Argentina remains a strong environment for unpredictable and competitive football. The Primera C continues to attract interest for its competitive balance and structure. We evaluate fixtures using structured data and observable performance trends.
| Form | Prediction | Result | Confidence % | |
|---|---|---|---|---|
| Round 32 | ||||
| Atlas El Porvenir 2026-10-12 |
LDLLD
DLLWL |
1X | 1:2 |
70.29%
|
| Berazategui Def. de Cambaceres 2026-10-12 |
LWDWW
LLDWW |
1 | 3:0 |
70.87%
|
| Canuelas Central Ballester 2026-10-12 |
DWWWW
LLDWW |
Handicap 2 (0:2) | 4:0 |
79.87%
|
| Deportivo Espanol Central Cordoba 2026-10-12 |
DWWDW
WWDWW |
X2 | 1:0 |
57.31%
|
| General Lamadrid Sportivo Barracas 2026-10-12 |
LDDDD
LDLLW |
1X | 0:0 |
70.87%
|
| Mercedes Lugano 2026-10-12 |
DWWDL
LDDDW |
X2 | 1:0 |
76.65%
|
| Form | Prediction | Result | Confidence % | |
|---|---|---|---|---|
| Round 31 | ||||
| Claypole Deportivo Espanol 2026-10-06 |
LWWLL
WWDWW |
X2 | 1:1 |
82.07%
|
| Round 28 | ||||
| Claypole Sacachispas 2026-09-15 |
LLDWL
DWLDW |
X2 | 2:0 |
83.55%
|
| Fenix CSR Espanol 2026-09-14 |
LLDDW
DDWDW |
Handicap 1 (2:0) | 1:1 |
55.53%
|
| Central Cordoba Argentino de Rosario 2026-09-13 |
WWWDW
DDLLW |
1 DNB | 3:0 |
92.02%
|
| Def. de Cambaceres Sportivo Barracas 2026-09-13 |
WDWLW
WLDLW |
X2 | 1:0 |
66.94%
|
| Justo Jose de Urquiza General Lamadrid 2026-09-13 |
WLWLL
DLWWD |
1X | 0:0 |
77.44%
|
| Berazategui Atlas 2026-09-12 |
WDLDL
DLWWW |
X2 | 2:0 |
50.86%
|
| Canuelas Victoriano A. 2026-09-12 |
WWWLL
DDWLD |
1X | 2:0 |
75.23%
|
| Central Ballester Puerto Nuevo 2026-09-12 |
WWDDW
LWLDD |
1X | 2:0 |
64.18%
|
| Club Lujan Deportivo Paraguayo 2026-09-12 |
LWDWL
WDWLL |
1 | 3:0 |
89.23%
|
| Estrella Del Sur Yupanqui 2026-09-12 |
LDLWW
LLLDW |
1X | 1:1 |
52.77%
|
| Juventud Unida S. M. El Porvenir 2026-09-12 |
LWWLW
LWDWL |
1X | 0:1 |
89.2%
|
| Lugano Leones de Rosario 2026-09-12 |
WDWLD
WWWLW |
X2 | 0:0 |
66.07%
|
Our methodology
Our Primera C predictions are built using a data-driven approach based on statistical modeling. Linear Regression models are used to break down performance indicators across teams. We focus on performance indicators including scoring rates and defensive stability. The goal is to offer a clearer and more objective view of each fixture.
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