· Primera Nacional
Acassuso
ATT
3/10
DEF
4/10
vs
Ferro Carril Oeste
8/10
ATT
7/10
DEF
Scheduled· Sat 31 Oct · 18:00
Best Pick
Under 2.5
73%
Correct Score
Top
0-1
20.4%
4.33
Top
0-0
16.7%
8.00
0-2
12.5%
4.00
1-1
11.6%
19.00
1-0
9.5%
23.00
1-2
7.1%
17.00
0-3
5.1%
5.00
2-1
3.3%
67.00
117%
29.00
X30%
7.50
252%
1.10TOP
1X47%
X282%
1269%
Probable XI4-4-2
Atamañuk
Ruiz
Ruiz
Dordoni
Cortés
Hermoso
González
Stéfano
Roberts
Duarte
Duarte
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Acassuso35 players
41.0/ 99Average
Top Rated
1Joel GhirardelloDEF75
2Natán AcostaDEF71
3Álex RuizDEF68
Ferro Carril Oeste31 players
56.6/ 99Strong
Top Rated
1Uriel PepeFWD90
2Matías KabalinMID85
3E. HoyosMID81
Acassuso43
PlayerOverall RatingImpactAggressionDiscipline
Joel Ghirardello75596288
Natán Acosta71599956
Álex Ruiz68527963
Lucas Pioli64804251
S. Bellatti63722774
Santiago Andrés Borges55465762
David De Stéfano54345873
Nicolás Stepanovitch52485156
Martín Schlotthauer51356778
Jorge Damián Luque50494550
Mariano Monllor5051950
Ramiro Reynoso49405062
Jonathan Raccio4887451
Ferro Carril Oeste37
PlayerOverall RatingImpactAggressionDiscipline
Uriel Pepe90998550
Matías Kabalin85845780
E. Hoyos81994960
Juan Orellana77607391
F. García73932136
M. Benegas73869629
N. Reniero73675883
J. Fernandes Pinto65407291
T. Esidín62763950
F. Tévez60603674
N. Kihm57338262
Matías Gabriel Zubowicz56496056
Gino Olguín56477552
Player Rankings
Snipers
Top goal threats
1
Uriel PepeFerro Carril Oeste
99
2
F. GarcíaFerro Carril Oeste
93
3
M. BenegasFerro Carril Oeste
86
4
Emanuel DeningFerro Carril Oeste
73
5
N. RenieroFerro Carril Oeste
67
Wall
Top defenders
1
Lucas PioliAcassuso
80
2
S. BellattiAcassuso
72
3
Leandro Peña BiaforeFerro Carril Oeste
61
4
F. TévezFerro Carril Oeste
60
5
Juan OrellanaFerro Carril Oeste
60
Control
Top playmakers
1
E. HoyosFerro Carril Oeste
99
2
Jonathan RaccioAcassuso
87
3
Matías KabalinFerro Carril Oeste
84
4
T. EsidínFerro Carril Oeste
76
5
E. OzunaFerro Carril Oeste
65
Bad Boys
Most likely to get booked
1
Román EtcheverriAcassuso
1
2
Joaquin PostigoAcassuso
1
3
Jonathan RaccioAcassuso
1
4
Manuel MónacoAcassuso
1
5
David EscalanteAcassuso
1
Power Rankings
Strongest overall
1
Uriel PepeFerro Carril Oeste
90
2
Matías KabalinFerro Carril Oeste
85
3
E. HoyosFerro Carril Oeste
81
4
Juan OrellanaFerro Carril Oeste
77
5
Joel GhirardelloAcassuso
75

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Acassuso vs Ferro Carril Oeste 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 Acassuso vs Ferro Carril Oeste 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 Acassuso 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 Acassuso vs Ferro Carril Oeste 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 Acassuso 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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