· AFC Champions League Elite
Công an Hà Nội
ATT
5/10
DEF
5/10
vs
京都不死鸟
5/10
ATT
5/10
DEF
未开始· Tue 3 Nov · 12:15
最佳推荐
小 2.5
53%
精确比分
顶级
1-1
12.3%
8.12
顶级
1-0
11.4%
8.74
2-1
9.2%
10.89
2-0
8.5%
11.73
0-1
8.3%
12.11
0-0
7.7%
13.04
1-2
6.6%
15.08
2-2
4.9%
20.22
147%
2.14TOP
X26%
3.86
227%
3.65
1X73%
X253%
1274%
Probable XI4-4-2
Nguyen
Anh
Tran
Hậu
Putros
Ly
Sabater
Long
Đô
Grafite
Vargas
Impact Player Hot Head
阵容尚未公布 — 下方为完整球队分析
Công an Hà Nội21 球员
46.8/ 99中等
最佳
1Dinh Bac NguyenFWD63
2Damià SabaterMID60
3Lê Phạm Thành LongMID60
京都不死鸟21 球员
54.8/ 99中等
最佳
1Rafael Elias da Silva BatagliaFWD81
2Marco Túlio Oliveira LemosFWD64
3Yusuke IshidaDEF61
Công an Hà Nội34
Player综合评分影响力侵略性纪律
Dinh Bac Nguyen63685050
Damià Sabater60514972
Lê Phạm Thành Long60625256
Brayan Andrés Perea Vargas59515078
Đoàn Văn Hậu52605541
Bùi Hoàng Việt Anh50534550
Frans Putros48406052
Ngoc Tin Phan48523950
Van Toan Hoang46454550
Alan Grafite46424161
David Henen46464550
Filip Nguyen45438250
Dinh Trong Tran45454550
京都不死鸟39
Player综合评分影响力侵略性纪律
Rafael Elias da Silva Bataglia81856267
Marco Túlio Oliveira Lemos64733950
Yusuke Ishida61595261
Hisashi Appiah Tawiah60465672
Maceió60477078
Sung-Jun Yoon57515661
Kyo Sato56495461
Thiago56475861
Alex Souza56535561
Hiroto Morooka55565750
Kodai Nagata53594350
Weverton53554456
Haruki Arai53485656
球员排名
狙击手
最大进球威胁
1
Rafael Elias da Silva Bataglia京都不死鸟
85
2
Marco Túlio Oliveira Lemos京都不死鸟
73
3
Dinh Bac NguyenCông an Hà Nội
68
4
Alex Souza京都不死鸟
53
5
Brayan Andrés Perea VargasCông an Hà Nội
51
城墙
最佳后卫
1
Đoàn Văn HậuCông an Hà Nội
60
2
Kodai Nagata京都不死鸟
59
3
Yusuke Ishida京都不死鸟
59
4
Weverton京都不死鸟
55
5
Bùi Hoàng Việt AnhCông an Hà Nội
53
控制
最佳组织者
1
João Pedro Mendes Santos京都不死鸟
65
2
Lê Phạm Thành LongCông an Hà Nội
62
3
Hiroto Morooka京都不死鸟
56
4
Ryuma Nakano京都不死鸟
54
5
Shimpei Fukuoka京都不死鸟
54
坏小子
最可能吃牌
1
Stefan MaukCông an Hà Nội
4
2
Leygley AdouCông an Hà Nội
24
3
Lê Văn ĐôCông an Hà Nội
27
4
Nguyễn Quang HảiCông an Hà Nội
27
5
João Pedro Mendes Santos京都不死鸟
27
力量排名
综合最强
1
Rafael Elias da Silva Bataglia京都不死鸟
81
2
Marco Túlio Oliveira Lemos京都不死鸟
64
3
Dinh Bac NguyenCông an Hà Nội
63
4
Yusuke Ishida京都不死鸟
61
5
Damià SabaterCông an Hà Nội
60

常见问题

Our Poisson model calculates the probability of every possible scoreline for Công an Hà Nội vs 京都不死鸟 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 Công an Hà Nội vs 京都不死鸟 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 Công an Hà Nội 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 Công an Hà Nội vs 京都不死鸟 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 Công an Hà Nội 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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