· UEFA U21 Championship Qualification
Moldova U21
ATT
5/10
DEF
4/10
vs
Kazakhstan U21
5/10
ATT
4/10
DEF
Scheduled· Tue 6 Oct · 14:00
Best Pick
BTTS
60%
Correct Score
Top
1-1
10.8%
9.24
Top
2-1
9.3%
10.74
1-0
8.1%
12.34
1-2
7.2%
13.84
2-0
7%
14.34
0-1
6.3%
15.90
2-2
6.2%
16.09
3-1
5.3%
18.72
147%
2.15TOP
X24%
4.24
230%
3.35
1X71%
X254%
1277%
Probable XI4-4-2
Nazarciuc
Dijinari
Gonta
Martin
Tonica
Mardari
Păscăluță
Forov
Grîu
Bețivu
Sprinsean
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Moldova U2121 players
57.9/ 99Strong
Top Rated
1Ovidiu DavidMID83
2Artiom DijinariDEF82
3Matteo ObleacDEF75
Kazakhstan U2125 players
54.5/ 99Average
Top Rated
1Artem LitoshFWD88
2Mansur BirkurmanovFWD85
3Magzhan ToktybayFWD82
Moldova U2151
PlayerOverall RatingImpactAggressionDiscipline
Ovidiu David83839963
Artiom Dijinari82995856
Matteo Obleac75768849
Nichita Caragheorghi73748750
Cristian Păscăluță70629956
Mihai Lupan68528172
Ivan Graminschii66538258
Vlad Pascari63459956
Vladislav Boico63469950
Vladislav Costin58497852
Artur Nazarciuc56579950
Vasile Luchita56565156
Leonard Saca52563956
Kazakhstan U2153
PlayerOverall RatingImpactAggressionDiscipline
Artem Litosh88939756
Mansur Birkurmanov85899956
Magzhan Toktybay82869950
Maksat Abraev80944550
Ali Turganov75974552
Sagi Malikaidar72805856
Zhansultan Mukhametkhanov72619667
Mukhamedali Abish62459961
Sultan Askarov61852350
Miras Rikhard59623950
Abinur Nurymbet59447561
Miras Omatay59527652
Sherkhan Kalmurza53539954
Player Rankings
Snipers
Top goal threats
1
Maksat AbraevKazakhstan U21
94
2
Artem LitoshKazakhstan U21
93
3
Mansur BirkurmanovKazakhstan U21
89
4
Magzhan ToktybayKazakhstan U21
86
5
Victor CiumasuMoldova U21
52
Wall
Top defenders
1
Artiom DijinariMoldova U21
99
2
Ali TurganovKazakhstan U21
97
3
Sultan AskarovKazakhstan U21
85
4
Sagi MalikaidarKazakhstan U21
80
5
Matteo ObleacMoldova U21
76
Control
Top playmakers
1
Ovidiu DavidMoldova U21
83
2
Nichita CaragheorghiMoldova U21
74
3
Cristian PăscăluțăMoldova U21
62
4
Zhansultan MukhametkhanovKazakhstan U21
61
5
Vasile LuchitaMoldova U21
56
Bad Boys
Most likely to get booked
1
Nicolae RotaruMoldova U21
28
2
Victor CiumasuMoldova U21
39
3
Olzhas BaybekKazakhstan U21
40
4
Ramazan BagdatKazakhstan U21
44
5
Danil AndreiciuMoldova U21
47
Power Rankings
Strongest overall
1
Artem LitoshKazakhstan U21
88
2
Mansur BirkurmanovKazakhstan U21
85
3
Ovidiu DavidMoldova U21
83
4
Artiom DijinariMoldova U21
82
5
Magzhan ToktybayKazakhstan U21
82

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Moldova U21 vs Kazakhstan U21 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 Moldova U21 vs Kazakhstan U21 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 Moldova U21 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 Moldova U21 vs Kazakhstan U21 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 Moldova U21 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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