Polissya’s second-place strength points to a front-foot win: 2.13 xG overall and 1.51 in the first half, against Obolon’s 0.54 and 0.00. That gulf supports an early home lead, while Polissya’s +38% attack power and Obolon’s defense 43% weaker than average make Over 2.5 a logical part of the builder.
1Match WinnerPolissya Zhytomyr
2Match GoalsOver 2.5
3First Half WinnerPolissya Zhytomyr
Probable XI4-4-2
Drobot
Hadroj
Araujo
Mykhaylichenko
Tomandzoto
Lednev
Panchyshyn
Krushynskyi
Mustafayev
Makouana
Krasnopir
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Polissya Zhytomyr33 players
50.2/ 99Average
Top Rated
1Mykola GayduchykFWD84
2Oleksandr AndriyevskyiMID73
3Igor KrasnopirFWD72
FC Obolon Kyiv32 players
50.2/ 99Average
Top Rated
1Oleg Slobodyan MID86
2Oleksandr FeshchenkoDEF80
3Andriy MatkevychFWD73
Polissya Zhytomyr45
PlayerOverall RatingImpactAggressionDiscipline
Mykola Gayduchyk84953469
Oleksandr Andriyevskyi73694087
Igor Krasnopir72635391
Kiril Kobernyuk65488361
Ruslan Babenko 64676452
Maksym Bragaru63594772
Bogdan Lednev60753450
João Vialle58506856
Oleksiy Gutsulyak58564467
Yevgen Volynets5759950
Eduard Sarapiy54404476
Georgiy Bushchan52529956
Danylo Beskorovaynyi51564550
FC Obolon Kyiv37
PlayerOverall RatingImpactAggressionDiscipline
Oleg Slobodyan 86844791
Oleksandr Feshchenko80999927
Andriy Matkevych73755961
Denys Ustymenko69586488
Taras Lyakh68599980
Andriy Lomnytskyi65694467
Dmytro Kapinus64607054
Ruslan Chernenko63377491
Kyrylo Prokopchuk59784937
R. Volokhatyi58369968
Dmytro Semenov57755931
Sergiy Sukhanov56478158
Vitaliy Yermakov55753738
Player Rankings
Snipers
Top goal threats
1
Mykola GayduchykPolissya Zhytomyr
95
2
Andriy MatkevychFC Obolon Kyiv
75
3
Igor KrasnopirPolissya Zhytomyr
63
4
Maksym BragaruPolissya Zhytomyr
59
5
Taras LyakhFC Obolon Kyiv
59
Wall
Top defenders
1
Oleksandr FeshchenkoFC Obolon Kyiv
99
2
Kyrylo ProkopchukFC Obolon Kyiv
78
3
Dmytro SemenovFC Obolon Kyiv
75
4
Vitaliy YermakovFC Obolon Kyiv
75
5
Andriy LomnytskyiFC Obolon Kyiv
69
Control
Top playmakers
1
Oleg Slobodyan FC Obolon Kyiv
84
2
Bogdan LednevPolissya Zhytomyr
75
3
Lindon EmërllahuPolissya Zhytomyr
69
4
Oleksandr AndriyevskyiPolissya Zhytomyr
69
5
Ruslan Babenko Polissya Zhytomyr
67
Bad Boys
Most likely to get booked
1
Lindon EmërllahuPolissya Zhytomyr
1
Aggression 9
2
Denys ShostakFC Obolon Kyiv
1
Aggression 86
3
Maksym ChekhFC Obolon Kyiv
1
Aggression 67
4
Taras Moroz FC Obolon Kyiv
1
Aggression 82
5
O. ZhovtenkoFC Obolon Kyiv
5
Aggression 82
Power Rankings
Strongest overall
1
Oleg Slobodyan FC Obolon Kyiv
86
2
Mykola GayduchykPolissya Zhytomyr
84
3
Oleksandr FeshchenkoFC Obolon Kyiv
80
4
Oleksandr AndriyevskyiPolissya Zhytomyr
73
5
Andriy MatkevychFC Obolon Kyiv
73
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for Polissya Zhytomyr vs FC Obolon Kyiv 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 Polissya Zhytomyr vs FC Obolon Kyiv 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 Polissya Zhytomyr 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 Polissya Zhytomyr vs FC Obolon Kyiv 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 Polissya Zhytomyr 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.