· UEFA Nations League
Italy
ATT
5/10
DEF
5/10
vs
Türkiye
5/10
ATT
5/10
DEF
Naplánováno· Mon 5 Oct · 18:45
Nejlepší tip
Pod 2.5
57%
Přesný výsledek
Výborná
1-1
13%
7.71
Výborná
1-0
12%
8.36
0-1
9.9%
10.10
0-0
9.1%
10.95
2-1
8.5%
11.79
2-0
7.8%
12.78
1-2
7%
14.23
0-2
5.4%
18.63
142%
2.40TOP
X27%
3.64
231%
3.25
1X69%
X258%
1273%
Probable XI4-4-2
Donnarumma
Bastoni
Favasuli
Ghilardi
Bartesaghi
Romano
Cacciamani
Vergara
Ndour
Camarda
Raspadori
Impact Player Hot Head
Sestavy ještě nebyly oznámeny — níže kompletní analýza kádru
Italy22 hráči
53.0/ 99Průměrný
Nejlepší
1Pio EspositoFWD69
2Nicolò CambiaghiFWD61
3Alessandro BastoniDEF60
Türkiye16 hráči
52.4/ 99Průměrný
Nejlepší
1Melih KabasakalMID84
2Demir Ege TıknazMID72
3İsmail YüksekMID63
Italy56
PlayerCelkové HodnoceníImpaktAgreseDisciplína
Pio Esposito69606083
Nicolò Cambiaghi61496978
Alessandro Bastoni60475672
Sandro Tonali60514972
Moise Kean57565061
Giorgio Scalvini 56545456
Nicolò Fagioli55505161
Diego Coppola54574950
Sebastiano Esposito54505561
Alessandro Romano53485456
Matteo Ruggeri52535050
Nicolò Barella52544850
Giovanni Di Lorenzo51494756
Türkiye39
PlayerCelkové HodnoceníImpaktAgreseDisciplína
Melih Kabasakal84909950
Demir Ege Tıknaz72885450
İsmail Yüksek63585667
Merih Demiral62595067
Zeki Çelik58485467
Uğurcan Çakır51499956
Kerem Aktürkoğlu49474756
Altay Bayındır48483950
Orkun Kökçü 48494550
Abdülkerim Bardakcı47454950
Eren Elmalı46454550
Ferdi Kadıoğlu45454550
Ozan Kabak 44485337
Žebříčky hráčů
Střelci
Největší gólová hrozba
1
Pio EspositoItaly
60
2
Moise KeanItaly
56
3
Sebastiano EspositoItaly
50
4
Giacomo RaspadoriItaly
49
5
Nicolò CambiaghiItaly
49
Zeď
Nejlepší obránci
1
Merih DemiralTürkiye
59
2
Diego CoppolaItaly
57
3
Giorgio Scalvini Italy
54
4
Matteo RuggeriItaly
53
5
Giovanni Di LorenzoItaly
49
Kontrola
Nejlepší tvůrci hry
1
Melih KabasakalTürkiye
90
2
Demir Ege TıknazTürkiye
88
3
İsmail YüksekTürkiye
58
4
Nicolò BarellaItaly
54
5
Issa DoumbiaItaly
53
Zlobivci
Nejpravděpodobnější žlutá karta
1
Salih Özcan Türkiye
24
2
Gianluca ManciniItaly
26
3
Barış Alper Yılmaz Türkiye
26
4
Ozan Kabak Türkiye
37
5
Gianluigi DonnarummaItaly
50
Žebříček síly
Nejsilnější celkově
1
Melih KabasakalTürkiye
84
2
Demir Ege TıknazTürkiye
72
3
Pio EspositoItaly
69
4
İsmail YüksekTürkiye
63
5
Merih DemiralTürkiye
62

Frequently Asked Questions

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