2015-16 1st Division Season

198 games

Final

Promoted

Lyngby

64 pts

Silkeborg · 63 pts

Horsens · 60 pts

Relegated

Vestsjaelland

withdrew

Biggest Overachiever

Silkeborg

11.92 points above expected

63 points · 51.08 expected points

Biggest Disappointment

Vestsjaelland

13.16 points below expected

7 points · 20.16 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	Lyngby Promoted	33	19	7	7	64	56	37	+19	55.72	+8.28
2	Silkeborg Promoted	33	18	9	6	63	53	29	+24	51.08	+11.92
3	Horsens Promoted	33	18	6	9	60	46	34	+12	53.20	+6.80
4	Vendsyssel	33	16	8	9	56	38	33	+5	47.74	+8.26
5	Vejle BK	33	16	5	12	53	50	46	+4	52.45	+0.55
6	Fredericia	33	12	11	10	47	42	45	-3	45.28	+1.72
7	Helsingor	33	14	5	14	47	34	42	-8	53.70	-6.70
8	HB Koge	33	13	6	14	45	31	35	-4	44.99	+0.01
9	FC Roskilde	33	10	9	14	39	48	58	-10	43.20	-4.20
10	Naestved	33	10	4	19	34	34	48	-14	40.51	-6.51
11	Skive	33	8	7	18	31	34	50	-16	39.82	-8.82
12	Vestsjaelland* Withdrew	33	2	7	24	7	19	28	-9	20.16	-13.16

* Point deductions: Vestsjaelland (-6 pts)

Notes

FC Vestsjaelland went bankrupt in the middle of the season, causing their 15 remaining matches to be forfeit losses. Their license was subsequently used as an amateur club in the Zealand Series.

Top Overachievers & Disappointments

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

Biggest Overachievers

#	Team	Actual	Sim	vsSim
1	Silkeborg	63	51.08	+11.92
2	Lyngby	64	55.72	+8.28
3	Vendsyssel	56	47.74	+8.26
4	Horsens	60	53.20	+6.80
5	Fredericia	47	45.28	+1.72

Biggest Disappointments

#	Team	Actual	Sim	vsSim
1	Vestsjaelland	7	20.16	-13.16
2	Skive	31	39.82	-8.82
3	Helsingor	47	53.70	-6.70
4	Naestved	34	40.51	-6.51
5	FC Roskilde	39	43.20	-4.20

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 consecutive wins or ties; Winless counts consecutive losses or ties.

Most Unlikely Winning Streaks

#	Team	Games	Dates	Probability
1	HB Koge	5	Nov 8 – Mar 13	1 in 277
2	Vendsyssel	5	Apr 24 – May 16	1 in 160
3	Horsens	5	Aug 16 – Sep 20	1 in 92
4	Silkeborg	5	Mar 28 – Apr 24	1 in 90
5	Naestved	2	Nov 29 – Mar 13	1 in 20

Most Unlikely Losing Streaks

#	Team	Games	Dates	Probability
1	Vestsjaelland	16	Nov 29 – May 28	1 in 1,239,039
2	FC Roskilde	6	Oct 4 – Nov 13	1 in 112
3	Naestved	5	Aug 2 – Aug 30	1 in 74
4	Vendsyssel	4	Nov 29 – Mar 17	1 in 73
5	HB Koge	4	Oct 18 – Nov 1	1 in 33

Most Unlikely Unbeaten Streaks

#	Team	Games	Dates	Probability
1	Vendsyssel	12	Aug 30 – Nov 12	1 in 213
2	Horsens	9	Aug 16 – Oct 9	1 in 84
3	Fredericia	7	Jul 26 – Sep 10	1 in 42
4	Silkeborg	7	Mar 20 – Apr 24	1 in 22
5	Lyngby	8	Sep 18 – Nov 5	1 in 20

Most Unlikely Winless Streaks

#	Team	Games	Dates	Probability
1	Vestsjaelland	16	Nov 29 – May 28	1 in 1,222
2	Vendsyssel	10	Nov 12 – Apr 20	1 in 178
3	FC Roskilde	9	Sep 18 – Nov 18	1 in 52
4	Fredericia	6	Sep 10 – Oct 14	1 in 25
5	Naestved	6	Aug 2 – Sep 6	1 in 15

Position Race

Each team’s league position across the whole season. Colored teams are the season’s headline finishers — champion, promoted, and relegated; everyone else is gray as context.

Recent Form

Each team’s last 5 games, hottest first. Form shows the results (navy win, gray draw, red loss, newest on the right); the bars show Elo change over the window, points taken, points expected from the odds, and actual minus expected. Beside each team: current Elo and season points.

Team	Elo	Season Pts	Elo Δ (5)	Points (5)	Expected (5)	Actual − Exp
Lyngby	1465	61	-11	7	9.3	-2.3
Silkeborg	1497	57	+62	15	8.5	+6.5
Horsens	1432	54	-40	6	9.2	-3.2
Vendsyssel	1393	53	+30	12	6.9	+5.1
Vejle BK	1385	47	-16	7	7.0	+0.0
Fredericia	1338	44	-3	6	5.8	+0.2
HB Koge	1366	42	-23	4	6.2	-2.2
Helsingor	1371	41	+4	9	6.9	+2.1
FC Roskilde	1339	36	-10	5	6.6	-1.6
Naestved	1325	31	+31	7	5.6	+1.4
Skive	1288	28	-20	1	4.6	-3.6
Vestsjaelland	1354	13	-19	5	7.5	-2.5

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	HK	HEL	HOR	LYN	NAE	SIL	SKI	VB	VEN	VES
FC Roskilde	—	0-2-1 4.13	2-1-0 4.12	1-0-2 3.81	0-0-3 3.46	1-1-1 3.41	1-1-1 4.16	0-1-2 4.09	2-1-0 4.35	1-0-2 3.67	1-0-2 3.40	1-2-0 3.63
Fredericia	1-2-0 4.20	—	0-1-2 4.74	2-1-0 3.85	0-1-2 3.30	2-0-1 3.44	2-1-0 4.41	1-1-1 3.84	1-1-1 4.58	0-2-1 3.57	1-0-2 3.75	2-1-0 3.84
HB Koge	0-1-2 4.21	2-1-0 3.59	—	1-0-2 4.01	0-0-3 3.68	0-1-2 3.31	2-0-1 4.63	0-1-2 3.71	1-2-0 4.72	1-0-2 3.41	3-0-0 3.90	3-0-0 4.28
Helsingor	2-0-1 4.52	0-1-2 4.48	2-0-1 4.32	—	0-2-1 4.38	0-0-3 3.86	2-0-1 5.16	0-1-2 4.40	3-0-0 5.31	2-0-1 4.41	1-1-1 4.36	2-0-1 4.53
Horsens	3-0-0 4.89	2-1-0 5.03	3-0-0 4.65	1-2-0 3.95	—	1-1-1 4.18	0-0-3 5.00	1-1-1 4.23	3-0-0 4.83	0-0-3 4.30	1-1-1 4.36	3-0-0 5.15
Lyngby	1-1-1 4.92	1-0-2 4.91	2-1-0 5.03	3-0-0 4.49	1-1-1 4.15	—	3-0-0 5.42	2-0-1 4.80	1-2-0 5.76	3-0-0 4.74	1-0-2 4.68	1-2-0 4.85
Naestved	1-1-1 4.16	0-1-2 3.92	1-0-2 3.70	1-0-2 3.18	3-0-0 3.35	0-0-3 2.93	—	0-1-2 3.24	1-0-2 4.42	1-0-2 2.89	0-1-2 3.81	2-0-1 3.40
Silkeborg	2-1-0 4.25	1-1-1 4.50	2-1-0 4.63	2-1-0 3.93	1-1-1 4.09	1-0-2 3.54	2-1-0 5.11	—	2-1-0 5.26	2-0-1 4.47	0-2-1 4.41	3-0-0 4.71
Skive	0-1-2 3.98	1-1-1 3.75	0-2-1 3.62	0-0-3 3.04	0-0-3 3.53	0-2-1 2.61	2-0-1 3.92	0-1-2 3.09	—	1-0-2 3.22	1-0-2 3.75	3-0-0 3.72
Vejle BK	2-0-1 4.67	1-2-0 4.76	2-0-1 4.93	1-0-2 3.93	3-0-0 4.04	0-0-3 3.59	2-0-1 5.47	1-0-2 3.87	2-0-1 5.12	—	0-2-1 4.46	2-1-0 4.15
Vendsyssel	2-0-1 4.94	2-0-1 4.58	0-0-3 4.43	1-1-1 3.98	1-1-1 3.97	2-0-1 3.66	2-1-0 4.53	1-2-0 3.92	2-0-1 4.59	1-2-0 3.88	—	2-1-0 4.55
Vestsjaelland	0-2-1 4.70	0-1-2 4.50	0-0-3 4.05	1-0-2 3.80	0-0-3 3.18	0-2-1 3.48	1-0-2 4.94	0-0-3 3.63	0-0-3 4.61	0-1-2 4.19	0-1-2 3.77	—

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	0.68	+12.5
Allowed	0.02	-2.6
Differential	0.62	+9.9

Scoreline Distribution

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

View:

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	7.07%	10.10%	5.81%	3.03%	0.51%	0.25%	26.77%
1	10.10%	9.60%	6.06%	4.04%	0.51%	0.25%	30.56%
2	5.81%	6.06%	3.03%	2.78%	1.26%	0.25%	19.19%
3	3.03%	4.04%	2.78%	0.51%	0.76%	—	11.11%
4	0.51%	0.51%	1.26%	0.76%	1.01%	—	4.04%
5+	0.25%	0.25%	0.25%	—	—	7.58%	8.33%
Total	26.77%	30.56%	19.19%	11.11%	4.04%	8.33%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.15	1.15	+0.00
SD	1.30	1.30	1.54
CV	1.13	1.13	—
Max	6	6	+5
Min	-1	-1	-5

Games Played: 198

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	6.06%	9.09%	3.03%	—	3.03%	—	21.21%
1	6.06%	9.09%	9.09%	6.06%	—	—	30.30%
2	6.06%	3.03%	6.06%	6.06%	3.03%	3.03%	27.27%
3	3.03%	3.03%	3.03%	3.03%	—	—	12.12%
4	—	—	—	3.03%	3.03%	—	6.06%
5+	—	—	—	—	—	3.03%	3.03%
Total	21.21%	24.24%	21.21%	18.18%	9.09%	6.06%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.42	1.73	-0.30
SD	1.23	1.55	1.57
CV	0.86	0.90	—
Max	4	6	+3
Min	-1	-1	-4

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	15.15%	3.03%	6.06%	6.06%	—	—	30.30%
1	9.09%	9.09%	6.06%	—	3.03%	—	27.27%
2	3.03%	12.12%	3.03%	3.03%	3.03%	—	24.24%
3	—	3.03%	3.03%	3.03%	—	—	9.09%
4	3.03%	—	—	—	3.03%	—	6.06%
5+	—	—	—	—	—	3.03%	3.03%
Total	30.30%	27.27%	18.18%	12.12%	9.09%	3.03%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.24	1.33	-0.09
SD	1.25	1.36	1.53
CV	1.01	1.02	—
Max	4	4	+4
Min	-1	-1	-3

Games Played: 33

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

Summary Statistics

	Scored	Allowed	Difference
Mean	0.91	1.03	-0.12
SD	0.98	1.13	1.63
CV	1.08	1.10	—
Max	3	4	+3
Min	-1	-1	-4

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	6.06%	18.18%	3.03%	12.12%	—	—	39.39%
1	12.12%	3.03%	3.03%	3.03%	3.03%	—	24.24%
2	3.03%	6.06%	6.06%	—	—	—	15.15%
3	—	6.06%	6.06%	—	—	—	12.12%
4	3.03%	—	—	—	—	—	3.03%
5+	—	—	—	—	—	6.06%	6.06%
Total	24.24%	33.33%	18.18%	15.15%	3.03%	6.06%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	0.97	1.21	-0.24
SD	1.26	1.24	1.71
CV	1.30	1.03	—
Max	4	4	+4
Min	-1	-1	-3

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	6.06%	6.06%	—	3.03%	—	—	15.15%
1	12.12%	12.12%	3.03%	6.06%	—	3.03%	36.36%
2	15.15%	6.06%	—	—	3.03%	—	24.24%
3	12.12%	—	3.03%	—	3.03%	—	18.18%
4	—	—	—	—	—	—	—
5+	—	—	—	—	—	6.06%	6.06%
Total	45.45%	24.24%	6.06%	9.09%	6.06%	9.09%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.33	0.97	+0.36
SD	1.14	1.49	1.76
CV	0.85	1.54	—
Max	3	5	+3
Min	-1	-1	-4

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	6.06%	3.03%	6.06%	—	—	—	15.15%
1	18.18%	9.09%	6.06%	3.03%	—	—	36.36%
2	9.09%	3.03%	3.03%	3.03%	—	—	18.18%
3	3.03%	6.06%	3.03%	—	—	—	12.12%
4	—	3.03%	6.06%	3.03%	3.03%	—	15.15%
5+	—	—	—	—	—	3.03%	3.03%
Total	36.36%	24.24%	24.24%	9.09%	3.03%	3.03%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.67	1.09	+0.58
SD	1.38	1.18	1.37
CV	0.83	1.08	—
Max	4	4	+3
Min	-1	-1	-2

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	6.06%	15.15%	15.15%	3.03%	—	—	39.39%
1	6.06%	6.06%	9.09%	9.09%	—	—	30.30%
2	3.03%	9.09%	—	6.06%	—	—	18.18%
3	—	3.03%	—	—	—	—	3.03%
4	—	—	3.03%	—	—	—	3.03%
5+	—	3.03%	—	—	—	3.03%	6.06%
Total	15.15%	36.36%	27.27%	18.18%	—	3.03%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.00	1.42	-0.42
SD	1.27	1.06	1.56
CV	1.27	0.75	—
Max	5	3	+4
Min	-1	-1	-3

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	6.06%	9.09%	6.06%	—	—	—	21.21%
1	9.09%	18.18%	3.03%	—	—	—	30.30%
2	3.03%	9.09%	3.03%	—	—	—	15.15%
3	3.03%	12.12%	3.03%	—	—	—	18.18%
4	—	3.03%	—	—	—	—	3.03%
5+	3.03%	—	3.03%	—	—	6.06%	12.12%
Total	24.24%	51.52%	18.18%	—	—	6.06%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.55	0.82	+0.73
SD	1.62	0.81	1.59
CV	1.05	0.99	—
Max	6	2	+5
Min	-1	-1	-2

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	3.03%	18.18%	9.09%	3.03%	—	—	33.33%
1	6.06%	15.15%	9.09%	6.06%	—	—	36.36%
2	3.03%	3.03%	3.03%	3.03%	3.03%	—	15.15%
3	—	6.06%	3.03%	—	3.03%	—	12.12%
4	—	—	—	—	—	—	—
5+	—	—	—	—	—	3.03%	3.03%
Total	12.12%	42.42%	24.24%	12.12%	6.06%	3.03%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.00	1.48	-0.48
SD	1.06	1.15	1.28
CV	1.06	0.77	—
Max	3	4	+2
Min	-1	-1	-3

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	6.06%	6.06%	6.06%	—	—	3.03%	21.21%
1	9.09%	6.06%	3.03%	6.06%	—	—	24.24%
2	3.03%	9.09%	3.03%	9.09%	—	—	24.24%
3	9.09%	6.06%	—	—	3.03%	—	18.18%
4	—	—	3.03%	3.03%	—	—	6.06%
5+	—	—	—	—	—	6.06%	6.06%
Total	27.27%	27.27%	15.15%	18.18%	3.03%	9.09%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.45	1.33	+0.12
SD	1.35	1.45	1.75
CV	0.93	1.09	—
Max	4	5	+3
Min	-1	-1	-5

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	9.09%	6.06%	6.06%	3.03%	—	—	24.24%
1	21.21%	12.12%	6.06%	3.03%	—	—	42.42%
2	9.09%	6.06%	3.03%	3.03%	—	—	21.21%
3	—	—	6.06%	—	—	—	6.06%
4	—	—	3.03%	—	—	—	3.03%
5+	—	—	—	—	—	3.03%	3.03%
Total	39.39%	24.24%	24.24%	9.09%	—	3.03%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	1.12	0.97	+0.15
SD	1.05	1.07	1.28
CV	0.94	1.11	—
Max	4	3	+2
Min	-1	-1	-3

Games Played: 33

↓ Scored \| Allowed →	0	1	2	3	4	5+	Total
0	9.09%	9.09%	6.06%	—	—	—	24.24%
1	—	6.06%	6.06%	3.03%	—	—	15.15%
2	—	—	3.03%	—	3.03%	—	6.06%
3	3.03%	—	3.03%	—	—	—	6.06%
4	—	—	—	—	3.03%	—	3.03%
5+	—	—	—	—	—	45.45%	45.45%
Total	12.12%	15.15%	18.18%	3.03%	6.06%	45.45%	100%

Summary Statistics

	Scored	Allowed	Difference
Mean	0.12	0.39	-0.27
SD	1.39	1.58	0.94
CV	11.44	4.01	—
Max	4	4	+3
Min	-1	-1	-2

Games Played: 33

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
Silkeborg	1497	63	51.08	+11.92	94.0%	35	40	46	50	56	64	66
Lyngby	1465	64	55.72	+8.28	84.0%	34	42	50	55	61	67	76
Horsens	1432	60	53.20	+6.80	84.0%	36	41	47	54	58	65	70
Vendsyssel	1393	56	47.74	+8.26	89.0%	25	36	42	48	53	58	67
Vejle BK	1385	53	52.45	+0.55	59.0%	33	40	48	52	57	63	67
Helsingor	1371	47	53.70	-6.70	26.0%	37	41	47	53	59	68	75
HB Koge	1366	45	44.99	+0.01	50.0%	30	33	39	45	50	59	62
Vestsjaelland	1354	7	20.16	-13.16	1.0%	7	11	16	20	24	29	31
FC Roskilde	1339	39	43.20	-4.20	30.0%	26	31	39	43	48	55	58
Fredericia	1338	47	45.28	+1.72	62.0%	29	31	39	45	51	59	62
Naestved	1325	34	40.51	-6.51	16.0%	23	30	36	39	45	53	59
Skive	1288	31	39.82	-8.82	10.0%	22	29	35	40	44	50	56

Point 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).

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.

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 11.5% * Slight Edge: 11.5% to 19% * Clear Edge: 19% to 28.5% * Strong Edge: 28.5% 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

+4.04%

No Edge

41.41%21.21%37.37%

Elo Value

Home Edge: 12.29 Elo pts.

356 Elo

0.003 goals per Elo point

01000

Scoring Tilt

Expected

+0.09 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 3 * Open: 3 to 4 * Wide Open: 4 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 0.06 * Tight Race: 0.06 to 0.13 * Comfortable: 0.13 to 0.24 * Runaway: 0.24 and up.

Title-Race Openness

5.4

Wide Open

123410

Champion Preseason Odds

29%

Lyngby, 1st of 12

LongshotFavorite

Title Margin

Expected

0.03/gm

Photo Finish

00.150.5/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 5.8 * Some Luck: 5.8 to 8.71 * Lucky: 8.71 to 11.61 * Wild Swing: 11.61 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 1.62 * Close: 1.62 to 2.43 * Off: 2.43 to 3.25 * Way Off: 3.25 and up.
Biggest Overachiever: The team that finished highest inside its own range of simulated outcomes. The gold line is where the top team in a league of 12 typically lands.
Biggest Underachiever: The team that finished lowest inside its own range of simulated outcomes. The gold line is where the bottom team in a league of 12 typically lands.
Season Outliers: Number of teams that finished above the 95th or below the 5th percentile of their own simulated range. Even a well-calibrated model expects about 10% of teams in the extremes.
Minimal Outliers: under 12 * As Expected: 12 to 19.2 * Several Outliers: 19.2 to 26.4 * Many Outliers: 26.4 and up.
Unexpected Relegations: Number of teams that were actually relegated but were not in the model's projected bottom field of the same size. The gold line is how many the model expected to miss on average.
As Expected: under 0.5 * A Surprise: 0.5 to 0.8 * Several Surprises: 0.8 to 1.1 * Many Surprises: 1.1 and up.

Luck Spread

Expected

7.55 points

Some Luck

07.2518

Average Finish Error

Expected

1.00

Pinpoint

02.035

Biggest Overachiever

Expected 95.83%

94.00%

Silkeborg

50100

Biggest Underachiever

Expected 4.17%

1.00%

Vestsjaelland

050

Season Outliers

Expected

1 of 12

Minimal Outliers

01.26

Unexpected Relegations

Expected 0.0

0 of 1

As Expected

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 0.13 * Even: 0.13 to 0.17 * Top-Heavy: 0.17 to 0.2 * Lopsided: 0.2 and up.
Noll-Scully: Take a league where every match is a coin flip and nobody is better than anybody. You would still get a table, with a leader and a bottom club, purely by luck, and you can work out how far apart that table would be. This is the real table measured against it. At 1.00 the season was no more spread out than pure chance; at 2.00 it was twice as spread out, which takes genuine differences in quality. Dividing by the luck figure is what makes seasons of different lengths comparable, because a short season spreads a table by luck alone far more than a long one does. Elo SD in the corner is the same question in rating points: how far apart the teams finished on the ratings.
Coin-Flip Parity: under 1.5 * Moderate Separation: 1.5 to 1.8 * Strong Separation: 1.8 to 2.15 * Wide Separation: 2.15 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 63.5% * Slight Edge: 63.5% to 68% * Clear Edge: 68% to 74.5% * Wide Edge: 74.5% 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 85% * Clear Edge: 85% to 91% * Strong Edge: 91% to 95% * Dominant: 95% 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 54.5% * Some Drama: 54.5% to 60% * Frequent: 60% to 65% * Very Frequent: 65% 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 14.5% * Occasional: 14.5% to 18.5% * Frequent: 18.5% to 23.5% * Very Frequent: 23.5% and up.

Gini Index

0.18

Top-Heavy

00.130.20.5

Noll-Scully

Elo SD: 60.08

2.10

Strong Separation

0.751.51.82.153

Interquartile Edge

59%

Even

50%63.5%74.5%100%

Best vs. Worst

Baseline

77%

Even

50%77%100%

Close Games

Expected

68%

Very Frequent

0%67%100%

Blowouts

Expected

10%

Rare

0%11%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 0.34 * Slight Separation: 0.34 to 0.42 * Notable Separation: 0.42 to 0.51 * Lopsided: 0.51 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 0.38 * Some Carryover: 0.38 to 0.57 * Strong Carryover: 0.57 to 0.7 * Near-Lock: 0.7 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 18% * As Expected: 18% to 22% * Upset-Prone: 22% to 25% * Very Upset-Prone: 25% 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 15.5% * As Expected: 15.5% to 18.5% * Shaky Favorites: 18.5% to 22% * Very Shaky: 22% and up.

Brier Score

Expected

0.64

Hard to Predict

00.632

Matchup Imbalance

0.21

Very Even

00.340.420.5

Strangeness

Expected

1.31

Wilder Than Modeled

01.002

Repeatability

0.08

Weak Carryover

00.380.570.71

Upset Rate

Expected

37%

Very Upset-Prone

0%31%50%

Clear Favorite Upset Rate

Expected

30%

Very Shaky

0%27%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 0.11 * Near Noise Ceiling: 0.11 to 0.16 * Above Noise: 0.16 to 0.21 * Well Above Noise: 0.21 and up.

Probability calibration

0.80

Excellent

0.010.050.10.51

Calibration slope

Ideal

0.75

Overconfident

0.51.001.5

Calibration error (ECE)

Noise ceiling

0.095

Well Within Noise

00.1070.25

Final Table Odds

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
Lyngby	29.00%	20.00%	14.00%	12.00%	10.00%	2.00%	2.00%	7.00%	2.00%	1.00%	1.00%	—
Silkeborg	10.00%	15.00%	14.00%	12.00%	12.00%	11.00%	12.00%	8.00%	4.00%	2.00%	—	—
Horsens	19.00%	20.00%	14.00%	5.00%	12.00%	11.00%	6.00%	3.00%	8.00%	1.00%	1.00%	—
Vendsyssel	2.00%	7.00%	14.00%	9.00%	9.00%	12.00%	14.00%	12.00%	12.00%	4.00%	4.00%	1.00%
Vejle BK	15.00%	11.00%	10.00%	21.00%	13.00%	11.00%	8.00%	2.00%	5.00%	3.00%	1.00%	—
Fredericia	5.00%	2.00%	8.00%	5.00%	12.00%	10.00%	15.00%	12.00%	10.00%	8.00%	12.00%	1.00%
Helsingor	17.00%	15.00%	11.00%	14.00%	11.00%	9.00%	8.00%	7.00%	4.00%	2.00%	2.00%	—
HB Koge	2.00%	5.00%	8.00%	8.00%	5.00%	14.00%	11.00%	8.00%	13.00%	13.00%	13.00%	—
FC Roskilde	—	2.00%	4.00%	8.00%	8.00%	12.00%	6.00%	14.00%	11.00%	20.00%	15.00%	—
Naestved	1.00%	2.00%	1.00%	4.00%	7.00%	1.00%	12.00%	8.00%	15.00%	28.00%	21.00%	—
Skive	—	1.00%	2.00%	2.00%	1.00%	7.00%	6.00%	19.00%	16.00%	18.00%	28.00%	—
Vestsjaelland	—	—	—	—	—	—	—	—	—	—	2.00%	98.00%

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.

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
Lyngby	63.00%	37.00%	—
Horsens	53.00%	47.00%	—
Helsingor	43.00%	57.00%	—
Silkeborg	39.00%	61.00%	—
Vejle BK	36.00%	64.00%	—
Vendsyssel	23.00%	76.00%	1.00%
HB Koge	15.00%	85.00%	—
Fredericia	15.00%	84.00%	1.00%
FC Roskilde	6.00%	94.00%	—
Naestved	4.00%	96.00%	—
Skive	3.00%	97.00%	—
Vestsjaelland	—	2.00%	98.00%

Filter by Team:

Overall Game Log

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

Date	Opponent	Score	Excitement	Pre Elo	Opp Elo	Win %	Tie %	Loss %	Proj. Margin	Elo Δ	Points
2015-07-23	HB Koge	W 1-0	41.3	1386	1374	46.76%	22.26%	30.98%	+0.07	+6.2	3
2015-07-23	@ Lyngby	L 0-1	41.3	1374	1386	30.98%	22.26%	46.76%	-0.07	-6.2	0
2015-07-25	Vestsjaelland	W 2-1	49.6	1384	1421	39.96%	22.50%	37.54%	-0.07	+6.8	3
2015-07-25	@ Silkeborg	L 1-2	49.6	1421	1384	37.54%	22.50%	39.96%	+0.07	-6.8	0
2015-07-26	Fredericia	D 1-1	56.8	1365	1336	49.03%	22.10%	28.87%	+0.12	-0.8	1
2015-07-26	@ Vejle BK	D 1-1	56.8	1336	1365	28.87%	22.10%	49.03%	-0.12	+0.8	1
2015-07-26	Helsingor	W 3-0	39.5	1369	1452	33.56%	22.40%	44.04%	-0.20	+22.4	3
2015-07-26	@ FC Roskilde	L 0-3	39.5	1452	1369	44.04%	22.40%	33.56%	+0.20	-22.4	0
2015-07-26	Horsens	W 2-1	46.8	1373	1371	45.57%	22.33%	32.10%	+0.04	+6.0	3
2015-07-26	@ Naestved	L 1-2	46.8	1371	1373	32.10%	22.33%	45.57%	-0.04	-6.0	0
2015-07-26	Vendsyssel	L 0-2	40.2	1359	1361	45.02%	22.36%	32.62%	+0.03	-15.7	0
2015-07-26	@ Skive	W 2-0	40.2	1361	1359	32.62%	22.36%	45.02%	-0.03	+15.7	3
2015-08-01	Vejle BK	D 2-2	63.6	1414	1364	51.77%	21.84%	26.39%	+0.18	-0.8	1
2015-08-01	@ Vestsjaelland	D 2-2	63.6	1364	1414	26.39%	21.84%	51.77%	-0.18	+0.8	2
2015-08-02	FC Roskilde	W 4-0	36.5	1337	1391	37.54%	22.50%	39.97%	-0.12	+27.1	4
2015-08-02	@ Fredericia	L 0-4	36.5	1391	1337	39.97%	22.50%	37.54%	+0.12	-27.1	3
2015-08-02	Lyngby	L 2-4	49.0	1365	1392	41.40%	22.48%	36.13%	-0.04	-11.4	0
2015-08-02	@ Horsens	W 4-2	49.0	1392	1365	36.13%	22.48%	41.40%	+0.04	+11.4	6
2015-08-02	Naestved	W 2-1	46.2	1430	1379	51.82%	21.83%	26.35%	+0.18	+5.1	3
2015-08-02	@ Helsingor	L 1-2	46.2	1379	1430	26.35%	21.83%	51.82%	-0.18	-5.1	3
2015-08-02	Silkeborg	D 1-1	57.8	1376	1391	43.24%	22.43%	34.33%	-0.01	-0.3	4
2015-08-02	@ Vendsyssel	D 1-1	57.8	1391	1376	34.33%	22.43%	43.24%	+0.01	+0.4	4
2015-08-02	Skive	D 1-1	56.9	1368	1344	48.47%	22.14%	29.39%	+0.10	-0.8	1
2015-08-02	@ HB Koge	D 1-1	56.9	1344	1368	29.39%	22.14%	48.47%	-0.10	+0.8	1
2015-08-07	HB Koge	W 1-0	41.4	1364	1367	44.80%	22.37%	32.84%	+0.03	+6.5	6
2015-08-07	@ FC Roskilde	L 0-1	41.4	1367	1364	32.84%	22.37%	44.80%	-0.03	-6.5	1
2015-08-07	Horsens	W 3-0	33.5	1365	1353	46.79%	22.26%	30.95%	+0.07	+16.9	5
2015-08-07	@ Vejle BK	L 0-3	33.5	1353	1365	30.95%	22.26%	46.79%	-0.07	-16.9	0
2015-08-07	Vestsjaelland	W 3-1	45.4	1344	1414	35.46%	22.46%	42.08%	-0.16	+12.9	4
2015-08-07	@ Skive	L 1-3	45.4	1414	1344	42.08%	22.46%	35.46%	+0.16	-12.9	1
2015-08-09	Helsingor	W 1-0	45.6	1364	1435	35.25%	22.46%	42.30%	-0.16	+7.9	7
2015-08-09	@ Fredericia	L 0-1	45.6	1435	1364	42.30%	22.46%	35.25%	+0.16	-7.9	3
2015-08-09	Silkeborg	W 1-0	42.0	1403	1391	46.88%	22.25%	30.86%	+0.07	+6.2	9
2015-08-09	@ Lyngby	L 0-1	42.0	1391	1403	30.86%	22.25%	46.88%	-0.07	-6.2	4
2015-08-09	Vendsyssel	L 0-1	45.4	1374	1376	44.96%	22.36%	32.68%	+0.03	-8.3	3
2015-08-09	@ Naestved	W 1-0	45.4	1376	1374	32.68%	22.36%	44.96%	-0.03	+8.3	7
2015-08-15	Fredericia	D 0-0	53.4	1401	1372	49.02%	22.10%	28.88%	+0.11	-0.9	2
2015-08-15	@ Vestsjaelland	D 0-0	53.4	1372	1401	28.88%	22.10%	49.02%	-0.11	+0.9	8
2015-08-16	FC Roskilde	W 1-0	42.4	1336	1371	40.38%	22.49%	37.13%	-0.06	+7.2	3
2015-08-16	@ Horsens	L 0-1	42.4	1371	1336	37.13%	22.49%	40.38%	+0.06	-7.2	6
2015-08-16	Lyngby	W 2-1	48.9	1384	1410	41.70%	22.47%	35.83%	-0.04	+6.6	10
2015-08-16	@ Vendsyssel	L 1-2	48.9	1410	1384	35.83%	22.47%	41.70%	+0.04	-6.6	9
2015-08-16	Naestved	W 2-0	36.7	1361	1366	44.49%	22.38%	33.13%	+0.02	+12.3	4
2015-08-16	@ HB Koge	L 0-2	36.7	1366	1361	33.13%	22.38%	44.49%	-0.02	-12.3	3
2015-08-16	Skive	W 2-1	45.7	1385	1357	48.87%	22.11%	29.02%	+0.11	+5.5	7
2015-08-16	@ Silkeborg	L 1-2	45.7	1357	1385	29.02%	22.11%	48.87%	-0.11	-5.5	4
2015-08-16	Vejle BK	W 3-1	41.8	1427	1382	51.14%	21.90%	26.95%	+0.16	+9.0	6
2015-08-16	@ Helsingor	L 1-3	41.8	1382	1427	26.95%	21.90%	51.14%	-0.16	-9.0	5
2015-08-19	Silkeborg	W 2-0	38.6	1343	1390	38.62%	22.50%	38.88%	-0.10	+14.0	6
2015-08-19	@ Horsens	L 0-2	38.6	1390	1343	38.88%	22.50%	38.62%	+0.10	-14.0	7
2015-08-20	Naestved	W 1-0	40.5	1373	1354	47.79%	22.19%	30.01%	+0.09	+6.0	8
2015-08-20	@ Vejle BK	L 0-1	40.5	1354	1373	30.01%	22.19%	47.79%	-0.09	-6.0	3
2015-08-23	FC Roskilde	D 0-0	53.3	1400	1364	49.99%	22.02%	27.99%	+0.14	-1.0	3
2015-08-23	@ Vestsjaelland	D 0-0	53.3	1364	1400	27.99%	22.02%	49.99%	-0.14	+1.0	7
2015-08-23	Lyngby	W 3-1	44.1	1373	1403	41.03%	22.48%	36.49%	-0.05	+11.5	11
2015-08-23	@ Fredericia	L 1-3	44.1	1403	1373	36.49%	22.48%	41.03%	+0.05	-11.5	9
2015-08-23	Skive	W 1-0	39.1	1436	1352	55.89%	21.30%	22.80%	+0.27	+4.8	9
2015-08-23	@ Helsingor	L 0-1	39.1	1352	1436	22.80%	21.30%	55.89%	-0.27	-4.8	4
2015-08-23	Vendsyssel	W 2-0	37.9	1373	1391	42.74%	22.44%	34.82%	-0.02	+12.8	7
2015-08-23	@ HB Koge	L 0-2	37.9	1391	1373	34.82%	22.44%	42.74%	+0.02	-12.8	10
2015-08-26	HB Koge	D 0-0	53.1	1376	1386	43.87%	22.41%	33.72%	+0.01	-0.4	8
2015-08-26	@ Silkeborg	D 0-0	53.1	1386	1376	33.72%	22.41%	43.87%	-0.01	+0.4	8
2015-08-28	Helsingor	W 4-1	43.2	1391	1441	38.25%	22.50%	39.25%	-0.10	+17.2	12
2015-08-28	@ Lyngby	L 1-4	43.2	1441	1391	39.25%	22.50%	38.25%	+0.10	-17.2	9
2015-08-28	Horsens	L 0-2	39.6	1347	1357	43.76%	22.41%	33.83%	+0.01	-15.4	4
2015-08-28	@ Skive	W 2-0	39.6	1357	1347	33.83%	22.41%	43.76%	-0.01	+15.4	9
2015-08-30	Fredericia	L 0-2	38.9	1348	1385	40.03%	22.50%	37.47%	-0.07	-14.3	3
2015-08-30	@ Naestved	W 2-0	38.9	1385	1348	37.47%	22.50%	40.03%	+0.07	+14.3	14
2015-08-30	Vestsjaelland	W 4-2	47.5	1378	1399	42.32%	22.45%	35.22%	-0.02	+10.0	13
2015-08-30	@ Vendsyssel	L 2-4	47.5	1399	1378	35.22%	22.45%	42.32%	+0.02	-10.0	3
2015-08-30	Vejle BK	L 1-2	50.1	1365	1379	43.23%	22.43%	34.34%	-0.01	-7.6	7
2015-08-30	@ FC Roskilde	W 2-1	50.1	1379	1365	34.34%	22.43%	43.23%	+0.01	+7.6	11
2015-09-06	FC Roskilde	D 1-1	56.6	1333	1357	41.90%	22.47%	35.63%	-0.03	-0.2	4
2015-09-06	@ Naestved	D 1-1	56.6	1357	1333	35.63%	22.47%	41.90%	+0.03	+0.2	8
2015-09-06	Vestsjaelland	W 2-1	47.5	1386	1389	44.87%	22.36%	32.77%	+0.03	+6.1	11
2015-09-06	@ HB Koge	L 1-2	47.5	1389	1386	32.77%	22.36%	44.87%	-0.03	-6.1	3
2015-09-06	Vejle BK	W 3-2	51.8	1409	1387	48.17%	22.17%	29.66%	+0.10	+5.3	15
2015-09-06	@ Lyngby	L 2-3	51.8	1387	1409	29.66%	22.17%	48.17%	-0.10	-5.3	11
2015-09-07	Helsingor	W 1-0	44.6	1376	1424	38.49%	22.50%	39.01%	-0.10	+7.4	11
2015-09-07	@ Silkeborg	L 0-1	44.6	1424	1376	39.01%	22.50%	38.49%	+0.10	-7.4	9
2015-09-10	Lyngby	W 2-0	39.7	1357	1414	37.21%	22.49%	40.30%	-0.12	+14.4	11
2015-09-10	@ FC Roskilde	L 0-2	39.7	1414	1357	40.30%	22.49%	37.21%	+0.12	-14.4	15
2015-09-10	Silkeborg	D 1-1	58.1	1399	1383	47.30%	22.23%	30.47%	+0.08	-0.7	15
2015-09-10	@ Fredericia	D 1-1	58.1	1383	1399	30.47%	22.23%	47.30%	-0.08	+0.7	12
2015-09-13	HB Koge	W 3-0	35.6	1373	1392	42.48%	22.45%	35.07%	-0.02	+18.7	12
2015-09-13	@ Horsens	L 0-3	35.6	1392	1373	35.07%	22.45%	42.48%	+0.02	-18.7	11
2015-09-13	Naestved	L 0-2	42.0	1383	1333	51.71%	21.85%	26.45%	+0.17	-17.7	3
2015-09-13	@ Vestsjaelland	W 2-0	42.0	1333	1383	26.45%	21.85%	51.71%	-0.17	+17.7	7
2015-09-13	Skive	W 3-1	39.8	1381	1332	51.70%	21.85%	26.46%	+0.17	+8.8	14
2015-09-13	@ Vejle BK	L 1-3	39.8	1332	1381	26.46%	21.85%	51.70%	-0.17	-8.9	4
2015-09-13	Vendsyssel	D 2-2	64.1	1416	1388	48.94%	22.11%	28.95%	+0.11	-0.6	10
2015-09-13	@ Helsingor	D 2-2	64.1	1388	1416	28.95%	22.11%	48.94%	-0.11	+0.6	14
2015-09-17	Vejle BK	L 0-2	40.9	1384	1390	44.36%	22.39%	33.25%	+0.02	-15.5	12
2015-09-17	@ Silkeborg	W 2-0	40.9	1390	1384	33.25%	22.39%	44.36%	-0.02	+15.5	17
2015-09-18	FC Roskilde	D 1-1	56.6	1323	1372	38.32%	22.50%	39.18%	-0.10	+0.0	5
2015-09-18	@ Skive	D 1-1	56.6	1372	1323	39.18%	22.50%	38.32%	+0.10	-0.0	12
2015-09-18	Fredericia	W 3-2	52.9	1389	1398	43.90%	22.40%	33.70%	+0.01	+5.9	17
2015-09-18	@ Vendsyssel	L 2-3	52.9	1398	1389	33.70%	22.40%	43.90%	-0.01	-5.9	15
2015-09-18	Naestved	W 3-1	40.5	1400	1351	51.61%	21.86%	26.53%	+0.17	+8.9	18
2015-09-18	@ Lyngby	L 1-3	40.5	1351	1400	26.53%	21.86%	51.61%	-0.17	-8.9	7
2015-09-20	Helsingor	L 0-4	37.4	1374	1416	39.34%	22.50%	38.16%	-0.08	-26.8	11
2015-09-20	@ HB Koge	W 4-0	37.4	1416	1374	38.16%	22.50%	39.34%	+0.08	+26.8	13
2015-09-20	Vestsjaelland	W 1-0	40.7	1392	1365	48.75%	22.12%	29.13%	+0.11	+5.9	15
2015-09-20	@ Horsens	L 0-1	40.7	1365	1392	29.13%	22.12%	48.75%	-0.11	-5.9	3
2015-09-26	Vendsyssel	D 1-1	58.5	1406	1395	46.68%	22.27%	31.05%	+0.07	-0.6	18
2015-09-26	@ Vejle BK	D 1-1	58.5	1395	1406	31.05%	22.27%	46.68%	-0.07	+0.6	18
2015-09-27	HB Koge	D 0-0	52.9	1392	1347	51.16%	21.90%	26.94%	+0.16	-1.1	16
2015-09-27	@ Fredericia	D 0-0	52.9	1347	1392	26.94%	21.90%	51.16%	-0.16	+1.1	12
2015-09-27	Horsens	D 1-1	59.6	1442	1397	51.10%	21.91%	26.99%	+0.16	-1.0	14
2015-09-27	@ Helsingor	D 1-1	59.6	1397	1442	26.99%	21.91%	51.10%	-0.16	+1.0	16
2015-09-27	Lyngby	D 1-1	57.7	1359	1409	38.25%	22.50%	39.25%	-0.10	+0.0	4
2015-09-27	@ Vestsjaelland	D 1-1	57.7	1409	1359	39.25%	22.50%	38.25%	+0.10	-0.0	19
2015-09-27	Silkeborg	D 2-2	62.8	1372	1368	45.64%	22.33%	32.03%	+0.04	-0.4	13
2015-09-27	@ FC Roskilde	D 2-2	62.8	1368	1372	32.03%	22.33%	45.64%	-0.04	+0.4	13
2015-09-27	Skive	L 0-1	45.0	1342	1323	47.76%	22.20%	30.04%	+0.09	-8.8	7
2015-09-27	@ Naestved	W 1-0	45.0	1323	1342	30.04%	22.20%	47.76%	-0.09	+8.8	8
2015-09-30	Fredericia	W 2-0	39.0	1332	1391	36.80%	22.49%	40.71%	-0.13	+14.5	11
2015-09-30	@ Skive	L 0-2	39.0	1391	1332	40.71%	22.49%	36.80%	+0.13	-14.5	16
2015-09-30	Horsens	D 1-1	58.3	1395	1398	44.77%	22.37%	32.87%	+0.03	-0.5	19
2015-09-30	@ Vendsyssel	D 1-1	58.3	1398	1395	32.87%	22.37%	44.77%	-0.03	+0.5	17
2015-10-03	Fredericia	D 1-1	58.0	1399	1377	48.18%	22.17%	29.66%	+0.10	-0.7	18
2015-10-03	@ Horsens	D 1-1	58.0	1377	1399	29.66%	22.17%	48.18%	-0.10	+0.7	17
2015-10-04	FC Roskilde	W 1-0	41.0	1395	1371	48.35%	22.15%	29.50%	+0.10	+5.9	22
2015-10-04	@ Vendsyssel	L 0-1	41.0	1371	1395	29.50%	22.15%	48.35%	-0.10	-5.9	13
2015-10-04	HB Koge	L 0-1	47.5	1405	1348	52.60%	21.75%	25.65%	+0.19	-9.5	18
2015-10-04	@ Vejle BK	W 1-0	47.5	1348	1405	25.65%	21.75%	52.60%	-0.19	+9.5	15
2015-10-04	Helsingor	W 3-0	39.0	1359	1441	33.72%	22.41%	43.88%	-0.20	+22.4	7
2015-10-04	@ Vestsjaelland	L 0-3	39.0	1441	1359	43.88%	22.41%	33.72%	+0.20	-22.4	14
2015-10-04	Skive	W 1-0	39.3	1408	1346	53.28%	21.66%	25.06%	+0.21	+5.2	22
2015-10-04	@ Lyngby	L 0-1	39.3	1346	1408	25.06%	21.66%	53.28%	-0.21	-5.2	11
2015-10-05	Naestved	D 1-1	56.8	1369	1333	49.93%	22.02%	28.05%	+0.13	-0.9	14
2015-10-05	@ Silkeborg	D 1-1	56.8	1333	1369	28.05%	22.02%	49.93%	-0.13	+0.9	8
2015-10-09	Horsens	L 0-2	38.2	1341	1398	37.14%	22.49%	40.37%	-0.13	-13.5	11
2015-10-09	@ Skive	W 2-0	38.2	1398	1341	40.37%	22.49%	37.14%	+0.13	+13.5	21
2015-10-11	Lyngby	L 0-1	47.4	1419	1414	45.93%	22.31%	31.76%	+0.05	-8.5	14
2015-10-11	@ Helsingor	W 1-0	47.4	1414	1419	31.76%	22.31%	45.93%	-0.05	+8.5	25
2015-10-14	Vejle BK	L 0-1	45.1	1377	1395	42.70%	22.44%	34.85%	-0.02	-8.0	17
2015-10-14	@ Fredericia	W 1-0	45.1	1395	1377	34.85%	22.44%	42.70%	+0.02	+8.0	21
2015-10-17	Helsingor	D 0-0	53.4	1368	1410	39.23%	22.50%	38.27%	-0.08	-0.0	15
2015-10-17	@ Silkeborg	D 0-0	53.4	1410	1368	38.27%	22.50%	39.23%	+0.08	+0.0	15
2015-10-18	FC Roskilde	W 3-1	40.9	1422	1365	52.59%	21.75%	25.66%	+0.19	+8.6	28
2015-10-18	@ Lyngby	L 1-3	40.9	1365	1422	25.66%	21.75%	52.59%	-0.19	-8.6	13
2015-10-18	Fredericia	L 0-1	45.9	1382	1369	46.84%	22.26%	30.90%	+0.07	-8.6	7
2015-10-18	@ Vestsjaelland	W 1-0	45.9	1369	1382	30.90%	22.26%	46.84%	-0.07	+8.6	20
2015-10-18	HB Koge	W 1-0	41.7	1334	1358	41.92%	22.46%	35.61%	-0.03	+6.9	11
2015-10-18	@ Naestved	L 0-1	41.7	1358	1334	35.61%	22.46%	41.92%	+0.03	-6.9	15
2015-10-18	Skive	L 2-3	57.9	1403	1327	54.91%	21.45%	23.64%	+0.25	-8.9	21
2015-10-18	@ Vejle BK	W 3-2	57.9	1327	1403	23.64%	21.45%	54.91%	-0.25	+8.8	14
2015-10-18	Vendsyssel	L 0-1	47.1	1412	1401	46.68%	22.27%	31.06%	+0.07	-8.6	21
2015-10-18	@ Horsens	W 1-0	47.1	1401	1412	31.06%	22.27%	46.68%	-0.07	+8.6	25
2015-10-21	Silkeborg	L 1-2	49.6	1351	1368	42.82%	22.44%	34.74%	-0.01	-7.5	15
2015-10-21	@ HB Koge	W 2-1	49.6	1368	1351	34.74%	22.44%	42.82%	+0.01	+7.5	18
2015-10-22	Vestsjaelland	D 4-4	69.6	1431	1373	52.72%	21.73%	25.55%	+0.20	-0.6	29
2015-10-22	@ Lyngby	D 4-4	69.6	1373	1431	25.55%	21.73%	52.72%	-0.20	+0.6	8
2015-10-22	Vejle BK	W 2-0	37.4	1409	1395	47.19%	22.23%	30.58%	+0.08	+11.5	28
2015-10-22	@ Vendsyssel	L 0-2	37.4	1395	1409	30.58%	22.23%	47.19%	-0.08	-11.5	21
2015-10-23	Fredericia	L 1-2	48.5	1336	1378	39.34%	22.50%	38.16%	-0.08	-7.1	14
2015-10-23	@ Skive	W 2-1	48.5	1378	1336	38.16%	22.50%	39.34%	+0.08	+7.0	23
2015-10-25	Horsens	L 0-1	42.9	1343	1403	36.76%	22.49%	40.76%	-0.13	-7.1	15
2015-10-25	@ HB Koge	W 1-0	42.9	1403	1343	40.76%	22.49%	36.76%	+0.13	+7.1	24
2015-10-25	Naestved	W 1-0	39.0	1411	1341	54.15%	21.55%	24.30%	+0.23	+5.0	18
2015-10-25	@ Helsingor	L 0-1	39.0	1341	1411	24.30%	21.55%	54.15%	-0.23	-5.0	11
2015-10-25	Silkeborg	L 2-3	54.6	1357	1375	42.61%	22.45%	34.94%	-0.02	-7.1	13
2015-10-25	@ FC Roskilde	W 3-2	54.6	1375	1357	34.94%	22.45%	42.61%	+0.02	+7.1	21
2015-10-28	Naestved	D 0-0	53.4	1421	1336	55.96%	21.29%	22.74%	+0.27	-1.5	29
2015-10-28	@ Vendsyssel	D 0-0	53.4	1336	1421	22.74%	21.29%	55.96%	-0.27	+1.5	12
2015-10-31	Lyngby	L 1-2	50.1	1383	1430	38.51%	22.50%	38.99%	-0.10	-6.9	21
2015-10-31	@ Silkeborg	W 2-1	50.1	1430	1383	38.99%	22.50%	38.51%	+0.10	+6.9	32
2015-11-01	FC Roskilde	W 2-1	46.6	1337	1350	43.53%	22.42%	34.05%	+0.00	+6.3	15
2015-11-01	@ Naestved	L 1-2	46.6	1350	1337	34.05%	22.42%	43.53%	+0.00	-6.3	13
2015-11-01	HB Koge	W 3-1	40.0	1383	1336	51.38%	21.88%	26.74%	+0.17	+8.9	24
2015-11-01	@ Vejle BK	L 1-3	40.0	1336	1383	26.74%	21.88%	51.38%	-0.17	-8.9	15
2015-11-01	Helsingor	D 0-0	54.5	1410	1416	44.46%	22.38%	33.16%	+0.02	-0.5	25
2015-11-01	@ Horsens	D 0-0	54.5	1416	1410	33.16%	22.38%	44.46%	-0.02	+0.5	19
2015-11-01	Skive	L 0-1	46.3	1374	1329	51.03%	21.92%	27.05%	+0.16	-9.3	8
2015-11-01	@ Vestsjaelland	W 1-0	46.3	1329	1374	27.05%	21.92%	51.03%	-0.16	+9.3	17
2015-11-01	Vendsyssel	L 1-2	50.5	1385	1419	40.42%	22.49%	37.09%	-0.06	-7.2	23
2015-11-01	@ Fredericia	W 2-1	50.5	1419	1385	37.09%	22.49%	40.42%	+0.06	+7.2	32
2015-11-05	Naestved	W 2-0	34.0	1437	1344	56.93%	21.14%	21.93%	+0.30	+8.6	35
2015-11-05	@ Lyngby	L 0-2	34.0	1344	1437	21.93%	21.14%	56.93%	-0.30	-8.6	15
2015-11-06	Horsens	L 0-1	42.7	1343	1410	35.86%	22.47%	41.67%	-0.15	-7.0	13
2015-11-06	@ FC Roskilde	W 1-0	42.7	1410	1343	41.67%	22.47%	35.86%	+0.15	+7.0	28
2015-11-08	Fredericia	W 2-1	48.4	1327	1378	38.05%	22.50%	39.45%	-0.11	+7.1	18
2015-11-08	@ HB Koge	L 1-2	48.4	1378	1327	39.45%	22.50%	38.05%	+0.11	-7.1	23
2015-11-08	Skive	W 1-0	38.6	1427	1338	56.32%	21.24%	22.44%	+0.28	+4.7	35
2015-11-08	@ Vendsyssel	L 0-1	38.6	1338	1427	22.44%	21.24%	56.32%	-0.28	-4.7	17
2015-11-08	Vejle BK	L 0-3	40.4	1416	1392	48.42%	22.15%	29.43%	+0.10	-24.3	19
2015-11-08	@ Helsingor	W 3-0	40.4	1392	1416	29.43%	22.15%	48.42%	-0.10	+24.3	27
2015-11-12	Vendsyssel	D 0-0	53.8	1364	1431	35.78%	22.47%	41.75%	-0.15	+0.3	9
2015-11-12	@ Vestsjaelland	D 0-0	53.8	1431	1364	41.75%	22.47%	35.78%	+0.15	-0.3	36
2015-11-13	FC Roskilde	W 4-2	43.1	1416	1336	55.38%	21.38%	23.24%	+0.26	+7.1	30
2015-11-13	@ Vejle BK	L 2-4	43.1	1336	1416	23.24%	21.38%	55.38%	-0.26	-7.1	13
2015-11-15	Helsingor	D 2-2	63.1	1371	1392	42.30%	22.46%	35.25%	-0.02	-0.2	24
2015-11-15	@ Fredericia	D 2-2	63.1	1392	1371	35.25%	22.46%	42.30%	+0.02	+0.2	20
2015-11-15	Silkeborg	L 1-3	43.9	1335	1376	39.52%	22.50%	37.98%	-0.08	-12.3	15
2015-11-15	@ Naestved	W 3-1	43.9	1376	1335	37.98%	22.50%	39.52%	+0.08	+12.3	24
2015-11-16	Lyngby	W 2-1	50.8	1417	1446	41.15%	22.48%	36.37%	-0.05	+6.6	31
2015-11-16	@ Horsens	L 1-2	50.8	1446	1417	36.37%	22.48%	41.15%	+0.05	-6.6	35
2015-11-18	Vestsjaelland	D 1-1	56.6	1329	1365	40.27%	22.49%	37.23%	-0.06	-0.1	14
2015-11-18	@ FC Roskilde	D 1-1	56.6	1365	1329	37.23%	22.49%	40.27%	+0.06	+0.1	10
2015-11-19	Vejle BK	W 1-0	43.6	1439	1423	47.32%	22.23%	30.45%	+0.08	+6.1	38
2015-11-19	@ Lyngby	L 0-1	43.6	1423	1439	30.45%	22.23%	47.32%	-0.08	-6.1	30
2015-11-22	Vestsjaelland	L 2-3	53.0	1323	1365	39.34%	22.50%	38.16%	-0.08	-6.7	15
2015-11-22	@ Naestved	W 3-2	53.0	1365	1323	38.16%	22.50%	39.34%	+0.08	+6.7	13
2015-11-25	HB Koge	L 0-1	44.3	1334	1334	45.15%	22.35%	32.50%	+0.03	-8.4	17
2015-11-25	@ Skive	W 1-0	44.3	1334	1334	32.50%	22.35%	45.15%	-0.03	+8.4	21
2015-11-28	Silkeborg	L 1-3	48.4	1417	1388	49.11%	22.09%	28.80%	+0.12	-14.7	30
2015-11-28	@ Vejle BK	W 3-1	48.4	1388	1417	28.80%	22.09%	49.11%	-0.12	+14.6	27
2015-11-29	FC Roskilde	L 0-1	44.0	1325	1329	44.69%	22.37%	32.93%	+0.02	-8.3	17
2015-11-29	@ Skive	W 1-0	44.0	1329	1325	32.93%	22.37%	44.69%	-0.02	+8.3	17
2015-11-29	HB Koge	L 0-2	41.2	1371	1343	49.04%	22.10%	28.86%	+0.12	-16.9	13
2015-11-29	@ Vestsjaelland	W 2-0	41.2	1343	1371	28.86%	22.10%	49.04%	-0.12	+16.9	24
2015-11-29	Helsingor	L 2-3	58.6	1431	1392	50.35%	21.98%	27.66%	+0.14	-8.2	36
2015-11-29	@ Vendsyssel	W 3-2	58.6	1392	1431	27.66%	21.98%	50.35%	-0.14	+8.2	23
2015-11-29	Lyngby	L 2-4	48.2	1371	1445	34.74%	22.44%	42.82%	-0.17	-9.9	24
2015-11-29	@ Fredericia	W 4-2	48.2	1445	1371	42.82%	22.44%	34.74%	+0.17	+9.9	41
2015-11-29	Naestved	L 1-3	49.6	1423	1316	58.47%	20.87%	20.65%	+0.33	-17.1	31
2015-11-29	@ Horsens	W 3-1	49.6	1316	1423	20.65%	20.87%	58.47%	-0.33	+17.1	18
2015-12-03	Fredericia	D 4-4	67.6	1337	1361	41.97%	22.46%	35.56%	-0.03	-0.1	18
2015-12-03	@ FC Roskilde	D 4-4	67.6	1361	1337	35.56%	22.46%	41.97%	+0.03	+0.1	25
2015-12-03	Horsens	D 1-1	58.7	1403	1406	44.70%	22.37%	32.92%	+0.02	-0.5	28
2015-12-03	@ Silkeborg	D 1-1	58.7	1406	1403	32.92%	22.37%	44.70%	-0.02	+0.5	32
2016-03-06	Skive	W 2-1	43.3	1400	1317	55.74%	21.33%	22.93%	+0.27	+4.5	26
2016-03-06	@ Helsingor	L 1-2	43.3	1317	1400	22.93%	21.33%	55.74%	-0.27	-4.5	17
2016-03-06	Vendsyssel	W 1-0	44.9	1359	1423	36.28%	22.48%	41.24%	-0.14	+7.8	27
2016-03-06	@ HB Koge	L 0-1	44.9	1423	1359	41.24%	22.48%	36.28%	+0.14	-7.8	36
2016-03-10	Vestsjaelland	FW	50.9	1402	1354	51.45%	21.87%	26.67%	+0.17	+0.0	31
2016-03-10	@ Silkeborg	FL	50.9	1354	1402	26.67%	21.87%	51.45%	-0.17	+0.0	13
2016-03-10	Fredericia	L 0-1	47.2	1402	1361	50.64%	21.95%	27.40%	+0.15	-9.2	31
2016-03-10	@ Silkeborg	W 1-0	47.2	1361	1402	27.40%	21.95%	50.64%	-0.15	+9.2	28
2016-03-13	Vestsjaelland	FW	51.0	1407	1354	52.02%	21.81%	26.17%	+0.18	+0.0	35
2016-03-13	@ Horsens	FL	51.0	1354	1407	26.17%	21.81%	52.02%	-0.18	+0.0	13
2016-03-13	HB Koge	L 1-3	48.0	1405	1367	50.16%	22.00%	27.84%	+0.14	-14.9	26
2016-03-13	@ Helsingor	W 3-1	48.0	1367	1405	27.84%	22.00%	50.16%	-0.14	+14.9	30
2016-03-13	Naestved	L 1-2	53.0	1403	1333	54.12%	21.56%	24.32%	+0.23	-9.2	30
2016-03-13	@ Vejle BK	W 2-1	53.0	1333	1403	24.32%	21.56%	54.12%	-0.23	+9.2	21
2016-03-13	Skive	D 1-1	58.5	1455	1313	62.22%	20.09%	17.68%	+0.43	-1.9	42
2016-03-13	@ Lyngby	D 1-1	58.5	1313	1455	17.68%	20.09%	62.22%	-0.43	+1.9	18
2016-03-13	Vendsyssel	W 3-1	45.7	1337	1415	34.32%	22.43%	43.26%	-0.18	+13.2	21
2016-03-13	@ FC Roskilde	L 1-3	45.7	1415	1337	43.26%	22.43%	34.32%	+0.18	-13.2	36
2016-03-17	Lyngby	L 0-2	41.0	1402	1453	37.96%	22.50%	39.54%	-0.11	-13.7	36
2016-03-17	@ Vendsyssel	W 2-0	41.0	1453	1402	39.54%	22.50%	37.96%	+0.11	+13.7	45
2016-03-20	Helsingor	FL	50.5	1354	1390	40.26%	22.49%	37.24%	-0.06	+0.0	13
2016-03-20	@ Vestsjaelland	FW	50.5	1390	1354	37.24%	22.49%	40.26%	+0.06	+0.0	29
2016-03-20	FC Roskilde	L 1-2	51.7	1382	1350	49.41%	22.07%	28.52%	+0.12	-8.5	30
2016-03-20	@ HB Koge	W 2-1	51.7	1350	1382	28.52%	22.07%	49.41%	-0.12	+8.5	24
2016-03-20	Horsens	W 4-3	55.9	1393	1407	43.37%	22.42%	34.21%	+0.00	+5.9	33
2016-03-20	@ Vejle BK	L 3-4	55.9	1407	1393	34.21%	22.42%	43.37%	+0.00	-5.9	35
2016-03-20	Naestved	W 2-1	45.3	1370	1342	48.89%	22.11%	29.00%	+0.11	+5.5	31
2016-03-20	@ Fredericia	L 1-2	45.3	1342	1370	29.00%	22.11%	48.89%	-0.11	-5.5	21
2016-03-20	Silkeborg	D 1-1	56.8	1315	1393	34.23%	22.42%	43.34%	-0.19	+0.4	19
2016-03-20	@ Skive	D 1-1	56.8	1393	1315	43.34%	22.42%	34.23%	+0.19	-0.4	32
2016-03-23	Vendsyssel	D 1-1	58.1	1393	1388	45.80%	22.32%	31.88%	+0.05	-0.5	33
2016-03-23	@ Silkeborg	D 1-1	58.1	1388	1393	31.88%	22.32%	45.80%	-0.05	+0.6	37
2016-03-24	Vejle BK	FL	50.7	1354	1399	38.91%	22.50%	38.59%	-0.09	+0.0	13
2016-03-24	@ Vestsjaelland	FW	50.7	1399	1354	38.59%	22.50%	38.91%	+0.09	+0.0	36
2016-03-24	Fredericia	W 3-0	34.0	1401	1376	48.57%	22.14%	29.29%	+0.11	+16.2	38
2016-03-24	@ Horsens	L 0-3	34.0	1376	1401	29.29%	22.14%	48.57%	-0.11	-16.2	31
2016-03-24	HB Koge	D 1-1	59.8	1467	1374	56.92%	21.14%	21.94%	+0.30	-1.5	46
2016-03-24	@ Lyngby	D 1-1	59.8	1374	1467	21.94%	21.14%	56.92%	-0.30	+1.5	31
2016-03-24	Helsingor	L 0-2	39.5	1359	1390	40.91%	22.48%	36.60%	-0.05	-14.5	24
2016-03-24	@ FC Roskilde	W 2-0	39.5	1390	1359	36.60%	22.48%	40.91%	+0.05	+14.5	32
2016-03-24	Skive	L 1-3	45.7	1337	1315	48.15%	22.17%	29.68%	+0.10	-14.4	21
2016-03-24	@ Naestved	W 3-1	45.7	1315	1337	29.68%	22.17%	48.15%	-0.10	+14.4	22
2016-03-28	Vestsjaelland	FW	50.1	1359	1354	45.88%	22.31%	31.81%	+0.05	+0.0	34
2016-03-28	@ Fredericia	FL	50.1	1354	1359	31.81%	22.31%	45.88%	-0.05	+0.0	13
2016-03-28	Lyngby	D 0-0	54.2	1344	1465	28.81%	22.09%	49.10%	-0.31	+0.9	25
2016-03-28	@ FC Roskilde	D 0-0	54.2	1465	1344	49.10%	22.09%	28.81%	+0.31	-0.9	47
2016-03-28	Naestved	W 2-0	34.1	1375	1322	52.07%	21.81%	26.12%	+0.18	+10.1	34
2016-03-28	@ HB Koge	L 0-2	34.1	1322	1375	26.12%	21.81%	52.07%	-0.18	-10.1	21
2016-03-28	Silkeborg	L 0-1	46.8	1404	1392	46.87%	22.25%	30.88%	+0.07	-8.6	32
2016-03-28	@ Helsingor	W 1-0	46.8	1392	1404	30.88%	22.25%	46.87%	-0.07	+8.6	36
2016-03-28	Vejle BK	L 0-3	35.1	1329	1399	35.37%	22.46%	42.17%	-0.16	-18.9	22
2016-03-28	@ Skive	W 3-0	35.1	1399	1329	42.17%	22.46%	35.37%	+0.16	+18.9	39
2016-03-31	Vendsyssel	D 0-0	54.2	1418	1389	49.13%	22.09%	28.78%	+0.12	-0.9	40
2016-03-31	@ Vejle BK	D 0-0	54.2	1389	1418	28.78%	22.09%	49.13%	-0.12	+0.9	38
2016-04-03	Lyngby	FL	52.1	1354	1465	30.13%	22.20%	47.67%	-0.27	+0.0	13
2016-04-03	@ Vestsjaelland	FW	52.1	1465	1354	47.67%	22.20%	30.13%	+0.27	+0.0	50
2016-04-03	FC Roskilde	W 6-2	37.7	1401	1345	52.40%	21.77%	25.83%	+0.19	+13.4	39
2016-04-03	@ Silkeborg	L 2-6	37.7	1345	1401	25.83%	21.77%	52.40%	-0.19	-13.4	25
2016-04-03	HB Koge	W 2-0	36.7	1417	1385	49.43%	22.07%	28.51%	+0.12	+10.9	41
2016-04-03	@ Horsens	L 0-2	36.7	1385	1417	28.51%	22.07%	49.43%	-0.12	-10.9	34
2016-04-03	Helsingor	W 1-0	44.5	1312	1396	33.57%	22.40%	44.03%	-0.20	+8.2	24
2016-04-03	@ Naestved	L 0-1	44.5	1396	1312	44.03%	22.40%	33.57%	+0.20	-8.2	32
2016-04-03	Skive	D 0-0	52.0	1359	1310	51.62%	21.86%	26.53%	+0.17	-1.1	35
2016-04-03	@ Fredericia	D 0-0	52.0	1310	1359	26.53%	21.86%	51.62%	-0.17	+1.1	23
2016-04-07	Silkeborg	L 0-2	45.6	1465	1414	51.82%	21.83%	26.35%	+0.18	-17.7	50
2016-04-07	@ Lyngby	W 2-0	45.6	1414	1465	26.35%	21.83%	51.82%	-0.18	+17.7	42
2016-04-10	Vestsjaelland	FW	49.4	1312	1354	39.19%	22.50%	38.31%	-0.09	+0.0	26
2016-04-10	@ Skive	FL	49.4	1354	1312	38.31%	22.50%	39.19%	+0.09	+0.0	13
2016-04-10	Fredericia	L 1-2	51.9	1390	1358	49.36%	22.07%	28.57%	+0.12	-8.5	38
2016-04-10	@ Vendsyssel	W 2-1	51.9	1358	1390	28.57%	22.07%	49.36%	-0.12	+8.5	38
2016-04-10	Horsens	L 0-3	38.0	1388	1428	39.55%	22.50%	37.95%	-0.08	-20.6	32
2016-04-10	@ Helsingor	W 3-0	38.0	1428	1388	37.95%	22.50%	39.55%	+0.08	+20.6	44
2016-04-10	Naestved	W 3-2	50.0	1332	1321	46.74%	22.26%	31.00%	+0.07	+5.5	28
2016-04-10	@ FC Roskilde	L 2-3	50.0	1321	1332	31.00%	22.26%	46.74%	-0.07	-5.5	24
2016-04-10	Vejle BK	L 1-2	49.8	1374	1417	39.18%	22.50%	38.32%	-0.09	-7.0	34
2016-04-10	@ HB Koge	W 2-1	49.8	1417	1374	38.32%	22.50%	39.18%	+0.09	+7.0	43
2016-04-14	Helsingor	L 2-3	58.5	1424	1367	52.65%	21.74%	25.61%	+0.20	-8.5	43
2016-04-14	@ Vejle BK	W 3-2	58.5	1367	1424	25.61%	21.74%	52.65%	-0.20	+8.5	35
2016-04-17	Silkeborg	FL	51.3	1354	1432	34.37%	22.43%	43.20%	-0.18	+0.0	13
2016-04-17	@ Vestsjaelland	FW	51.3	1432	1354	43.20%	22.43%	34.37%	+0.18	+0.0	45
2016-04-17	FC Roskilde	W 2-1	43.5	1448	1337	58.90%	20.79%	20.31%	+0.35	+4.0	47
2016-04-17	@ Horsens	L 1-2	43.5	1337	1448	20.31%	20.79%	58.90%	-0.35	-4.0	28
2016-04-17	HB Koge	L 0-3	38.1	1367	1367	45.13%	22.35%	32.52%	+0.03	-22.9	38
2016-04-17	@ Fredericia	W 3-0	38.1	1367	1367	32.52%	22.35%	45.13%	-0.03	+22.9	37
2016-04-17	Lyngby	L 0-2	35.5	1315	1447	27.54%	21.97%	50.49%	-0.34	-10.6	24
2016-04-17	@ Naestved	W 2-0	35.5	1447	1315	50.49%	21.97%	27.54%	+0.34	+10.6	53
2016-04-17	Vendsyssel	W 2-1	49.0	1312	1381	35.44%	22.46%	42.10%	-0.16	+7.5	29
2016-04-17	@ Skive	L 1-2	49.0	1381	1312	42.10%	22.46%	35.44%	+0.16	-7.5	38
2016-04-20	Horsens	L 0-3	36.7	1374	1452	34.16%	22.42%	43.42%	-0.19	-18.3	38
2016-04-20	@ Vendsyssel	W 3-0	36.7	1452	1374	43.42%	22.42%	34.16%	+0.19	+18.3	50
2016-04-21	Vejle BK	W 2-0	40.3	1333	1416	33.71%	22.40%	43.89%	-0.20	+15.4	31
2016-04-21	@ FC Roskilde	L 0-2	40.3	1416	1333	43.89%	22.40%	33.71%	+0.20	-15.4	43
2016-04-24	Vestsjaelland	FW	50.0	1355	1354	45.31%	22.34%	32.35%	+0.04	+0.0	41
2016-04-24	@ Vendsyssel	FL	50.0	1354	1355	32.35%	22.34%	45.31%	-0.04	+0.0	13
2016-04-24	Fredericia	L 1-2	51.5	1375	1344	49.40%	22.07%	28.53%	+0.12	-8.5	35
2016-04-24	@ Helsingor	W 2-1	51.5	1344	1375	28.53%	22.07%	49.40%	-0.12	+8.5	41
2016-04-24	Horsens	D 0-0	57.6	1457	1471	43.36%	22.42%	34.22%	+0.00	-0.4	54
2016-04-24	@ Lyngby	D 0-0	57.6	1471	1457	34.22%	22.42%	43.36%	+0.00	+0.4	51
2016-04-24	Naestved	W 3-0	29.7	1432	1304	60.65%	20.44%	18.91%	+0.39	+10.9	48
2016-04-24	@ Silkeborg	L 0-3	29.7	1304	1432	18.91%	20.44%	60.65%	-0.39	-10.9	24
2016-04-24	Skive	D 1-1	57.1	1390	1319	54.33%	21.53%	24.14%	+0.23	-1.2	38
2016-04-24	@ HB Koge	D 1-1	57.1	1319	1390	24.14%	21.53%	54.33%	-0.23	+1.2	30
2016-04-28	Silkeborg	W 1-0	46.2	1355	1443	33.02%	22.38%	44.60%	-0.21	+8.3	44
2016-04-28	@ Vendsyssel	L 0-1	46.2	1443	1355	44.60%	22.38%	33.02%	+0.21	-8.3	48
2016-05-01	Vestsjaelland	FW	50.9	1400	1354	51.23%	21.90%	26.88%	+0.16	+0.0	46
2016-05-01	@ Vejle BK	FL	50.9	1354	1400	26.88%	21.90%	51.23%	-0.16	+0.0	13
2016-05-01	FC Roskilde	W 3-1	41.1	1367	1349	47.63%	22.20%	30.16%	+0.09	+9.9	38
2016-05-01	@ Helsingor	L 1-3	41.1	1349	1367	30.16%	22.20%	47.63%	-0.09	-9.9	31
2016-05-01	Horsens	L 0-2	37.4	1352	1471	29.07%	22.12%	48.81%	-0.30	-11.1	41
2016-05-01	@ Fredericia	W 2-0	37.4	1471	1352	48.81%	22.12%	29.07%	+0.30	+11.1	54
2016-05-01	Lyngby	L 0-3	37.6	1389	1457	35.62%	22.46%	41.91%	-0.16	-19.0	38
2016-05-01	@ HB Koge	W 3-0	37.6	1457	1389	41.91%	22.46%	35.62%	+0.16	+19.0	57
2016-05-01	Naestved	L 2-4	49.3	1320	1294	48.77%	22.12%	29.11%	+0.11	-13.0	30
2016-05-01	@ Skive	W 4-2	49.3	1294	1320	29.11%	22.12%	48.77%	-0.11	+13.0	27
2016-05-05	Fredericia	D 0-0	53.0	1400	1341	52.86%	21.72%	25.43%	+0.20	-1.2	47
2016-05-05	@ Vejle BK	D 0-0	53.0	1341	1400	25.43%	21.72%	52.86%	-0.20	+1.3	42
2016-05-08	FC Roskilde	FL	49.8	1354	1339	47.29%	22.23%	30.49%	+0.08	+0.0	13
2016-05-08	@ Vestsjaelland	FW	49.8	1339	1354	30.49%	22.23%	47.29%	-0.08	+0.0	34
2016-05-08	HB Koge	W 1-0	40.2	1434	1370	53.53%	21.63%	24.84%	+0.22	+5.1	51
2016-05-08	@ Silkeborg	L 0-1	40.2	1370	1434	24.84%	21.63%	53.53%	-0.22	-5.1	38
2016-05-08	Helsingor	W 1-0	40.2	1476	1377	57.59%	21.03%	21.38%	+0.31	+4.5	60
2016-05-08	@ Lyngby	L 0-1	40.2	1377	1476	21.38%	21.03%	57.59%	-0.31	-4.5	38
2016-05-08	Skive	W 3-2	46.5	1482	1307	65.42%	19.28%	15.31%	+0.53	+2.9	57
2016-05-08	@ Horsens	L 2-3	46.5	1307	1482	15.31%	19.28%	65.42%	-0.53	-2.9	30
2016-05-08	Vendsyssel	L 0-1	42.0	1307	1364	37.18%	22.49%	40.33%	-0.13	-7.2	27
2016-05-08	@ Naestved	W 1-0	42.0	1364	1307	40.33%	22.49%	37.18%	+0.13	+7.2	47
2016-05-11	Vestsjaelland	FW	50.3	1372	1354	47.61%	22.21%	30.18%	+0.08	+0.0	41
2016-05-11	@ Helsingor	FL	50.3	1354	1372	30.18%	22.21%	47.61%	-0.08	+0.0	13
2016-05-11	Fredericia	D 0-0	51.5	1299	1343	39.14%	22.50%	38.35%	-0.09	-0.0	28
2016-05-11	@ Naestved	D 0-0	51.5	1343	1299	38.35%	22.50%	39.14%	+0.09	+0.0	43
2016-05-11	HB Koge	D 2-2	62.2	1339	1365	41.61%	22.47%	35.92%	-0.04	-0.2	35
2016-05-11	@ FC Roskilde	D 2-2	62.2	1365	1339	35.92%	22.47%	41.61%	+0.04	+0.2	39
2016-05-11	Skive	W 3-1	37.4	1440	1304	61.47%	20.26%	18.26%	+0.41	+6.3	54
2016-05-11	@ Silkeborg	L 1-3	37.4	1304	1440	18.26%	20.26%	61.47%	-0.41	-6.3	30
2016-05-11	Vejle BK	L 0-1	51.6	1485	1399	56.10%	21.27%	22.63%	+0.28	-10.1	57
2016-05-11	@ Horsens	W 1-0	51.6	1399	1485	22.63%	21.27%	56.10%	-0.28	+10.1	50
2016-05-11	Vendsyssel	L 0-1	51.3	1480	1371	58.77%	20.82%	20.42%	+0.34	-10.5	60
2016-05-11	@ Lyngby	W 1-0	51.3	1371	1480	20.42%	20.82%	58.77%	-0.34	+10.5	50
2016-05-16	Horsens	FL	52.4	1354	1475	28.85%	22.10%	49.05%	-0.30	+0.0	13
2016-05-16	@ Vestsjaelland	FW	52.4	1475	1354	49.05%	22.10%	28.85%	+0.30	+0.0	60
2016-05-16	FC Roskilde	W 3-2	49.6	1381	1339	50.79%	21.94%	27.27%	+0.15	+5.0	53
2016-05-16	@ Vendsyssel	L 2-3	49.6	1339	1381	27.27%	21.94%	50.79%	-0.15	-5.0	35
2016-05-16	Helsingor	L 0-1	44.9	1365	1372	44.19%	22.39%	33.42%	+0.01	-8.2	39
2016-05-16	@ HB Koge	W 1-0	44.9	1372	1365	33.42%	22.39%	44.19%	-0.01	+8.2	44
2016-05-16	Lyngby	D 2-2	63.6	1298	1470	23.22%	21.38%	55.40%	-0.45	+1.0	31
2016-05-16	@ Skive	D 2-2	63.6	1470	1298	55.40%	21.38%	23.22%	+0.45	-1.0	61
2016-05-16	Naestved	W 2-1	42.3	1409	1299	58.76%	20.82%	20.42%	+0.34	+4.1	53
2016-05-16	@ Vejle BK	L 1-2	42.3	1299	1409	20.42%	20.82%	58.76%	-0.34	-4.0	28
2016-05-16	Silkeborg	L 1-4	39.6	1343	1446	30.99%	22.26%	46.75%	-0.26	-14.3	43
2016-05-16	@ Fredericia	W 4-1	39.6	1446	1343	46.75%	22.26%	30.99%	+0.26	+14.3	57
2016-05-22	Naestved	FL	49.5	1354	1295	52.87%	21.71%	25.41%	+0.20	+0.0	13
2016-05-22	@ Vestsjaelland	FW	49.5	1295	1354	25.41%	21.71%	52.87%	-0.20	+0.0	31
2016-05-22	FC Roskilde	D 3-3	65.5	1328	1334	44.44%	22.38%	33.18%	+0.02	-0.3	44
2016-05-22	@ Fredericia	D 3-3	65.5	1334	1328	33.18%	22.38%	44.44%	-0.02	+0.3	36
2016-05-22	HB Koge	L 0-1	46.4	1386	1357	49.12%	22.09%	28.79%	+0.12	-9.0	53
2016-05-22	@ Vendsyssel	W 1-0	46.4	1357	1386	28.79%	22.09%	49.12%	-0.12	+9.0	42
2016-05-22	Helsingor	L 0-1	41.0	1299	1381	33.80%	22.41%	43.79%	-0.19	-6.6	31
2016-05-22	@ Skive	W 1-0	41.0	1381	1299	43.79%	22.41%	33.80%	+0.19	+6.7	47
2016-05-22	Lyngby	L 3-4	59.1	1413	1469	37.33%	22.49%	40.17%	-0.12	-6.3	53
2016-05-22	@ Vejle BK	W 4-3	59.1	1469	1413	40.17%	22.49%	37.33%	+0.12	+6.3	64
2016-05-22	Silkeborg	L 1-3	51.7	1475	1460	47.22%	22.23%	30.55%	+0.08	-14.2	60
2016-05-22	@ Horsens	W 3-1	51.7	1460	1475	30.55%	22.23%	47.22%	-0.08	+14.2	60
2016-05-28	Vestsjaelland	FW	50.2	1366	1354	46.73%	22.26%	31.00%	+0.07	+0.0	45
2016-05-28	@ HB Koge	FL	50.2	1354	1366	31.00%	22.26%	46.73%	-0.07	+0.0	13
2016-05-28	Fredericia	L 2-3	61.2	1475	1328	62.71%	19.98%	17.32%	+0.45	-10.0	64
2016-05-28	@ Lyngby	W 3-2	61.2	1328	1475	17.32%	19.98%	62.71%	-0.45	+10.0	47
2016-05-28	Horsens	W 5-1	44.8	1295	1461	23.87%	21.49%	54.65%	-0.43	+29.4	34
2016-05-28	@ Naestved	L 1-5	44.8	1461	1295	54.65%	21.49%	23.87%	+0.43	-29.4	60
2016-05-28	Skive	W 4-3	51.2	1334	1292	50.70%	21.95%	27.35%	+0.15	+4.9	39
2016-05-28	@ FC Roskilde	L 3-4	51.2	1292	1334	27.35%	21.95%	50.70%	-0.15	-4.9	31
2016-05-28	Vejle BK	W 5-0	36.2	1474	1407	53.88%	21.59%	24.53%	+0.22	+22.3	63
2016-05-28	@ Silkeborg	L 0-5	36.2	1407	1474	24.53%	21.59%	53.88%	-0.22	-22.3	53
2016-05-28	Vendsyssel	L 0-2	41.4	1387	1377	46.55%	22.27%	31.17%	+0.06	-16.2	47
2016-05-28	@ Helsingor	W 2-0	41.4	1377	1387	31.17%	22.27%	46.55%	-0.06	+16.2	56

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	2016-05-28	17.32%	Fredericia	1328	3	@ Lyngby	1475	2
2	2016-05-11	20.42%	Vendsyssel	1371	1	@ Lyngby	1480	0
3	2015-11-29	20.65%	Naestved	1316	3	@ Horsens	1423	1
4	2016-05-11	22.63%	Vejle BK	1399	1	@ Horsens	1485	0
5	2015-10-18	23.64%	Skive	1327	3	@ Vejle BK	1403	2
6	2016-05-28	23.87%	@ Naestved	1295	5	Horsens	1461	1
7	2016-03-13	24.32%	Naestved	1333	2	@ Vejle BK	1403	1
8	2016-04-14	25.61%	Helsingor	1367	3	@ Vejle BK	1424	2
9	2015-10-04	25.65%	HB Koge	1348	1	@ Vejle BK	1405	0
10	2016-04-07	26.35%	Silkeborg	1414	2	@ Lyngby	1465	0
11	2015-09-13	26.45%	Naestved	1333	2	@ Vestsjaelland	1383	0
12	2015-11-01	27.05%	Skive	1329	1	@ Vestsjaelland	1374	0
13	2016-03-10	27.40%	Fredericia	1361	1	@ Silkeborg	1402	0
14	2015-11-29	27.66%	Helsingor	1392	3	@ Vendsyssel	1431	2
15	2016-03-13	27.84%	HB Koge	1367	3	@ Helsingor	1405	1
16	2016-03-20	28.52%	FC Roskilde	1350	2	@ HB Koge	1382	1
17	2016-04-24	28.53%	Fredericia	1344	2	@ Helsingor	1375	1
18	2016-04-10	28.57%	Fredericia	1358	2	@ Vendsyssel	1390	1
19	2016-05-22	28.79%	HB Koge	1357	1	@ Vendsyssel	1386	0
20	2015-11-28	28.80%	Silkeborg	1388	3	@ Vejle BK	1417	1
21	2015-11-29	28.86%	HB Koge	1343	2	@ Vestsjaelland	1371	0
22	2016-05-01	29.11%	Naestved	1294	4	@ Skive	1320	2
23	2015-11-08	29.43%	Vejle BK	1392	3	@ Helsingor	1416	0
24	2016-03-24	29.68%	Skive	1315	3	@ Naestved	1337	1
25	2015-09-27	30.04%	Skive	1323	1	@ Naestved	1342	0

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	2016-05-28	29.37	@ Naestved	5	1295	23.87%	Horsens	1	1461	54.65%	21.49%
2	2015-08-02	27.11	@ Fredericia	4	1337	37.54%	FC Roskilde	0	1391	39.97%	22.50%
3	2015-09-20	26.78	Helsingor	4	1416	38.16%	@ HB Koge	0	1374	39.34%	22.50%
4	2015-11-08	24.28	Vejle BK	3	1392	29.43%	@ Helsingor	0	1416	48.42%	22.15%
5	2016-04-17	22.89	HB Koge	3	1367	32.52%	@ Fredericia	0	1367	45.13%	22.35%
6	2015-07-26	22.44	@ FC Roskilde	3	1369	33.56%	Helsingor	0	1452	44.04%	22.40%
7	2015-10-04	22.37	@ Vestsjaelland	3	1359	33.72%	Helsingor	0	1441	43.88%	22.41%
8	2016-05-28	22.35	@ Silkeborg	5	1474	53.88%	Vejle BK	0	1407	24.53%	21.59%
9	2016-04-10	20.59	Horsens	3	1428	37.95%	@ Helsingor	0	1388	39.55%	22.50%
10	2016-05-01	18.97	Lyngby	3	1457	41.91%	@ HB Koge	0	1389	35.62%	22.46%
11	2016-03-28	18.86	Vejle BK	3	1399	42.17%	@ Skive	0	1329	35.37%	22.46%
12	2015-09-13	18.74	@ Horsens	3	1373	42.48%	HB Koge	0	1392	35.07%	22.45%
13	2016-04-20	18.34	Horsens	3	1452	43.42%	@ Vendsyssel	0	1374	34.16%	22.42%
14	2016-04-07	17.73	Silkeborg	2	1414	26.35%	@ Lyngby	0	1465	51.82%	21.83%
15	2015-09-13	17.69	Naestved	2	1333	26.45%	@ Vestsjaelland	0	1383	51.71%	21.85%
16	2015-08-28	17.25	@ Lyngby	4	1391	38.25%	Helsingor	1	1441	39.25%	22.50%
17	2015-11-29	17.08	Naestved	3	1316	20.65%	@ Horsens	1	1423	58.47%	20.87%
18	2015-08-07	16.93	@ Vejle BK	3	1365	46.79%	Horsens	0	1353	30.95%	22.26%
19	2015-11-29	16.91	HB Koge	2	1343	28.86%	@ Vestsjaelland	0	1371	49.04%	22.10%
20	2016-05-28	16.18	Vendsyssel	2	1377	31.17%	@ Helsingor	0	1387	46.55%	22.27%
21	2016-03-24	16.17	@ Horsens	3	1401	48.57%	Fredericia	0	1376	29.29%	22.14%
22	2015-07-26	15.73	Vendsyssel	2	1361	32.62%	@ Skive	0	1359	45.02%	22.36%
23	2015-09-17	15.54	Vejle BK	2	1390	33.25%	@ Silkeborg	0	1384	44.36%	22.39%
24	2016-04-21	15.41	@ FC Roskilde	2	1333	33.71%	Vejle BK	0	1416	43.89%	22.40%
25	2015-08-28	15.37	Horsens	2	1357	33.83%	@ Skive	0	1347	43.76%	22.41%

Most & Least Exciting Games

The season’s games ranked by excitement rating (0–100) — a blend of closeness, upset, team quality, and scoring. An asterisk (*) after the date indicates a playoff game.

#	Date	Excitement	Winning Team				Losing Team				Tie %
#	Date	Excitement	Team	Score	Elo	Win %	Team	Score	Elo	Win %	Tie %
1	2015-10-22	69.6	@ Lyngby	4	1431	52.72%	Vestsjaelland	4	1373	25.55%	21.73%
2	2015-12-03	67.6	@ FC Roskilde	4	1337	41.97%	Fredericia	4	1361	35.56%	22.46%
3	2016-05-22	65.5	@ Fredericia	3	1328	44.44%	FC Roskilde	3	1334	33.18%	22.38%
4	2015-09-13	64.1	@ Helsingor	2	1416	48.94%	Vendsyssel	2	1388	28.95%	22.11%
5	2015-08-01	63.6	@ Vestsjaelland	2	1414	51.77%	Vejle BK	2	1364	26.39%	21.84%
6	2016-05-16	63.6	@ Skive	2	1298	23.22%	Lyngby	2	1470	55.40%	21.38%
7	2015-11-15	63.1	@ Fredericia	2	1371	42.30%	Helsingor	2	1392	35.25%	22.46%
8	2015-09-27	62.8	@ FC Roskilde	2	1372	45.64%	Silkeborg	2	1368	32.03%	22.33%
9	2016-05-11	62.2	@ FC Roskilde	2	1339	41.61%	HB Koge	2	1365	35.92%	22.47%
10	2016-05-28	61.2	Fredericia	3	1328	17.32%	@ Lyngby	2	1475	62.71%	19.98%
11	2016-03-24	59.8	@ Lyngby	1	1467	56.92%	HB Koge	1	1374	21.94%	21.14%
12	2015-09-27	59.6	@ Helsingor	1	1442	51.10%	Horsens	1	1397	26.99%	21.91%
13	2016-05-22	59.1	Lyngby	4	1469	40.17%	@ Vejle BK	3	1413	37.33%	22.49%
14	2015-12-03	58.7	@ Silkeborg	1	1403	44.70%	Horsens	1	1406	32.92%	22.37%
15	2015-11-29	58.6	Helsingor	3	1392	27.66%	@ Vendsyssel	2	1431	50.35%	21.98%
16	2015-09-26	58.5	@ Vejle BK	1	1406	46.68%	Vendsyssel	1	1395	31.05%	22.27%
17	2016-03-13	58.5	@ Lyngby	1	1455	62.22%	Skive	1	1313	17.68%	20.09%
18	2016-04-14	58.5	Helsingor	3	1367	25.61%	@ Vejle BK	2	1424	52.65%	21.74%
19	2015-09-30	58.3	@ Vendsyssel	1	1395	44.77%	Horsens	1	1398	32.87%	22.37%
20	2015-09-10	58.1	@ Fredericia	1	1399	47.30%	Silkeborg	1	1383	30.47%	22.23%
21	2016-03-23	58.1	@ Silkeborg	1	1393	45.80%	Vendsyssel	1	1388	31.88%	22.32%
22	2015-10-03	58.0	@ Horsens	1	1399	48.18%	Fredericia	1	1377	29.66%	22.17%
23	2015-10-18	57.9	Skive	3	1327	23.64%	@ Vejle BK	2	1403	54.91%	21.45%
24	2015-08-02	57.8	@ Vendsyssel	1	1376	43.24%	Silkeborg	1	1391	34.33%	22.43%
25	2015-09-27	57.7	@ Vestsjaelland	1	1359	38.25%	Lyngby	1	1409	39.25%	22.50%

#	Date	Excitement	Winning Team				Losing Team				Tie %
#	Date	Excitement	Team	Score	Elo	Win %	Team	Score	Elo	Win %	Tie %
1	2016-04-24	29.7	@ Silkeborg	3	1432	60.65%	Naestved	0	1304	18.91%	20.44%
2	2015-08-07	33.5	@ Vejle BK	3	1365	46.79%	Horsens	0	1353	30.95%	22.26%
3	2015-11-05	34.0	@ Lyngby	2	1437	56.93%	Naestved	0	1344	21.93%	21.14%
4	2016-03-24	34.0	@ Horsens	3	1401	48.57%	Fredericia	0	1376	29.29%	22.14%
5	2016-03-28	34.1	@ HB Koge	2	1375	52.07%	Naestved	0	1322	26.12%	21.81%
6	2016-03-28	35.1	Vejle BK	3	1399	42.17%	@ Skive	0	1329	35.37%	22.46%
7	2016-04-17	35.5	Lyngby	2	1447	50.49%	@ Naestved	0	1315	27.54%	21.97%
8	2015-09-13	35.6	@ Horsens	3	1373	42.48%	HB Koge	0	1392	35.07%	22.45%
9	2016-05-28	36.2	@ Silkeborg	5	1474	53.88%	Vejle BK	0	1407	24.53%	21.59%
10	2015-08-02	36.5	@ Fredericia	4	1337	37.54%	FC Roskilde	0	1391	39.97%	22.50%
11	2015-08-16	36.7	@ HB Koge	2	1361	44.49%	Naestved	0	1366	33.13%	22.38%
12	2016-04-03	36.7	@ Horsens	2	1417	49.43%	HB Koge	0	1385	28.51%	22.07%
13	2016-04-20	36.7	Horsens	3	1452	43.42%	@ Vendsyssel	0	1374	34.16%	22.42%
14	2015-09-20	37.4	Helsingor	4	1416	38.16%	@ HB Koge	0	1374	39.34%	22.50%
15	2015-10-22	37.4	@ Vendsyssel	2	1409	47.19%	Vejle BK	0	1395	30.58%	22.23%
16	2016-05-01	37.4	Horsens	2	1471	48.81%	@ Fredericia	0	1352	29.07%	22.12%
17	2016-05-11	37.4	@ Silkeborg	3	1440	61.47%	Skive	1	1304	18.26%	20.26%
18	2016-05-01	37.6	Lyngby	3	1457	41.91%	@ HB Koge	0	1389	35.62%	22.46%
19	2016-04-03	37.7	@ Silkeborg	6	1401	52.40%	FC Roskilde	2	1345	25.83%	21.77%
20	2015-08-23	37.9	@ HB Koge	2	1373	42.74%	Vendsyssel	0	1391	34.82%	22.44%
21	2016-04-10	38.0	Horsens	3	1428	37.95%	@ Helsingor	0	1388	39.55%	22.50%
22	2016-04-17	38.1	HB Koge	3	1367	32.52%	@ Fredericia	0	1367	45.13%	22.35%
23	2015-10-09	38.2	Horsens	2	1398	40.37%	@ Skive	0	1341	37.14%	22.49%
24	2015-08-19	38.6	@ Horsens	2	1343	38.62%	Silkeborg	0	1390	38.88%	22.50%
25	2015-11-08	38.6	@ Vendsyssel	1	1427	56.32%	Skive	0	1338	22.44%	21.24%