· UEFA Youth League
Hammarby U19
ATT
5/10
DEF
5/10
1:0
Super Nova U19
5/10
ATT
6/10
DEF
Fine
Migliore scelta
Oltre 2.5
69%
Risultato esatto91′
Eccellente
2-1
9%
11.00
Eccellente
1-1
8.7%
13.00
2-2
6.9%
21.00
1-2
6.7%
29.00
3-1
6.2%
12.00
2-0
5.9%
8.50
1-0
5.7%
11.00
3-2
4.7%
23.00
150%
2.00TOP
X21%
4.70
229%
3.48
1X71%
X250%
1279%
xG — Gol attesi
Hammarby U19
Hammarby U19 att.
Super Nova U19
Super Nova U19 att.
goal
ogni 15 min
0–15′
15–30′
30–45′
45–60′
60–75′
75–90′
Hammarby U19
Super Nova U19
Probable XI4-4-2
Norell
Fors
Balagadde
Reyes
Andersson-Lönnergård
Jakobsson
Elezaj
Dawlat
Karlsson
Nord
Koné
Impact Player Hot Head
Formazioni non ancora annunciate — analisi completa della rosa sotto
Hammarby U1914 giocatori
Nessuna metrica rosa
B. NorellGK
Liam RönnelöwGK
Svante StangenbergGK
Theodor LindgrenGK
Dino ForsDEF
Elijah BalagaddeDEF
Emil ReyesDEF
Kevin Andersson-LönnergårdDEF
Robin Bravo NordDEF
Filip JakobssonMID
Gent ElezajMID
Mansoor DawlatMID
Markus KarlssonMID
Super Nova U195 giocatori
42.0/ 99Medio
Migliori
1Fabio Rosa DEF45
2Georgijs BombānsFWD42
3Toms PetrovičsGK41
Hammarby U1914
PlayerValutazioneImpattoAggressivitàDisciplina
B. Norell————
Liam Rönnelöw————
Svante Stangenberg————
Theodor Lindgren————
Dino Fors————
Elijah Balagadde————
Emil Reyes————
Kevin Andersson-Lönnergård————
Robin Bravo Nord————
Filip Jakobsson————
Gent Elezaj————
Mansoor Dawlat————
Markus Karlsson————
Super Nova U197
PlayerValutazioneImpattoAggressivitàDisciplina
Fabio Rosa 45454550
Georgijs Bombāns42413950
Toms Petrovičs41393950
Kristaps Rekmanis41393950
Alens Grikovs41393950
Kristaps Secis————
Viesturs Pipcans————
Classifiche giocatori
Cecchini
Maggior minaccia di gol
1
Georgijs BombānsSuper Nova U19
41
Muro
Migliori difensori
1
Fabio Rosa Super Nova U19
45
2
Kristaps RekmanisSuper Nova U19
39
Controllo
Migliori registi
1
Alens GrikovsSuper Nova U19
39
Cattivi ragazzi
Più propensi ai cartellini
1
Toms PetrovičsSuper Nova U19
50
2
Fabio Rosa Super Nova U19
50
3
Kristaps RekmanisSuper Nova U19
50
4
Alens GrikovsSuper Nova U19
50
5
Georgijs BombānsSuper Nova U19
50
Classifica potenza
Più forti in assoluto
1
Fabio Rosa Super Nova U19
45
2
Georgijs BombānsSuper Nova U19
42
3
Toms PetrovičsSuper Nova U19
41
4
Kristaps RekmanisSuper Nova U19
41
5
Alens GrikovsSuper Nova U19
41

Domande frequenti

Our Poisson model calculates the probability of every possible scoreline for Hammarby U19 vs Super Nova 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 Hammarby U19 vs Super Nova 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 Hammarby 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 Hammarby U19 vs Super Nova 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 Hammarby 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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