· UEFA Youth League
Bayern U19
ATT
6/10
DEF
4/10
vs
Arsenal U19
5/10
ATT
5/10
DEF
Scheduled· Wed 21 Oct · 14:00
Best Pick
Over 2.5
71%
Correct Score
Top
2-1
9%
10.00
Top
1-1
8.2%
11.00
2-2
6.7%
11.00
3-1
6.6%
12.00
1-2
6.1%
19.00
2-0
6%
15.00
1-0
5.5%
19.00
3-2
4.9%
15.00
153%
1.50TOP
X21%
4.00
226%
4.75
1X74%
X247%
1279%
Probable XI4-4-2
Podar-Stiube
El-Chaar
Gashi
Kiala
Kim
Lambe
Olychenko
Daiber
Osmani
Nseke
Assomo
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Bayern U1922 players
61.9/ 99Strong
Top Rated
1Bastian AssomoFWD96
2Yil GashiFWD95
3Michael MatosevicMID81
Arsenal U1922 players
46.5/ 99Average
Top Rated
1Marli SalmonDEF66
2Ceadach O'NeillFWD57
3Josiah KingDEF54
Bayern U1933
PlayerOverall RatingImpactAggressionDiscipline
Bastian Assomo96988483
Yil Gashi95999772
Michael Matosevic81829956
Uzorchukwu Frank Egwuatu79943950
Filip Pavic70558367
Cassiano Kiala65804650
Noah Codjo-Evora64704750
Bogdan Olychenko63418078
R. Pavlić62684956
Allen Junior Lambe60408267
Rayan Moumouni59616150
David Santos Daiber59369973
Erblin Osmani58585456
Arsenal U1933
PlayerOverall RatingImpactAggressionDiscipline
Marli Salmon66725456
Ceadach O'Neill57585256
Josiah King54604550
Josh Ogunnaike50484756
Jack Porter49482350
Callan Hamill47455150
Ife Ibrahim47324967
Elijah Upson46434949
Maalik Hashi46454550
Saurap Sampang46454550
Alhamdulillah Hakeem Abubakar45454550
Marcell Washington45454550
Andre Harriman-Annous45454550
Player Rankings
Snipers
Top goal threats
1
Yil GashiBayern U19
99
2
Bastian AssomoBayern U19
98
3
Uzorchukwu Frank EgwuatuBayern U19
94
4
Noah Codjo-EvoraBayern U19
70
5
Ceadach O'NeillArsenal U19
58
Wall
Top defenders
1
Cassiano KialaBayern U19
80
2
Marli SalmonArsenal U19
72
3
R. PavlićBayern U19
68
4
Rayan MoumouniBayern U19
61
5
Josiah KingArsenal U19
60
Control
Top playmakers
1
Michael MatosevicBayern U19
82
2
Erblin OsmaniBayern U19
58
3
Matouš SrbBayern U19
57
4
Guido Della RovereBayern U19
45
5
Maalik HashiArsenal U19
45
Bad Boys
Most likely to get booked
1
Elijah UpsonArsenal U19
49
2
David Podar-StiubeBayern U19
50
3
Leon KlanacBayern U19
50
4
Leonard RulandBayern U19
50
5
Adam El-ChaarBayern U19
50
Power Rankings
Strongest overall
1
Bastian AssomoBayern U19
96
2
Yil GashiBayern U19
95
3
Michael MatosevicBayern U19
81
4
Uzorchukwu Frank EgwuatuBayern U19
79
5
Filip PavicBayern U19
70

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Bayern U19 vs Arsenal 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 Bayern U19 vs Arsenal 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 Bayern 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 Bayern U19 vs Arsenal 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 Bayern 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.

HubTodayLiveCorrect ScoreAI SignalsPlayer props