· AFC Cup
Seoul
ATT
5/10
DEF
5/10
vs
Thể Công-Viettel
5/10
ATT
5/10
DEF
Scheduled· Wed 4 Nov · 10:00
Best Pick
BTTS
53%
Correct Score
Top
1-1
12.1%
8.30
Top
1-0
10.6%
9.43
2-1
9.3%
10.79
2-0
8.2%
12.27
0-1
7.8%
12.76
0-0
6.9%
14.51
1-2
6.9%
14.59
2-2
5.3%
18.98
147%
2.15TOP
X25%
3.94
228%
3.56
1X72%
X253%
1275%
Probable XI4-4-2
Kang
Lee
Ahn
Kim
Ros
Jung
Babec
Do-yoon
Kim
Oliveira
Samuel
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Seoul27 players
50.2/ 99Average
Top Rated
1Jeong Hyeon-WooFWD65
2Young-wook ChoFWD62
3Min-jun KimMID59
Thể Công-Viettel21 players
49.9/ 99Average
Top Rated
1HernandesFWD65
2Huu Thang NguyenMID55
3Ngoc Tan DoanMID55
Seoul35
PlayerOverall RatingImpactAggressionDiscipline
Jeong Hyeon-Woo65617161
Young-wook Cho62694550
Min-jun Kim59476067
Sang-min Lee 58585356
Park Jang Han-Gyeol55524661
Pil-gwan Ko55455961
Han-do Lee54484961
Leonardo Ruiz53484867
Seon-min Moon53525156
Jong-beom Baek52534550
Hrvoje Babec52544950
Jae-min Ahn51426352
Juan Antonio Ros51484956
Thể Công-Viettel34
PlayerOverall RatingImpactAggressionDiscipline
Hernandes65733950
Huu Thang Nguyen55614550
Ngoc Tan Doan55486256
Kyle Colonna54525752
Van Khang Khuat54464767
Viet Tu Dinh53465161
Quoc Nhat Nam Le53495356
Duc Hoang Minh Nguyen52488940
Paulinho Curuá52514756
Viet Hung Trieu50464856
André Luiz50524650
Lucas49533556
Tuan Tai Phan49435056
Player Rankings
Snipers
Top goal threats
1
HernandesThể Công-Viettel
73
2
Young-wook ChoSeoul
69
3
Jeong Hyeon-WooSeoul
61
4
Seon-min MoonSeoul
52
5
Leonardo RuizSeoul
48
Wall
Top defenders
1
Sang-min Lee Seoul
58
2
LucasThể Công-Viettel
53
3
Kyle ColonnaThể Công-Viettel
52
4
Jin-su KimSeoul
50
5
Han-do LeeSeoul
48
Control
Top playmakers
1
Huu Thang NguyenThể Công-Viettel
61
2
Hrvoje BabecSeoul
54
3
Park Jang Han-GyeolSeoul
52
4
André LuizThể Công-Viettel
52
5
Paulinho CuruáThể Công-Viettel
51
Bad Boys
Most likely to get booked
1
Ha-won JungSeoul
18
2
Son Jeong-beomSeoul
24
3
Rimario GordonThể Công-Viettel
25
4
Van Tu NguyenThể Công-Viettel
35
5
Yazan AlarabSeoul
36
Power Rankings
Strongest overall
1
Jeong Hyeon-WooSeoul
65
2
HernandesThể Công-Viettel
65
3
Young-wook ChoSeoul
62
4
Min-jun KimSeoul
59
5
Sang-min Lee Seoul
58

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Seoul vs Thể Công-Viettel 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 Seoul vs Thể Công-Viettel 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 Seoul 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 Seoul vs Thể Công-Viettel 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 Seoul 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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