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HB Køge 0 - 2 Nykøbing

15.11.2019 - 17:30

Stadium : Capelli Sport Stadion, Køge Idrætspark (Køge)

Finished

Table Corners Cards 1st Half 2nd Half Over Under Goals Clean Sheet BTTS Match Scoring / Conceding First

Over 2.5
Over 0.5
Over 1.5
Over 3.5
Over 4.5
Over 5.5

#	Team	MP	Over 2.5	Overall	Home	Away
1	Viborg	33	21	64%	65%	63%
2	Vejle	33	17	52%	53%	50%
3	Fremad Amager	33	20	61%	59%	63%
4	Fredericia	33	23	70%	82%	56%
5	HB Køge	33	17	52%	50%	53%
6	Skive Women	33	16	48%	56%	41%
7	Vendsyssel	33	12	36%	18%	56%
8	Nykøbing	33	23	70%	69%	71%
9	Roskilde Women	33	20	61%	63%	59%
10	Næstved	33	13	39%	29%	50%
11	Hvidovre	33	15	45%	50%	41%
12	Kolding IF	33	19	58%	63%	53%

#	Team	MP	Over 0.5	Overall	Home	Away
1	Viborg	33	32	97%	94%	100%
2	Vejle	33	30	91%	100%	81%
3	Fremad Amager	33	29	88%	88%	88%
4	Fredericia	33	33	100%	100%	100%
5	HB Køge	33	31	94%	94%	94%
6	Skive Women	33	30	91%	94%	88%
7	Vendsyssel	33	31	94%	94%	94%
8	Nykøbing	33	30	91%	94%	88%
9	Roskilde Women	33	32	97%	100%	94%
10	Næstved	33	31	94%	88%	100%
11	Hvidovre	33	31	94%	88%	100%
12	Kolding IF	33	32	97%	94%	100%

#	Team	MP	Over 1.5	Overall	Home	Away
1	Viborg	33	29	88%	94%	81%
2	Vejle	33	24	73%	76%	69%
3	Fremad Amager	33	23	70%	76%	63%
4	Fredericia	33	29	88%	100%	75%
5	HB Køge	33	27	82%	81%	82%
6	Skive Women	33	25	76%	81%	71%
7	Vendsyssel	33	23	70%	53%	88%
8	Nykøbing	33	26	79%	81%	76%
9	Roskilde Women	33	29	88%	88%	88%
10	Næstved	33	24	73%	65%	81%
11	Hvidovre	33	27	82%	69%	94%
12	Kolding IF	33	28	85%	88%	82%

#	Team	MP	Over 1.5	Overall	Home	Away
1	Viborg	33	14	42%	41%	44%
2	Vejle	33	10	30%	29%	31%
3	Fremad Amager	33	13	39%	41%	38%
4	Fredericia	33	17	52%	59%	44%
5	HB Køge	33	11	33%	25%	41%
6	Skive Women	33	11	33%	44%	24%
7	Vendsyssel	33	5	15%	6%	25%
8	Nykøbing	33	19	58%	50%	65%
9	Roskilde Women	33	15	45%	50%	41%
10	Næstved	33	9	27%	18%	38%
11	Hvidovre	33	9	27%	38%	18%
12	Kolding IF	33	11	33%	38%	29%

#	Team	MP	Over 1.5	Overall	Home	Away
1	Viborg	33	8	24%	35%	13%
2	Vejle	33	7	21%	24%	19%
3	Fremad Amager	33	4	12%	18%	6%
4	Fredericia	33	7	21%	24%	19%
5	HB Køge	33	3	9%	0%	18%
6	Skive Women	33	6	18%	19%	18%
7	Vendsyssel	33	2	6%	0%	13%
8	Nykøbing	33	8	24%	19%	29%
9	Roskilde Women	33	5	15%	6%	24%
10	Næstved	33	2	6%	6%	6%
11	Hvidovre	33	6	18%	25%	12%
12	Kolding IF	33	4	12%	13%	12%

#	Team	MP	Over 1.5	Overall	Home	Away
1	Viborg	33	4	12%	24%	0%
2	Vejle	33	3	9%	12%	6%
3	Fremad Amager	33	1	3%	6%	0%
4	Fredericia	33	3	9%	12%	6%
5	HB Køge	33	1	3%	0%	6%
6	Skive Women	33	4	12%	6%	18%
7	Vendsyssel	33	1	3%	0%	6%
8	Nykøbing	33	4	12%	6%	18%
9	Roskilde Women	33	2	6%	0%	12%
10	Næstved	33	0	0%	0%	0%
11	Hvidovre	33	2	6%	6%	6%
12	Kolding IF	33	3	9%	13%	6%

Under 2.5
Under 0.5
Under 1.5
Under 3.5
Under 4.5
Under 5.5

#	Team	MP	Over 2.5	Overall	Home	Away
1	Viborg	33	12	36%	35%	38%
2	Vejle	33	16	48%	47%	50%
3	Fremad Amager	33	13	39%	41%	38%
4	Fredericia	33	10	30%	18%	44%
5	HB Køge	33	16	48%	50%	47%
6	Skive Women	33	17	52%	44%	59%
7	Vendsyssel	33	21	64%	82%	44%
8	Nykøbing	33	10	30%	31%	29%
9	Roskilde Women	33	13	39%	38%	41%
10	Næstved	33	20	61%	71%	50%
11	Hvidovre	33	18	55%	50%	59%
12	Kolding IF	33	14	42%	38%	47%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Viborg	33	1	3%	6%	0%
2	Vejle	33	3	9%	0%	19%
3	Fremad Amager	33	4	12%	12%	13%
4	Fredericia	33	0	0%	0%	0%
5	HB Køge	33	2	6%	6%	6%
6	Skive Women	33	3	9%	6%	12%
7	Vendsyssel	33	2	6%	6%	6%
8	Nykøbing	33	3	9%	6%	12%
9	Roskilde Women	33	1	3%	0%	6%
10	Næstved	33	2	6%	12%	0%
11	Hvidovre	33	2	6%	13%	0%
12	Kolding IF	33	1	3%	6%	0%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Viborg	33	4	12%	6%	19%
2	Vejle	33	9	27%	24%	31%
3	Fremad Amager	33	10	30%	24%	38%
4	Fredericia	33	4	12%	0%	25%
5	HB Køge	33	6	18%	19%	18%
6	Skive Women	33	8	24%	19%	29%
7	Vendsyssel	33	10	30%	47%	13%
8	Nykøbing	33	7	21%	19%	24%
9	Roskilde Women	33	4	12%	13%	12%
10	Næstved	33	9	27%	35%	19%
11	Hvidovre	33	6	18%	31%	6%
12	Kolding IF	33	5	15%	13%	18%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Viborg	33	19	58%	59%	56%
2	Vejle	33	23	70%	71%	69%
3	Fremad Amager	33	20	61%	59%	63%
4	Fredericia	33	16	48%	41%	56%
5	HB Køge	33	22	67%	75%	59%
6	Skive Women	33	22	67%	56%	76%
7	Vendsyssel	33	28	85%	94%	75%
8	Nykøbing	33	14	42%	50%	35%
9	Roskilde Women	33	18	55%	50%	59%
10	Næstved	33	24	73%	82%	63%
11	Hvidovre	33	24	73%	63%	82%
12	Kolding IF	33	22	67%	63%	71%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Viborg	33	25	76%	65%	88%
2	Vejle	33	26	79%	76%	81%
3	Fremad Amager	33	29	88%	82%	94%
4	Fredericia	33	26	79%	76%	81%
5	HB Køge	33	30	91%	100%	82%
6	Skive Women	33	27	82%	81%	82%
7	Vendsyssel	33	31	94%	100%	88%
8	Nykøbing	33	25	76%	81%	71%
9	Roskilde Women	33	28	85%	94%	76%
10	Næstved	33	31	94%	94%	94%
11	Hvidovre	33	27	82%	75%	88%
12	Kolding IF	33	29	88%	88%	88%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Viborg	33	29	88%	76%	100%
2	Vejle	33	30	91%	88%	94%
3	Fremad Amager	33	32	97%	94%	100%
4	Fredericia	33	30	91%	88%	94%
5	HB Køge	33	32	97%	100%	94%
6	Skive Women	33	29	88%	94%	82%
7	Vendsyssel	33	32	97%	100%	94%
8	Nykøbing	33	29	88%	94%	82%
9	Roskilde Women	33	31	94%	100%	88%
10	Næstved	33	33	100%	100%	100%
11	Hvidovre	33	31	94%	94%	94%
12	Kolding IF	33	30	91%	88%	94%

You can check out how often HB Køge and Nykøbing matches in the 1st Division go over or under a certain number of goals on this page. This is a handy tool to help predict how many goals they could potentially score when they meet in their upcoming match.

Here is an example of what we mean:

82% of HB Køge’s matches in the 1st Division have had more than 1.5 goals scored. And when we raise the line of total goals to 2.5, then the rate of matches that go over that number is 52%.

You can also check how many times the total has gone under. In Nykøbing’s case, 21% of their matches have seen under 1.5 goals scored. And in terms of matches with under 2.5 goals, the rate is 30%.

You can also see whether HB Køge and Nykøbing matches go over or under a total number of goals when playing at home, away, or overall in 1st Division.

The over/under goal analysis for HB Køge vs Nykøbing offers valuable insights into each club's playing styles as well as a reasonable estimation of goals we could see in the match.

However, predicting the number of goals in a match does not always go the way the math says it will. It’s important to always consider other factors as well in order to get the best chance of making an accurate prediction.

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