· Major League Soccer
明尼苏达联足球俱乐部
ATT
4/10
DEF
5/10
vs
体育堪萨斯城
3/10
ATT
2/10
DEF
未开始· Thu 29 Oct · 00:30
最佳推荐
两队进球
58%
精确比分
顶级
1-1
10.7%
10.00
顶级
2-1
9.7%
8.50
1-0
9%
12.00
2-0
8.1%
10.00
1-2
6.4%
21.00
3-1
5.9%
10.00
0-1
5.9%
26.00
2-2
5.8%
15.00
152%
1.40TOP
X23%
5.00
225%
7.00
1X75%
X248%
1277%
Probable XI3-5-2
Smir
Markanich
Valerius
Taylor
Jackson
Harvey
Gressel
Pereyra
Triantis
Hlongwane
Randell
Impact Player Hot Head Shooter

Dieng Mamadou — always sniffing around the box — the kind of player who just ends up on the scoresheet

Jordan Adebayo-Smith — the go-to finisher in this side, and he loves these nights

K. Yeboah — gets in behind constantly, so a goal here would surprise nobody

阵容尚未公布 — 下方为完整球队分析
明尼苏达联足球俱乐部31 球员
55.3/ 99强
最佳
1J. PereyraMID83
2Marcus CaldeiraFWD72
3Mauricio GonzalezFWD68
体育堪萨斯城30 球员
52.4/ 99中等
最佳
1Jansen MillerDEF82
2Moisés MosqueraDEF76
3Lasse Berg JohnsenMID62
明尼苏达联足球俱乐部36
Player综合评分影响力侵略性纪律
J. Pereyra83765785
Marcus Caldeira72703689
Mauricio Gonzalez68718147
Cherrion Valerius66425889
Dominik Fitz66529961
Dieng Mamadou66764247
C. Harvey65589948
James Rodríguez65754856
D. Padelford63427770
Kyle Duncan63427567
Maurice Malone63569961
AZ Jackson61374991
D. Taylor60508549
体育堪萨斯城36
Player综合评分影响力侵略性纪律
Jansen Miller82807265
Moisés Mosquera76877048
Lasse Berg Johnsen62744449
Jayden Reid61327689
Owen Wolff61417670
Ethan Bartlow60712866
Ian James60414391
Or Blorian60554078
Kwaku Agyabeng59506759
Zorhan Bassong59638141
Dejan Joveljić57523978
Jake Davis54327063
Macielo Tschantret54475261
赛前预测· 阵容尚未公布
最佳射手
高影响力前锋
1
Dieng Mamadou
明尼苏达联足球俱乐部
影响
76
1.83
2
Mauricio Gonzalez
明尼苏达联足球俱乐部
影响
71
2.40
3
Marcus Caldeira
明尼苏达联足球俱乐部
影响
70
2.00
4
Stephen Afrifa
体育堪萨斯城
影响
63
4.00
5
K. Yeboah
明尼苏达联足球俱乐部
影响
58
1.80
6
Maurice Malone
明尼苏达联足球俱乐部
影响
56
2.40
球员排名
狙击手
最大进球威胁
1
Dieng Mamadou明尼苏达联足球俱乐部
76
2
Mauricio Gonzalez明尼苏达联足球俱乐部
71
3
Marcus Caldeira明尼苏达联足球俱乐部
70
4
Mauricio González明尼苏达联足球俱乐部
63
5
Stephen Afrifa体育堪萨斯城
63
城墙
最佳后卫
1
Moisés Mosquera体育堪萨斯城
87
2
Jansen Miller体育堪萨斯城
80
3
Ethan Bartlow体育堪萨斯城
71
4
M. Duggan明尼苏达联足球俱乐部
65
5
Kieran Chandler明尼苏达联足球俱乐部
64
控制
最佳组织者
1
J. Pereyra明尼苏达联足球俱乐部
76
2
James Rodríguez明尼苏达联足球俱乐部
75
3
Lasse Berg Johnsen体育堪萨斯城
74
4
Manu García体育堪萨斯城
71
5
Zorhan Bassong体育堪萨斯城
63
坏小子
最可能吃牌
1
Taylor Calheira体育堪萨斯城
1
2
N. Romero明尼苏达联足球俱乐部
20
3
Stephen Afrifa体育堪萨斯城
22
4
B. Hlongwane明尼苏达联足球俱乐部
25
5
Manu García体育堪萨斯城
25
力量排名
综合最强
1
J. Pereyra明尼苏达联足球俱乐部
83
2
Jansen Miller体育堪萨斯城
82
3
Moisés Mosquera体育堪萨斯城
76
4
Marcus Caldeira明尼苏达联足球俱乐部
72
5
Mauricio Gonzalez明尼苏达联足球俱乐部
68

常见问题

Our Poisson model calculates the probability of every possible scoreline for 明尼苏达联足球俱乐部 vs 体育堪萨斯城 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 明尼苏达联足球俱乐部 vs 体育堪萨斯城 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 明尼苏达联足球俱乐部 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 明尼苏达联足球俱乐部 vs 体育堪萨斯城 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 明尼苏达联足球俱乐部 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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