· UEFA Youth League
Kairat U19
ATT
5/10
DEF
5/10
8:1
Mladost DG U19
5/10
ATT
5/10
DEF
Bitti
En iyi tahmin
Üst 2.5
70%
Doğru skor93′
Mükemmel
2-1
8.7%
11.43
Mükemmel
1-1
8.6%
11.59
2-2
7%
14.27
1-2
6.9%
14.47
3-1
5.9%
16.91
2-0
5.5%
18.31
1-0
5.4%
18.57
3-2
4.7%
21.11
148%
2.09TOP
X21%
4.67
231%
3.25
1X69%
X252%
1279%
xG — Beklenen goller
Kairat U19
Kairat U19 bekl.
Mladost DG U19
Mladost DG U19 bekl.
goal
15 dk'da bir
0–15′
15–30′
30–45′
45–60′
60–75′
75–90′
Kairat U19
Mladost DG U19
Probable XI4-4-2
Aldashev
Mamedov
Nurgaliyev
Kalikulov
Bazarbaev
Musaipov
Darkhan
Petrik
Malaev
Toleukhan
Zhilkaydar
Impact Player Hot Head
Kadrolar henüz açıklanmadı — aşağıda tam kadro analizi
Kairat U1915 oyuncu
41.5/ 99Orta
En İyiler
1Sanat NurbolatDEF51
2Ermukhammad KerimkhanFWD47
3Danila BuchGK46
Mladost DG U192 oyuncu
45.0/ 99Orta
En İyiler
1Petar PantićDEF45
2Vuk KosticDEF45
Kairat U1922
PlayerGenel DereceEtkiAgresyonDisiplin
Sanat Nurbolat51368249
Ermukhammad Kerimkhan47474550
Danila Buch46454550
Aliden Musaipov46454550
Kaisar Malaev46454550
Nurzhan Erkin46454550
Tore Ismagulov46454550
Rayimbek Kenesbekov41393950
Daniyar Tashpulatov40353756
Abylay Toleukhan39404938
Amirbek Bazarbaev38363550
Adlan Nurgaliyev36343450
Daulet Orynbasar36343450
Mladost DG U1924
PlayerGenel DereceEtkiAgresyonDisiplin
Petar Pantić45454550
Vuk Kostic45454550
Lazar Zivanovic————
Nikola Baukovic————
Stefan Nikezic————
Bosko Rasovic————
Ivan Aleksic————
Jovan Jablan————
Lazar Tomic————
Luka Rakocevic————
Milos Darmanovic————
Nebojsa Coguric————
Nebojsa Scekic————
Oyuncu sıralamaları
Keskin nişancılar
En büyük gol tehdidi
1
Ermukhammad KerimkhanKairat U19
47
2
Abylay ToleukhanKairat U19
40
3
Daulet OrynbasarKairat U19
34
Duvar
En iyi savunmacılar
1
Petar PantićMladost DG U19
45
2
Vuk KosticMladost DG U19
45
3
Rayimbek KenesbekovKairat U19
39
4
Amirbek BazarbaevKairat U19
36
5
Sanat NurbolatKairat U19
36
Kontrol
En iyi oyun kurucular
1
Aliden MusaipovKairat U19
45
2
Kaisar MalaevKairat U19
45
3
Nurzhan ErkinKairat U19
45
4
Tore IsmagulovKairat U19
45
5
Evgeniy PetrikKairat U19
25
Yaramaz çocuklar
Kart görme olasılığı en yüksek
1
Abylay ToleukhanKairat U19
38
2
Sanat NurbolatKairat U19
49
3
Danila BuchKairat U19
50
4
Adlan NurgaliyevKairat U19
50
5
Akezhan KalikulovKairat U19
50
Güç sıralaması
Genel olarak en güçlüler
1
Sanat NurbolatKairat U19
51
2
Ermukhammad KerimkhanKairat U19
47
3
Danila BuchKairat U19
46
4
Aliden MusaipovKairat U19
46
5
Kaisar MalaevKairat U19
46

Sıkça Sorulan Sorular

Our Poisson model calculates the probability of every possible scoreline for Kairat U19 vs Mladost DG 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 Kairat U19 vs Mladost DG 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 Kairat 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 Kairat U19 vs Mladost DG 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 Kairat 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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