· Primera Nacional
San Martin de Tucuman
ATT
6/10
DEF
5/10
vs
Colegiales
3/10
ATT
5/10
DEF
Scheduled· Sun 1 Nov · 21:30
Best Pick
Under 2.5
66%
Correct Score
Top
1-0
19.3%
5.50
Top
2-0
14.7%
9.50
0-0
12.7%
5.50
1-1
10.4%
6.00
2-1
8%
11.00
3-0
7.5%
23.00
0-1
6.8%
7.00
3-1
4%
26.00
162%
2.30TOP
X25%
2.70
213%
3.25
1X87%
X238%
1275%
Probable XI4-4-2
Sand
Paz
Cabrera
López
López
Graneros
Cisneros
Carabajal
Soraire
Veliez
Borasi
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
San Martin de Tucuman35 players
43.2/ 99Average
Top Rated
1Nicolás CastroMID79
2Gonzalo RodríguezFWD70
3Alan CisnerosMID64
Colegiales33 players
45.1/ 99Average
Top Rated
1Leo OrtizDEF74
2Elias OcampoMID73
3Gonzalo Leandro BaglivoMID71
San Martin de Tucuman43
PlayerOverall RatingImpactAggressionDiscipline
Nicolás Castro79655890
Gonzalo Rodríguez70774061
Alan Cisneros64642391
Facundo Pons62656946
Elías López61377072
Juan Bautista Valdez60458756
Kevin López59656844
Tiago Penalba58465767
B. Borasi56456872
Guillermo Sebastian Rodríguez52414967
Nahuel Manganelli4949950
Matias Garcia48604536
Darío Sand4645950
Colegiales44
PlayerOverall RatingImpactAggressionDiscipline
Leo Ortiz74843674
Elias Ocampo73595289
Gonzalo Leandro Baglivo71972850
Mauro Albertengo67574891
J. Mamani61526472
Tomás Perri57536161
Ezequiel Adriel Aguirre Quirós56485167
Federico Marín55268580
Franco Zicarelli55872822
Emilio Di Fulvio51517650
Nicolas Toloza51595829
Lucas Juárez48493556
Gastón Cortés46483950
Player Rankings
Snipers
Top goal threats
1
Gonzalo RodríguezSan Martin de Tucuman
77
2
Facundo PonsSan Martin de Tucuman
65
3
Nicolas TolozaColegiales
59
4
Mauro AlbertengoColegiales
57
5
D. DiellosSan Martin de Tucuman
56
Wall
Top defenders
1
Leo OrtizColegiales
84
2
F. RiveroColegiales
75
3
Ezequiel Adriel Aguirre QuirósColegiales
48
4
Gastón CortésColegiales
48
5
Tiago PenalbaSan Martin de Tucuman
46
Control
Top playmakers
1
Gonzalo Leandro BaglivoColegiales
97
2
Franco ZicarelliColegiales
87
3
Kevin LópezSan Martin de Tucuman
65
4
Nicolás CastroSan Martin de Tucuman
65
5
Alan CisnerosSan Martin de Tucuman
64
Bad Boys
Most likely to get booked
1
G. CarabajalSan Martin de Tucuman
1
2
Jorge Emanuel JuárezSan Martin de Tucuman
1
3
Leonardo Gastón MonroySan Martin de Tucuman
1
4
Santiago BriñoneSan Martin de Tucuman
1
5
D. DiellosSan Martin de Tucuman
1
Power Rankings
Strongest overall
1
Nicolás CastroSan Martin de Tucuman
79
2
Leo OrtizColegiales
74
3
Elias OcampoColegiales
73
4
Gonzalo Leandro BaglivoColegiales
71
5
Gonzalo RodríguezSan Martin de Tucuman
70

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for San Martin de Tucuman vs Colegiales 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 San Martin de Tucuman vs Colegiales 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 San Martin de Tucuman 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 San Martin de Tucuman vs Colegiales 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 San Martin de Tucuman 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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