· Slovakia Cup
TJ Slavoj Boleráz
ATT
6/10
DEF
6/10
vs
MFK Skalica
4/10
ATT
6/10
DEF
Scheduled· Wed 4 Nov · 12:00
Best Pick
Over 2.5
80%
Correct Score
2-1
7.6%
13.11
2-2
7%
14.29
1-1
6.2%
16.01
3-1
6.2%
16.09
1-2
5.7%
17.46
3-2
5.7%
17.54
2-3
4.3%
23.38
2-0
4.2%
24.04
152%
1.94TOP
X19%
5.18
229%
3.43
1X71%
X248%
1281%
Probable XI4-4-2
Vahalík
Kitta
Dzibela
Onder
Lalík
Krč
Daniš
Planeta
Kolóny
Klačo
Mikinič
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
TJ Slavoj Boleráz15 players
No squad metrics available
Tomáš VahalíkGK
Adam KittaDEF
Dusan DzibelaDEF
Marcel OnderDEF
Martin LalíkDEF
Radovan KlačoDEF
Jakub KrčMID
Marek DanišMID
Marek PlanetaMID
Marián KolónyMID
Michal MikiničMID
Patrik NeštickýMID
Pavol MackovcinMID
MFK Skalica10 players
49.8/ 99Average
Top Rated
1Oliver PodhorínDEF59
2Petr PudhorockýMID57
3Adam RavasMID53
TJ Slavoj Boleráz15
PlayerOverall RatingImpactAggressionDiscipline
Tomáš Vahalík————
Adam Kitta————
Dusan Dzibela————
Marcel Onder————
Martin Lalík————
Radovan Klačo————
Jakub Krč————
Marek Daniš————
Marek Planeta————
Marián Kolóny————
Michal Mikinič————
Patrik Neštický————
Pavol Mackovcin————
MFK Skalica27
PlayerOverall RatingImpactAggressionDiscipline
Oliver Podhorín59535861
Petr Pudhorocký57565656
Adam Ravas53505256
Peter Guinari51534850
Erik Daniel51524550
Samuel Suľa50465056
Martin Mášik50514850
Adam Morong50475156
Erik Riška49494550
Damián Bariš2846575
Martin Junas————
Richard Ludha————
L. Šimko————
Player Rankings
Snipers
Top goal threats
1
Erik DanielMFK Skalica
52
2
Adam MorongMFK Skalica
47
Wall
Top defenders
1
Oliver PodhorínMFK Skalica
53
2
Peter GuinariMFK Skalica
53
3
Samuel SuľaMFK Skalica
46
Control
Top playmakers
1
Petr PudhorockýMFK Skalica
56
2
Martin MášikMFK Skalica
51
3
Adam RavasMFK Skalica
50
4
Damián BarišMFK Skalica
46
Bad Boys
Most likely to get booked
1
Damián BarišMFK Skalica
5
2
Erik RiškaMFK Skalica
50
3
Peter GuinariMFK Skalica
50
4
Martin MášikMFK Skalica
50
5
Erik DanielMFK Skalica
50
Power Rankings
Strongest overall
1
Oliver PodhorínMFK Skalica
59
2
Petr PudhorockýMFK Skalica
57
3
Adam RavasMFK Skalica
53
4
Peter GuinariMFK Skalica
51
5
Erik DanielMFK Skalica
51

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for TJ Slavoj Boleráz vs MFK Skalica 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 TJ Slavoj Boleráz vs MFK Skalica 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 TJ Slavoj Boleráz 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 TJ Slavoj Boleráz vs MFK Skalica 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 TJ Slavoj Boleráz 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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