· UEFA Youth League
Kairat U19
ATT
5/10
DEF
5/10
8:1
Mladost DG U19
5/10
ATT
5/10
DEF
Fine
Migliore scelta
Oltre 2.5
70%
Risultato esatto93′
Eccellente
2-1
8.7%
11.43
Eccellente
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 — Gol attesi
Kairat U19
Kairat U19 att.
Mladost DG U19
Mladost DG U19 att.
goal
ogni 15 min
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
Formazioni non ancora annunciate — analisi completa della rosa sotto
Kairat U1915 giocatori
41.5/ 99Medio
Migliori
1Sanat NurbolatDEF51
2Ermukhammad KerimkhanFWD47
3Danila BuchGK46
Mladost DG U192 giocatori
45.0/ 99Medio
Migliori
1Petar PantićDEF45
2Vuk KosticDEF45
Kairat U1922
PlayerValutazioneImpattoAggressivitàDisciplina
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
PlayerValutazioneImpattoAggressivitàDisciplina
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————
Classifiche giocatori
Cecchini
Maggior minaccia di gol
1
Ermukhammad KerimkhanKairat U19
47
2
Abylay ToleukhanKairat U19
40
3
Daulet OrynbasarKairat U19
34
Muro
Migliori difensori
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
Controllo
Migliori registi
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
Cattivi ragazzi
Più propensi ai cartellini
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
Classifica potenza
Più forti in assoluto
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

Domande frequenti

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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