· UEFA Youth League
Göztepe U19
ATT
5/10
DEF
5/10
1:2
AZ U19
6/10
ATT
5/10
DEF
Konec
Nejlepší tip
Nad 2.5
70%
Přesný výsledek94′
Výborná
1-1
8.8%
11.00
Výborná
2-1
8.7%
29.00
1-2
7.2%
10.00
2-2
7.1%
19.00
3-1
5.7%
51.00
1-0
5.4%
34.00
2-0
5.3%
41.00
3-2
4.6%
51.00
146%
2.17TOP
X22%
4.62
232%
3.10
1X68%
X254%
1278%
xG — Očekávané góly
Göztepe U19
Göztepe U19 oček.
AZ U19
AZ U19 oček.
goal
za 15 min
0–15′
15–30′
30–45′
45–60′
60–75′
75–90′
Göztepe U19
AZ U19
Probable XI4-4-2
Ercan
5
Eligüzel
13
Yildirim
Yavuz
6
Atalay
2
Sarıgül
8
Akin
25
Şirin
11
Şeker
23
Turan
Özen
Impact Player Hot Head
Sestavy ještě nebyly oznámeny — níže kompletní analýza kádru
Göztepe U196 hráči
45.5/ 99Průměrný
Nejlepší
1B. SarıgülMID46
2Ö. AkinMID46
3Y. ŞirinMID46
AZ U1915 hráči
61.3/ 99Silný
Nejlepší
1Andrea NataliDEF93
2Saviola SaffoeDEF86
3Hessel de WitMID80
Göztepe U1913
PlayerCelkové HodnoceníImpaktAgreseDisciplína
2
B. Sarıgül46454550
8
Ö. Akin46454550
25
Y. Şirin46454550
5
B. Eligüzel45454550
11
Deniz Şeker45454550
23
Ömür Osman Turan45454550
A. Ercan————
N. Uzel————
1
Tolga Sancak————
13
Ege Yildirim————
F. Yavuz————
6
K. Atalay————
F. Özen————
AZ U1921
PlayerCelkové HodnoceníImpaktAgreseDisciplína
3
Andrea Natali93998561
Saviola Saffoe86817478
10
Hessel de Wit80739378
6
Julian Sijbrands78928146
Bogdan Budko76719962
14
Calvin Carrilho72589567
4
Kevin van Ouytsel 61586354
1
Nick Homan56583450
Rayan Zekri54459956
11
Chavarello Hellings47484350
5
Eliav Adjedj45454550
12
Timme Elffers45454550
8
Adam Ouaziz45516830
Žebříčky hráčů
Střelci
Největší gólová hrozba
1
Chavarello HellingsAZ U19
48
2
Deniz ŞekerGöztepe U19
45
3
Ömür Osman TuranGöztepe U19
45
4
Gijs GrootAZ U19
45
5
Rayan ZekriAZ U19
45
Zeď
Nejlepší obránci
1
Andrea NataliAZ U19
99
2
Saviola SaffoeAZ U19
81
3
Calvin CarrilhoAZ U19
58
4
Kevin van Ouytsel AZ U19
58
5
B. EligüzelGöztepe U19
45
Kontrola
Nejlepší tvůrci hry
1
Julian SijbrandsAZ U19
92
2
Hessel de WitAZ U19
73
3
Bogdan BudkoAZ U19
71
4
Adam OuazizAZ U19
51
5
B. SarıgülGöztepe U19
45
Zlobivci
Nejpravděpodobnější žlutá karta
1
Adam OuazizAZ U19
30
2
Julian SijbrandsAZ U19
46
3
B. EligüzelGöztepe U19
50
4
B. SarıgülGöztepe U19
50
5
Ö. AkinGöztepe U19
50
Žebříček síly
Nejsilnější celkově
1
Andrea NataliAZ U19
93
2
Saviola SaffoeAZ U19
86
3
Hessel de WitAZ U19
80
4
Julian SijbrandsAZ U19
78
5
Bogdan BudkoAZ U19
76

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Göztepe U19 vs AZ U19 based on each team's expected goals (xG). The top-3 most likely scorelines are shown above with their probability percentages. Even the highest-probability score rarely exceeds 12-15% chance, as football is a low-scoring sport.

The Bet Builder combines multiple selections from the Göztepe U19 vs AZ U19 match into one bet. Our AI identifies outcomes that complement each other — for example, a home win paired with over 2.5 goals and both teams to score. All selections must win for the combined bet to pay out, which means higher combined odds but also higher risk.

xG (expected goals) measures the quality of scoring chances. If Göztepe U19 has an xG of 1.8, it means they are expected to score around 1-2 goals based on chance quality. The model uses pre-match xG to build a probability table for all possible scorelines. A team can overperform xG (score more than expected) or underperform xG (score fewer).

Yes, all predictions on ProSoccer are completely free. You can view predicted scorelines, over/under probabilities, and bet builder suggestions for Göztepe U19 vs AZ U19 without paying or registering. Odds values are revealed after a tap to keep the page compliant with search engine guidelines.

A value bet occurs when the probability of an outcome is higher than what the bookmaker odds suggest. For example, if our model gives Göztepe U19 a 55% chance of winning but the odds imply only 40%, that is a value bet. Our AI highlights selections where the model probability exceeds the implied probability from the odds.

AI predictions use statistical modeling (Poisson distribution) based on historical data and pre-match metrics. They are more reliable over large samples than for any single match. No prediction is guaranteed — football has inherent unpredictability. Our model typically identifies the correct top-3 scorelines in 25-30% of matches, which is significantly better than random chance.

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