· AFC Champions League Two
Al Rayyan
ATT
6/10
DEF
4/10
vs
Al-Nahda
5/10
ATT
5/10
DEF
Programmata· Wed 4 Nov · 16:00
Migliore scelta
GOL DI ENTRAMBE
54%
Risultato esatto
Eccellente
1-1
11.1%
9.04
Eccellente
1-0
10.5%
9.54
2-1
9.8%
10.19
2-0
9.3%
10.76
0-1
6.2%
16.02
0-0
5.9%
16.92
1-2
5.8%
17.12
3-1
5.8%
17.25
154%
1.85TOP
X23%
4.28
222%
4.45
1X77%
X245%
1276%
Probable XI4-4-2
Abunad
Yousif
Ali
Amaro
Ellethy
Hatem
Gregore
Hassan
Sharshani
Ganehi
Al-Rawi
Impact Player Hot Head
Formazioni non ancora annunciate — analisi completa della rosa sotto
Al Rayyan18 giocatori
52.5/ 99Medio
Migliori
1GregoreMID74
2Julien De SartMID73
3Ahmed AlaaeldinFWD58
Al-Nahda14 giocatori
55.5/ 99Forte
Migliori
1Ahed Al-MashaikiMID89
2Karim DarwichFWD65
3Marwan MubarakFWD64
Al Rayyan36
PlayerValutazioneImpattoAggressivitàDisciplina
Gregore74608183
Julien De Sart73566189
Ahmed Alaaeldin58565261
Diogo Amaro56495561
Daoud Soliman55475661
André Amaro52575941
Róger Guedes52504756
Wesley51485356
Jassem Gaber Abdulsallam50485450
Mahmud Abunad49493450
David García48464356
Youssef Ayman48544841
Aleksandar Mitrović  48464756
Al-Nahda25
PlayerValutazioneImpattoAggressivitàDisciplina
Ahed Al-Mashaiki89999950
Karim Darwich65565978
Marwan Mubarak64723950
Harib Al-Saadi 62495772
Ahmed Al Rawahi59545361
Amjad Al-Harthi56515161
Valon Ahmedi52494956
Faiyz Al-Rusheidi51514550
Ghanim Al Habashi51474361
Mohamed Al-Ghafri49514150
Ahmed Al-Kaabi48483856
Al Faraj Al Kiyumi48474850
Petrus Boumal44495535
Classifiche giocatori
Cecchini
Maggior minaccia di gol
1
Marwan MubarakAl-Nahda
72
2
Ahmed AlaaeldinAl Rayyan
56
3
Karim DarwichAl-Nahda
56
4
Mohamed Al-GhafriAl-Nahda
51
5
Khalid Ali SabahAl Rayyan
49
Muro
Migliori difensori
1
André AmaroAl Rayyan
57
2
Youssef AymanAl Rayyan
54
3
Ahmed Al RawahiAl-Nahda
54
4
Amjad Al-HarthiAl-Nahda
51
5
Diogo AmaroAl Rayyan
49
Controllo
Migliori registi
1
Ahed Al-MashaikiAl-Nahda
99
2
OtávioAl Rayyan
63
3
GregoreAl Rayyan
60
4
Julien De SartAl Rayyan
56
5
Róger GuedesAl Rayyan
50
Cattivi ragazzi
Più propensi ai cartellini
1
OtávioAl Rayyan
18
2
Abdul Aziz KhalfanAl-Nahda
25
3
Petrus BoumalAl-Nahda
35
4
André AmaroAl Rayyan
41
5
Youssef AymanAl Rayyan
41
Classifica potenza
Più forti in assoluto
1
Ahed Al-MashaikiAl-Nahda
89
2
GregoreAl Rayyan
74
3
Julien De SartAl Rayyan
73
4
Karim DarwichAl-Nahda
65
5
Marwan MubarakAl-Nahda
64

Domande frequenti

Our Poisson model calculates the probability of every possible scoreline for Al Rayyan vs Al-Nahda 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 Al Rayyan vs Al-Nahda 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 Al Rayyan 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 Al Rayyan vs Al-Nahda 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 Al Rayyan 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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