Over Under Goals: Roskilde 2 - 2 Vendsyssel

Sun, 08 April 2018 - 07:45

Stadium: Roskilde Idrætspark

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	19	58%	41%	75%
2	Esbjerg	33	20	61%	65%	56%
3	Vejle	33	12	36%	38%	35%
4	Fremad Amager	33	10	30%	31%	29%
5	Fredericia	33	17	52%	56%	47%
6	Thisted	33	20	61%	56%	65%
7	Brabrand Women	33	19	58%	56%	59%
8	HB Køge	33	16	48%	53%	44%
9	Skive IK	33	17	52%	59%	44%
10	Vendsyssel	33	18	55%	47%	63%
11	Nykøbing	33	20	61%	69%	53%
12	Roskilde	33	16	48%	47%	50%

#	Team	MP	Over 0.5	Overall	Home	Away
1	Viborg	33	29	88%	88%	88%
2	Esbjerg	33	29	88%	88%	88%
3	Vejle	33	29	88%	81%	94%
4	Fremad Amager	33	29	88%	88%	88%
5	Fredericia	33	30	91%	94%	88%
6	Thisted	33	31	94%	100%	88%
7	Brabrand Women	33	32	97%	94%	100%
8	HB Køge	33	27	82%	76%	88%
9	Skive IK	33	30	91%	100%	81%
10	Vendsyssel	33	30	91%	88%	94%
11	Nykøbing	33	31	94%	94%	94%
12	Roskilde	33	31	94%	94%	94%

#	Team	MP	Over 1.5	Overall	Home	Away
1	Viborg	33	25	76%	71%	81%
2	Esbjerg	33	25	76%	82%	69%
3	Vejle	33	22	67%	63%	71%
4	Fremad Amager	33	17	52%	44%	59%
5	Fredericia	33	24	73%	75%	71%
6	Thisted	33	28	85%	88%	82%
7	Brabrand Women	33	27	82%	81%	82%
8	HB Køge	33	21	64%	65%	63%
9	Skive IK	33	23	70%	82%	56%
10	Vendsyssel	33	30	91%	88%	94%
11	Nykøbing	33	26	79%	81%	76%
12	Roskilde	33	26	79%	71%	88%

#	Team	MP	Over 1.5	Overall	Home	Away
1	Viborg	33	14	42%	41%	44%
2	Esbjerg	33	12	36%	41%	31%
3	Vejle	33	6	18%	25%	12%
4	Fremad Amager	33	6	18%	13%	24%
5	Fredericia	33	14	42%	50%	35%
6	Thisted	33	11	33%	19%	47%
7	Brabrand Women	33	9	27%	13%	41%
8	HB Køge	33	8	24%	29%	19%
9	Skive IK	33	11	33%	35%	31%
10	Vendsyssel	33	12	36%	29%	44%
11	Nykøbing	33	16	48%	56%	41%
12	Roskilde	33	11	33%	41%	25%

#	Team	MP	Over 1.5	Overall	Home	Away
1	Viborg	33	7	21%	24%	19%
2	Esbjerg	33	5	15%	24%	6%
3	Vejle	33	2	6%	13%	0%
4	Fremad Amager	33	4	12%	6%	18%
5	Fredericia	33	7	21%	25%	18%
6	Thisted	33	4	12%	0%	24%
7	Brabrand Women	33	4	12%	0%	24%
8	HB Køge	33	1	3%	6%	0%
9	Skive IK	33	6	18%	18%	19%
10	Vendsyssel	33	7	21%	18%	25%
11	Nykøbing	33	11	33%	44%	24%
12	Roskilde	33	4	12%	12%	13%

#	Team	MP	Over 1.5	Overall	Home	Away
1	Viborg	33	5	15%	12%	19%
2	Esbjerg	33	4	12%	24%	0%
3	Vejle	33	0	0%	0%	0%
4	Fremad Amager	33	2	6%	6%	6%
5	Fredericia	33	3	9%	13%	6%
6	Thisted	33	1	3%	0%	6%
7	Brabrand Women	33	3	9%	0%	18%
8	HB Køge	33	0	0%	0%	0%
9	Skive IK	33	3	9%	18%	0%
10	Vendsyssel	33	4	12%	6%	19%
11	Nykøbing	33	6	18%	19%	18%
12	Roskilde	33	1	3%	0%	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	14	42%	59%	25%
2	Esbjerg	33	13	39%	35%	44%
3	Vejle	33	21	64%	63%	65%
4	Fremad Amager	33	23	70%	69%	71%
5	Fredericia	33	16	48%	44%	53%
6	Thisted	33	13	39%	44%	35%
7	Brabrand Women	33	14	42%	44%	41%
8	HB Køge	33	17	52%	47%	56%
9	Skive IK	33	16	48%	41%	56%
10	Vendsyssel	33	15	45%	53%	38%
11	Nykøbing	33	13	39%	31%	47%
12	Roskilde	33	17	52%	53%	50%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Viborg	33	4	12%	12%	13%
2	Esbjerg	33	4	12%	12%	13%
3	Vejle	33	4	12%	19%	6%
4	Fremad Amager	33	4	12%	13%	12%
5	Fredericia	33	3	9%	6%	12%
6	Thisted	33	2	6%	0%	12%
7	Brabrand Women	33	1	3%	6%	0%
8	HB Køge	33	6	18%	24%	13%
9	Skive IK	33	3	9%	0%	19%
10	Vendsyssel	33	3	9%	12%	6%
11	Nykøbing	33	2	6%	6%	6%
12	Roskilde	33	2	6%	6%	6%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Viborg	33	8	24%	29%	19%
2	Esbjerg	33	8	24%	18%	31%
3	Vejle	33	11	33%	38%	29%
4	Fremad Amager	33	16	48%	56%	41%
5	Fredericia	33	9	27%	25%	29%
6	Thisted	33	5	15%	13%	18%
7	Brabrand Women	33	6	18%	19%	18%
8	HB Køge	33	12	36%	35%	38%
9	Skive IK	33	10	30%	18%	44%
10	Vendsyssel	33	3	9%	12%	6%
11	Nykøbing	33	7	21%	19%	24%
12	Roskilde	33	7	21%	29%	13%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Viborg	33	19	58%	59%	56%
2	Esbjerg	33	21	64%	59%	69%
3	Vejle	33	27	82%	75%	88%
4	Fremad Amager	33	27	82%	88%	76%
5	Fredericia	33	19	58%	50%	65%
6	Thisted	33	22	67%	81%	53%
7	Brabrand Women	33	24	73%	88%	59%
8	HB Køge	33	25	76%	71%	81%
9	Skive IK	33	22	67%	65%	69%
10	Vendsyssel	33	21	64%	71%	56%
11	Nykøbing	33	17	52%	44%	59%
12	Roskilde	33	22	67%	59%	75%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Viborg	33	26	79%	76%	81%
2	Esbjerg	33	28	85%	76%	94%
3	Vejle	33	31	94%	88%	100%
4	Fremad Amager	33	29	88%	94%	82%
5	Fredericia	33	26	79%	75%	82%
6	Thisted	33	29	88%	100%	76%
7	Brabrand Women	33	29	88%	100%	76%
8	HB Køge	33	32	97%	94%	100%
9	Skive IK	33	27	82%	82%	81%
10	Vendsyssel	33	26	79%	82%	75%
11	Nykøbing	33	22	67%	56%	76%
12	Roskilde	33	29	88%	88%	88%

#	Team	MP	Over 2.5	Overall	Home	Away
1	Viborg	33	28	85%	88%	81%
2	Esbjerg	33	29	88%	76%	100%
3	Vejle	33	33	100%	100%	100%
4	Fremad Amager	33	31	94%	94%	94%
5	Fredericia	33	30	91%	88%	94%
6	Thisted	33	32	97%	100%	94%
7	Brabrand Women	33	30	91%	100%	82%
8	HB Køge	33	33	100%	100%	100%
9	Skive IK	33	30	91%	82%	100%
10	Vendsyssel	33	29	88%	94%	81%
11	Nykøbing	33	27	82%	81%	82%
12	Roskilde	33	32	97%	100%	94%

You can check out how often Roskilde and Vendsyssel 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:

79% of Roskilde’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 48%.

You can also check how many times the total has gone under. In Vendsyssel’s case, 9% of their matches have seen under 1.5 goals scored. And in terms of matches with under 2.5 goals, the rate is 45%.

You can also see whether Roskilde and Vendsyssel 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 Roskilde vs Vendsyssel 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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