· Primera Nacional
玛伦
ATT
8/10
DEF
5/10
vs
Club Ciudad de Bolivar
5/10
ATT
7/10
DEF
未开始· Sat 31 Oct · 19:00
最佳推荐
小 2.5
61%
精确比分
顶级
1-0
16.3%
5.00
顶级
2-0
12.4%
6.50
1-1
11.6%
7.00
0-0
10.7%
6.50
2-1
8.8%
9.50
0-1
7.6%
10.00
3-0
6.3%
13.00
3-1
4.5%
19.00
157%
1.73TOP
X26%
3.30
217%
5.50
1X83%
X243%
1274%
Probable XI4-4-2
Díaz
Salvareschi
Iñíguez
Lazza
Flores
Franco
Gonzalez
Berterame
Olivares
Bulacio
Contreras
Impact Player Hot Head
阵容尚未公布 — 下方为完整球队分析
玛伦31 球员
52.6/ 99中等
最佳
1David Ezequiel BulacioFWD85
2Francisco FloresDEF78
3Emanuel IñíguezDEF71
Club Ciudad de Bolivar26 球员
45.0/ 99中等
最佳
1Elias MartinezDEF77
2Agustín ParedesDEF63
3Carlos Emanuel Cuello AzambuyaDEF60
玛伦38
Player综合评分影响力侵略性纪律
David Ezequiel Bulacio85966553
Francisco Flores78687373
Emanuel Iñíguez71539982
Joaquin Zappacosta71926219
Tomás Ariel Ramírez70656267
Leonel Maximiliano Ramírez69577961
Franco Fagúndez62626256
Matías Benítez62645156
Joaquín Livera59316881
Mauro Burruchaga57417958
Iván Manuel Vaquero55415072
Juan Manuel Olivares55755926
Gonzalo Berterame54565449
Club Ciudad de Bolivar39
Player综合评分影响力侵略性纪律
Elias Martinez77996044
Agustín Paredes63418170
Carlos Emanuel Cuello Azambuya60338484
Tobías Fernandez58386272
Tomás Guiacobini55415669
Agustín Rufinetti5454950
Nicolas Ihitz54528340
Federico Peralta53437847
Khalil Caraballo51369771
J. Chacón48523950
Maximiliano Gutiérrez47412177
Arnaldo Gonzalez4683321
A. Carrión45454550
球员排名
狙击手
最大进球威胁
1
David Ezequiel Bulacio玛伦
96
2
Joaquin Zappacosta玛伦
92
3
Matías Benítez玛伦
64
4
Franco Fagúndez玛伦
62
5
Franco Toloza玛伦
54
城墙
最佳后卫
1
Elias MartinezClub Ciudad de Bolivar
99
2
Ezequiel NavarroClub Ciudad de Bolivar
70
3
Francisco Flores玛伦
68
4
B. Salvareschi玛伦
64
5
Emilio Lazza玛伦
58
控制
最佳组织者
1
Arnaldo GonzalezClub Ciudad de Bolivar
83
2
Juan Manuel Olivares玛伦
75
3
Tomás Ariel Ramírez玛伦
65
4
Gaston Gonzalez玛伦
60
5
Gonzalo Berterame玛伦
56
坏小子
最可能吃牌
1
Arnaldo GonzalezClub Ciudad de Bolivar
1
2
Guillermo SánchezClub Ciudad de Bolivar
1
3
Emilio Lazza玛伦
5
4
Ezequiel NavarroClub Ciudad de Bolivar
7
5
Cristian Vega Club Ciudad de Bolivar
13
力量排名
综合最强
1
David Ezequiel Bulacio玛伦
85
2
Francisco Flores玛伦
78
3
Elias MartinezClub Ciudad de Bolivar
77
4
Emanuel Iñíguez玛伦
71
5
Joaquin Zappacosta玛伦
71

常见问题

Our Poisson model calculates the probability of every possible scoreline for 玛伦 vs Club Ciudad de Bolivar 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 玛伦 vs Club Ciudad de Bolivar 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 玛伦 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 玛伦 vs Club Ciudad de Bolivar 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 玛伦 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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