· Champions League
Viking FK
ATT
9/10
DEF
6/10
vs
Bayern München
10/10
ATT
8/10
DEF
未开始· Tue 13 Oct · 19:00
最佳推荐
两队进球
69%
精确比分
顶级
1-1
8.9%
23.00
顶级
2-1
8.3%
41.00
1-2
7.7%
13.00
2-2
7.1%
29.00
1-0
5.2%
51.00
3-1
5.2%
81.00
0-1
4.8%
19.00
2-0
4.8%
81.00
142%
15.00TOP
X22%
11.00
236%
1.10
1X64%
X258%
1278%
Probable XI3-5-2
Østbø
Bærtelsen
Bassey
Stensness
Bjørdal
Hansen
Bell
Askildsen
Kvia-Egeskog
Cosic
Botheim
Impact Player Hot Head Shooter

Henrik Bjørdal — always sniffing around the box — the kind of player who just ends up on the scoresheet

Erik Botheim  — the danger man up front — one good ball and he is away

Peter Christiansen — always sniffing around the box — the kind of player who just ends up on the scoresheet

阵容尚未公布 — 下方为完整球队分析
Viking FK32 球员
52.2/ 99中等
最佳
1Erik Botheim FWD80
2Nick D'AgostinoFWD78
3Henrik FalchenerDEF75
Bayern München32 球员
51.4/ 99中等
最佳
1Harry Kane FWD90
2Aleksandar PavlovicMID87
3Tom BischofMID80
Viking FK35
Player综合评分影响力侵略性纪律
Erik Botheim 80766183
Nick D'Agostino78767667
Henrik Falchener75994549
Henrik Heggheim74639964
Niklas Fuglestad70646772
Arild Østbø68718753
Vetle Auklend67428291
Jesper Daland66516372
Sondre Bjørshol65688940
Peter Christiansen61535379
Anders Bærtelsen60734647
Lubomir Belko5758950
Viljar Vevatne57703750
Bayern München67
Player综合评分影响力侵略性纪律
Harry Kane 90993481
Aleksandar Pavlovic87975174
Tom Bischof80993867
Armindo Sieb74649991
Luis Díaz73666578
David Santos Daiber65577361
Joshua Kimmich 63992426
Leon Goretzka61683660
Felipe Chavez60625356
Bara Sapoko Ndiaye56545556
Ismael Saibari55574756
Serge Gnabry55545056
Deniz Ofli51564550
赛前预测· 阵容尚未公布
最佳射手
高影响力前锋
1
Harry Kane 
Bayern München
影响
99
1.36
2
Erik Botheim 
Viking FK
影响
76
4.14
3
Luis Díaz
Bayern München
影响
66
1.89
4
Niklas Fuglestad
Viking FK
影响
64
6.70
5
Peter Christiansen
Viking FK
影响
53
4.48
6
A. Cosic
Viking FK
影响
48
6.70
球员排名
狙击手
最大进球威胁
1
Harry Kane Bayern München
99
2
Erik Botheim Viking FK
76
3
Nick D'AgostinoViking FK
76
4
Luis DíazBayern München
66
5
Niklas FuglestadViking FK
64
城墙
最佳后卫
1
Henrik FalchenerViking FK
99
2
Anders BærtelsenViking FK
73
3
Gianni Stensness Viking FK
71
4
Viljar VevatneViking FK
70
5
Sondre BjørsholViking FK
68
控制
最佳组织者
1
Joshua Kimmich Bayern München
99
2
Tom BischofBayern München
99
3
Aleksandar PavlovicBayern München
97
4
Leon GoretzkaBayern München
68
5
Henrik BjørdalViking FK
65
坏小子
最可能吃牌
1
Tobias MoiViking FK
1
2
Edvin AustbøViking FK
1
3
Ruben AlteViking FK
8
4
Kristoffer AskildsenViking FK
14
5
Herman HaugenViking FK
18
力量排名
综合最强
1
Harry Kane Bayern München
90
2
Aleksandar PavlovicBayern München
87
3
Erik Botheim Viking FK
80
4
Tom BischofBayern München
80
5
Nick D'AgostinoViking FK
78

常见问题

Our Poisson model calculates the probability of every possible scoreline for Viking FK vs Bayern München 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 Viking FK vs Bayern München 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 Viking FK 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 Viking FK vs Bayern München 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 Viking FK 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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