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HB Køge 0 - 2 Hobro

14.10.2013 - 17:00

Stadium : Castus Park (Herfølge)

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	Horsens	33	20	61%	53%	69%
2	Silkeborg	33	20	61%	65%	56%
3	Hobro	33	20	61%	63%	59%
4	Lyngby	33	22	67%	65%	69%
5	Vejle	33	18	55%	35%	75%
6	Fredericia	33	17	52%	59%	44%
7	HB Køge	33	13	39%	35%	44%
8	Vendsyssel	33	21	64%	69%	59%
9	AB Women	33	20	61%	69%	53%
10	Brønshøj Women	33	19	58%	63%	53%
11	Hvidovre	33	18	55%	63%	47%
12	Marienlyst Women	33	24	73%	69%	76%

#	Team	MP	Over 0.5	Overall	Home	Away
1	Horsens	33	31	94%	88%	100%
2	Silkeborg	33	32	97%	100%	94%
3	Hobro	33	32	97%	94%	100%
4	Lyngby	33	31	94%	94%	94%
5	Vejle	33	28	85%	76%	94%
6	Fredericia	33	31	94%	94%	94%
7	HB Køge	33	29	88%	94%	81%
8	Vendsyssel	33	32	97%	100%	94%
9	AB Women	33	31	94%	100%	88%
10	Brønshøj Women	33	32	97%	100%	94%
11	Hvidovre	33	32	97%	94%	100%
12	Marienlyst Women	33	33	100%	100%	100%

#	Team	MP	Over 1.5	Overall	Home	Away
1	Horsens	33	26	79%	76%	81%
2	Silkeborg	33	29	88%	94%	81%
3	Hobro	33	27	82%	75%	88%
4	Lyngby	33	29	88%	88%	88%
5	Vejle	33	23	70%	53%	88%
6	Fredericia	33	25	76%	82%	69%
7	HB Køge	33	23	70%	76%	63%
8	Vendsyssel	33	26	79%	81%	76%
9	AB Women	33	26	79%	94%	65%
10	Brønshøj Women	33	25	76%	75%	76%
11	Hvidovre	33	30	91%	88%	94%
12	Marienlyst Women	33	31	94%	88%	100%

#	Team	MP	Over 1.5	Overall	Home	Away
1	Horsens	33	14	42%	47%	38%
2	Silkeborg	33	13	39%	47%	31%
3	Hobro	33	7	21%	19%	24%
4	Lyngby	33	11	33%	41%	25%
5	Vejle	33	12	36%	24%	50%
6	Fredericia	33	10	30%	35%	25%
7	HB Køge	33	3	9%	6%	13%
8	Vendsyssel	33	9	27%	25%	29%
9	AB Women	33	11	33%	44%	24%
10	Brønshøj Women	33	8	24%	19%	29%
11	Hvidovre	33	14	42%	44%	41%
12	Marienlyst Women	33	12	36%	25%	47%

#	Team	MP	Over 1.5	Overall	Home	Away
1	Horsens	33	10	30%	29%	31%
2	Silkeborg	33	8	24%	24%	25%
3	Hobro	33	4	12%	19%	6%
4	Lyngby	33	4	12%	12%	13%
5	Vejle	33	4	12%	6%	19%
6	Fredericia	33	5	15%	29%	0%
7	HB Køge	33	1	3%	0%	6%
8	Vendsyssel	33	4	12%	6%	18%
9	AB Women	33	6	18%	25%	12%
10	Brønshøj Women	33	2	6%	0%	12%
11	Hvidovre	33	8	24%	25%	24%
12	Marienlyst Women	33	6	18%	13%	24%

#	Team	MP	Over 1.5	Overall	Home	Away
1	Horsens	33	4	12%	6%	19%
2	Silkeborg	33	3	9%	6%	13%
3	Hobro	33	2	6%	6%	6%
4	Lyngby	33	1	3%	0%	6%
5	Vejle	33	1	3%	0%	6%
6	Fredericia	33	2	6%	12%	0%
7	HB Køge	33	1	3%	0%	6%
8	Vendsyssel	33	1	3%	0%	6%
9	AB Women	33	4	12%	25%	0%
10	Brønshøj Women	33	0	0%	0%	0%
11	Hvidovre	33	3	9%	13%	6%
12	Marienlyst Women	33	0	0%	0%	0%

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	Horsens	33	13	39%	47%	31%
2	Silkeborg	33	13	39%	35%	44%
3	Hobro	33	13	39%	38%	41%
4	Lyngby	33	11	33%	35%	31%
5	Vejle	33	15	45%	65%	25%
6	Fredericia	33	16	48%	41%	56%
7	HB Køge	33	20	61%	65%	56%
8	Vendsyssel	33	12	36%	31%	41%
9	AB Women	33	13	39%	31%	47%
10	Brønshøj Women	33	14	42%	38%	47%
11	Hvidovre	33	15	45%	38%	53%
12	Marienlyst Women	33	9	27%	31%	24%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Horsens	33	2	6%	12%	0%
2	Silkeborg	33	1	3%	0%	6%
3	Hobro	33	1	3%	6%	0%
4	Lyngby	33	2	6%	6%	6%
5	Vejle	33	5	15%	24%	6%
6	Fredericia	33	2	6%	6%	6%
7	HB Køge	33	4	12%	6%	19%
8	Vendsyssel	33	1	3%	0%	6%
9	AB Women	33	2	6%	0%	12%
10	Brønshøj Women	33	1	3%	0%	6%
11	Hvidovre	33	1	3%	6%	0%
12	Marienlyst Women	33	0	0%	0%	0%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Horsens	33	7	21%	24%	19%
2	Silkeborg	33	4	12%	6%	19%
3	Hobro	33	6	18%	25%	12%
4	Lyngby	33	4	12%	12%	13%
5	Vejle	33	10	30%	47%	13%
6	Fredericia	33	8	24%	18%	31%
7	HB Køge	33	10	30%	24%	38%
8	Vendsyssel	33	7	21%	19%	24%
9	AB Women	33	7	21%	6%	35%
10	Brønshøj Women	33	8	24%	25%	24%
11	Hvidovre	33	3	9%	13%	6%
12	Marienlyst Women	33	2	6%	13%	0%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Horsens	33	19	58%	53%	63%
2	Silkeborg	33	20	61%	53%	69%
3	Hobro	33	26	79%	81%	76%
4	Lyngby	33	22	67%	59%	75%
5	Vejle	33	21	64%	76%	50%
6	Fredericia	33	23	70%	65%	75%
7	HB Køge	33	30	91%	94%	88%
8	Vendsyssel	33	24	73%	75%	71%
9	AB Women	33	22	67%	56%	76%
10	Brønshøj Women	33	25	76%	81%	71%
11	Hvidovre	33	19	58%	56%	59%
12	Marienlyst Women	33	21	64%	75%	53%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Horsens	33	23	70%	71%	69%
2	Silkeborg	33	25	76%	76%	75%
3	Hobro	33	29	88%	81%	94%
4	Lyngby	33	29	88%	88%	88%
5	Vejle	33	29	88%	94%	81%
6	Fredericia	33	28	85%	71%	100%
7	HB Køge	33	32	97%	100%	94%
8	Vendsyssel	33	29	88%	94%	82%
9	AB Women	33	27	82%	75%	88%
10	Brønshøj Women	33	31	94%	100%	88%
11	Hvidovre	33	25	76%	75%	76%
12	Marienlyst Women	33	27	82%	88%	76%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Horsens	33	29	88%	94%	81%
2	Silkeborg	33	30	91%	94%	88%
3	Hobro	33	31	94%	94%	94%
4	Lyngby	33	32	97%	100%	94%
5	Vejle	33	32	97%	100%	94%
6	Fredericia	33	31	94%	88%	100%
7	HB Køge	33	32	97%	100%	94%
8	Vendsyssel	33	32	97%	100%	94%
9	AB Women	33	29	88%	75%	100%
10	Brønshøj Women	33	33	100%	100%	100%
11	Hvidovre	33	30	91%	88%	94%
12	Marienlyst Women	33	33	100%	100%	100%

You can check out how often HB Køge and Hobro 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:

70% 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 39%.

You can also check how many times the total has gone under. In Hobro’s case, 18% of their matches have seen under 1.5 goals scored. And in terms of matches with under 2.5 goals, the rate is 39%.

You can also see whether HB Køge and Hobro 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 Hobro 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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