2014-15 1st Division Season

198 games

Final

Promoted

Viborg

65 pts

Aarhus GF · 61 pts

Relegated

Bronshoj

23 pts

AB Gladsaxe · 32 pts

Biggest Overachiever

Vendsyssel

6.35 points above expected

49 points · 42.65 expected points

Biggest Disappointment

Bronshoj

16.09 points below expected

23 points · 39.09 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	Viborg Promoted	33	17	14	2	65	47	20	+27	60.22	+4.78
2	Aarhus GF Promoted	33	17	10	6	61	59	33	+26	59.04	+1.96
3	Lyngby	33	14	9	10	51	49	37	+12	52.71	-1.71
4	Vendsyssel	33	13	10	10	49	35	29	+6	42.65	+6.35
5	Vejle BK	33	11	12	10	45	41	46	-5	41.89	+3.11
6	Horsens	33	10	12	11	42	43	42	+1	48.41	-6.41
7	HB Koge	33	10	12	11	42	33	35	-2	41.31	+0.69
8	Skive	33	8	17	8	41	40	42	-2	42.14	-1.14
9	FC Roskilde	33	10	8	15	38	40	38	+2	42.51	-4.51
10	Fredericia	33	6	16	11	34	28	40	-12	39.60	-5.60
11	AB Gladsaxe Relegated	33	8	8	17	32	35	61	-26	33.97	-1.97
12	Bronshoj Relegated	33	3	14	16	23	20	47	-27	39.09	-16.09

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
Viborg	1494	65	60.22	+4.78	76.4%	33	48	55	60	65	72	87
Aarhus GF	1485	61	59.04	+1.96	62.9%	29	47	54	59	64	71	86
Lyngby	1377	51	52.71	-1.71	43.1%	25	41	48	53	58	65	78
HB Koge	1356	42	41.31	+0.69	56.8%	16	30	36	41	46	53	72
Horsens	1349	42	48.41	-6.41	21.2%	23	36	43	48	53	61	78
FC Roskilde	1346	38	42.51	-4.51	29.4%	17	31	38	42	47	54	72
Vejle BK	1339	45	41.89	+3.11	69.4%	17	30	37	42	47	54	72
Vendsyssel	1331	49	42.65	+6.35	83.0%	19	31	38	43	47	55	75
Skive	1328	41	42.14	-1.14	47.0%	15	30	37	42	47	54	67
Fredericia	1286	34	39.60	-5.60	24.2%	17	28	35	39	44	52	66
AB Gladsaxe	1230	32	33.97	-1.97	42.2%	6	23	29	34	39	46	63
Bronshoj	1221	23	39.09	-16.09	1.2%	14	27	34	39	44	51	65

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	AG	AG	BRO	FR	FRE	HK	HOR	LYN	SKI	VB	VEN	VIB
AB Gladsaxe	—	0-0-3 2.48	0-2-1 3.50	1-0-2 3.53	2-0-1 3.77	1-1-1 3.24	0-0-3 2.91	0-1-2 2.58	1-1-1 3.16	0-2-1 3.00	2-1-0 3.52	1-0-2 2.22
Aarhus GF	3-0-0 5.82	—	3-0-0 5.33	2-1-0 5.50	2-0-1 5.93	1-2-0 5.36	1-1-1 4.75	1-1-1 4.92	0-3-0 5.89	2-0-1 5.87	2-1-0 5.36	0-1-2 4.37
Bronshoj	1-2-0 4.72	0-0-3 2.95	—	0-2-1 4.06	0-3-0 3.86	1-0-2 3.80	0-2-1 3.45	0-2-1 2.66	1-2-0 3.37	0-0-3 3.63	0-1-2 4.06	0-0-3 2.58
FC Roskilde	2-0-1 4.69	0-1-2 2.76	1-2-0 4.14	—	2-0-1 4.46	0-1-2 4.40	1-0-2 3.89	0-1-2 3.11	1-0-2 4.43	2-1-0 4.18	1-0-2 3.91	0-2-1 2.57
Fredericia	1-0-2 4.44	1-0-2 2.38	0-3-0 4.36	1-0-2 3.76	—	0-3-0 4.06	0-3-0 3.28	2-1-0 3.05	1-2-0 4.08	0-1-2 4.19	0-2-1 3.61	0-1-2 2.46
HB Koge	1-1-1 4.98	0-2-1 2.90	2-0-1 4.41	2-1-0 3.80	0-3-0 4.14	—	1-1-1 3.89	1-0-2 2.95	1-2-0 3.71	1-1-1 3.79	1-1-1 3.92	0-0-3 2.91
Horsens	3-0-0 5.34	1-1-1 3.46	1-2-0 4.77	2-0-1 4.32	0-3-0 4.95	1-1-1 4.34	—	1-0-2 4.02	0-1-2 4.53	1-1-1 4.91	0-0-3 4.63	0-3-0 2.99
Lyngby	2-1-0 5.69	1-1-1 3.29	1-2-0 5.60	2-1-0 5.13	0-1-2 5.19	2-0-1 5.29	2-0-1 4.18	—	2-0-1 5.32	0-2-1 5.22	2-0-1 4.57	0-1-2 3.24
Skive	1-1-1 5.06	0-3-0 2.40	0-2-1 4.84	2-0-1 3.78	0-2-1 4.13	0-2-1 4.50	2-1-0 3.69	1-0-2 2.92	—	1-1-1 3.99	1-2-0 4.20	0-3-0 2.43
Vejle BK	1-2-0 5.25	1-0-2 2.42	3-0-0 4.61	0-1-2 4.03	2-1-0 4.02	1-1-1 4.42	1-1-1 3.30	1-2-0 3.01	1-1-1 4.22	—	0-1-2 4.43	0-2-1 2.25
Vendsyssel	0-1-2 4.72	0-1-2 2.88	2-1-0 4.15	2-0-1 4.30	1-2-0 4.60	1-1-1 4.30	3-0-0 3.59	1-0-2 3.65	0-2-1 4.01	2-1-0 3.78	—	1-1-1 2.73
Viborg	2-0-1 6.11	2-1-0 3.85	3-0-0 5.70	1-2-0 5.70	2-1-0 5.82	3-0-0 5.36	0-3-0 5.24	2-1-0 5.00	0-3-0 5.86	1-2-0 6.06	1-1-1 5.54	—

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	0.71	+9.8
Allowed	0.57	-8.8
Differential	0.91	+6.7

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.

View:

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	11.11%	8.59%	6.82%	2.78%	1.52%	0.25%	31.06%
1	8.59%	17.68%	7.32%	2.27%	—	0.76%	36.62%
2	6.82%	7.32%	5.56%	0.76%	0.25%	0.25%	20.96%
3	2.78%	2.27%	0.76%	1.52%	—	0.25%	7.58%
4	1.52%	—	0.25%	—	—	0.25%	2.02%
5+	0.25%	0.76%	0.25%	0.25%	0.25%	—	1.77%
Total	31.06%	36.62%	20.96%	7.58%	2.02%	1.77%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.19	1.19	+0.00
SD	1.14	1.14	1.62
CV	0.96	0.96	—
Max	7	7	+6
Min	0	0	-6

Games Played: 198

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	6.06%	15.15%	3.03%	9.09%	3.03%	—	36.36%
1	6.06%	12.12%	3.03%	6.06%	—	6.06%	33.33%
2	9.09%	6.06%	3.03%	—	—	3.03%	21.21%
3	—	—	—	3.03%	—	3.03%	6.06%
4	—	—	3.03%	—	—	—	3.03%
5+	—	—	—	—	—	—	—
Total	21.21%	33.33%	12.12%	18.18%	3.03%	12.12%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.06	1.85	-0.79
SD	1.06	1.62	1.82
CV	1.00	0.88	—
Max	4	5	+2
Min	0	0	-4

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	9.09%	6.06%	3.03%	3.03%	—	—	21.21%
1	9.09%	12.12%	3.03%	—	—	—	24.24%
2	3.03%	18.18%	9.09%	3.03%	—	—	33.33%
3	3.03%	3.03%	—	—	—	—	6.06%
4	6.06%	—	—	—	—	—	6.06%
5+	3.03%	3.03%	—	3.03%	—	—	9.09%
Total	33.33%	42.42%	15.15%	9.09%	—	—	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.79	1.00	+0.79
SD	1.49	0.94	1.76
CV	0.84	0.94	—
Max	5	3	+5
Min	0	0	-3

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	18.18%	9.09%	9.09%	6.06%	3.03%	3.03%	48.48%
1	6.06%	18.18%	15.15%	3.03%	—	—	42.42%
2	3.03%	—	6.06%	—	—	—	9.09%
3	—	—	—	—	—	—	—
4	—	—	—	—	—	—	—
5+	—	—	—	—	—	—	—
Total	27.27%	27.27%	30.30%	9.09%	3.03%	3.03%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	0.61	1.42	-0.82
SD	0.66	1.25	1.45
CV	1.09	0.88	—
Max	2	5	+2
Min	0	0	-5

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	6.06%	21.21%	12.12%	—	—	—	39.39%
1	3.03%	12.12%	3.03%	3.03%	—	—	21.21%
2	12.12%	3.03%	6.06%	3.03%	3.03%	—	27.27%
3	6.06%	—	—	—	—	—	6.06%
4	3.03%	—	—	—	—	—	3.03%
5+	—	—	3.03%	—	—	—	3.03%
Total	30.30%	36.36%	24.24%	6.06%	3.03%	—	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.21	1.15	+0.06
SD	1.29	1.03	1.71
CV	1.07	0.90	—
Max	5	4	+4
Min	0	0	-2

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	18.18%	3.03%	9.09%	3.03%	3.03%	—	36.36%
1	9.09%	21.21%	12.12%	3.03%	—	—	45.45%
2	6.06%	3.03%	6.06%	—	—	—	15.15%
3	—	—	—	3.03%	—	—	3.03%
4	—	—	—	—	—	—	—
5+	—	—	—	—	—	—	—
Total	33.33%	27.27%	27.27%	9.09%	3.03%	—	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	0.85	1.21	-0.36
SD	0.80	1.11	1.29
CV	0.94	0.92	—
Max	3	4	+2
Min	0	0	-4

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	15.15%	12.12%	12.12%	—	—	—	39.39%
1	9.09%	21.21%	3.03%	—	—	3.03%	36.36%
2	3.03%	6.06%	—	3.03%	—	—	12.12%
3	3.03%	6.06%	—	—	—	—	9.09%
4	3.03%	—	—	—	—	—	3.03%
5+	—	—	—	—	—	—	—
Total	33.33%	45.45%	15.15%	3.03%	—	3.03%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.00	1.06	-0.06
SD	1.09	1.32	1.77
CV	1.09	1.25	—
Max	4	7	+4
Min	0	0	-6

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	3.03%	6.06%	6.06%	3.03%	—	—	18.18%
1	9.09%	21.21%	15.15%	3.03%	—	—	48.48%
2	3.03%	6.06%	9.09%	—	—	—	18.18%
3	6.06%	6.06%	—	3.03%	—	—	15.15%
4	—	—	—	—	—	—	—
5+	—	—	—	—	—	—	—
Total	21.21%	39.39%	30.30%	9.09%	—	—	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.30	1.27	+0.03
SD	0.95	0.91	1.40
CV	0.73	0.72	—
Max	3	3	+3
Min	0	0	-3

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	12.12%	12.12%	9.09%	—	—	—	33.33%
1	3.03%	12.12%	6.06%	3.03%	—	—	24.24%
2	9.09%	15.15%	3.03%	—	—	—	27.27%
3	—	3.03%	3.03%	—	—	—	6.06%
4	—	—	—	—	—	—	—
5+	—	6.06%	—	—	3.03%	—	9.09%
Total	24.24%	48.48%	21.21%	3.03%	3.03%	—	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.48	1.12	+0.36
SD	1.66	0.93	1.71
CV	1.12	0.83	—
Max	7	4	+6
Min	0	0	-2

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	12.12%	3.03%	6.06%	3.03%	—	—	24.24%
1	9.09%	27.27%	9.09%	—	—	—	45.45%
2	—	9.09%	9.09%	—	—	—	18.18%
3	3.03%	—	3.03%	3.03%	—	—	9.09%
4	—	—	—	—	—	3.03%	3.03%
5+	—	—	—	—	—	—	—
Total	24.24%	39.39%	27.27%	6.06%	—	3.03%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.21	1.27	-0.06
SD	1.02	1.10	1.09
CV	0.84	0.86	—
Max	4	5	+3
Min	0	0	-3

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	6.06%	3.03%	9.09%	3.03%	6.06%	—	27.27%
1	9.09%	21.21%	3.03%	6.06%	—	—	39.39%
2	3.03%	9.09%	6.06%	—	—	—	18.18%
3	—	9.09%	—	3.03%	—	—	12.12%
4	3.03%	—	—	—	—	—	3.03%
5+	—	—	—	—	—	—	—
Total	21.21%	42.42%	18.18%	12.12%	6.06%	—	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.24	1.39	-0.15
SD	1.09	1.14	1.77
CV	0.88	0.82	—
Max	4	4	+4
Min	0	0	-4

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	15.15%	9.09%	3.03%	3.03%	3.03%	—	33.33%
1	15.15%	9.09%	12.12%	—	—	—	36.36%
2	12.12%	6.06%	6.06%	—	—	—	24.24%
3	3.03%	—	—	—	—	—	3.03%
4	3.03%	—	—	—	—	—	3.03%
5+	—	—	—	—	—	—	—
Total	48.48%	24.24%	21.21%	3.03%	3.03%	—	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.06	0.88	+0.18
SD	1.00	1.05	1.61
CV	0.94	1.20	—
Max	4	4	+4
Min	0	0	-4

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	12.12%	3.03%	—	—	—	—	15.15%
1	15.15%	24.24%	3.03%	—	—	—	42.42%
2	18.18%	6.06%	3.03%	—	—	—	27.27%
3	9.09%	—	3.03%	3.03%	—	—	15.15%
4	—	—	—	—	—	—	—
5+	—	—	—	—	—	—	—
Total	54.55%	33.33%	9.09%	3.03%	—	—	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.42	0.61	+0.82
SD	0.94	0.79	1.10
CV	0.66	1.30	—
Max	3	3	+3
Min	0	0	-1

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	Vendsyssel	49	42.65	+6.35
2	Viborg	65	60.22	+4.78
3	Vejle BK	45	41.89	+3.11
4	Aarhus GF	61	59.04	+1.96
5	HB Koge	42	41.31	+0.69

Biggest Disappointments

#	Team	Actual	Sim	vsSim
1	Bronshoj	23	39.09	-16.09
2	Horsens	42	48.41	-6.41
3	Fredericia	34	39.60	-5.60
4	FC Roskilde	38	42.51	-4.51
5	AB Gladsaxe	32	33.97	-1.97

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	AB Gladsaxe	4	Nov 2 – Nov 23	1 in 176
2	Vendsyssel	3	Aug 10 – Aug 24	1 in 41
3	Aarhus GF	5	Nov 6 – Mar 15	1 in 34
4	Lyngby	4	Oct 15 – Nov 2	1 in 32
5	Horsens	3	Aug 31 – Sep 17	1 in 19

Most Unlikely Losing Streaks

#	Team	Games	Dates	Probability
1	Horsens	3	Nov 24 – Mar 15	1 in 75
2	Lyngby	3	Apr 2 – Apr 11	1 in 57
3	Vendsyssel	3	May 24 – Jun 6	1 in 43
4	Bronshoj	4	Nov 23 – Mar 21	1 in 39
5	FC Roskilde	3	Nov 2 – Nov 14	1 in 19

Most Unlikely Unbeaten Streaks

#	Team	Games	Dates	Probability
1	Bronshoj	9	Sep 13 – Nov 9	1 in 83
2	Viborg	16	Nov 2 – May 20	1 in 54
3	Aarhus GF	14	Nov 6 – May 7	1 in 52
4	Vejle BK	7	Apr 30 – Jun 6	1 in 46
5	Fredericia	6	Apr 19 – May 20	1 in 41

Most Unlikely Winless Streaks

#	Team	Games	Dates	Probability
1	Bronshoj	20	Oct 24 – Jun 6	1 in 1,338
2	Fredericia	16	Sep 28 – Apr 19	1 in 544
3	Lyngby	10	Nov 23 – May 2	1 in 516
4	Vendsyssel	10	Nov 9 – Apr 16	1 in 153
5	Horsens	9	Apr 12 – May 30	1 in 87

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).

Team	1	2	3	4	5	6	7	8	9	10	11	12
Viborg	44.75%	28.88%	13.93%	6.26%	2.91%	1.67%	0.69%	0.47%	0.28%	0.10%	0.04%	0.02%
Aarhus GF	37.00%	31.14%	15.22%	7.82%	4.17%	1.96%	1.34%	0.81%	0.30%	0.14%	0.05%	0.05%
Lyngby	10.92%	18.49%	24.54%	16.06%	10.51%	6.90%	4.82%	3.31%	2.08%	1.28%	0.78%	0.31%
Vendsyssel	0.89%	2.22%	6.00%	8.86%	11.43%	12.79%	11.80%	12.04%	10.91%	9.99%	7.51%	5.56%
Vejle BK	0.50%	2.11%	4.92%	8.07%	9.68%	11.04%	11.74%	11.93%	11.94%	11.04%	10.46%	6.57%
Horsens	3.69%	8.80%	15.16%	17.78%	14.78%	11.22%	8.96%	7.01%	5.28%	3.88%	2.23%	1.21%
HB Koge	0.46%	1.56%	4.31%	7.11%	9.54%	10.99%	12.21%	12.07%	11.88%	11.45%	10.57%	7.85%
Skive	0.57%	2.33%	5.01%	8.27%	10.83%	11.15%	11.66%	12.05%	11.14%	10.90%	9.73%	6.36%
FC Roskilde	0.64%	2.45%	5.49%	9.19%	11.21%	12.31%	12.12%	11.16%	11.25%	9.47%	8.51%	6.20%
Fredericia	0.28%	1.06%	2.67%	5.09%	6.64%	8.85%	10.11%	11.72%	12.95%	14.08%	14.39%	12.16%
AB Gladsaxe	0.02%	0.12%	0.48%	1.27%	2.13%	3.33%	4.60%	6.32%	9.28%	13.41%	20.19%	38.85%
Bronshoj	0.28%	0.84%	2.27%	4.22%	6.17%	7.79%	9.95%	11.11%	12.71%	14.26%	15.54%	14.86%

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

+1.52%

No Edge

32.83%35.86%31.31%

Elo Value

Home Edge: 5.26 Elo pts.

208 Elo

0.005 goals per Elo point

0600

Scoring Tilt

Expected

+0.02 goals

Neutral

-2+0.03+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%

Viborg, 1st of 12

LongshotFavorite

Title Margin

Expected

0.12/gm

Photo Finish

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 5.75 * Some Luck: 5.75 to 8.63 * Lucky: 8.63 to 11.5 * Wild Swing: 11.5 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 1.66 * Close: 1.66 to 2.49 * Off: 2.49 to 3.33 * Way Off: 3.33 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 1.11 * A Surprise: 1.11 to 1.77 * Several Surprises: 1.77 to 2.43 * Many Surprises: 2.43 and up.

Luck Spread

Expected

5.79 points

Some Luck

07.1918

Average Finish Error

Expected

1.17

Pinpoint

02.085

Biggest Overachiever

Expected 95.83%

83.01%

Vendsyssel

50100

Biggest Underachiever

Expected 4.17%

1.22%

Bronshoj

050

Season Outliers

Expected

1 of 12

Minimal Outliers

01.26

Unexpected Relegations

Expected

0 of 2

As Expected

01.12

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

1.37

Moderate Separation

01.003

Interquartile Edge

56%

Even

50%60%70%80%100%

Best vs. Worst

Baseline

83%

Strong Edge

50%84%100%

Close Games

Expected

70%

Very Frequent

0%65%100%

Blowouts

Expected

11%

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

0.67

Coin-Flip

00.622

Matchup Imbalance

0.30

Lopsided

00.10.180.280.5

Strangeness

Expected

0.71

Very Predictable

01.002

Repeatability

0.57

Some Carryover

00.30.60.851

Upset Rate

Expected

25%

Chalky

0%26%50%

Clear Favorite Upset Rate

Expected

23%

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 0.13 * Near Noise Ceiling: 0.13 to 0.19 * Above Noise: 0.19 to 0.26 * Well Above Noise: 0.26 and up.

Probability calibration

0.10

Borderline

0.010.050.10.51

Calibration slope

Ideal

0.87

Calibrated

0.51.001.5

Calibration error (ECE)

Noise ceiling

0.098

Well Within Noise

00.1280.3

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
Viborg	73.63%	26.31%	0.06%
Aarhus GF	68.14%	31.76%	0.10%
Lyngby	29.41%	69.50%	1.09%
Horsens	12.49%	84.07%	3.44%
Vendsyssel	3.11%	83.82%	13.07%
FC Roskilde	3.09%	82.20%	14.71%
Skive	2.90%	81.01%	16.09%
Vejle BK	2.61%	80.36%	17.03%
HB Koge	2.02%	79.56%	18.42%
Fredericia	1.34%	72.11%	26.55%
Bronshoj	1.12%	68.48%	30.40%
AB Gladsaxe	0.14%	40.82%	59.04%

Filter by Team:

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
2014-07-25	AB Gladsaxe	W 1-0	1366	1297	52.95%	25.06%	22.00%	+5.0	3
2014-07-25	@ Lyngby	L 0-1	1297	1366	22.00%	25.06%	52.95%	-5.0	0
2014-07-25	HB Koge	D 1-1	1325	1340	42.06%	26.61%	31.33%	-0.4	1
2014-07-25	@ Skive	D 1-1	1340	1325	31.33%	26.61%	42.06%	+0.4	1
2014-07-26	Bronshoj	W 2-1	1361	1352	45.23%	26.33%	28.44%	+5.8	3
2014-07-26	@ Vejle BK	L 1-2	1352	1361	28.44%	26.33%	45.23%	-5.9	0
2014-07-27	FC Roskilde	D 2-2	1412	1324	55.17%	24.52%	20.31%	-1.1	1
2014-07-27	@ Aarhus GF	D 2-2	1324	1412	20.31%	24.52%	55.17%	+1.1	1
2014-07-27	Horsens	D 1-1	1394	1366	47.76%	26.01%	26.23%	-0.9	1
2014-07-27	@ Viborg	D 1-1	1366	1394	26.23%	26.01%	47.76%	+0.9	1
2014-07-28	Fredericia	W 2-1	1270	1333	35.22%	26.78%	38.00%	+7.3	3
2014-07-28	@ Vendsyssel	L 1-2	1333	1270	38.00%	26.78%	35.22%	-7.3	0
2014-08-01	Skive	L 2-3	1325	1325	44.13%	26.45%	29.43%	-7.8	1
2014-08-01	@ FC Roskilde	W 3-2	1325	1325	29.43%	26.45%	44.13%	+7.8	4
2014-08-03	Aarhus GF	L 0-5	1347	1411	34.98%	26.78%	38.24%	-32.1	0
2014-08-03	@ Bronshoj	W 5-0	1411	1347	38.24%	26.78%	34.98%	+32.1	4
2014-08-03	Lyngby	L 0-2	1367	1371	43.50%	26.50%	30.00%	-16.2	1
2014-08-03	@ Horsens	W 2-0	1371	1367	30.00%	26.50%	43.50%	+16.2	6
2014-08-03	Vejle BK	D 1-1	1325	1367	38.25%	26.78%	34.97%	-0.1	1
2014-08-03	@ Fredericia	D 1-1	1367	1325	34.97%	26.78%	38.25%	+0.1	4
2014-08-03	Vendsyssel	D 2-2	1292	1277	45.98%	26.25%	27.77%	-0.5	1
2014-08-03	@ AB Gladsaxe	D 2-2	1277	1292	27.77%	26.25%	45.98%	+0.5	4
2014-08-03	Viborg	L 2-3	1340	1393	36.62%	26.79%	36.59%	-6.7	1
2014-08-03	@ HB Koge	W 3-2	1393	1340	36.59%	26.79%	36.62%	+6.7	4
2014-08-08	Bronshoj	D 1-1	1387	1314	53.35%	24.96%	21.68%	-1.3	7
2014-08-08	@ Lyngby	D 1-1	1314	1387	21.68%	24.96%	53.35%	+1.3	1
2014-08-08	HB Koge	L 1-3	1367	1333	48.52%	25.90%	25.58%	-15.3	4
2014-08-08	@ Vejle BK	W 3-1	1333	1367	25.58%	25.90%	48.52%	+15.3	4
2014-08-08	Horsens	W 1-0	1333	1351	41.54%	26.65%	31.81%	+6.8	7
2014-08-08	@ Skive	L 0-1	1351	1333	31.81%	26.65%	41.54%	-6.8	1
2014-08-10	FC Roskilde	W 2-0	1278	1318	38.51%	26.77%	34.72%	+13.7	7
2014-08-10	@ Vendsyssel	L 0-2	1318	1278	34.72%	26.77%	38.51%	-13.6	1
2014-08-10	Fredericia	W 2-0	1291	1325	39.29%	26.75%	33.96%	+13.4	4
2014-08-10	@ AB Gladsaxe	L 0-2	1325	1291	33.96%	26.75%	39.29%	-13.4	1
2014-08-10	Viborg	L 0-2	1443	1400	49.74%	25.70%	24.57%	-18.0	4
2014-08-10	@ Aarhus GF	W 2-0	1400	1443	24.57%	25.70%	49.74%	+18.0	7
2014-08-14	Aarhus GF	D 0-0	1349	1425	33.34%	26.73%	39.94%	+0.3	5
2014-08-14	@ HB Koge	D 0-0	1425	1349	39.94%	26.73%	33.34%	-0.3	5
2014-08-16	Vendsyssel	L 0-1	1316	1291	47.29%	26.08%	26.63%	-9.2	1
2014-08-16	@ Bronshoj	W 1-0	1291	1316	26.63%	26.08%	47.29%	+9.2	10
2014-08-17	AB Gladsaxe	W 1-0	1344	1305	49.24%	25.78%	24.98%	+5.6	4
2014-08-17	@ Horsens	L 0-1	1305	1344	24.98%	25.78%	49.24%	-5.6	4
2014-08-17	Lyngby	W 2-1	1312	1386	33.69%	26.74%	39.57%	+7.5	4
2014-08-17	@ Fredericia	L 1-2	1386	1312	39.57%	26.74%	33.69%	-7.5	7
2014-08-17	Skive	D 0-0	1418	1339	54.06%	24.80%	21.14%	-1.6	8
2014-08-17	@ Viborg	D 0-0	1339	1418	21.14%	24.80%	54.06%	+1.6	8
2014-08-17	Vejle BK	D 2-2	1304	1352	37.34%	26.79%	35.87%	-0.0	2
2014-08-17	@ FC Roskilde	D 2-2	1352	1304	35.87%	26.79%	37.34%	+0.1	5
2014-08-22	HB Koge	D 1-1	1304	1349	37.75%	26.78%	35.46%	-0.1	3
2014-08-22	@ FC Roskilde	D 1-1	1349	1304	35.46%	26.78%	37.75%	+0.1	6
2014-08-23	Viborg	L 1-2	1307	1416	29.07%	26.41%	44.52%	-6.0	1
2014-08-23	@ Bronshoj	W 2-1	1416	1307	44.52%	26.41%	29.07%	+6.0	11
2014-08-24	Aarhus GF	L 1-5	1299	1425	27.15%	26.16%	46.69%	-17.8	4
2014-08-24	@ AB Gladsaxe	W 5-1	1425	1299	46.69%	26.16%	27.15%	+17.8	8
2014-08-24	Skive	W 2-0	1319	1341	41.09%	26.67%	32.24%	+12.9	7
2014-08-24	@ Fredericia	L 0-2	1341	1319	32.24%	26.67%	41.09%	-12.9	8
2014-08-24	Vejle BK	D 0-0	1378	1352	47.55%	26.04%	26.41%	-1.0	8
2014-08-24	@ Lyngby	D 0-0	1352	1378	26.41%	26.04%	47.55%	+1.0	6
2014-08-24	Vendsyssel	L 0-1	1350	1301	50.43%	25.57%	24.00%	-9.7	4
2014-08-24	@ Horsens	W 1-0	1301	1350	24.00%	25.57%	50.43%	+9.7	13
2014-08-28	Fredericia	W 2-1	1443	1332	57.73%	23.80%	18.47%	+4.0	11
2014-08-28	@ Aarhus GF	L 1-2	1332	1443	18.47%	23.80%	57.73%	-4.0	7
2014-08-29	AB Gladsaxe	D 1-1	1353	1281	53.24%	24.99%	21.77%	-1.3	7
2014-08-29	@ Vejle BK	D 1-1	1281	1353	21.77%	24.99%	53.24%	+1.3	5
2014-08-29	Lyngby	L 1-2	1310	1377	34.64%	26.77%	38.59%	-6.8	13
2014-08-29	@ Vendsyssel	W 2-1	1377	1310	38.59%	26.77%	34.64%	+6.8	11
2014-08-31	Bronshoj	D 1-1	1328	1301	47.67%	26.02%	26.30%	-0.9	9
2014-08-31	@ Skive	D 1-1	1301	1328	26.30%	26.02%	47.67%	+0.9	2
2014-08-31	FC Roskilde	D 1-1	1422	1304	58.63%	23.52%	17.85%	-1.8	12
2014-08-31	@ Viborg	D 1-1	1304	1422	17.85%	23.52%	58.63%	+1.8	4
2014-08-31	Horsens	L 0-2	1349	1340	45.28%	26.33%	28.39%	-16.7	6
2014-08-31	@ HB Koge	W 2-0	1340	1349	28.39%	26.33%	45.28%	+16.7	7
2014-09-04	Vendsyssel	L 0-2	1352	1304	50.33%	25.59%	24.08%	-18.2	7
2014-09-04	@ Vejle BK	W 2-0	1304	1352	24.08%	25.59%	50.33%	+18.2	16
2014-09-05	AB Gladsaxe	D 3-3	1327	1283	49.89%	25.67%	24.44%	-0.7	10
2014-09-05	@ Skive	D 3-3	1283	1327	24.44%	25.67%	49.89%	+0.7	6
2014-09-05	Bronshoj	W 2-1	1332	1302	48.13%	25.96%	25.92%	+5.4	9
2014-09-05	@ HB Koge	L 1-2	1302	1332	25.92%	25.96%	48.13%	-5.4	2
2014-09-07	Fredericia	D 2-2	1420	1328	55.65%	24.39%	19.96%	-1.1	13
2014-09-07	@ Viborg	D 2-2	1328	1420	19.96%	24.39%	55.65%	+1.1	8
2014-09-11	Aarhus GF	L 0-3	1322	1447	27.27%	26.18%	46.56%	-16.4	16
2014-09-11	@ Vendsyssel	W 3-0	1447	1322	46.56%	26.18%	27.27%	+16.4	14
2014-09-12	Viborg	L 0-2	1283	1419	26.00%	25.97%	48.03%	-10.9	6
2014-09-12	@ AB Gladsaxe	W 2-0	1419	1283	48.03%	25.97%	26.00%	+10.9	16
2014-09-13	FC Roskilde	D 1-1	1296	1306	42.76%	26.56%	30.68%	-0.5	3
2014-09-13	@ Bronshoj	D 1-1	1306	1296	30.68%	26.56%	42.76%	+0.5	5
2014-09-13	Skive	W 5-4	1384	1326	51.53%	25.36%	23.11%	+4.5	14
2014-09-13	@ Lyngby	L 4-5	1326	1384	23.11%	25.36%	51.53%	-4.5	10
2014-09-14	HB Koge	D 0-0	1329	1338	42.92%	26.55%	30.53%	-0.5	9
2014-09-14	@ Fredericia	D 0-0	1338	1329	30.53%	26.55%	42.92%	+0.5	10
2014-09-14	Vejle BK	W 3-0	1357	1333	47.15%	26.10%	26.75%	+16.2	10
2014-09-14	@ Horsens	L 0-3	1333	1357	26.75%	26.10%	47.15%	-16.2	7
2014-09-17	Horsens	L 0-1	1306	1373	34.68%	26.77%	38.55%	-7.2	5
2014-09-17	@ FC Roskilde	W 1-0	1373	1306	38.55%	26.77%	34.68%	+7.2	13
2014-09-18	Vejle BK	L 0-1	1463	1317	61.59%	22.51%	15.90%	-11.4	14
2014-09-18	@ Aarhus GF	W 1-0	1317	1463	15.90%	22.51%	61.59%	+11.4	10
2014-09-19	AB Gladsaxe	D 0-0	1338	1272	52.55%	25.14%	22.30%	-1.4	11
2014-09-19	@ HB Koge	D 0-0	1272	1338	22.30%	25.14%	52.55%	+1.4	7
2014-09-19	Vendsyssel	D 1-1	1322	1305	46.27%	26.21%	27.51%	-0.8	11
2014-09-19	@ Skive	D 1-1	1305	1322	27.51%	26.21%	46.27%	+0.8	17
2014-09-20	Horsens	D 1-1	1296	1380	32.29%	26.67%	41.04%	+0.3	4
2014-09-20	@ Bronshoj	D 1-1	1380	1296	41.04%	26.67%	32.29%	-0.3	14
2014-09-21	Fredericia	L 0-1	1299	1329	39.88%	26.73%	33.40%	-8.0	5
2014-09-21	@ FC Roskilde	W 1-0	1329	1299	33.40%	26.73%	39.88%	+8.0	12
2014-09-21	Lyngby	W 2-0	1430	1389	49.52%	25.73%	24.75%	+10.4	19
2014-09-21	@ Viborg	L 0-2	1389	1430	24.75%	25.73%	49.52%	-10.4	14
2014-09-26	Skive	L 1-2	1329	1321	45.05%	26.35%	28.60%	-8.3	10
2014-09-26	@ Vejle BK	W 2-1	1321	1329	28.60%	26.35%	45.05%	+8.3	14
2014-09-26	Viborg	L 0-1	1306	1441	26.18%	26.00%	47.82%	-5.8	17
2014-09-26	@ Vendsyssel	W 1-0	1441	1306	47.82%	26.00%	26.18%	+5.8	22
2014-09-28	Aarhus GF	L 1-2	1380	1452	33.94%	26.75%	39.31%	-6.7	14
2014-09-28	@ Horsens	W 2-1	1452	1380	39.31%	26.75%	33.94%	+6.7	17
2014-09-28	Bronshoj	D 0-0	1337	1296	49.43%	25.75%	24.82%	-1.2	13
2014-09-28	@ Fredericia	D 0-0	1296	1337	24.82%	25.75%	49.43%	+1.2	5
2014-09-28	FC Roskilde	L 0-4	1274	1291	41.70%	26.64%	31.66%	-29.7	7
2014-09-28	@ AB Gladsaxe	W 4-0	1291	1274	31.66%	26.64%	41.70%	+29.7	8
2014-09-28	HB Koge	W 2-0	1378	1337	49.47%	25.74%	24.79%	+10.4	17
2014-09-28	@ Lyngby	L 0-2	1337	1378	24.79%	25.74%	49.47%	-10.4	11
2014-10-03	FC Roskilde	W 3-1	1389	1321	52.80%	25.09%	22.11%	+8.2	20
2014-10-03	@ Lyngby	L 1-3	1321	1389	22.11%	25.09%	52.80%	-8.2	8
2014-10-04	AB Gladsaxe	W 1-0	1297	1244	50.97%	25.47%	23.56%	+5.3	8
2014-10-04	@ Bronshoj	L 0-1	1244	1297	23.56%	25.47%	50.97%	-5.3	7
2014-10-05	Fredericia	D 3-3	1373	1336	48.94%	25.83%	25.23%	-0.6	15
2014-10-05	@ Horsens	D 3-3	1336	1373	25.23%	25.83%	48.94%	+0.6	14
2014-10-05	Skive	D 0-0	1458	1329	59.77%	23.15%	17.08%	-2.1	18
2014-10-05	@ Aarhus GF	D 0-0	1329	1458	17.08%	23.15%	59.77%	+2.1	15
2014-10-05	Vendsyssel	L 0-1	1326	1300	47.53%	26.05%	26.43%	-9.2	11
2014-10-05	@ HB Koge	W 1-0	1300	1326	26.43%	26.05%	47.53%	+9.2	20
2014-10-06	Vejle BK	D 1-1	1446	1320	59.48%	23.25%	17.28%	-1.8	23
2014-10-06	@ Viborg	D 1-1	1320	1446	17.28%	23.25%	59.48%	+1.8	11
2014-10-12	Bronshoj	L 0-2	1332	1302	47.91%	25.99%	26.10%	-17.5	15
2014-10-12	@ Skive	W 2-0	1302	1332	26.10%	25.99%	47.91%	+17.5	11
2014-10-12	Lyngby	D 0-0	1239	1397	23.69%	25.50%	50.81%	+1.3	8
2014-10-12	@ AB Gladsaxe	D 0-0	1397	1239	50.81%	25.50%	23.69%	-1.3	21
2014-10-12	Vejle BK	D 1-1	1317	1322	43.38%	26.51%	30.11%	-0.5	12
2014-10-12	@ HB Koge	D 1-1	1322	1317	30.11%	26.51%	43.38%	+0.5	12
2014-10-12	Viborg	D 1-1	1312	1445	26.44%	26.05%	47.52%	+0.9	9
2014-10-12	@ FC Roskilde	D 1-1	1445	1312	47.52%	26.05%	26.44%	-0.9	24
2014-10-15	Lyngby	L 2-3	1456	1395	51.91%	25.28%	22.81%	-8.9	18
2014-10-15	@ Aarhus GF	W 3-2	1395	1456	22.81%	25.28%	51.91%	+8.9	24
2014-10-17	Fredericia	W 2-0	1444	1336	57.38%	23.90%	18.71%	+8.1	27
2014-10-17	@ Viborg	L 0-2	1336	1444	18.71%	23.90%	57.38%	-8.1	14
2014-10-18	FC Roskilde	L 0-2	1323	1313	45.32%	26.32%	28.36%	-16.7	12
2014-10-18	@ Vejle BK	W 2-0	1313	1323	28.36%	26.32%	45.32%	+16.7	12
2014-10-18	HB Koge	W 1-0	1320	1317	44.49%	26.41%	29.10%	+6.3	14
2014-10-18	@ Bronshoj	L 0-1	1317	1320	29.10%	26.41%	44.49%	-6.3	12
2014-10-18	Skive	W 2-1	1404	1314	55.44%	24.45%	20.12%	+4.3	27
2014-10-18	@ Lyngby	L 1-2	1314	1404	20.12%	24.45%	55.44%	-4.3	15
2014-10-19	AB Gladsaxe	W 3-1	1372	1240	60.15%	23.02%	16.83%	+6.3	18
2014-10-19	@ Horsens	L 1-3	1240	1372	16.83%	23.02%	60.15%	-6.3	8
2014-10-19	Vendsyssel	D 0-0	1447	1310	60.74%	22.81%	16.44%	-2.2	19
2014-10-19	@ Aarhus GF	D 0-0	1310	1447	16.44%	22.81%	60.74%	+2.2	21
2014-10-23	Horsens	L 1-2	1445	1379	52.63%	25.13%	22.25%	-9.5	19
2014-10-23	@ Aarhus GF	W 2-1	1379	1445	22.25%	25.13%	52.63%	+9.5	21
2014-10-24	AB Gladsaxe	W 2-1	1310	1234	53.76%	24.87%	21.37%	+4.6	18
2014-10-24	@ Skive	L 1-2	1234	1310	21.37%	24.87%	53.76%	-4.6	8
2014-10-24	Bronshoj	D 0-0	1330	1326	44.54%	26.41%	29.05%	-0.7	13
2014-10-24	@ FC Roskilde	D 0-0	1326	1330	29.05%	26.41%	44.54%	+0.7	15
2014-10-24	Lyngby	L 1-7	1310	1409	30.48%	26.55%	42.98%	-28.3	12
2014-10-24	@ HB Koge	W 7-1	1409	1310	42.98%	26.55%	30.48%	+28.3	30
2014-10-24	Viborg	W 1-0	1312	1452	25.57%	25.89%	48.54%	+9.4	24
2014-10-24	@ Vendsyssel	L 0-1	1452	1312	48.54%	25.89%	25.57%	-9.4	27
2014-10-26	Vejle BK	L 1-3	1328	1306	47.04%	26.11%	26.85%	-14.9	14
2014-10-26	@ Fredericia	W 3-1	1306	1328	26.85%	26.11%	47.04%	+14.9	15
2014-11-01	Fredericia	D 0-0	1327	1313	45.87%	26.26%	27.87%	-0.8	16
2014-11-01	@ Bronshoj	D 0-0	1313	1327	27.87%	26.26%	45.87%	+0.8	15
2014-11-02	Aarhus GF	W 3-0	1442	1436	44.94%	26.37%	28.70%	+17.1	30
2014-11-02	@ Viborg	L 0-3	1436	1442	28.70%	26.37%	44.94%	-17.1	19
2014-11-02	FC Roskilde	W 2-0	1437	1329	57.44%	23.88%	18.67%	+8.0	33
2014-11-02	@ Lyngby	L 0-2	1329	1437	18.67%	23.88%	57.44%	-8.1	13
2014-11-02	HB Koge	W 2-0	1229	1282	36.62%	26.79%	36.58%	+14.2	11
2014-11-02	@ AB Gladsaxe	L 0-2	1282	1229	36.58%	26.79%	36.62%	-14.2	12
2014-11-02	Skive	D 2-2	1388	1314	53.48%	24.93%	21.59%	-1.0	22
2014-11-02	@ Horsens	D 2-2	1314	1388	21.59%	24.93%	53.48%	+1.0	19
2014-11-02	Vendsyssel	L 0-4	1321	1321	44.02%	26.46%	29.53%	-31.0	15
2014-11-02	@ Vejle BK	W 4-0	1321	1321	29.53%	26.46%	44.02%	+31.0	27
2014-11-05	Horsens	W 2-1	1352	1387	39.19%	26.75%	34.06%	+6.7	30
2014-11-05	@ Vendsyssel	L 1-2	1387	1352	34.06%	26.75%	39.19%	-6.7	22
2014-11-06	Vejle BK	W 4-0	1419	1290	59.77%	23.15%	17.08%	+14.0	22
2014-11-06	@ Aarhus GF	L 0-4	1290	1419	17.08%	23.15%	59.77%	-14.0	15
2014-11-09	AB Gladsaxe	L 2-4	1321	1243	53.98%	24.82%	21.21%	-14.9	13
2014-11-09	@ FC Roskilde	W 4-2	1243	1321	21.21%	24.82%	53.98%	+15.0	14
2014-11-09	Bronshoj	D 2-2	1359	1326	48.37%	25.92%	25.71%	-0.7	31
2014-11-09	@ Vendsyssel	D 2-2	1326	1359	25.71%	25.92%	48.37%	+0.7	17
2014-11-09	Lyngby	D 1-1	1314	1445	26.60%	26.07%	47.33%	+0.9	16
2014-11-09	@ Fredericia	D 1-1	1445	1314	47.33%	26.07%	26.60%	-0.8	34
2014-11-09	Skive	W 1-0	1268	1315	37.40%	26.79%	35.81%	+7.4	15
2014-11-09	@ HB Koge	L 0-1	1315	1268	35.81%	26.79%	37.40%	-7.4	19
2014-11-10	Horsens	D 1-1	1459	1380	54.14%	24.78%	21.09%	-1.4	31
2014-11-10	@ Viborg	D 1-1	1380	1459	21.09%	24.78%	54.14%	+1.4	23
2014-11-14	FC Roskilde	W 1-0	1308	1306	44.28%	26.43%	29.29%	+6.3	22
2014-11-14	@ Skive	L 0-1	1306	1308	29.29%	26.43%	44.28%	-6.3	13
2014-11-15	Viborg	L 0-1	1276	1458	21.37%	24.87%	53.77%	-4.8	15
2014-11-15	@ Vejle BK	W 1-0	1458	1276	53.77%	24.87%	21.37%	+4.9	34
2014-11-16	Fredericia	W 1-0	1258	1315	36.06%	26.79%	37.15%	+7.6	17
2014-11-16	@ AB Gladsaxe	L 0-1	1315	1258	37.15%	26.79%	36.06%	-7.6	16
2014-11-16	Vendsyssel	W 2-1	1444	1358	54.95%	24.57%	20.47%	+4.4	37
2014-11-16	@ Lyngby	L 1-2	1358	1444	20.47%	24.57%	54.95%	-4.4	31
2014-11-23	AB Gladsaxe	L 0-1	1354	1266	55.15%	24.52%	20.33%	-10.4	31
2014-11-23	@ Vendsyssel	W 1-0	1266	1354	20.33%	24.52%	55.15%	+10.4	20
2014-11-23	Bronshoj	W 2-0	1463	1327	60.55%	22.88%	16.57%	+7.1	37
2014-11-23	@ Viborg	L 0-2	1327	1463	16.57%	22.88%	60.55%	-7.1	17
2014-11-23	Lyngby	W 1-0	1433	1449	41.86%	26.63%	31.52%	+6.7	25
2014-11-23	@ Aarhus GF	L 0-1	1449	1433	31.52%	26.63%	41.86%	-6.7	37
2014-11-23	Skive	D 1-1	1308	1314	43.12%	26.53%	30.35%	-0.5	17
2014-11-23	@ Fredericia	D 1-1	1314	1308	30.35%	26.53%	43.12%	+0.5	23
2014-11-24	Horsens	W 3-1	1271	1382	28.94%	26.39%	44.67%	+14.3	18
2014-11-24	@ Vejle BK	L 1-3	1382	1271	44.67%	26.39%	28.94%	-14.3	23
2014-11-26	Aarhus GF	L 1-2	1320	1439	27.87%	26.26%	45.87%	-5.8	17
2014-11-26	@ Bronshoj	W 2-1	1439	1320	45.87%	26.26%	27.87%	+5.8	28
2014-11-30	Aarhus GF	L 1-2	1307	1445	25.78%	25.93%	48.29%	-5.4	17
2014-11-30	@ Fredericia	W 2-1	1445	1307	48.29%	25.93%	25.78%	+5.4	31
2014-11-30	HB Koge	L 1-2	1367	1275	55.65%	24.39%	19.96%	-9.9	23
2014-11-30	@ Horsens	W 2-1	1275	1367	19.96%	24.39%	55.65%	+9.9	18
2015-03-12	Viborg	L 0-1	1442	1470	40.14%	26.72%	33.14%	-8.1	37
2015-03-12	@ Lyngby	W 1-0	1470	1442	33.14%	26.72%	40.14%	+8.1	40
2015-03-13	Vendsyssel	D 1-1	1315	1343	40.11%	26.72%	33.17%	-0.3	24
2015-03-13	@ Skive	D 1-1	1343	1315	33.17%	26.72%	40.11%	+0.3	32
2015-03-14	Vejle BK	L 0-1	1314	1285	47.85%	26.00%	26.15%	-9.3	17
2015-03-14	@ Bronshoj	W 1-0	1285	1314	26.15%	26.00%	47.85%	+9.3	21
2015-03-15	Aarhus GF	L 0-1	1276	1450	22.11%	25.09%	52.80%	-5.0	20
2015-03-15	@ AB Gladsaxe	W 1-0	1450	1276	52.80%	25.09%	22.11%	+5.0	34
2015-03-15	FC Roskilde	L 1-2	1357	1300	51.52%	25.36%	23.12%	-9.3	23
2015-03-15	@ Horsens	W 2-1	1300	1357	23.12%	25.36%	51.52%	+9.3	16
2015-03-15	Fredericia	D 0-0	1285	1302	41.79%	26.63%	31.58%	-0.5	19
2015-03-15	@ HB Koge	D 0-0	1302	1285	31.58%	26.63%	41.79%	+0.5	18
2015-03-19	Lyngby	D 1-1	1295	1434	25.66%	25.91%	48.43%	+1.0	22
2015-03-19	@ Vejle BK	D 1-1	1434	1295	48.43%	25.91%	25.66%	-1.0	38
2015-03-20	AB Gladsaxe	W 3-0	1478	1271	67.66%	20.02%	12.32%	+7.6	43
2015-03-20	@ Viborg	L 0-3	1271	1478	12.32%	20.02%	67.66%	-7.5	20
2015-03-21	Horsens	L 1-3	1305	1348	37.97%	26.78%	35.25%	-12.6	17
2015-03-21	@ Bronshoj	W 3-1	1348	1305	35.25%	26.78%	37.97%	+12.6	26
2015-03-22	FC Roskilde	L 0-3	1302	1309	43.08%	26.54%	30.39%	-23.3	18
2015-03-22	@ Fredericia	W 3-0	1309	1302	30.39%	26.54%	43.08%	+23.3	19
2015-03-22	HB Koge	D 0-0	1344	1285	51.67%	25.33%	23.00%	-1.3	33
2015-03-22	@ Vendsyssel	D 0-0	1285	1344	23.00%	25.33%	51.67%	+1.3	20
2015-03-22	Skive	D 2-2	1455	1315	61.08%	22.70%	16.23%	-1.5	35
2015-03-22	@ Aarhus GF	D 2-2	1315	1455	16.23%	22.70%	61.08%	+1.5	25
2015-03-25	HB Koge	L 0-1	1332	1286	50.12%	25.63%	24.25%	-9.6	19
2015-03-25	@ FC Roskilde	W 1-0	1286	1332	24.25%	25.63%	50.12%	+9.6	23
2015-03-26	Bronshoj	D 0-0	1433	1292	61.06%	22.70%	16.24%	-2.2	39
2015-03-26	@ Lyngby	D 0-0	1292	1433	16.24%	22.70%	61.06%	+2.2	18
2015-03-27	Viborg	D 0-0	1316	1486	22.54%	25.21%	52.25%	+1.4	26
2015-03-27	@ Skive	D 0-0	1486	1316	52.25%	25.21%	22.54%	-1.4	44
2015-03-29	Fredericia	D 1-1	1361	1279	54.48%	24.69%	20.82%	-1.4	27
2015-03-29	@ Horsens	D 1-1	1279	1361	20.82%	24.69%	54.48%	+1.4	19
2015-03-29	Vendsyssel	W 1-0	1323	1342	41.39%	26.66%	31.96%	+6.8	22
2015-03-29	@ FC Roskilde	L 0-1	1342	1323	31.96%	26.66%	41.39%	-6.8	33
2015-04-02	AB Gladsaxe	D 1-1	1294	1264	48.09%	25.96%	25.95%	-0.9	19
2015-04-02	@ Bronshoj	D 1-1	1264	1294	25.95%	25.96%	48.09%	+0.9	21
2015-04-02	FC Roskilde	W 1-0	1454	1330	59.29%	23.31%	17.40%	+4.0	38
2015-04-02	@ Aarhus GF	L 0-1	1330	1454	17.40%	23.31%	59.29%	-4.0	22
2015-04-02	Fredericia	D 0-0	1335	1280	51.23%	25.42%	23.35%	-1.3	34
2015-04-02	@ Vendsyssel	D 0-0	1280	1335	23.35%	25.42%	51.23%	+1.3	20
2015-04-02	HB Koge	W 2-0	1484	1296	65.91%	20.79%	13.29%	+5.7	47
2015-04-02	@ Viborg	L 0-2	1296	1484	13.29%	20.79%	65.91%	-5.7	23
2015-04-02	Lyngby	W 1-0	1359	1431	34.05%	26.75%	39.20%	+7.9	30
2015-04-02	@ Horsens	L 0-1	1431	1359	39.20%	26.75%	34.05%	-7.9	39
2015-04-02	Skive	W 2-1	1296	1317	41.03%	26.68%	32.30%	+6.5	25
2015-04-02	@ Vejle BK	L 1-2	1317	1296	32.30%	26.68%	41.03%	-6.5	26
2015-04-05	Aarhus GF	L 1-2	1334	1458	27.37%	26.19%	46.44%	-5.7	34
2015-04-05	@ Vendsyssel	W 2-1	1458	1334	46.44%	26.19%	27.37%	+5.7	41
2015-04-06	Bronshoj	W 1-0	1290	1293	43.60%	26.49%	29.91%	+6.5	26
2015-04-06	@ HB Koge	L 0-1	1293	1290	29.91%	26.49%	43.60%	-6.5	19
2015-04-06	Horsens	L 0-3	1265	1367	29.93%	26.50%	43.57%	-17.7	21
2015-04-06	@ AB Gladsaxe	W 3-0	1367	1265	43.57%	26.50%	29.93%	+17.7	33
2015-04-06	Lyngby	W 1-0	1311	1423	28.81%	26.38%	44.81%	+8.8	29
2015-04-06	@ Skive	L 0-1	1423	1311	44.81%	26.38%	28.81%	-8.8	39
2015-04-06	Vejle BK	W 2-0	1326	1302	47.21%	26.09%	26.70%	+11.1	25
2015-04-06	@ FC Roskilde	L 0-2	1302	1326	26.70%	26.09%	47.21%	-11.1	25
2015-04-06	Viborg	L 1-2	1281	1490	19.11%	24.06%	56.83%	-4.1	20
2015-04-06	@ Fredericia	W 2-1	1490	1281	56.83%	24.06%	19.11%	+4.1	50
2015-04-09	Vendsyssel	D 0-0	1494	1328	63.63%	21.73%	14.63%	-2.4	51
2015-04-09	@ Viborg	D 0-0	1328	1494	14.63%	21.73%	63.63%	+2.4	35
2015-04-11	FC Roskilde	L 0-2	1287	1337	37.06%	26.79%	36.15%	-14.3	19
2015-04-11	@ Bronshoj	W 2-0	1337	1287	36.15%	26.79%	37.06%	+14.3	28
2015-04-11	Fredericia	W 4-0	1291	1277	45.86%	26.26%	27.88%	+21.8	28
2015-04-11	@ Vejle BK	L 0-4	1277	1291	27.88%	26.26%	45.86%	-21.8	20
2015-04-11	HB Koge	L 1-3	1414	1296	58.52%	23.55%	17.92%	-17.9	39
2015-04-11	@ Lyngby	W 3-1	1296	1414	17.92%	23.55%	58.52%	+17.9	29
2015-04-12	Aarhus GF	D 1-1	1385	1464	33.03%	26.71%	40.25%	+0.3	34
2015-04-12	@ Horsens	D 1-1	1464	1385	40.25%	26.71%	33.03%	-0.3	42
2015-04-12	Skive	W 2-1	1247	1320	33.84%	26.75%	39.42%	+7.5	24
2015-04-12	@ AB Gladsaxe	L 1-2	1320	1247	39.42%	26.75%	33.84%	-7.5	29
2015-04-16	Aarhus GF	L 1-2	1314	1463	24.61%	25.71%	49.68%	-5.2	29
2015-04-16	@ HB Koge	W 2-1	1463	1314	49.68%	25.71%	24.61%	+5.2	45
2015-04-16	Vejle BK	D 0-0	1331	1313	46.47%	26.19%	27.34%	-0.9	36
2015-04-16	@ Vendsyssel	D 0-0	1313	1331	27.34%	26.19%	46.47%	+0.9	29
2015-04-17	Horsens	W 3-0	1312	1385	33.80%	26.74%	39.46%	+21.8	32
2015-04-17	@ Skive	L 0-3	1385	1312	39.46%	26.74%	33.80%	-21.8	34
2015-04-17	Lyngby	D 0-0	1351	1396	37.77%	26.78%	35.45%	-0.1	29
2015-04-17	@ FC Roskilde	D 0-0	1396	1351	35.45%	26.78%	37.77%	+0.1	40
2015-04-19	AB Gladsaxe	W 3-0	1309	1254	51.16%	25.43%	23.40%	+14.4	32
2015-04-19	@ HB Koge	L 0-3	1254	1309	23.40%	25.43%	51.16%	-14.4	24
2015-04-19	Bronshoj	D 0-0	1256	1273	41.71%	26.64%	31.66%	-0.4	21
2015-04-19	@ Fredericia	D 0-0	1273	1256	31.66%	26.64%	41.71%	+0.5	20
2015-04-19	Viborg	D 1-1	1469	1492	40.87%	26.68%	32.45%	-0.3	46
2015-04-19	@ Aarhus GF	D 1-1	1492	1469	32.45%	26.68%	40.87%	+0.3	52
2015-04-22	Vejle BK	D 1-1	1240	1314	33.73%	26.74%	39.52%	+0.2	25
2015-04-22	@ AB Gladsaxe	D 1-1	1314	1240	39.52%	26.74%	33.73%	-0.2	30
2015-04-24	HB Koge	D 1-1	1334	1324	45.45%	26.31%	28.24%	-0.7	33
2015-04-24	@ Skive	D 1-1	1324	1334	28.24%	26.31%	45.45%	+0.7	33
2015-04-25	Fredericia	L 0-2	1396	1255	61.09%	22.69%	16.22%	-21.4	40
2015-04-25	@ Lyngby	W 2-0	1255	1396	16.22%	22.69%	61.09%	+21.4	24
2015-04-25	Vendsyssel	L 0-3	1273	1330	36.05%	26.79%	37.16%	-20.4	20
2015-04-25	@ Bronshoj	W 3-0	1330	1273	37.16%	26.79%	36.05%	+20.4	39
2015-04-25	Viborg	D 0-0	1363	1492	26.86%	26.11%	47.03%	+0.9	35
2015-04-25	@ Horsens	D 0-0	1492	1363	47.03%	26.11%	26.86%	-0.9	53
2015-04-26	Aarhus GF	L 1-3	1313	1468	24.00%	25.57%	50.42%	-8.8	30
2015-04-26	@ Vejle BK	W 3-1	1468	1313	50.42%	25.57%	24.00%	+8.8	49
2015-04-26	FC Roskilde	L 2-5	1240	1351	28.94%	26.39%	44.67%	-12.6	25
2015-04-26	@ AB Gladsaxe	W 5-2	1351	1240	44.67%	26.39%	28.94%	+12.6	32
2015-04-30	Vejle BK	D 3-3	1491	1305	65.71%	20.88%	13.41%	-1.4	54
2015-04-30	@ Viborg	D 3-3	1305	1491	13.41%	20.88%	65.71%	+1.4	31
2015-05-02	Lyngby	W 2-0	1350	1375	40.67%	26.69%	32.63%	+13.0	42
2015-05-02	@ Vendsyssel	L 0-2	1375	1350	32.63%	26.69%	40.67%	-13.0	40
2015-05-03	AB Gladsaxe	W 1-0	1276	1228	50.42%	25.57%	24.01%	+5.4	27
2015-05-03	@ Fredericia	L 0-1	1228	1276	24.01%	25.57%	50.42%	-5.4	25
2015-05-03	Bronshoj	W 4-0	1477	1253	69.30%	19.26%	11.44%	+9.1	52
2015-05-03	@ Aarhus GF	L 0-4	1253	1477	11.44%	19.26%	69.30%	-9.1	20
2015-05-03	Horsens	D 1-1	1324	1364	38.46%	26.77%	34.77%	-0.1	34
2015-05-03	@ HB Koge	D 1-1	1364	1324	34.77%	26.77%	38.46%	+0.1	36
2015-05-03	Skive	W 3-0	1364	1333	48.06%	25.97%	25.97%	+15.8	35
2015-05-03	@ FC Roskilde	L 0-3	1333	1364	25.97%	25.97%	48.06%	-15.8	33
2015-05-07	Aarhus GF	L 0-2	1379	1486	29.42%	26.45%	44.14%	-12.0	35
2015-05-07	@ FC Roskilde	W 2-0	1486	1379	44.14%	26.45%	29.42%	+12.0	55
2015-05-08	Vejle BK	D 1-1	1318	1306	45.60%	26.29%	28.11%	-0.7	34
2015-05-08	@ Skive	D 1-1	1306	1318	28.11%	26.29%	45.60%	+0.7	32
2015-05-08	Viborg	L 0-1	1324	1490	22.95%	25.32%	51.73%	-5.2	34
2015-05-08	@ HB Koge	W 1-0	1490	1324	51.73%	25.32%	22.95%	+5.2	57
2015-05-10	Bronshoj	D 1-1	1222	1244	41.12%	26.67%	32.21%	-0.3	26
2015-05-10	@ AB Gladsaxe	D 1-1	1244	1222	32.21%	26.67%	41.12%	+0.4	21
2015-05-10	Horsens	W 2-1	1362	1365	43.66%	26.49%	29.85%	+6.1	43
2015-05-10	@ Lyngby	L 1-2	1365	1362	29.85%	26.49%	43.66%	-6.1	36
2015-05-10	Vendsyssel	D 1-1	1282	1363	32.66%	26.69%	40.65%	+0.3	28
2015-05-10	@ Fredericia	D 1-1	1363	1282	40.65%	26.69%	32.66%	-0.3	43
2015-05-13	FC Roskilde	W 1-0	1495	1367	59.62%	23.20%	17.18%	+3.9	60
2015-05-13	@ Viborg	L 0-1	1367	1495	17.18%	23.20%	59.62%	-3.9	35
2015-05-14	AB Gladsaxe	W 5-1	1368	1222	61.59%	22.51%	15.90%	+10.8	46
2015-05-14	@ Lyngby	L 1-5	1222	1368	15.90%	22.51%	61.59%	-10.8	26
2015-05-14	Fredericia	L 0-1	1498	1282	68.52%	19.62%	11.86%	-12.4	55
2015-05-14	@ Aarhus GF	W 1-0	1282	1498	11.86%	19.62%	68.52%	+12.4	31
2015-05-14	HB Koge	W 1-0	1307	1319	42.36%	26.59%	31.04%	+6.6	35
2015-05-14	@ Vejle BK	L 0-1	1319	1307	31.04%	26.59%	42.36%	-6.7	34
2015-05-16	Skive	D 0-0	1244	1317	33.81%	26.74%	39.44%	+0.2	22
2015-05-16	@ Bronshoj	D 0-0	1317	1244	39.44%	26.74%	33.81%	-0.2	35
2015-05-17	Vendsyssel	L 0-2	1358	1363	43.43%	26.51%	30.07%	-16.2	36
2015-05-17	@ Horsens	W 2-0	1363	1358	30.07%	26.51%	43.43%	+16.2	46
2015-05-19	HB Koge	D 1-1	1486	1312	64.41%	21.42%	14.17%	-2.2	56
2015-05-19	@ Aarhus GF	D 1-1	1312	1486	14.17%	21.42%	64.41%	+2.2	35
2015-05-20	AB Gladsaxe	W 3-1	1313	1211	56.82%	24.07%	19.12%	+7.1	38
2015-05-20	@ Vejle BK	L 1-3	1211	1313	19.12%	24.07%	56.82%	-7.1	26
2015-05-20	FC Roskilde	W 1-0	1379	1363	46.17%	26.23%	27.61%	+6.0	49
2015-05-20	@ Vendsyssel	L 0-1	1363	1379	27.61%	26.23%	46.17%	-6.1	35
2015-05-20	Horsens	D 1-1	1295	1342	37.37%	26.79%	35.84%	-0.1	32
2015-05-20	@ Fredericia	D 1-1	1342	1295	35.84%	26.79%	37.37%	+0.1	37
2015-05-20	Lyngby	L 1-2	1244	1379	26.18%	26.00%	47.82%	-5.5	22
2015-05-20	@ Bronshoj	W 2-1	1379	1244	47.82%	26.00%	26.18%	+5.5	49
2015-05-20	Skive	D 1-1	1499	1317	65.28%	21.06%	13.66%	-2.3	61
2015-05-20	@ Viborg	D 1-1	1317	1499	13.66%	21.06%	65.28%	+2.3	36
2015-05-23	Aarhus GF	D 1-1	1319	1483	23.03%	25.34%	51.63%	+1.2	37
2015-05-23	@ Skive	D 1-1	1483	1319	51.63%	25.34%	23.03%	-1.2	57
2015-05-24	Bronshoj	D 2-2	1342	1239	56.98%	24.02%	19.00%	-1.2	38
2015-05-24	@ Horsens	D 2-2	1239	1342	19.00%	24.02%	56.98%	+1.2	23
2015-05-24	Fredericia	W 2-0	1357	1295	52.16%	25.23%	22.61%	+9.6	38
2015-05-24	@ FC Roskilde	L 0-2	1295	1357	22.61%	25.23%	52.16%	-9.6	32
2015-05-24	Vendsyssel	W 4-0	1315	1385	34.12%	26.75%	39.12%	+28.3	38
2015-05-24	@ HB Koge	L 0-4	1385	1315	39.12%	26.75%	34.12%	-28.3	49
2015-05-24	Viborg	W 2-1	1204	1496	13.42%	20.89%	65.69%	+11.3	29
2015-05-24	@ AB Gladsaxe	L 1-2	1496	1204	65.69%	20.89%	13.42%	-11.3	61
2015-05-25	Vejle BK	L 1-2	1384	1321	52.26%	25.21%	22.53%	-9.4	49
2015-05-25	@ Lyngby	W 2-1	1321	1384	22.53%	25.21%	52.26%	+9.4	41
2015-05-30	Aarhus GF	D 2-2	1375	1482	29.32%	26.44%	44.24%	+0.5	50
2015-05-30	@ Lyngby	D 2-2	1482	1375	44.24%	26.44%	29.32%	-0.5	58
2015-05-30	FC Roskilde	W 2-0	1343	1367	40.72%	26.69%	32.59%	+13.0	41
2015-05-30	@ HB Koge	L 0-2	1367	1343	32.59%	26.69%	40.72%	-13.0	38
2015-05-30	Fredericia	D 2-2	1320	1285	48.70%	25.87%	25.43%	-0.7	38
2015-05-30	@ Skive	D 2-2	1285	1320	25.43%	25.87%	48.70%	+0.7	33
2015-05-30	Vejle BK	D 2-2	1341	1330	45.56%	26.30%	28.14%	-0.5	39
2015-05-30	@ Horsens	D 2-2	1330	1341	28.14%	26.30%	45.56%	+0.5	42
2015-05-30	Vendsyssel	W 2-0	1215	1357	25.40%	25.86%	48.74%	+17.7	32
2015-05-30	@ AB Gladsaxe	L 0-2	1357	1215	48.74%	25.86%	25.40%	-17.7	49
2015-05-30	Viborg	L 0-3	1240	1485	16.36%	22.77%	60.87%	-10.3	23
2015-05-30	@ Bronshoj	W 3-0	1485	1240	60.87%	22.77%	16.36%	+10.2	64
2015-06-06	AB Gladsaxe	W 5-3	1482	1233	71.47%	18.19%	10.34%	+3.1	61
2015-06-06	@ Aarhus GF	L 3-5	1233	1482	10.34%	18.19%	71.47%	-3.1	32
2015-06-06	Bronshoj	W 2-0	1330	1230	56.65%	24.11%	19.23%	+8.3	45
2015-06-06	@ Vejle BK	L 0-2	1230	1330	19.23%	24.11%	56.65%	-8.3	23
2015-06-06	HB Koge	D 1-1	1286	1356	34.18%	26.76%	39.06%	+0.2	34
2015-06-06	@ Fredericia	D 1-1	1356	1286	39.06%	26.76%	34.18%	-0.2	42
2015-06-06	Horsens	L 1-2	1354	1341	45.84%	26.26%	27.90%	-8.4	38
2015-06-06	@ FC Roskilde	W 2-1	1341	1354	27.90%	26.26%	45.84%	+8.4	42
2015-06-06	Lyngby	D 1-1	1495	1375	58.83%	23.46%	17.72%	-1.8	65
2015-06-06	@ Viborg	D 1-1	1375	1495	17.72%	23.46%	58.83%	+1.8	51
2015-06-06	Skive	L 1-2	1339	1319	46.70%	26.16%	27.14%	-8.6	49
2015-06-06	@ Vendsyssel	W 2-1	1319	1339	27.14%	26.16%	46.70%	+8.6	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
#	Date	Underdog Win %	Team	Elo	Score	Team	Elo	Score
1	2015-05-14	11.86%	Fredericia	1282	1	@ Aarhus GF	1498	0
2	2015-05-24	13.42%	@ AB Gladsaxe	1204	2	Viborg	1496	1
3	2014-09-18	15.90%	Vejle BK	1317	1	@ Aarhus GF	1463	0
4	2015-04-25	16.22%	Fredericia	1255	2	@ Lyngby	1396	0
5	2015-04-11	17.92%	HB Koge	1296	3	@ Lyngby	1414	1
6	2014-11-30	19.96%	HB Koge	1275	2	@ Horsens	1367	1
7	2014-11-23	20.33%	AB Gladsaxe	1266	1	@ Vendsyssel	1354	0
8	2014-11-09	21.21%	AB Gladsaxe	1243	4	@ FC Roskilde	1321	2
9	2014-10-23	22.25%	Horsens	1379	2	@ Aarhus GF	1445	1
10	2015-05-25	22.53%	Vejle BK	1321	2	@ Lyngby	1384	1
11	2014-10-15	22.81%	Lyngby	1395	3	@ Aarhus GF	1456	2
12	2015-03-15	23.12%	FC Roskilde	1300	2	@ Horsens	1357	1
13	2014-08-24	24.00%	Vendsyssel	1301	1	@ Horsens	1350	0
14	2014-09-04	24.08%	Vendsyssel	1304	2	@ Vejle BK	1352	0
15	2015-03-25	24.25%	HB Koge	1286	1	@ FC Roskilde	1332	0
16	2014-08-10	24.57%	Viborg	1400	2	@ Aarhus GF	1443	0
17	2015-05-30	25.40%	@ AB Gladsaxe	1215	2	Vendsyssel	1357	0
18	2014-10-24	25.57%	@ Vendsyssel	1312	1	Viborg	1452	0
19	2014-08-08	25.58%	HB Koge	1333	3	@ Vejle BK	1367	1
20	2014-10-12	26.10%	Bronshoj	1302	2	@ Skive	1332	0
21	2015-03-14	26.15%	Vejle BK	1285	1	@ Bronshoj	1314	0
22	2014-10-05	26.43%	Vendsyssel	1300	1	@ HB Koge	1326	0
23	2014-08-16	26.63%	Vendsyssel	1291	1	@ Bronshoj	1316	0
24	2014-10-26	26.85%	Vejle BK	1306	3	@ Fredericia	1328	1
25	2015-06-06	27.14%	Skive	1319	2	@ Vendsyssel	1339	1

Biggest Elo Changes

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

#	Date	Elo Δ	Winning Team				Losing Team				Tie %
#	Date	Elo Δ	Team	Score	Elo	Win %	Team	Score	Elo	Win %	Tie %
1	2014-08-03	32.10	Aarhus GF	5	1411	38.24%	@ Bronshoj	0	1347	34.98%	26.78%
2	2014-11-02	30.99	Vendsyssel	4	1321	29.53%	@ Vejle BK	0	1321	44.02%	26.46%
3	2014-09-28	29.71	FC Roskilde	4	1291	31.66%	@ AB Gladsaxe	0	1274	41.70%	26.64%
4	2014-10-24	28.31	Lyngby	7	1409	42.98%	@ HB Koge	1	1310	30.48%	26.55%
5	2015-05-24	28.29	@ HB Koge	4	1315	34.12%	Vendsyssel	0	1385	39.12%	26.75%
6	2015-03-22	23.32	FC Roskilde	3	1309	30.39%	@ Fredericia	0	1302	43.08%	26.54%
7	2015-04-11	21.83	@ Vejle BK	4	1291	45.86%	Fredericia	0	1277	27.88%	26.26%
8	2015-04-17	21.80	@ Skive	3	1312	33.80%	Horsens	0	1385	39.46%	26.74%
9	2015-04-25	21.38	Fredericia	2	1255	16.22%	@ Lyngby	0	1396	61.09%	22.69%
10	2015-04-25	20.38	Vendsyssel	3	1330	37.16%	@ Bronshoj	0	1273	36.05%	26.79%
11	2014-09-04	18.20	Vendsyssel	2	1304	24.08%	@ Vejle BK	0	1352	50.33%	25.59%
12	2014-08-10	18.03	Viborg	2	1400	24.57%	@ Aarhus GF	0	1443	49.74%	25.70%
13	2015-04-11	17.88	HB Koge	3	1296	17.92%	@ Lyngby	1	1414	58.52%	23.55%
14	2014-08-24	17.81	Aarhus GF	5	1425	46.69%	@ AB Gladsaxe	1	1299	27.15%	26.16%
15	2015-05-30	17.73	@ AB Gladsaxe	2	1215	25.40%	Vendsyssel	0	1357	48.74%	25.86%
16	2015-04-06	17.69	Horsens	3	1367	43.57%	@ AB Gladsaxe	0	1265	29.93%	26.50%
17	2014-10-12	17.49	Bronshoj	2	1302	26.10%	@ Skive	0	1332	47.91%	25.99%
18	2014-11-02	17.11	@ Viborg	3	1442	44.94%	Aarhus GF	0	1436	28.70%	26.37%
19	2014-10-18	16.72	FC Roskilde	2	1313	28.36%	@ Vejle BK	0	1323	45.32%	26.32%
20	2014-08-31	16.71	Horsens	2	1340	28.39%	@ HB Koge	0	1349	45.28%	26.33%
21	2014-09-11	16.41	Aarhus GF	3	1447	46.56%	@ Vendsyssel	0	1322	27.27%	26.18%
22	2014-08-03	16.18	Lyngby	2	1371	30.00%	@ Horsens	0	1367	43.50%	26.50%
23	2015-05-17	16.16	Vendsyssel	2	1363	30.07%	@ Horsens	0	1358	43.43%	26.51%
24	2014-09-14	16.16	@ Horsens	3	1357	47.15%	Vejle BK	0	1333	26.75%	26.10%
25	2015-05-03	15.76	@ FC Roskilde	3	1364	48.06%	Skive	0	1333	25.97%	25.97%