Slovakia offers a football scene where consistency and form often decide outcomes. As the top competition, the 2. Liga draws consistent attention from fans and analysts. We evaluate fixtures using structured data and observable performance trends. From title races to tight mid-table battles, each match adds context to the season.
| Form | Prediction | Result | Confidence % | |
|---|---|---|---|---|
| Round 30 | ||||
| I. Bratislava Banska Bystrica 2026-05-15 |
LLLWW
WWDDW |
2 DNB | 1:0 |
82.41%
|
| Lubovna Malzenice 2026-05-15 |
LDLWL
LDDWD |
X2 | 1:2 |
91.05%
|
| Petrzalka L. Mikulas 2026-05-15 |
LLWWL
WDWDW |
1 DNB | 2:1 |
66.07%
|
| Puchov Povazska Bystrica 2026-05-15 |
LWDLD
DDDDL |
X2 | 0:1 |
55.53%
|
| Samorin Zvolen 2026-05-15 |
WDDDW
WWDLW |
X2 | 3:1 |
73.42%
|
| Zilina B Slavia TU Kosice 2026-05-15 |
LDLDL
WLDLW |
Handicap 2 (0:2) | 4:0 |
68.52%
|
| Form | Prediction | Result | Confidence % | |
|---|---|---|---|---|
| Round 29 | ||||
| L. Mikulas Puchov 2026-05-08 |
DWDWL
WDLDL |
1 DNB | 3:0 |
53.32%
|
| Malzenice Samorin 2026-05-08 |
DDWDW
DDDWL |
1X | 2:3 |
65.36%
|
| Slavia TU Kosice I. Bratislava 2026-05-08 |
LDLWL
LLWWD |
X2 | 2:1 |
52.77%
|
| Slovan Bratislava B Lubovna 2026-05-08 |
DWWDW
DLWLD |
1 DNB | 5:1 |
92.79%
|
| Z. Moravce-Vrable Petrzalka 2026-05-08 |
LLLLW
LWWLW |
X2 | 1:0 |
77.44%
|
| Zvolen Zilina B 2026-05-08 |
WDLWW
DLDLL |
1 DNB | 5:2 |
92.34%
|
| Round 28 | ||||
| Petrzalka Banska Bystrica 2026-05-03 |
WWLWW
DDWWW |
1X | 1:3 |
76.35%
|
| Zilina B Malzenice 2026-05-03 |
LDLLL
DWDWW |
X2 | 1:1 |
79.32%
|
| Samorin Slovan Bratislava B 2026-05-02 |
DDWLW
WWDWD |
Handicap 2 (0:2) | 1:1 |
81.09%
|
| Lubovna Povazska Bystrica 2026-05-02 |
LWLDW
DDLLD |
X2 | 1:1 |
75.24%
|
| Zvolen Slavia TU Kosice 2026-05-02 |
DLWWD
DLWLD |
1 | 2:1 |
77.25%
|
| Puchov Z. Moravce-Vrable 2026-05-02 |
DLDLD
LLLWL |
X2 | 1:0 |
66.07%
|
| Lehota p. V. I. Bratislava 2026-05-01 |
DLDWL
LWWDL |
X2 | 1:0 |
52.78%
|
Our methodology
We use statistical modeling techniques to produce consistent 2. Liga predictions. Techniques such as Linear Regression help capture underlying trends in match data. Our models assess goal output, defensive records, and consistency over time. The result is a more consistent and data-backed match analysis.
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