· UEFA U21 Championship Qualification
Italy U21
ATT
7/10
DEF
6/10
vs
Poland U21
6/10
ATT
7/10
DEF
Naplánováno· Mon 5 Oct · 16:15
Nejlepší tip
Nad 2.5
65%
Přesný výsledek
Výborná
2-1
9.6%
10.45
Výborná
1-1
9.5%
10.52
2-0
7.3%
13.75
1-0
7.2%
13.83
3-1
6.4%
15.58
2-2
6.3%
15.90
1-2
6.2%
16.00
3-0
4.9%
20.48
154%
1.87TOP
X22%
4.63
225%
4.02
1X76%
X247%
1279%
Probable XI4-4-2
Mascardi
Bakoune
Dellavalle
Fontanarosa
Moruzzi
Ciammaglichella
Bianco
Nunzio
Cacciamani
Okoro
Raimondo
Impact Player Hot Head
Sestavy ještě nebyly oznámeny — níže kompletní analýza kádru
Italy U2130 hráči
78.1/ 99Elitní
Nejlepší
1Francesco CamardaFWD98
2Seydou FiniFWD98
3Cher NdourMID97
Poland U2126 hráči
49.4/ 99Průměrný
Nejlepší
1Marcel LubikGK83
2Kacper DudaMID83
3Maciej KuziemkaFWD83
Italy U2197
PlayerCelkové HodnoceníImpaktAgreseDisciplína
Francesco Camarda98999991
Seydou Fini98999991
Cher Ndour97999991
Pietro Comuzzo96999178
Lorenzo Venturino94999967
Filippo Mane93999261
Luca Lipani93999970
Matteo Dagasso 93999968
Luca Koleosho90899988
Niccolò Pisilli89996467
Davide Bartesaghi88909961
Tommaso Berti88999946
Jeff Ekhator86995356
Poland U2175
PlayerCelkové HodnoceníImpaktAgreseDisciplína
Marcel Lubik83912350
Kacper Duda83999933
Maciej Kuziemka83994550
Wiktor Bogacz72843950
Kamil Jakubczyk60459956
Jan Faberski57605150
Mariusz Kutwa55604650
Tomasz Pienko54494961
Filip Kocaba53604150
Daniel Mikolajewski53573950
Slawomir Abramowicz46454550
K. Potulski45454550
Wojciech Monka45454550
Žebříčky hráčů
Střelci
Největší gólová hrozba
1
Antonio RaimondoItaly U21
99
2
Francesco CamardaItaly U21
99
3
Jeff EkhatorItaly U21
99
4
Lorenzo VenturinoItaly U21
99
5
Seydou FiniItaly U21
99
Zeď
Nejlepší obránci
1
Filippo ManeItaly U21
99
2
Pietro ComuzzoItaly U21
99
3
Davide BartesaghiItaly U21
90
4
Gabriele CalvaniItaly U21
82
5
Gabriele Guarino Italy U21
79
Kontrola
Nejlepší tvůrci hry
1
Cher NdourItaly U21
99
2
Giacomo FaticantiItaly U21
99
3
Luca LipaniItaly U21
99
4
Matteo Dagasso Italy U21
99
5
Niccolò PisilliItaly U21
99
Zlobivci
Nejpravděpodobnější žlutá karta
1
Kacper DudaPoland U21
33
2
Tommaso BertiItaly U21
46
3
Lorenzo PalmisaniItaly U21
50
4
Gabriele CalvaniItaly U21
50
5
Gabriele Guarino Italy U21
50
Žebříček síly
Nejsilnější celkově
1
Francesco CamardaItaly U21
98
2
Seydou FiniItaly U21
98
3
Cher NdourItaly U21
97
4
Pietro ComuzzoItaly U21
96
5
Lorenzo VenturinoItaly U21
94

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Italy U21 vs Poland 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 Italy U21 vs Poland 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 Italy 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 Italy U21 vs Poland 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 Italy 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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