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2019-20 1st Division Season

198 games

Final

Promoted

Vejle BK

68 pts

Relegated

Naestved

26 pts

FC Roskilde · 31 pts

Nyboking · 33 pts

Biggest Overachiever

Skive

14.55 points above expected

48 points · 33.45 expected points

Biggest Disappointment

Naestved

14.92 points below expected

26 points · 40.92 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 Vejle BK Promoted 33 20 8 5 68 63 31 +32 60.06 +7.94
2 Viborg 33 17 8 8 59 66 44 +22 59.12 -0.12
3 Fredericia 33 15 7 11 52 61 52 +9 49.85 +2.15
4 Fremad Amager 33 13 10 10 49 45 45 0 44.34 +4.66
5 Skive 33 13 9 11 48 46 46 0 33.45 +14.55
6 Kolding IF 33 13 8 12 47 50 49 +1 48.51 -1.51
7 Vendsyssel 33 12 8 13 44 35 40 -5 48.15 -4.15
8 Hvidovre 33 10 11 12 41 46 46 0 39.10 +1.90
9 HB Koge 33 9 13 11 40 43 47 -4 44.96 -4.96
10 Nykobing 33 7 12 14 33 47 64 -17 41.17 -8.17
11 FC Roskilde Relegated 33 8 7 18 31 44 61 -17 38.93 -7.93
12 Naestved Relegated 33 5 11 17 26 29 50 -21 40.92 -14.92

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
Vejle BK 1590 68 60.06 +7.94 87.5% 33 48 55 60 65 72 86
Viborg 1548 59 59.12 -0.12 51.7% 29 47 54 59 64 71 85
Fredericia 1476 52 49.85 +2.15 63.8% 21 38 45 50 55 62 76
Fremad Amager 1473 49 44.34 +4.66 75.7% 18 32 39 44 49 57 74
Kolding IF 1462 47 48.51 -1.51 44.9% 24 36 43 48 54 61 79
Vendsyssel 1454 44 48.15 -4.15 31.4% 22 36 43 48 53 60 80
HB Koge 1445 40 44.96 -4.96 27.8% 16 33 40 45 50 57 74
Hvidovre 1434 41 39.10 +1.90 63.1% 12 27 34 39 44 51 70
Skive 1408 48 33.45 +14.55 98.0% 12 22 28 33 38 45 63
FC Roskilde 1392 31 38.93 -7.93 15.4% 14 27 34 39 44 51 69
Nykobing 1367 33 41.17 -8.17 14.9% 16 29 36 41 46 53 68
Naestved 1353 26 40.92 -14.92 2.3% 12 29 36 41 46 53 73

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 FR FRE FA HK HVI KI NAE NYK SKI VB VEN VIB
FC Roskilde —
1-0-2
3.44
1-0-2
3.50
0-1-2
3.97
0-1-2
3.96
1-1-1
3.07
2-0-1
3.66
1-2-0
4.01
1-2-0
4.82
0-0-3
2.30
1-0-2
3.51
0-0-3
2.67
Fredericia
2-0-1
4.86
 —
1-0-2
4.94
1-2-0
4.28
2-0-1
5.24
3-0-0
4.28
2-1-0
5.17
1-2-0
5.10
0-1-2
5.46
1-0-2
2.97
1-0-2
4.42
1-1-1
3.18
Fremad Amager
2-0-1
4.79
2-0-1
3.36
 —
1-1-1
3.87
0-1-2
4.45
0-3-0
3.73
3-0-0
4.53
1-1-1
4.52
0-2-1
5.35
1-1-1
3.09
3-0-0
3.36
0-1-2
3.25
HB Koge
2-1-0
4.32
0-2-1
4.02
1-1-1
4.41
 —
0-3-0
4.47
2-0-1
3.98
0-2-1
4.93
1-0-2
4.56
0-1-2
4.97
1-1-1
2.90
1-1-1
3.67
1-1-1
2.76
Hvidovre
2-1-0
4.33
1-0-2
3.07
2-1-0
3.84
0-3-0
3.82
 —
1-0-2
3.26
2-1-0
3.83
1-2-0
3.70
1-0-2
4.90
0-0-3
2.28
0-2-1
3.30
0-1-2
2.83
Kolding IF
1-1-1
5.24
0-0-3
4.01
0-3-0
4.56
1-0-2
4.31
2-0-1
5.04
 —
2-1-0
4.63
1-1-1
4.57
3-0-0
5.48
0-1-2
3.64
0-1-2
4.09
3-0-0
2.92
Naestved
1-0-2
4.63
0-1-2
3.14
0-0-3
3.76
1-2-0
3.37
0-1-2
4.46
0-1-2
3.67
 —
1-1-1
3.71
1-0-2
4.81
0-2-1
2.65
1-2-0
3.90
0-1-2
2.72
Nykobing
0-2-1
4.28
0-2-1
3.21
1-1-1
3.77
2-0-1
3.74
0-2-1
4.60
1-1-1
3.72
1-1-1
4.58
 —
1-1-1
4.65
1-1-1
2.41
0-1-2
3.72
0-0-3
2.47
Skive
0-2-1
3.48
2-1-0
2.88
1-2-0
2.97
2-1-0
3.33
2-0-1
3.40
0-0-3
2.85
2-0-1
3.49
1-1-1
3.65
 —
1-1-1
2.04
2-0-1
2.95
0-1-2
2.40
Vejle BK
3-0-0
6.08
2-0-1
5.35
1-1-1
5.23
1-1-1
5.43
3-0-0
6.09
2-1-0
4.66
1-2-0
5.70
1-1-1
5.97
1-1-1
6.37
 —
3-0-0
4.73
2-1-0
4.46
Vendsyssel
2-0-1
4.79
2-0-1
3.87
0-0-3
4.94
1-1-1
4.62
1-2-0
5.00
2-1-0
4.19
0-2-1
4.39
2-1-0
4.58
1-0-2
5.37
0-0-3
3.58
 —
1-1-1
2.85
Viborg
3-0-0
5.67
1-1-1
5.13
2-1-0
5.05
1-1-1
5.57
2-1-0
5.50
0-0-3
5.41
2-1-0
5.62
3-0-0
5.88
2-1-0
5.97
0-1-2
3.83
1-1-1
5.48
 —

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 0.65 +8.8
Allowed 0.53 -9.9
Differential 0.95 +7.5

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%7.32%7.07%2.27%1.01%0.51%24.24%
17.32%10.61%6.82%3.54%1.26%—29.55%
27.07%6.82%11.11%2.78%2.02%—29.80%
32.27%3.54%2.78%0.51%1.01%0.25%10.35%
41.01%1.26%2.02%1.01%——5.30%
5+0.51%——0.25%——0.76%
Total24.24%29.55%29.80%10.35%5.30%0.76%100%

Summary Statistics

Scored Allowed Difference
Mean 1.45 1.45 +0.00
SD 1.16 1.16 1.61
CV 0.80 0.80 —
Max 5 5 +5
Min 0 0 -5

Games Played: 198

↓ Scored | Allowed →012345+Total
03.03%3.03%18.18%3.03%3.03%—30.30%
16.06%3.03%9.09%—3.03%—21.21%
26.06%3.03%15.15%9.09%3.03%—36.36%
3—6.06%——3.03%—9.09%
43.03%—————3.03%
5+———————
Total18.18%15.15%42.42%12.12%12.12%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.33 1.85 -0.52
SD 1.11 1.23 1.73
CV 0.83 0.66 —
Max 4 4 +4
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
0—6.06%6.06%———12.12%
16.06%3.03%12.12%3.03%——24.24%
29.09%6.06%18.18%—6.06%—39.39%
3—9.09%9.09%———18.18%
4———3.03%——3.03%
5+3.03%—————3.03%
Total18.18%24.24%45.45%6.06%6.06%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.85 1.58 +0.27
SD 1.15 1.06 1.59
CV 0.62 0.67 —
Max 5 4 +5
Min 0 0 -2

Games Played: 33

↓ Scored | Allowed →012345+Total
012.12%—3.03%3.03%3.03%3.03%24.24%
118.18%3.03%9.09%6.06%——36.36%
23.03%6.06%15.15%3.03%——27.27%
33.03%—————3.03%
43.03%3.03%3.03%———9.09%
5+———————
Total39.39%12.12%30.30%12.12%3.03%3.03%100%

Summary Statistics

Scored Allowed Difference
Mean 1.36 1.36 +0.00
SD 1.17 1.37 1.90
CV 0.86 1.00 —
Max 4 5 +4
Min 0 0 -5

Games Played: 33

↓ Scored | Allowed →012345+Total
06.06%6.06%6.06%———18.18%
16.06%18.18%9.09%6.06%——39.39%
26.06%9.09%15.15%3.03%3.03%—36.36%
3—3.03%3.03%———6.06%
4———————
5+———————
Total18.18%36.36%33.33%9.09%3.03%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.30 1.42 -0.12
SD 0.85 1.00 1.17
CV 0.65 0.70 —
Max 3 4 +2
Min 0 0 -2

Games Played: 33

↓ Scored | Allowed →012345+Total
06.06%9.09%3.03%3.03%——21.21%
13.03%21.21%9.09%—3.03%—36.36%
212.12%3.03%6.06%6.06%——27.27%
33.03%3.03%3.03%——3.03%12.12%
4——3.03%———3.03%
5+———————
Total24.24%36.36%24.24%9.09%3.03%3.03%100%

Summary Statistics

Scored Allowed Difference
Mean 1.39 1.39 +0.00
SD 1.06 1.22 1.48
CV 0.76 0.88 —
Max 4 5 +3
Min 0 0 -3

Games Played: 33

↓ Scored | Allowed →012345+Total
03.03%9.09%12.12%3.03%——27.27%
13.03%6.06%6.06%———15.15%
29.09%15.15%15.15%3.03%——42.42%
3—6.06%——3.03%—9.09%
4——3.03%3.03%——6.06%
5+———————
Total15.15%36.36%36.36%9.09%3.03%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.52 1.48 +0.03
SD 1.18 0.97 1.40
CV 0.78 0.65 —
Max 4 4 +2
Min 0 0 -3

Games Played: 33

↓ Scored | Allowed →012345+Total
06.06%12.12%12.12%—3.03%—33.33%
19.09%21.21%6.06%12.12%——48.48%
2—3.03%6.06%6.06%——15.15%
33.03%—————3.03%
4———————
5+———————
Total18.18%36.36%24.24%18.18%3.03%—100%

Summary Statistics

Scored Allowed Difference
Mean 0.88 1.52 -0.64
SD 0.78 1.09 1.32
CV 0.89 0.72 —
Max 3 4 +3
Min 0 0 -4

Games Played: 33

↓ Scored | Allowed →012345+Total
09.09%12.12%3.03%6.06%3.03%3.03%36.36%
1—3.03%3.03%6.06%——12.12%
23.03%3.03%21.21%3.03%——30.30%
3—3.03%6.06%3.03%3.03%—15.15%
4——6.06%———6.06%
5+———————
Total12.12%21.21%39.39%18.18%6.06%3.03%100%

Summary Statistics

Scored Allowed Difference
Mean 1.42 1.94 -0.52
SD 1.30 1.20 1.66
CV 0.91 0.62 —
Max 4 5 +2
Min 0 0 -5

Games Played: 33

↓ Scored | Allowed →012345+Total
09.09%6.06%6.06%———21.21%
19.09%9.09%6.06%6.06%——30.30%
212.12%9.09%6.06%—9.09%—36.36%
3—3.03%6.06%3.03%——12.12%
4———————
5+———————
Total30.30%27.27%24.24%9.09%9.09%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.39 1.39 +0.00
SD 0.97 1.27 1.37
CV 0.69 0.91 —
Max 3 4 +2
Min 0 0 -2

Games Played: 33

↓ Scored | Allowed →012345+Total
09.09%3.03%————12.12%
115.15%12.12%6.06%—3.03%—36.36%
29.09%6.06%3.03%—3.03%—21.21%
39.09%—3.03%———12.12%
46.06%6.06%—3.03%——15.15%
5+———3.03%——3.03%
Total48.48%27.27%12.12%6.06%6.06%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.91 0.94 +0.97
SD 1.38 1.20 1.65
CV 0.72 1.27 —
Max 5 4 +4
Min 0 0 -3

Games Played: 33

↓ Scored | Allowed →012345+Total
06.06%18.18%9.09%9.09%——42.42%
16.06%15.15%——3.03%—24.24%
29.09%9.09%3.03%———21.21%
33.03%3.03%3.03%———9.09%
4——3.03%———3.03%
5+———————
Total24.24%45.45%18.18%9.09%3.03%—100%

Summary Statistics

Scored Allowed Difference
Mean 1.06 1.21 -0.15
SD 1.14 1.02 1.66
CV 1.08 0.84 —
Max 4 4 +3
Min 0 0 -3

Games Played: 33

↓ Scored | Allowed →012345+Total
03.03%3.03%6.06%———12.12%
16.06%12.12%6.06%3.03%3.03%—30.30%
26.06%9.09%9.09%———24.24%
36.06%6.06%——3.03%—15.15%
4—6.06%6.06%3.03%——15.15%
5+3.03%—————3.03%
Total24.24%36.36%27.27%6.06%6.06%—100%

Summary Statistics

Scored Allowed Difference
Mean 2.00 1.33 +0.67
SD 1.37 1.11 1.78
CV 0.68 0.83 —
Max 5 4 +5
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 Skive 48 33.45 +14.55
2 Vejle BK 68 60.06 +7.94
3 Fremad Amager 49 44.34 +4.66
4 Fredericia 52 49.85 +2.15
5 Hvidovre 41 39.10 +1.90

Biggest Disappointments

# Team Actual Sim vsSim
1 Naestved 26 40.92 -14.92
2 Nykobing 33 41.17 -8.17
3 FC Roskilde 31 38.93 -7.93
4 HB Koge 40 44.96 -4.96
5 Vendsyssel 44 48.15 -4.15

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 Skive 3 Jul 16 – Jul 25 1 in 47
2 Fredericia 3 Aug 4 – Aug 14 1 in 29
3 Kolding IF 4 Jul 28 – Aug 16 1 in 27
4 Vejle BK 5 Aug 25 – Sep 29 1 in 26
5 Fremad Amager 3 Jun 28 – Jul 4 1 in 16

Most Unlikely Losing Streaks

# Team Games Dates Probability
1 Vendsyssel 5 Aug 4 – Aug 24 1 in 385
2 Fredericia 4 Jun 23 – Jul 5 1 in 95
3 FC Roskilde 4 Aug 21 – Sep 8 1 in 41
4 Naestved 4 Jul 3 – Jul 19 1 in 36
5 Nykobing 5 Jul 1 – Jul 19 1 in 28

Most Unlikely Unbeaten Streaks

# Team Games Dates Probability
1 Vejle BK 18 Aug 21 – Jun 5 1 in 230
2 Fredericia 8 Aug 4 – Sep 13 1 in 58
3 Fremad Amager 7 Jun 28 – Jul 25 1 in 51
4 Nykobing 6 Nov 3 – May 31 1 in 37
5 Skive 5 Jul 5 – Jul 25 1 in 25

Most Unlikely Winless Streaks

# Team Games Dates Probability
1 Naestved 11 Jun 10 – Jul 25 1 in 59
2 Nykobing 10 Sep 8 – Nov 7 1 in 53
3 HB Koge 6 Aug 30 – Oct 6 1 in 28
4 Vendsyssel 5 Aug 4 – Aug 24 1 in 22
5 FC Roskilde 8 Jun 11 – Jul 12 1 in 18

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
Vejle BK45.13%26.96%12.88%6.93%3.50%2.20%1.17%0.60%0.40%0.14%0.07%0.02%
Viborg36.52%30.41%14.88%8.10%4.29%2.63%1.59%0.77%0.48%0.24%0.07%0.02%
Fredericia5.88%11.73%16.83%16.04%14.27%10.68%8.12%6.16%4.92%3.09%1.47%0.81%
Fremad Amager1.26%4.05%7.44%9.77%11.30%12.39%12.15%11.42%10.65%8.83%6.80%3.94%
Skive0.02%0.15%0.53%1.00%1.71%2.79%4.44%6.70%9.05%13.29%21.36%38.96%
Kolding IF4.36%8.99%14.49%14.07%14.50%11.13%9.98%7.95%6.16%4.37%2.71%1.29%
Vendsyssel3.79%8.20%13.34%14.97%13.33%12.13%9.75%8.47%6.72%4.66%3.26%1.38%
Hvidovre0.24%1.02%2.27%4.21%6.22%7.80%9.35%11.72%13.13%14.83%15.62%13.59%
HB Koge1.64%4.40%8.32%10.36%11.66%12.47%12.19%10.95%9.73%8.56%6.14%3.58%
Nykobing0.46%1.64%3.42%5.39%7.59%9.48%11.07%12.64%13.17%13.06%12.19%9.89%
FC Roskilde0.26%0.75%2.28%3.66%4.93%7.28%8.98%10.85%13.03%15.22%17.30%15.46%
Naestved0.44%1.70%3.32%5.50%6.70%9.02%11.21%11.77%12.56%13.71%13.01%11.06%

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
+7.07%
Slight Edge
39.39%28.28%32.32%
Elo Value
Home Edge: 24.61 Elo pts.
242 Elo
0.004 goals per Elo point
0800
Scoring Tilt
Expected
+0.19 goals
Neutral
-2+0.10+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
45%
Vejle BK, 1st of 12
LongshotFavorite
Title Margin
Expected
0.27/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 5.89 * Some Luck: 5.89 to 8.83 * Lucky: 8.83 to 11.77 * Wild Swing: 11.77 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 1.61 * Close: 1.61 to 2.42 * Off: 2.42 to 3.23 * Way Off: 3.23 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 1.34 * A Surprise: 1.34 to 2.15 * Several Surprises: 2.15 to 2.96 * Many Surprises: 2.96 and up.
Luck Spread
Expected
7.59 points
Some Luck
07.3618
Average Finish Error
Expected
2.00
Close
02.025
Biggest Overachiever
Expected 95.83%
98.00%
Skive
50100
Biggest Underachiever
Expected 4.17%
2.34%
Naestved
050
Season Outliers
Expected
2 of 12
Minimal Outliers
01.26
Unexpected Relegations
Expected
2 of 3
A Surprise
01.33

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
0.14
Even
00.120.180.260.5
Noll-Scully
Coin-flip
1.38
Moderate Separation
01.003
Interquartile Edge
60%
Even
50%60%70%80%100%
Best vs. Worst
Baseline
80%
Clear Edge
50%80%100%
Close Games
Expected
64%
Very Frequent
0%65%100%
Blowouts
Expected
10%
Occasional
0%12%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
00.622
Matchup Imbalance
0.27
Notable Separation
00.10.180.280.5
Strangeness
Expected
1.10
As Expected
01.002
Repeatability
0.41
Some Carryover
00.30.60.851
Upset Rate
Expected
26%
As Expected
0%28%50%
Clear Favorite Upset Rate
Expected
28%
Shaky Favorites
0%24%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
0.22
Well Calibrated
0.010.050.10.51
Calibration slope
Ideal
0.80
Overconfident
0.51.001.5
Calibration error (ECE)
Noise ceiling
0.136
Near Noise Ceiling
00.1200.3

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 Direct promotion Same level Direct relegation
Vejle BK 45.13% 54.64% 0.23%
Viborg 36.52% 63.15% 0.33%
Fredericia 5.88% 88.75% 5.37%
Kolding IF 4.36% 87.27% 8.37%
Vendsyssel 3.79% 86.91% 9.30%
HB Koge 1.64% 80.08% 18.28%
Fremad Amager 1.26% 79.17% 19.57%
Nykobing 0.46% 64.40% 35.14%
Naestved 0.44% 61.78% 37.78%
Hvidovre 0.24% 55.72% 44.04%
FC Roskilde 0.26% 51.76% 47.98%
Skive 0.02% 26.37% 73.61%

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
2019-07-26 FC Roskilde W 3-0 1515 1434 55.65% 22.23% 22.12% +10.8 3
2019-07-26 @ Viborg L 0-3 1434 1515 22.12% 22.23% 55.65% -10.8 0
2019-07-27 Vejle BK D 2-2 1450 1504 37.69% 24.00% 38.30% +0.0 1
2019-07-27 @ Fremad Amager D 2-2 1504 1450 38.30% 24.00% 37.69% -0.0 1
2019-07-28 HB Koge W 3-1 1440 1464 41.97% 23.92% 34.11% +9.4 3
2019-07-28 @ Nykobing L 1-3 1464 1440 34.11% 23.92% 41.97% -9.4 0
2019-07-28 Hvidovre W 2-1 1469 1399 54.37% 22.49% 23.13% +3.9 3
2019-07-28 @ Kolding IF L 1-2 1399 1469 23.13% 22.49% 54.37% -3.9 0
2019-07-28 Naestved W 3-1 1338 1456 29.17% 23.54% 47.29% +12.1 3
2019-07-28 @ Skive L 1-3 1456 1338 47.29% 23.54% 29.17% -12.1 0
2019-07-28 Vendsyssel L 1-2 1443 1489 38.86% 24.00% 37.14% -6.0 0
2019-07-28 @ Fredericia W 2-1 1489 1443 37.14% 24.00% 38.86% +6.0 3
2019-08-03 Viborg L 0-1 1395 1526 27.69% 23.35% 48.96% -4.8 0
2019-08-03 @ Hvidovre W 1-0 1526 1395 48.96% 23.35% 27.69% +4.8 6
2019-08-04 Fredericia L 1-3 1455 1437 47.65% 23.50% 28.84% -12.2 0
2019-08-04 @ HB Koge W 3-1 1437 1455 28.84% 23.50% 47.65% +12.2 3
2019-08-04 Fremad Amager L 0-1 1495 1450 51.29% 23.03% 25.68% -7.9 3
2019-08-04 @ Vendsyssel W 1-0 1450 1495 25.68% 23.03% 51.29% +7.9 4
2019-08-04 Kolding IF L 0-2 1444 1473 41.21% 23.95% 34.84% -12.5 0
2019-08-04 @ Naestved W 2-0 1473 1444 34.84% 23.95% 41.21% +12.5 6
2019-08-04 Nykobing L 2-4 1504 1450 52.33% 22.86% 24.81% -11.8 1
2019-08-04 @ Vejle BK W 4-2 1450 1504 24.81% 22.86% 52.33% +11.8 6
2019-08-04 Skive D 1-1 1424 1350 54.79% 22.41% 22.80% -1.1 1
2019-08-04 @ FC Roskilde D 1-1 1350 1424 22.80% 22.41% 54.79% +1.1 4
2019-08-10 Vejle BK L 0-3 1487 1492 44.66% 23.77% 31.57% -19.4 3
2019-08-10 @ Vendsyssel W 3-0 1492 1487 31.57% 23.77% 44.66% +19.4 4
2019-08-11 FC Roskilde W 4-3 1486 1422 53.51% 22.66% 23.83% +3.7 9
2019-08-11 @ Kolding IF L 3-4 1422 1486 23.83% 22.66% 53.51% -3.7 1
2019-08-11 HB Koge D 0-0 1531 1443 56.49% 22.05% 21.46% -1.4 7
2019-08-11 @ Viborg D 0-0 1443 1531 21.46% 22.05% 56.49% +1.4 1
2019-08-11 Hvidovre D 2-2 1462 1390 54.49% 22.47% 23.04% -0.8 7
2019-08-11 @ Nykobing D 2-2 1390 1462 23.04% 22.47% 54.49% +0.8 1
2019-08-11 Naestved W 3-1 1450 1431 47.74% 23.49% 28.77% +8.1 6
2019-08-11 @ Fredericia L 1-3 1431 1450 28.77% 23.49% 47.74% -8.2 0
2019-08-11 Skive D 0-0 1458 1351 58.64% 21.53% 19.82% -1.5 5
2019-08-11 @ Fremad Amager D 0-0 1351 1458 19.82% 21.53% 58.64% +1.5 5
2019-08-14 Fredericia L 0-1 1511 1458 52.28% 22.87% 24.85% -8.1 4
2019-08-14 @ Vejle BK W 1-0 1458 1511 24.85% 22.87% 52.28% +8.1 9
2019-08-16 Kolding IF L 1-2 1444 1489 38.95% 24.00% 37.05% -6.0 1
2019-08-16 @ HB Koge W 2-1 1489 1444 37.05% 24.00% 38.95% +6.0 12
2019-08-16 Nykobing L 2-3 1456 1461 44.64% 23.77% 31.59% -6.3 5
2019-08-16 @ Fremad Amager W 3-2 1461 1456 31.59% 23.77% 44.64% +6.3 10
2019-08-16 Viborg L 0-2 1352 1530 22.81% 22.42% 54.77% -7.7 5
2019-08-16 @ Skive W 2-0 1530 1352 54.77% 22.42% 22.81% +7.7 10
2019-08-18 Hvidovre D 1-1 1423 1391 49.58% 23.27% 27.15% -0.8 1
2019-08-18 @ Naestved D 1-1 1391 1423 27.15% 23.27% 49.58% +0.8 2
2019-08-18 Vendsyssel W 2-0 1419 1468 38.37% 24.00% 37.62% +11.7 4
2019-08-18 @ FC Roskilde L 0-2 1468 1419 37.62% 24.00% 38.37% -11.7 3
2019-08-21 Fredericia D 2-2 1537 1466 54.50% 22.47% 23.03% -0.8 11
2019-08-21 @ Viborg D 2-2 1466 1537 23.03% 22.47% 54.50% +0.8 10
2019-08-21 Fremad Amager D 0-0 1392 1450 37.12% 24.00% 38.88% +0.1 3
2019-08-21 @ Hvidovre D 0-0 1450 1392 38.88% 24.00% 37.12% -0.1 6
2019-08-21 HB Koge L 2-3 1430 1438 44.20% 23.80% 32.00% -6.3 4
2019-08-21 @ FC Roskilde W 3-2 1438 1430 32.00% 23.80% 44.20% +6.3 4
2019-08-21 Naestved L 0-1 1467 1423 51.17% 23.05% 25.78% -7.9 10
2019-08-21 @ Nykobing W 1-0 1423 1467 25.78% 23.05% 51.17% +7.9 4
2019-08-21 Skive L 0-1 1456 1345 59.18% 21.39% 19.43% -9.0 3
2019-08-21 @ Vendsyssel W 1-0 1345 1456 19.43% 21.39% 59.18% +9.0 8
2019-08-21 Vejle BK D 1-1 1495 1503 44.23% 23.80% 31.97% -0.4 13
2019-08-21 @ Kolding IF D 1-1 1503 1495 31.97% 23.80% 44.23% +0.4 5
2019-08-24 Vendsyssel W 3-0 1430 1447 42.96% 23.88% 33.16% +15.4 7
2019-08-24 @ Naestved L 0-3 1447 1430 33.16% 23.88% 42.96% -15.4 3
2019-08-25 FC Roskilde W 3-2 1392 1424 40.80% 23.96% 35.23% +5.3 6
2019-08-25 @ Hvidovre L 2-3 1424 1392 35.23% 23.96% 40.80% -5.3 4
2019-08-25 Fremad Amager W 3-1 1445 1450 44.58% 23.78% 31.65% +8.8 7
2019-08-25 @ HB Koge L 1-3 1450 1445 31.65% 23.78% 44.58% -8.8 6
2019-08-25 Kolding IF W 4-3 1467 1495 41.35% 23.95% 34.71% +5.1 13
2019-08-25 @ Fredericia L 3-4 1495 1467 34.71% 23.95% 41.35% -5.1 13
2019-08-25 Nykobing W 3-0 1537 1459 55.21% 22.33% 22.47% +11.0 14
2019-08-25 @ Viborg L 0-3 1459 1537 22.47% 22.33% 55.21% -11.0 10
2019-08-25 Skive W 2-1 1504 1354 63.32% 20.17% 16.51% +2.8 8
2019-08-25 @ Vejle BK L 1-2 1354 1504 16.51% 20.17% 63.32% -2.8 8
2019-08-29 Viborg W 4-1 1506 1548 39.53% 23.99% 36.48% +14.0 11
2019-08-29 @ Vejle BK L 1-4 1548 1506 36.48% 23.99% 39.53% -14.0 14
2019-08-30 HB Koge D 1-1 1351 1453 31.13% 23.73% 45.13% +0.5 9
2019-08-30 @ Skive D 1-1 1453 1351 45.13% 23.73% 31.13% -0.5 8
2019-08-30 Kolding IF W 3-2 1448 1490 39.45% 23.99% 36.56% +5.4 13
2019-08-30 @ Nykobing L 2-3 1490 1448 36.56% 23.99% 39.45% -5.4 13
2019-08-30 Naestved W 1-0 1441 1446 44.61% 23.77% 31.61% +5.4 9
2019-08-30 @ Fremad Amager L 0-1 1446 1441 31.61% 23.77% 44.61% -5.4 7
2019-09-01 Fredericia L 1-2 1419 1472 37.84% 24.00% 38.16% -5.9 4
2019-09-01 @ FC Roskilde W 2-1 1472 1419 38.16% 24.00% 37.84% +5.9 16
2019-09-01 Hvidovre W 1-0 1432 1397 49.91% 23.23% 26.86% +4.7 6
2019-09-01 @ Vendsyssel L 0-1 1397 1432 26.86% 23.23% 49.91% -4.7 6
2019-09-06 Skive W 4-2 1392 1351 50.73% 23.11% 26.16% +6.7 9
2019-09-06 @ Hvidovre L 2-4 1351 1392 26.16% 23.11% 50.73% -6.7 9
2019-09-08 FC Roskilde W 1-0 1440 1413 48.95% 23.35% 27.69% +4.8 10
2019-09-08 @ Naestved L 0-1 1413 1440 27.69% 23.35% 48.95% -4.8 4
2019-09-08 Fremad Amager D 0-0 1485 1446 50.36% 23.17% 26.47% -0.9 14
2019-09-08 @ Kolding IF D 0-0 1446 1485 26.47% 23.17% 50.36% +0.9 10
2019-09-08 Nykobing D 2-2 1478 1454 48.50% 23.41% 28.09% -0.5 17
2019-09-08 @ Fredericia D 2-2 1454 1478 28.09% 23.41% 48.50% +0.5 14
2019-09-13 Fredericia D 2-2 1344 1477 27.53% 23.33% 49.14% +0.5 10
2019-09-13 @ Skive D 2-2 1477 1344 49.14% 23.33% 27.53% -0.5 18
2019-09-14 Viborg L 1-2 1447 1534 33.22% 23.88% 42.90% -5.3 10
2019-09-14 @ Fremad Amager W 2-1 1534 1447 42.90% 23.88% 33.22% +5.3 17
2019-09-15 Hvidovre W 3-0 1520 1399 60.24% 21.11% 18.66% +9.2 14
2019-09-15 @ Vejle BK L 0-3 1399 1520 18.66% 21.11% 60.24% -9.2 9
2019-09-15 Kolding IF W 2-0 1437 1484 38.69% 24.00% 37.31% +11.6 9
2019-09-15 @ Vendsyssel L 0-2 1484 1437 37.31% 24.00% 38.69% -11.6 14
2019-09-15 Naestved D 1-1 1453 1445 46.32% 23.63% 30.04% -0.5 9
2019-09-15 @ HB Koge D 1-1 1445 1453 30.04% 23.63% 46.32% +0.5 11
2019-09-15 Nykobing W 3-1 1408 1454 38.83% 24.00% 37.17% +10.0 7
2019-09-15 @ FC Roskilde L 1-3 1454 1408 37.17% 24.00% 38.83% -10.0 14
2019-09-19 FC Roskilde W 2-0 1530 1418 59.15% 21.40% 19.45% +6.6 17
2019-09-19 @ Vejle BK L 0-2 1418 1530 19.45% 21.40% 59.15% -6.6 7
2019-09-19 Vendsyssel D 1-1 1539 1448 56.78% 21.98% 21.23% -1.3 18
2019-09-19 @ Viborg D 1-1 1448 1539 21.23% 21.98% 56.78% +1.2 10
2019-09-20 HB Koge D 2-2 1390 1452 36.50% 23.99% 39.51% +0.1 10
2019-09-20 @ Hvidovre D 2-2 1452 1390 39.51% 23.99% 36.50% -0.1 10
2019-09-22 Fremad Amager L 1-2 1477 1442 49.89% 23.23% 26.88% -7.3 18
2019-09-22 @ Fredericia W 2-1 1442 1477 26.88% 23.23% 49.89% +7.3 13
2019-09-22 Skive W 4-2 1472 1345 60.87% 20.92% 18.20% +4.8 17
2019-09-22 @ Kolding IF L 2-4 1345 1472 18.20% 20.92% 60.87% -4.8 10
2019-09-22 Vendsyssel L 0-3 1444 1449 44.55% 23.78% 31.67% -19.4 14
2019-09-22 @ Nykobing W 3-0 1449 1444 31.67% 23.78% 44.55% +19.3 13
2019-09-28 Kolding IF L 1-2 1538 1477 53.20% 22.71% 24.09% -7.7 18
2019-09-28 @ Viborg W 2-1 1477 1538 24.09% 22.71% 53.20% +7.7 20
2019-09-28 Nykobing D 3-3 1340 1425 33.47% 23.89% 42.64% +0.2 11
2019-09-28 @ Skive D 3-3 1425 1340 42.64% 23.89% 33.47% -0.2 15
2019-09-29 FC Roskilde W 2-1 1449 1412 50.28% 23.18% 26.54% +4.4 16
2019-09-29 @ Fremad Amager L 1-2 1412 1449 26.54% 23.18% 50.28% -4.4 7
2019-09-29 HB Koge W 2-1 1469 1452 47.52% 23.52% 28.97% +4.7 16
2019-09-29 @ Vendsyssel L 1-2 1452 1469 28.97% 23.52% 47.52% -4.7 10
2019-09-29 Hvidovre W 3-2 1469 1390 55.43% 22.28% 22.29% +3.6 21
2019-09-29 @ Fredericia L 2-3 1390 1469 22.29% 22.28% 55.43% -3.6 10
2019-09-29 Vejle BK L 2-3 1446 1536 32.70% 23.85% 43.45% -5.0 11
2019-09-29 @ Naestved W 3-2 1536 1446 43.45% 23.85% 32.70% +5.0 20
2019-10-02 Vejle BK D 0-0 1448 1541 32.28% 23.82% 43.90% +0.4 11
2019-10-02 @ HB Koge D 0-0 1541 1448 43.90% 23.82% 32.28% -0.4 21
2019-10-03 Fredericia L 1-2 1485 1473 46.87% 23.58% 29.54% -7.0 20
2019-10-03 @ Kolding IF W 2-1 1473 1485 29.54% 23.58% 46.87% +6.9 24
2019-10-05 Naestved W 3-1 1386 1441 37.64% 24.00% 38.36% +10.3 13
2019-10-05 @ Hvidovre L 1-3 1441 1386 38.36% 24.00% 37.64% -10.2 11
2019-10-06 Fremad Amager D 2-2 1425 1454 41.26% 23.95% 34.79% -0.2 16
2019-10-06 @ Nykobing D 2-2 1454 1425 34.79% 23.95% 41.26% +0.2 17
2019-10-06 Skive L 0-2 1448 1340 58.71% 21.51% 19.77% -16.8 11
2019-10-06 @ HB Koge W 2-0 1340 1448 19.77% 21.51% 58.71% +16.8 14
2019-10-06 Vendsyssel W 1-0 1541 1474 53.97% 22.57% 23.46% +4.2 24
2019-10-06 @ Vejle BK L 0-1 1474 1541 23.46% 22.57% 53.97% -4.2 16
2019-10-06 Viborg L 0-2 1407 1530 28.66% 23.48% 47.86% -9.4 7
2019-10-06 @ FC Roskilde W 2-0 1530 1407 47.86% 23.48% 28.66% +9.4 21
2019-10-11 Nykobing W 3-1 1478 1424 52.25% 22.88% 24.88% +7.2 23
2019-10-11 @ Kolding IF L 1-3 1424 1478 24.88% 22.88% 52.25% -7.2 16
2019-10-12 Hvidovre W 2-1 1357 1397 39.78% 23.99% 36.24% +5.7 17
2019-10-12 @ Skive L 1-2 1397 1357 36.24% 23.99% 39.78% -5.7 13
2019-10-13 Naestved D 1-1 1469 1431 50.43% 23.16% 26.42% -0.8 17
2019-10-13 @ Vendsyssel D 1-1 1431 1469 26.42% 23.16% 50.43% +0.8 12
2019-10-18 Fremad Amager W 5-0 1539 1454 56.18% 22.12% 21.70% +17.2 24
2019-10-18 @ Viborg L 0-5 1454 1539 21.70% 22.12% 56.18% -17.2 17
2019-10-18 Vendsyssel D 1-1 1391 1469 34.41% 23.93% 41.66% +0.2 14
2019-10-18 @ Hvidovre D 1-1 1469 1391 41.66% 23.93% 34.41% -0.2 18
2019-10-19 Kolding IF W 3-0 1545 1485 53.10% 22.73% 24.17% +11.8 27
2019-10-19 @ Vejle BK L 0-3 1485 1545 24.17% 22.73% 53.10% -11.8 23
2019-10-20 FC Roskilde W 2-1 1431 1398 49.73% 23.25% 27.02% +4.5 14
2019-10-20 @ HB Koge L 1-2 1398 1431 27.02% 23.25% 49.73% -4.5 7
2019-10-20 Fredericia L 0-5 1417 1480 36.48% 23.99% 39.52% -26.7 16
2019-10-20 @ Nykobing W 5-0 1480 1417 39.52% 23.99% 36.48% +26.7 27
2019-10-20 Skive W 1-0 1431 1363 54.13% 22.54% 23.33% +4.2 15
2019-10-20 @ Naestved L 0-1 1363 1431 23.33% 22.54% 54.13% -4.2 17
2019-10-24 HB Koge D 2-2 1506 1436 54.41% 22.49% 23.10% -0.8 28
2019-10-24 @ Fredericia D 2-2 1436 1506 23.10% 22.49% 54.41% +0.8 15
2019-10-24 Viborg L 1-3 1436 1556 28.89% 23.51% 47.61% -8.2 15
2019-10-24 @ Naestved W 3-1 1556 1436 47.61% 23.51% 28.89% +8.2 27
2019-10-25 Hvidovre L 0-2 1437 1391 51.26% 23.04% 25.70% -15.0 17
2019-10-25 @ Fremad Amager W 2-0 1391 1437 25.70% 23.04% 51.26% +15.0 17
2019-10-25 Vejle BK D 0-0 1359 1557 20.92% 21.89% 57.19% +1.4 18
2019-10-25 @ Skive D 0-0 1557 1359 57.19% 21.89% 20.92% -1.4 28
2019-10-25 Vendsyssel L 1-2 1473 1468 45.93% 23.67% 30.40% -6.8 23
2019-10-25 @ Kolding IF W 2-1 1468 1473 30.40% 23.67% 45.93% +6.8 21
2019-10-27 Naestved W 4-0 1393 1427 40.54% 23.97% 35.49% +21.2 10
2019-10-27 @ FC Roskilde L 0-4 1427 1393 35.49% 23.97% 40.54% -21.2 15
2019-10-27 Viborg L 1-2 1391 1565 23.14% 22.50% 54.36% -3.9 16
2019-10-27 @ Nykobing W 2-1 1565 1391 54.36% 22.50% 23.14% +3.9 30
2019-11-01 FC Roskilde L 1-3 1360 1414 37.65% 24.00% 38.34% -10.1 18
2019-11-01 @ Skive W 3-1 1414 1360 38.34% 24.00% 37.65% +10.1 13
2019-11-01 Kolding IF L 1-2 1406 1466 36.83% 24.00% 39.17% -5.7 17
2019-11-01 @ Hvidovre W 2-1 1466 1406 39.17% 24.00% 36.83% +5.7 26
2019-11-02 Fremad Amager W 4-0 1555 1422 61.58% 20.72% 17.70% +11.4 31
2019-11-02 @ Vejle BK L 0-4 1422 1555 17.70% 20.72% 61.58% -11.4 17
2019-11-02 Viborg W 2-1 1436 1569 27.58% 23.34% 49.08% +7.2 18
2019-11-02 @ HB Koge L 1-2 1569 1436 49.08% 23.34% 27.58% -7.2 30
2019-11-03 Fredericia D 1-1 1406 1506 31.54% 23.77% 44.70% +0.4 16
2019-11-03 @ Naestved D 1-1 1506 1406 44.70% 23.77% 31.54% -0.4 29
2019-11-03 Nykobing D 0-0 1475 1387 56.52% 22.04% 21.44% -1.4 22
2019-11-03 @ Vendsyssel D 0-0 1387 1475 21.44% 22.04% 56.52% +1.4 17
2019-11-06 FC Roskilde W 2-0 1505 1425 55.59% 22.25% 22.16% +7.5 32
2019-11-06 @ Fredericia L 0-2 1425 1505 22.16% 22.25% 55.59% -7.5 13
2019-11-07 Vejle BK D 0-0 1388 1567 22.71% 22.39% 54.90% +1.2 18
2019-11-07 @ Nykobing D 0-0 1567 1388 54.90% 22.39% 22.71% -1.3 32
2019-11-08 Naestved W 2-1 1561 1407 63.81% 20.01% 16.18% +2.8 33
2019-11-08 @ Viborg L 1-2 1407 1561 16.18% 20.01% 63.81% -2.8 16
2019-11-10 HB Koge L 0-2 1472 1444 49.07% 23.34% 27.59% -14.4 26
2019-11-10 @ Kolding IF W 2-0 1444 1472 27.59% 23.34% 49.07% +14.4 21
2019-11-10 Hvidovre L 0-2 1417 1400 47.52% 23.52% 28.96% -14.0 13
2019-11-10 @ FC Roskilde W 2-0 1400 1417 28.96% 23.52% 47.52% +14.0 20
2019-11-10 Skive L 0-2 1513 1350 64.61% 19.74% 15.65% -18.2 32
2019-11-10 @ Fredericia W 2-0 1350 1513 15.65% 19.74% 64.61% +18.2 21
2019-11-10 Vendsyssel W 3-0 1410 1474 36.34% 23.99% 39.67% +17.7 20
2019-11-10 @ Fremad Amager L 0-3 1474 1410 39.67% 23.99% 36.34% -17.7 22
2019-11-15 Fredericia L 2-3 1415 1494 34.09% 23.92% 41.99% -5.1 20
2019-11-15 @ Hvidovre W 3-2 1494 1415 41.99% 23.92% 34.09% +5.1 35
2019-11-15 Nykobing L 0-2 1458 1389 54.16% 22.53% 23.30% -15.7 21
2019-11-15 @ HB Koge W 2-0 1389 1458 23.30% 22.53% 54.16% +15.7 21
2019-11-17 Fremad Amager L 0-1 1404 1428 41.96% 23.92% 34.11% -6.7 16
2019-11-17 @ Naestved W 1-0 1428 1404 34.11% 23.92% 41.96% +6.7 23
2019-11-17 Kolding IF L 0-2 1368 1458 32.84% 23.86% 43.30% -10.5 21
2019-11-17 @ Skive W 2-0 1458 1368 43.30% 23.86% 32.84% +10.5 29
2019-11-21 HB Koge D 1-1 1435 1442 44.22% 23.80% 31.98% -0.4 24
2019-11-21 @ Fremad Amager D 1-1 1442 1435 31.98% 23.80% 44.22% +0.4 22
2019-11-23 Vejle BK L 0-4 1403 1565 24.31% 22.76% 52.92% -15.4 13
2019-11-23 @ FC Roskilde W 4-0 1565 1403 52.92% 22.76% 24.31% +15.4 35
2019-11-23 Viborg W 1-0 1456 1564 30.46% 23.67% 45.86% +7.2 25
2019-11-23 @ Vendsyssel L 0-1 1564 1456 45.86% 23.67% 30.46% -7.2 33
2019-11-28 Vejle BK L 3-4 1557 1581 41.97% 23.92% 34.11% -5.9 33
2019-11-28 @ Viborg W 4-3 1581 1557 34.11% 23.92% 41.97% +5.9 38
2020-02-28 Hvidovre D 1-1 1551 1409 62.44% 20.46% 17.11% -1.6 34
2020-02-28 @ Viborg D 1-1 1409 1551 17.11% 20.46% 62.44% +1.6 21
2020-02-29 Fredericia W 4-2 1434 1500 36.09% 23.98% 39.93% +9.5 27
2020-02-29 @ Fremad Amager L 2-4 1500 1434 39.93% 23.98% 36.09% -9.5 35
2020-02-29 Naestved D 1-1 1468 1397 54.43% 22.48% 23.09% -1.1 30
2020-02-29 @ Kolding IF D 1-1 1397 1468 23.09% 22.48% 54.43% +1.1 17
2020-03-01 FC Roskilde W 2-0 1463 1388 55.02% 22.37% 22.62% +7.6 28
2020-03-01 @ Vendsyssel L 0-2 1388 1463 22.62% 22.37% 55.02% -7.6 13
2020-03-01 HB Koge W 1-0 1587 1443 62.68% 20.38% 16.94% +3.0 41
2020-03-01 @ Vejle BK L 0-1 1443 1587 16.94% 20.38% 62.68% -3.0 22
2020-03-01 Skive W 4-2 1405 1358 51.50% 23.00% 25.50% +6.6 24
2020-03-01 @ Nykobing L 2-4 1358 1405 25.50% 23.00% 51.50% -6.6 21
2020-03-06 Kolding IF W 1-0 1380 1467 33.14% 23.87% 42.98% +6.9 16
2020-03-06 @ FC Roskilde L 0-1 1467 1380 42.98% 23.87% 33.14% -6.9 30
2020-03-06 Vendsyssel W 2-0 1440 1471 40.94% 23.96% 35.10% +11.0 25
2020-03-06 @ HB Koge L 0-2 1471 1440 35.10% 23.96% 40.94% -11.1 28
2020-03-07 Vejle BK L 0-1 1411 1590 22.69% 22.38% 54.92% -4.0 21
2020-03-07 @ Hvidovre W 1-0 1590 1411 54.92% 22.38% 22.69% +4.1 44
2020-03-07 Viborg L 1-3 1490 1549 36.94% 24.00% 39.06% -10.0 35
2020-03-07 @ Fredericia W 3-1 1549 1490 39.06% 24.00% 36.94% +10.0 37
2020-03-08 Fremad Amager W 2-1 1351 1444 32.43% 23.83% 43.74% +6.6 24
2020-03-08 @ Skive L 1-2 1444 1351 43.74% 23.83% 32.43% -6.6 27
2020-03-08 Nykobing D 0-0 1398 1412 43.45% 23.85% 32.70% -0.4 18
2020-03-08 @ Naestved D 0-0 1412 1398 32.70% 23.85% 43.45% +0.4 25
2020-05-29 Skive D 1-1 1559 1358 68.41% 18.33% 13.26% -2.0 38
2020-05-29 @ Viborg D 1-1 1358 1559 13.26% 18.33% 68.41% +2.0 25
2020-05-30 Hvidovre D 1-1 1451 1407 51.07% 23.06% 25.87% -0.9 26
2020-05-30 @ HB Koge D 1-1 1407 1451 25.87% 23.06% 51.07% +0.9 22
2020-05-30 Kolding IF D 2-2 1437 1460 42.10% 23.92% 33.98% -0.2 28
2020-05-30 @ Fremad Amager D 2-2 1460 1437 33.98% 23.92% 42.10% +0.2 31
2020-05-31 FC Roskilde D 2-2 1412 1387 48.66% 23.39% 27.95% -0.5 26
2020-05-31 @ Nykobing D 2-2 1387 1412 27.95% 23.39% 48.66% +0.5 17
2020-05-31 Fredericia L 0-1 1460 1480 42.49% 23.90% 33.61% -6.8 28
2020-05-31 @ Vendsyssel W 1-0 1480 1460 33.61% 23.90% 42.49% +6.8 38
2020-05-31 Naestved D 1-1 1594 1398 67.88% 18.54% 13.58% -2.0 45
2020-05-31 @ Vejle BK D 1-1 1398 1594 13.58% 18.54% 67.88% +2.0 19
2020-06-05 Vejle BK L 1-2 1487 1592 30.85% 23.71% 45.44% -5.0 38
2020-06-05 @ Fredericia W 2-1 1592 1487 45.44% 23.71% 30.85% +5.0 48
2020-06-05 Vendsyssel L 1-3 1360 1453 32.31% 23.82% 43.86% -9.0 25
2020-06-05 @ Skive W 3-1 1453 1360 43.86% 23.82% 32.31% +9.0 31
2020-06-05 Viborg W 3-1 1460 1557 31.84% 23.79% 44.37% +11.5 34
2020-06-05 @ Kolding IF L 1-3 1557 1460 44.37% 23.79% 31.84% -11.5 38
2020-06-06 Fremad Amager W 2-1 1387 1437 38.33% 24.00% 37.67% +5.8 20
2020-06-06 @ FC Roskilde L 1-2 1437 1387 37.67% 24.00% 38.33% -5.8 28
2020-06-06 Nykobing W 1-0 1408 1411 44.79% 23.76% 31.45% +5.4 25
2020-06-06 @ Hvidovre L 0-1 1411 1408 31.45% 23.76% 44.79% -5.4 26
2020-06-07 HB Koge W 2-1 1400 1450 38.25% 24.00% 37.75% +5.9 22
2020-06-07 @ Naestved L 1-2 1450 1400 37.75% 24.00% 38.25% -5.9 26
2020-06-09 Hvidovre D 1-1 1462 1413 51.71% 22.97% 25.33% -0.9 32
2020-06-09 @ Vendsyssel D 1-1 1413 1462 25.33% 22.97% 51.71% +0.9 26
2020-06-09 Kolding IF D 2-2 1406 1472 36.04% 23.98% 39.98% +0.1 27
2020-06-09 @ Nykobing D 2-2 1472 1406 39.98% 23.98% 36.04% -0.1 35
2020-06-09 Skive L 1-2 1597 1351 72.41% 16.64% 10.96% -10.0 48
2020-06-09 @ Vejle BK W 2-1 1351 1597 10.96% 16.64% 72.41% +10.0 28
2020-06-10 Fredericia D 2-2 1444 1482 40.00% 23.98% 36.01% -0.1 27
2020-06-10 @ HB Koge D 2-2 1482 1444 36.01% 23.98% 40.00% +0.1 39
2020-06-10 Naestved W 1-0 1431 1406 48.68% 23.39% 27.93% +4.9 31
2020-06-10 @ Fremad Amager L 0-1 1406 1431 27.93% 23.39% 48.68% -4.9 22
2020-06-11 FC Roskilde W 4-2 1546 1393 63.59% 20.09% 16.32% +4.3 41
2020-06-11 @ Viborg L 2-4 1393 1546 16.32% 20.09% 63.59% -4.3 20
2020-06-12 Nykobing W 3-2 1361 1406 38.92% 24.00% 37.08% +5.5 31
2020-06-12 @ Skive L 2-3 1406 1361 37.08% 24.00% 38.92% -5.5 27
2020-06-13 Fremad Amager W 3-1 1482 1436 51.36% 23.02% 25.62% +7.4 42
2020-06-13 @ Fredericia L 1-3 1436 1482 25.62% 23.02% 51.36% -7.4 31
2020-06-13 Vejle BK L 0-1 1472 1587 29.58% 23.59% 46.83% -5.1 35
2020-06-13 @ Kolding IF W 1-0 1587 1472 46.83% 23.59% 29.58% +5.1 51
2020-06-13 Vendsyssel D 0-0 1401 1461 36.78% 24.00% 39.22% +0.1 23
2020-06-13 @ Naestved D 0-0 1461 1401 39.22% 24.00% 36.78% -0.1 33
2020-06-14 HB Koge D 2-2 1389 1444 37.53% 24.00% 38.46% +0.0 21
2020-06-14 @ FC Roskilde D 2-2 1444 1389 38.46% 24.00% 37.53% -0.0 28
2020-06-14 Viborg L 1-4 1414 1550 27.14% 23.27% 49.58% -11.0 26
2020-06-14 @ Hvidovre W 4-1 1550 1414 49.58% 23.27% 27.14% +11.0 44
2020-06-19 FC Roskilde D 2-2 1467 1389 55.24% 22.32% 22.44% -0.9 36
2020-06-19 @ Kolding IF D 2-2 1389 1467 22.44% 22.32% 55.24% +0.9 22
2020-06-19 Fredericia L 0-2 1561 1489 54.52% 22.47% 23.01% -15.8 44
2020-06-19 @ Viborg W 2-0 1489 1561 23.01% 22.47% 54.52% +15.8 45
2020-06-19 HB Koge D 1-1 1461 1444 47.59% 23.51% 28.90% -0.6 34
2020-06-19 @ Vendsyssel D 1-1 1444 1461 28.90% 23.51% 47.59% +0.6 29
2020-06-20 Skive D 0-0 1429 1366 53.38% 22.68% 23.94% -1.1 32
2020-06-20 @ Fremad Amager D 0-0 1366 1429 23.94% 22.68% 53.38% +1.1 32
2020-06-21 Hvidovre W 5-3 1592 1403 67.20% 18.80% 14.00% +3.4 54
2020-06-21 @ Vejle BK L 3-5 1403 1592 14.00% 18.80% 67.20% -3.5 26
2020-06-21 Naestved W 2-1 1401 1401 45.26% 23.72% 31.01% +5.0 30
2020-06-21 @ Nykobing L 1-2 1401 1401 31.01% 23.72% 45.26% -5.0 23
2020-06-23 Kolding IF W 1-0 1445 1466 42.37% 23.90% 33.73% +5.7 32
2020-06-23 @ HB Koge L 0-1 1466 1445 33.73% 23.90% 42.37% -5.7 36
2020-06-23 Vendsyssel L 2-4 1505 1460 51.18% 23.05% 25.77% -11.6 45
2020-06-23 @ Fredericia W 4-2 1460 1505 25.77% 23.05% 51.18% +11.6 37
2020-06-24 Vejle BK D 1-1 1396 1595 20.78% 21.85% 57.38% +1.3 24
2020-06-24 @ Naestved D 1-1 1595 1396 57.38% 21.85% 20.78% -1.3 55
2020-06-24 Viborg L 0-1 1367 1545 22.77% 22.40% 54.83% -4.1 32
2020-06-24 @ Skive W 1-0 1545 1367 54.83% 22.40% 22.77% +4.1 47
2020-06-25 Fremad Amager W 3-0 1400 1427 41.44% 23.94% 34.62% +15.9 29
2020-06-25 @ Hvidovre L 0-3 1427 1400 34.62% 23.94% 41.44% -15.9 32
2020-06-25 Nykobing D 2-2 1390 1406 43.09% 23.87% 33.05% -0.2 23
2020-06-25 @ FC Roskilde D 2-2 1406 1390 33.05% 23.87% 43.09% +0.2 31
2020-06-26 Kolding IF D 2-2 1472 1460 46.91% 23.58% 29.51% -0.4 38
2020-06-26 @ Vendsyssel D 2-2 1460 1472 29.51% 23.58% 46.91% +0.4 37
2020-06-27 Fredericia W 2-0 1594 1494 57.91% 21.72% 20.37% +6.9 58
2020-06-27 @ Vejle BK L 0-2 1494 1594 20.37% 21.72% 57.91% -6.9 45
2020-06-27 HB Koge W 4-2 1549 1450 57.75% 21.75% 20.49% +5.4 50
2020-06-27 @ Viborg L 2-4 1450 1549 20.49% 21.75% 57.75% -5.4 32
2020-06-27 Naestved W 2-0 1363 1397 40.57% 23.97% 35.46% +11.1 35
2020-06-27 @ Skive L 0-2 1397 1363 35.46% 23.97% 40.57% -11.2 24
2020-06-28 FC Roskilde W 2-0 1412 1390 48.24% 23.44% 28.33% +9.3 35
2020-06-28 @ Fremad Amager L 0-2 1390 1412 28.33% 23.44% 48.24% -9.3 23
2020-06-28 Hvidovre D 1-1 1406 1416 43.97% 23.82% 32.22% -0.4 32
2020-06-28 @ Nykobing D 1-1 1416 1406 32.22% 23.82% 43.97% +0.4 30
2020-06-30 Skive W 2-1 1460 1374 56.23% 22.11% 21.66% +3.7 40
2020-06-30 @ Kolding IF L 1-2 1374 1460 21.66% 22.11% 56.23% -3.7 35
2020-06-30 Vejle BK W 2-1 1445 1601 24.96% 22.89% 52.15% +7.6 35
2020-06-30 @ HB Koge L 1-2 1601 1445 52.15% 22.89% 24.96% -7.6 58
2020-06-30 Viborg D 2-2 1386 1555 23.67% 22.62% 53.71% +0.8 25
2020-06-30 @ Naestved D 2-2 1555 1386 53.71% 22.62% 23.67% -0.8 51
2020-07-01 Nykobing W 4-0 1421 1406 47.34% 23.54% 29.13% +18.0 38
2020-07-01 @ Fremad Amager L 0-4 1406 1421 29.13% 23.54% 47.34% -18.1 32
2020-07-01 Vendsyssel L 2-3 1380 1472 32.57% 23.84% 43.59% -4.9 23
2020-07-01 @ FC Roskilde W 3-2 1472 1380 43.59% 23.84% 32.57% +5.0 41
2020-07-02 Hvidovre L 1-2 1487 1416 54.41% 22.49% 23.10% -7.9 45
2020-07-02 @ Fredericia W 2-1 1416 1487 23.10% 22.49% 54.41% +7.9 33
2020-07-03 Kolding IF L 0-1 1387 1464 34.44% 23.94% 41.63% -5.8 25
2020-07-03 @ Naestved W 1-0 1464 1387 41.63% 23.94% 34.44% +5.8 43
2020-07-04 Fremad Amager L 0-1 1477 1439 50.28% 23.18% 26.54% -7.8 41
2020-07-04 @ Vendsyssel W 1-0 1439 1477 26.54% 23.18% 50.28% +7.8 41
2020-07-05 Fredericia W 1-0 1371 1479 30.45% 23.67% 45.87% +7.2 38
2020-07-05 @ Skive L 0-1 1479 1371 45.87% 23.67% 30.45% -7.2 45
2020-07-05 HB Koge D 1-1 1424 1452 41.31% 23.95% 34.75% -0.2 34
2020-07-05 @ Hvidovre D 1-1 1452 1424 34.75% 23.95% 41.31% +0.2 36
2020-07-06 FC Roskilde W 4-1 1593 1375 69.94% 17.71% 12.35% +5.0 61
2020-07-06 @ Vejle BK L 1-4 1375 1593 12.35% 17.71% 69.94% -5.0 23
2020-07-06 Nykobing W 4-3 1554 1388 65.00% 19.61% 15.39% +2.4 54
2020-07-06 @ Viborg L 3-4 1388 1554 15.39% 19.61% 65.00% -2.4 32
2020-07-10 Naestved W 3-2 1472 1381 56.77% 21.99% 21.24% +3.4 48
2020-07-10 @ Fredericia L 2-3 1381 1472 21.24% 21.99% 56.77% -3.4 25
2020-07-10 Vejle BK L 0-2 1469 1598 27.86% 23.38% 48.76% -9.2 41
2020-07-10 @ Vendsyssel W 2-0 1598 1469 48.76% 23.38% 27.86% +9.2 64
2020-07-10 Viborg D 2-2 1447 1556 30.24% 23.65% 46.10% +0.4 42
2020-07-10 @ Fremad Amager D 2-2 1556 1447 46.10% 23.65% 30.24% -0.4 55
2020-07-11 HB Koge L 0-1 1385 1453 35.77% 23.98% 40.25% -6.0 32
2020-07-11 @ Nykobing W 1-0 1453 1385 40.25% 23.98% 35.77% +6.0 39
2020-07-11 Hvidovre L 0-2 1470 1424 51.38% 23.02% 25.60% -15.0 43
2020-07-11 @ Kolding IF W 2-0 1424 1470 25.60% 23.02% 51.38% +15.0 37
2020-07-12 Skive D 2-2 1370 1378 44.22% 23.80% 31.98% -0.3 24
2020-07-12 @ FC Roskilde D 2-2 1378 1370 31.98% 23.80% 44.22% +0.3 39
2020-07-14 Kolding IF W 2-0 1475 1455 47.98% 23.47% 28.55% +9.3 51
2020-07-14 @ Fredericia L 0-2 1455 1475 28.55% 23.47% 47.98% -9.4 43
2020-07-14 Nykobing W 1-0 1608 1379 70.87% 17.31% 11.82% +2.1 67
2020-07-14 @ Vejle BK L 0-1 1379 1608 11.82% 17.31% 70.87% -2.1 32
2020-07-14 Vendsyssel W 4-1 1556 1460 57.45% 21.83% 20.72% +8.6 58
2020-07-14 @ Viborg L 1-4 1460 1556 20.72% 21.83% 57.45% -8.6 41
2020-07-15 Fremad Amager L 0-1 1459 1447 46.85% 23.58% 29.56% -7.4 39
2020-07-15 @ HB Koge W 1-0 1447 1459 29.56% 23.58% 46.85% +7.4 45
2020-07-16 FC Roskilde L 0-2 1378 1370 46.32% 23.63% 30.04% -13.8 25
2020-07-16 @ Naestved W 2-0 1370 1378 30.04% 23.63% 46.32% +13.8 27
2020-07-16 Skive L 0-2 1439 1378 53.13% 22.73% 24.14% -15.4 37
2020-07-16 @ Hvidovre W 2-0 1378 1439 24.14% 22.73% 53.13% +15.4 42
2020-07-19 Fredericia W 1-0 1384 1484 31.38% 23.75% 44.87% +7.1 30
2020-07-19 @ FC Roskilde L 0-1 1484 1384 44.87% 23.75% 31.38% -7.1 51
2020-07-19 Fremad Amager D 2-2 1446 1454 44.07% 23.81% 32.12% -0.3 44
2020-07-19 @ Kolding IF D 2-2 1454 1446 32.12% 23.81% 44.07% +0.3 46
2020-07-19 HB Koge W 3-2 1394 1451 37.18% 24.00% 38.82% +5.7 45
2020-07-19 @ Skive L 2-3 1451 1394 38.82% 24.00% 37.18% -5.7 39
2020-07-19 Hvidovre L 0-2 1364 1423 36.93% 24.00% 39.07% -11.5 25
2020-07-19 @ Naestved W 2-0 1423 1364 39.07% 24.00% 36.93% +11.5 40
2020-07-19 Vendsyssel L 0-2 1377 1451 34.91% 23.95% 41.14% -11.0 32
2020-07-19 @ Nykobing W 2-0 1451 1377 41.14% 23.95% 34.91% +11.0 44
2020-07-19 Viborg D 1-1 1610 1565 51.23% 23.04% 25.73% -0.9 68
2020-07-19 @ Vejle BK D 1-1 1565 1610 25.73% 23.04% 51.23% +0.9 59
2020-07-23 Nykobing D 2-2 1477 1366 59.12% 21.41% 19.47% -1.0 52
2020-07-23 @ Fredericia D 2-2 1366 1477 19.47% 21.41% 59.12% +1.1 33
2020-07-25 FC Roskilde D 0-0 1435 1391 51.07% 23.06% 25.86% -1.0 41
2020-07-25 @ Hvidovre D 0-0 1391 1435 25.86% 23.06% 51.07% +1.0 31
2020-07-25 Kolding IF L 0-2 1565 1445 60.13% 21.14% 18.74% -17.2 59
2020-07-25 @ Viborg W 2-0 1445 1565 18.74% 21.14% 60.13% +17.2 47
2020-07-25 Naestved D 2-2 1446 1352 57.08% 21.91% 21.00% -0.9 40
2020-07-25 @ HB Koge D 2-2 1352 1446 21.00% 21.91% 57.08% +1.0 26
2020-07-25 Skive L 0-1 1462 1399 53.41% 22.68% 23.91% -8.2 44
2020-07-25 @ Vendsyssel W 1-0 1399 1462 23.91% 22.68% 53.41% +8.2 48
2020-07-25 Vejle BK W 4-1 1455 1609 25.17% 22.94% 51.89% +18.5 49
2020-07-25 @ Fremad Amager L 1-4 1609 1455 51.89% 22.94% 25.17% -18.5 68

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 2020-06-09 10.96% Skive 1351 2 @ Vejle BK 1597 1
2 2019-11-10 15.65% Skive 1350 2 @ Fredericia 1513 0
3 2020-07-25 18.74% Kolding IF 1445 2 @ Viborg 1565 0
4 2019-08-21 19.43% Skive 1345 1 @ Vendsyssel 1456 0
5 2019-10-06 19.77% Skive 1340 2 @ HB Koge 1448 0
6 2020-06-19 23.01% Fredericia 1489 2 @ Viborg 1561 0
7 2020-07-02 23.10% Hvidovre 1416 2 @ Fredericia 1487 1
8 2019-11-15 23.30% Nykobing 1389 2 @ HB Koge 1458 0
9 2020-07-25 23.91% Skive 1399 1 @ Vendsyssel 1462 0
10 2019-09-28 24.09% Kolding IF 1477 2 @ Viborg 1538 1
11 2020-07-16 24.14% Skive 1378 2 @ Hvidovre 1439 0
12 2019-08-04 24.81% Nykobing 1450 4 @ Vejle BK 1504 2
13 2019-08-14 24.85% Fredericia 1458 1 @ Vejle BK 1511 0
14 2020-06-30 24.96% @ HB Koge 1445 2 Vejle BK 1601 1
15 2020-07-25 25.17% @ Fremad Amager 1455 4 Vejle BK 1609 1
16 2020-07-11 25.60% Hvidovre 1424 2 @ Kolding IF 1470 0
17 2019-08-04 25.68% Fremad Amager 1450 1 @ Vendsyssel 1495 0
18 2019-10-25 25.70% Hvidovre 1391 2 @ Fremad Amager 1437 0
19 2020-06-23 25.77% Vendsyssel 1460 4 @ Fredericia 1505 2
20 2019-08-21 25.78% Naestved 1423 1 @ Nykobing 1467 0
21 2020-07-04 26.54% Fremad Amager 1439 1 @ Vendsyssel 1477 0
22 2019-09-22 26.88% Fremad Amager 1442 2 @ Fredericia 1477 1
23 2019-11-02 27.58% @ HB Koge 1436 2 Viborg 1569 1
24 2019-11-10 27.59% HB Koge 1444 2 @ Kolding IF 1472 0
25 2019-08-04 28.84% Fredericia 1437 3 @ HB Koge 1455 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 2019-10-20 26.65 Fredericia 5 1480 39.52% @ Nykobing 0 1417 36.48% 23.99%
2 2019-10-27 21.17 @ FC Roskilde 4 1393 40.54% Naestved 0 1427 35.49% 23.97%
3 2019-08-10 19.38 Vejle BK 3 1492 31.57% @ Vendsyssel 0 1487 44.66% 23.77%
4 2019-09-22 19.35 Vendsyssel 3 1449 31.67% @ Nykobing 0 1444 44.55% 23.78%
5 2020-07-25 18.52 @ Fremad Amager 4 1455 25.17% Vejle BK 1 1609 51.89% 22.94%
6 2019-11-10 18.21 Skive 2 1350 15.65% @ Fredericia 0 1513 64.61% 19.74%
7 2020-07-01 18.06 @ Fremad Amager 4 1421 47.34% Nykobing 0 1406 29.13% 23.54%
8 2019-11-10 17.65 @ Fremad Amager 3 1410 36.34% Vendsyssel 0 1474 39.67% 23.99%
9 2019-10-18 17.15 @ Viborg 5 1539 56.18% Fremad Amager 0 1454 21.70% 22.12%
10 2020-07-25 17.15 Kolding IF 2 1445 18.74% @ Viborg 0 1565 60.13% 21.14%
11 2019-10-06 16.80 Skive 2 1340 19.77% @ HB Koge 0 1448 58.71% 21.51%
12 2020-06-25 15.89 @ Hvidovre 3 1400 41.44% Fremad Amager 0 1427 34.62% 23.94%
13 2020-06-19 15.76 Fredericia 2 1489 23.01% @ Viborg 0 1561 54.52% 22.47%
14 2019-11-15 15.68 Nykobing 2 1389 23.30% @ HB Koge 0 1458 54.16% 22.53%
15 2019-11-23 15.44 Vejle BK 4 1565 52.92% @ FC Roskilde 0 1403 24.31% 22.76%
16 2020-07-16 15.42 Skive 2 1378 24.14% @ Hvidovre 0 1439 53.13% 22.73%
17 2019-08-24 15.36 @ Naestved 3 1430 42.96% Vendsyssel 0 1447 33.16% 23.88%
18 2020-07-11 14.99 Hvidovre 2 1424 25.60% @ Kolding IF 0 1470 51.38% 23.02%
19 2019-10-25 14.96 Hvidovre 2 1391 25.70% @ Fremad Amager 0 1437 51.26% 23.04%
20 2019-11-10 14.42 HB Koge 2 1444 27.59% @ Kolding IF 0 1472 49.07% 23.34%
21 2019-11-10 14.04 Hvidovre 2 1400 28.96% @ FC Roskilde 0 1417 47.52% 23.52%
22 2019-08-29 13.96 @ Vejle BK 4 1506 39.53% Viborg 1 1548 36.48% 23.99%
23 2020-07-16 13.75 FC Roskilde 2 1370 30.04% @ Naestved 0 1378 46.32% 23.63%
24 2019-08-04 12.51 Kolding IF 2 1473 34.84% @ Naestved 0 1444 41.21% 23.95%
25 2019-08-04 12.19 Fredericia 3 1437 28.84% @ HB Koge 1 1455 47.65% 23.50%