· Primera Nacional
San Telmo
ATT
4/10
DEF
3/10
vs
Central Norte
4/10
ATT
5/10
DEF
Scheduled· Sat 31 Oct · 18:00
Best Pick
Under 2.5
71%
Correct Score
Top
1-0
16.6%
5.50
Top
0-0
15.1%
5.00
1-1
13.1%
5.50
0-1
11.9%
7.00
2-0
9.1%
9.50
2-1
7.2%
11.00
1-2
5.2%
15.00
0-2
4.7%
15.00
143%
2.35TOP
X31%
2.70
226%
3.50
1X74%
X257%
1269%
Probable XI4-4-2
Bertoli
Jazbec
Diaz
Paredes
Pusula
Britez
Peralta
Henríquez
Tello
Núñez
Ortiz
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
San Telmo30 players
45.7/ 99Average
Top Rated
1Román GamarraMID72
2Lázaro BritezDEF71
3Cristian NúñezFWD69
Central Norte31 players
50.5/ 99Average
Top Rated
1Matías MoravecMID87
2Maximiliano AlvezMID80
3Elias CalderónDEF75
San Telmo49
PlayerOverall RatingImpactAggressionDiscipline
Román Gamarra72685178
Lázaro Britez71675473
Cristian Núñez69658761
Leonel Pollacchi60685745
Nicolás Darío Ortiz60366971
Ian Pardo56634550
Lucas Baldunciel56525661
Juan Ignacio Motroni54474865
Elias Peralta54546247
Iñaki Lartirigoyen54535056
Joaquín Enrico53519960
Martín Batallini53378981
Franco Tisera51498840
Central Norte36
PlayerOverall RatingImpactAggressionDiscipline
Matías Moravec87906970
Maximiliano Alvez80716578
Elias Calderón75766859
S. Quiroga75923763
Agustín Aleo67457085
Agustín Bustinduy64684167
Franco Vedoya64732461
Julián López60468491
F. Borda59487273
Leonardo Felissia58525264
Joaquín Hass5759950
Érik Bodencer57728216
Joaquín Mateo García56632850
Player Rankings
Snipers
Top goal threats
1
Franco VedoyaCentral Norte
73
2
Érik BodencerCentral Norte
72
3
Cristian NúñezSan Telmo
65
4
Joaquín Mateo GarcíaCentral Norte
63
5
K. Isa LunaCentral Norte
54
Wall
Top defenders
1
Elias CalderónCentral Norte
76
2
Leonel PollacchiSan Telmo
68
3
Lázaro BritezSan Telmo
67
4
Emanuel DiazSan Telmo
59
5
Gonzalo CatillaCentral Norte
55
Control
Top playmakers
1
S. QuirogaCentral Norte
92
2
Matías MoravecCentral Norte
90
3
Maximiliano AlvezCentral Norte
71
4
Román GamarraSan Telmo
68
5
Agustín BustinduyCentral Norte
68
Bad Boys
Most likely to get booked
1
Maximiliano PadillaCentral Norte
1
2
Kevin Juan Cruz FernándezCentral Norte
1
3
K. Isa LunaCentral Norte
1
4
Pablo Mauricio RosalesCentral Norte
12
5
Érik BodencerCentral Norte
16
Power Rankings
Strongest overall
1
Matías MoravecCentral Norte
87
2
Maximiliano AlvezCentral Norte
80
3
Elias CalderónCentral Norte
75
4
S. QuirogaCentral Norte
75
5
Román GamarraSan Telmo
72

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for San Telmo vs Central Norte 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 Telmo vs Central Norte 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 Telmo 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 Telmo vs Central Norte 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 Telmo 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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