens 1 1619 45.16% 27.07%
18 2012-07-14 13.98 @ Sonderjyske 6 1593 49.84% Randers 1 1531 24.12% 26.05%
19 2012-10-06 13.56 Aarhus GF 3 1633 39.31% @ Sonderjyske 0 1573 32.93% 27.76%
20 2013-05-16 13.40 @ Aarhus GF 3 1592 39.85% Aalborg 0 1603 32.42% 27.72%
21 2012-12-01 12.86 @ Odense 3 1607 41.75% Esbjerg 0 1604 30.70% 27.55%
22 2012-09-14 12.53 @ Esbjerg 3 1583 42.84% Odense 0 1573 29.74% 27.42%
23 2013-04-21 12.48 @ Sonderjyske 4 1562 35.09% Odense 1 1606 37.06% 27.85%
24 2012-09-24 12.36 Esbjerg 2 1596 25.60% @ Aalborg 0 1643 47.87% 26.53%
25 2012-09-15 11.85 Silkeborg 2 1545 27.62% @ Sonderjyske 0 1574 45.34% 27.04%