· AFC Cup
Melbourne Victory
ATT
5/10
DEF
6/10
vs
Persib Bandung
5/10
ATT
4/10
DEF
Planlandı· Wed 4 Nov · 07:45
En iyi tahmin
KG VAR
54%
Doğru skor
Mükemmel
1-1
11.4%
8.75
Mükemmel
1-0
10.4%
9.59
2-1
9.7%
10.36
2-0
8.8%
11.37
0-1
6.8%
14.76
1-2
6.3%
15.94
0-0
6.2%
16.19
3-1
5.4%
18.41
151%
1.95TOP
X24%
4.14
225%
4.05
1X75%
X249%
1276%
Probable XI4-4-2
Graskoski
Hamill
Lino
Shillington
Davidson
Genreau
Mihailidis
Valadon
Mata
Nduka
Clarismario
Impact Player Hot Head
Kadrolar henüz açıklanmadı — aşağıda tam kadro analizi
Melbourne Victory20 oyuncu
54.4/ 99Orta
En İyiler
1Jordi ValadonMID65
2Tom LockyerDEF64
3Brendan HamillDEF62
Persib Bandung30 oyuncu
52.6/ 99Orta
En İyiler
1RamonFWD89
2Luciano GuaycocheaMID77
3Gabriel MutomboDEF76
Melbourne Victory26
PlayerGenel DereceEtkiAgresyonDisiplin
Jordi Valadon65605567
Tom Lockyer64595467
Brendan Hamill62704656
Guillermo May61635056
Jason Davidson58604956
Clarismario58634550
Mikey Ghossaini58595156
Denis Genreau57604656
Keegan Jelacic57475467
Philipp Ziereis 55574656
Louis D'Arrigo54524956
Matthew Grimaldi53544850
Jack Duncan52533950
Persib Bandung48
PlayerGenel DereceEtkiAgresyonDisiplin
Ramon89996061
Luciano Guaycochea77636391
Gabriel Mutombo76824969
Nazriel Alfaro Syahdan76994550
Dedi Kusnandar65537861
Júlio César 60673961
Patricio Matricardi59773547
Gufeng Zhu56437056
Saddil Ramdani56555856
Balša Sekulić54514367
Beckham Putra Nugraha53564550
Fitrah Maulana Muhammad Ridwan52534550
Gakuto Notsuda52495056
Oyuncu sıralamaları
Keskin nişancılar
En büyük gol tehdidi
1
RamonPersib Bandung
99
2
Harrison SawyerMelbourne Victory
65
3
ClarismarioMelbourne Victory
63
4
Guillermo MayMelbourne Victory
63
5
Mikey GhossainiMelbourne Victory
59
Duvar
En iyi savunmacılar
1
Gabriel MutomboPersib Bandung
82
2
Patricio MatricardiPersib Bandung
77
3
Brendan HamillMelbourne Victory
70
4
Júlio César Persib Bandung
67
5
Jason DavidsonMelbourne Victory
60
Kontrol
En iyi oyun kurucular
1
Nazriel Alfaro SyahdanPersib Bandung
99
2
T. HayePersib Bandung
78
3
Luciano GuaycocheaPersib Bandung
63
4
Denis GenreauMelbourne Victory
60
5
Jordi ValadonMelbourne Victory
60
Yaramaz çocuklar
Kart görme olasılığı en yüksek
1
Harrison SawyerMelbourne Victory
1
2
Adam Alis SetyanoPersib Bandung
1
3
T. HayePersib Bandung
3
4
Joshua InserraMelbourne Victory
25
5
Kakang RudiantoPersib Bandung
30
Güç sıralaması
Genel olarak en güçlüler
1
RamonPersib Bandung
89
2
Luciano GuaycocheaPersib Bandung
77
3
Gabriel MutomboPersib Bandung
76
4
Nazriel Alfaro SyahdanPersib Bandung
76
5
Jordi ValadonMelbourne Victory
65

Sıkça Sorulan Sorular

Our Poisson model calculates the probability of every possible scoreline for Melbourne Victory vs Persib Bandung 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 Melbourne Victory vs Persib Bandung 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 Melbourne Victory 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 Melbourne Victory vs Persib Bandung 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 Melbourne Victory 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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