Both sides may lack a cutting edge, but the defensive cracks tell the story: Newell’s are especially vulnerable, and San Lorenzo’s back line is far from watertight. That gives each team a route to trouble, with chances likely to build as the game opens up. The second goal looks well within reach—Over 1.5 is the logical call.
Match GoalsOver 1.5
Best Pick
Under 2.5
66%
Correct Score
Top
1-0
15.2%
6.00
Top
1-1
13.1%
6.00
0-0
12.5%
5.50
0-1
10.8%
6.50
2-0
9.2%
11.00
2-1
8%
11.00
1-2
5.7%
13.00
0-2
4.7%
13.00
144%
2.60TOP
X30%
2.88
226%
3.10
1X74%
X256%
1270%
Combined Bet Builder
3 legs
San Lorenzo’s home advantage should keep them on the front foot, while Newell’s equally vulnerable defence offers a route to the breakthrough. Both attacks have shown limits, but neither back line looks secure enough to contain the contest; a response from Newell’s appears likely, making goals at both ends and a San Lorenzo-or-draw finish the compelling script.
1Double ChanceSan Lorenzo or Draw
2Match GoalsOver 1.5
3Both Teams to ScoreYes
Confirmed XI5-3-2
25
Devecchi
2
Arboleda
16
Corujo
19
Amor
32
Herrera
3
Pagano
24
Tripichio
10
Gulli
14
Ladstatter
9
Cuello
29
Auzmendi
Impact Player Hot Head
San Lorenzo44 players
48.0/ 99Average
Top Rated
1Gastón Hernández DEF74
2Manuel Insaurralde MID68
3Danilo ArboledaDEF67
Newell's39 players
52.5/ 99Average
Top Rated
1David SoteloMID75
2Lautaro GiannettiDEF73
3Marcelo EspondaMID69
Player Rankings
Snipers
Top goal threats
1
Rodrigo AuzmendiSan Lorenzo
76
2
Luciano HerreraNewell's
58
3
F. ScarpeccioNewell's
56
4
Ignacio RamírezNewell's
54
5
Matías CóccaroNewell's
54
Wall
Top defenders
1
Danilo ArboledaSan Lorenzo
87
2
Nicolás Goitea Newell's
86
3
Lautaro GiannettiNewell's
83
4
Gastón Hernández San Lorenzo
82
5
Saúl SalcedoNewell's
82
Control
Top playmakers
1
David SoteloNewell's
71
2
Juan Ignacio MéndezNewell's
69
3
Jerónimo Gómez MattarNewell's
63
4
Alan SoñoraNewell's
57
5
John David Carabalí CarabalíSan Lorenzo
55
Bad Boys
Most likely to get booked
1
Jhohan RomañaSan Lorenzo
1
Aggression 37
2
Matías RealiSan Lorenzo
1
Aggression 99
3
Jerónimo Gómez MattarNewell's
1
Aggression 48
4
Martín RíoSan Lorenzo
8
Aggression 57
5
Ezequiel CeruttiSan Lorenzo
10
Aggression 63
Power Rankings
Strongest overall
1
David SoteloNewell's
75
2
Gastón Hernández San Lorenzo
74
3
Lautaro GiannettiNewell's
73
4
Marcelo EspondaNewell's
69
5
Manuel Insaurralde San Lorenzo
68
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for San Lorenzo vs Newell's 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 Lorenzo vs Newell's 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 Lorenzo 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 Lorenzo vs Newell's 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 Lorenzo 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.