· AFC Champions League Two
Melbourne Victory
ATT
5/10
DEF
6/10
vs
Persib
5/10
ATT
4/10
DEF
Запланирован· Wed 4 Nov · 07:45
Лучший выбор
ОБЕ ЗАБЬЮТ
54%
Точный счёт
Отличная
1-1
11.4%
8.75
Отличная
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
Составы ещё не объявлены — полный анализ игроков ниже
Melbourne Victory20 игроков
54.4/ 99Средний
Лучшие
1Jordi ValadonMID65
2Tom LockyerDEF64
3Brendan HamillDEF62
Persib30 игроков
52.6/ 99Средний
Лучшие
1RamonFWD89
2Luciano GuaycocheaMID77
3Gabriel MutomboDEF76
Melbourne Victory26
PlayerОбщий РейтингВлияниеАгрессияДисциплина
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
Persib48
PlayerОбщий РейтингВлияниеАгрессияДисциплина
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
Рейтинги игроков
Снайперы
Главные голевые угрозы
1
RamonPersib
99
2
Harrison SawyerMelbourne Victory
65
3
ClarismarioMelbourne Victory
63
4
Guillermo MayMelbourne Victory
63
5
Mikey GhossainiMelbourne Victory
59
Стена
Лучшие защитники
1
Gabriel MutomboPersib
82
2
Patricio MatricardiPersib
77
3
Brendan HamillMelbourne Victory
70
4
Júlio César Persib
67
5
Jason DavidsonMelbourne Victory
60
Контроль
Лучшие плеймейкеры
1
Nazriel Alfaro SyahdanPersib
99
2
T. HayePersib
78
3
Luciano GuaycocheaPersib
63
4
Denis GenreauMelbourne Victory
60
5
Jordi ValadonMelbourne Victory
60
Хулиганы
Склонны к карточкам
1
Harrison SawyerMelbourne Victory
1
2
Adam Alis SetyanoPersib
1
3
T. HayePersib
3
4
Joshua InserraMelbourne Victory
25
5
Kakang RudiantoPersib
30
Рейтинг силы
Сильнейшие в целом
1
RamonPersib
89
2
Luciano GuaycocheaPersib
77
3
Gabriel MutomboPersib
76
4
Nazriel Alfaro SyahdanPersib
76
5
Jordi ValadonMelbourne Victory
65

Часто задаваемые вопросы

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