· UEFA Women's Champions League
Køge
ATT
6/10
DEF
6/10
vs
Barcelona W
8/10
ATT
7/10
DEF
Запланирован· Thu 29 Oct · 20:00
Лучший выбор
ОБЕ ЗАБЬЮТ
69%
Точный счёт
Отличная
1-1
9.1%
10.93
Отличная
2-1
8.3%
12.06
1-2
7.8%
12.76
2-2
7.1%
14.08
1-0
5.3%
18.73
0-1
5%
19.82
3-1
5%
19.96
2-0
4.8%
20.66
141%
2.44TOP
X22%
4.51
237%
2.71
1X63%
X259%
1278%
Probable XI4-4-2
Andersen
Termansen
Callesen
Brandt
Ruedt
Oyama
Guy
Paulson
Pelgander
Korhonen
Óskarsdóttir
Impact Player Hot Head
Составы ещё не объявлены — полный анализ игроков ниже
Køge22 игроков
51.1/ 99Средний
Лучшие
1Yukina SakabeDEF76
2Sarah Katrine ThygesenMID71
3Mwanalima JerekoMID67
Barcelona W24 игроков
66.1/ 99Сильный
Лучшие
1Caroline Graham HansenFWD97
2Clàudia Pina i MedinaFWD97
3Irene Paredes HernándezDEF95
Køge52
PlayerОбщий РейтингВлияниеАгрессияДисциплина
Yukina Sakabe76679961
Sarah Katrine Thygesen71628561
Mwanalima Jereko67487678
Emelía Óskarsdóttir66685956
Emma Flick64587356
Zuzanna Witek62526067
Rikke Marie Madsen55565950
Maria Uhre Nielsen53543961
Nadia Nadim53415478
Aemu Oyama50485250
Kyra Taylor Carusa48433867
Alberte Vingum Andersen46453450
Emma Pelkowski46454550
Barcelona W40
PlayerОбщий РейтингВлияниеАгрессияДисциплина
Caroline Graham Hansen97999989
Clàudia Pina i Medina97998689
Irene Paredes Hernández95977978
Ewa Barbara Pajor93994483
Patricia Guijarro Gutiérrez92999965
Francisca Ramos Ribeiro Nazareth de Sousa90978961
Lua Calo73815750
Clara Serrajordi Díaz67994232
Victoria López Serrano Félix66994826
Catalina Thomás Coll Lluch6569950
Aitana Bonmatí i Conca64694661
Marta Torrejón63685950
Sydney Joy Schertenleib63705850
Рейтинги игроков
Снайперы
Главные голевые угрозы
1
Caroline Graham HansenBarcelona W
99
2
Clàudia Pina i MedinaBarcelona W
99
3
Ewa Barbara PajorBarcelona W
99
4
Lua CaloBarcelona W
81
5
Emelía ÓskarsdóttirKøge
68
Стена
Лучшие защитники
1
Irene Paredes HernándezBarcelona W
97
2
Marta TorrejónBarcelona W
68
3
Yukina SakabeKøge
67
4
Emma FlickKøge
58
5
Maria Uhre NielsenKøge
54
Контроль
Лучшие плеймейкеры
1
Clara Serrajordi DíazBarcelona W
99
2
Patricia Guijarro GutiérrezBarcelona W
99
3
Victoria López Serrano FélixBarcelona W
99
4
Francisca Ramos Ribeiro Nazareth de SousaBarcelona W
97
5
Sydney Joy SchertenleibBarcelona W
70
Хулиганы
Склонны к карточкам
1
Victoria López Serrano FélixBarcelona W
26
2
L. HermannKøge
32
3
Mille Gejl JensenKøge
32
4
Clara Serrajordi DíazBarcelona W
32
5
Alberte Vingum AndersenKøge
50
Рейтинг силы
Сильнейшие в целом
1
Caroline Graham HansenBarcelona W
97
2
Clàudia Pina i MedinaBarcelona W
97
3
Irene Paredes HernándezBarcelona W
95
4
Ewa Barbara PajorBarcelona W
93
5
Patricia Guijarro GutiérrezBarcelona W
92

Часто задаваемые вопросы

Our Poisson model calculates the probability of every possible scoreline for Køge vs Barcelona W 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 Køge vs Barcelona W 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 Køge 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 Køge vs Barcelona W 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 Køge 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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