· UEFA U21 Championship Qualification
Latvia U21
ATT
4/10
DEF
4/10
vs
Malta U21
3/10
ATT
2/10
DEF
Scheduled· Tue 6 Oct · 16:00
Best Pick
BTTS
58%
Correct Score
Top
1-1
11%
9.09
Top
2-1
9.6%
10.47
1-0
8.9%
11.18
2-0
7.8%
12.88
1-2
6.8%
14.78
0-1
6.3%
15.78
2-2
5.9%
17.02
3-1
5.5%
18.09
149%
2.03TOP
X24%
4.23
227%
3.69
1X73%
X251%
1276%
Probable XI4-4-2
Veisbuks
Kangars
Molotkovs
Kudelkins
Susko
Zaleiko
Patrikejevs
Minins
Anmanis
Puzirevskis
Melnis
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Latvia U2122 players
35.8/ 99Developing
Top Rated
1Ralfs KragliksDEF68
2Alans KangarsDEF56
3Rudolfs KlavinskisMID48
Malta U2132 players
44.4/ 99Average
Top Rated
1Sven XerriDEF65
2Jake VassalloDEF64
3Deacon AbelaDEF60
Latvia U2154
PlayerOverall RatingImpactAggressionDiscipline
Ralfs Kragliks68864550
Alans Kangars56535656
Rudolfs Klavinskis48416350
Aleksandrs Molotkovs45454550
Bruno Melnis45454550
Rodrigo Gaucis45454550
Glebs Mihalcovs42423950
Nils Henrijs Veinbergs41393950
Maksims Semeško40521250
Emils Evelons38239950
Glebs Patika38373450
Martins Lormanis36343450
Nikita Parfjonovs3227950
Malta U2160
PlayerOverall RatingImpactAggressionDiscipline
Sven Xerri65467567
Jake Vassallo64715352
Deacon Abela60635356
Alfie Bridgman57429650
Liam Frendo54554550
Basil Tuma54518445
Bjorn Buhagiar53329952
Lucas Caruana52494956
Shaisen Attard50524550
Keyon Ewurum48514150
Brooklyn Borg47455750
Hugo Sacco46454550
Karl Sargent46454550
Player Rankings
Snipers
Top goal threats
1
Basil TumaMalta U21
51
2
Bruno MelnisLatvia U21
45
3
Rodrigo GaucisLatvia U21
45
4
Brooklyn BorgMalta U21
45
5
Ensell AttardMalta U21
45
Wall
Top defenders
1
Ralfs KragliksLatvia U21
86
2
Jake VassalloMalta U21
71
3
Deacon AbelaMalta U21
63
4
Alans KangarsLatvia U21
53
5
Maksims SemeškoLatvia U21
52
Control
Top playmakers
1
Shaisen AttardMalta U21
52
2
Keyon EwurumMalta U21
51
3
Lucas CaruanaMalta U21
49
4
Alfie BridgmanMalta U21
42
5
Rudolfs KlavinskisLatvia U21
41
Bad Boys
Most likely to get booked
1
Daniel LetherbyMalta U21
37
2
Ivans PatrikejevsLatvia U21
42
3
Basil TumaMalta U21
45
4
Nikita ParfjonovsLatvia U21
50
5
Aleksandrs MolotkovsLatvia U21
50
Power Rankings
Strongest overall
1
Ralfs KragliksLatvia U21
68
2
Sven XerriMalta U21
65
3
Jake VassalloMalta U21
64
4
Deacon AbelaMalta U21
60
5
Alfie BridgmanMalta U21
57

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Latvia U21 vs Malta 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 Latvia U21 vs Malta 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 Latvia 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 Latvia U21 vs Malta 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 Latvia 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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