Panathinaikos carry the sharper attacking threat, while Volos’ fragile defence offers a clear route to chances. The hosts’ own back line could make this a two-way contest despite their blunt attack; if the visitors’ pressure breaks through early, Volos may have to open up, making a lively, high-scoring finish the likeliest script.
Match GoalsOver 2.5
Лучший выбор
Меньше 2.5
59%
Точный счёт
Отличная
0-1
13%
7.50
Отличная
1-1
13%
9.00
0-0
9.9%
13.00
1-0
9.9%
21.00
0-2
8.6%
7.00
1-2
8.5%
8.50
2-1
6.5%
23.00
2-0
4.9%
41.00
128%
8.00
X28%
4.50
244%
1.36TOP
1X56%
X272%
1272%
Экспресс
3 выборов
Panathinaikos look the logical winners: their 1.78 xG is more than double Volos’s 0.77, while their attack is 11% stronger and Volos’s defence 33% weaker than average. That defensive fragility gives Volos a route to score, supporting both teams to score; with Panathinaikos likely to drive the game, over 2.5 goals also fits.
1Match WinnerPanathinaikos
2Match GoalsOver 2.5
3Both Teams to ScoreYes
Confirmed XI4-5-1
1
Siampanis
28
Katsikas
99
Agyakwa
4
Kargas
37
Mendyl
8
Comba
27
Burnic
21
González
20
Regis
14
Cabella
9
Hurtado
Impact Player Hot Head
Volos NFC31 игроков
47.6/ 99Средний
Лучшие
1Georgios CharalampoglouFWD97
2Derek AgyakwaDEF62
3Rémy CabellaMID58
Panathinaikos37 игроков
52.6/ 99Средний
Лучшие
1Cyriel DessersFWD93
2Adam Gnezda CerinMID68
3Manolis SiopisMID67
Рейтинги игроков
Снайперы
Главные голевые угрозы
1
Georgios CharalampoglouVolos NFC
99
2
Cyriel DessersPanathinaikos
99
3
Elmin RastoderPanathinaikos
68
4
Andreas TettehPanathinaikos
67
5
Levi GarcíaPanathinaikos
57
Стена
Лучшие защитники
1
Erik Palmer-BrownPanathinaikos
86
2
Giannis KargasVolos NFC
76
3
Tin JedvajPanathinaikos
70
4
Sverrir Ingi IngasonPanathinaikos
58
5
Hatim Es-SaoubiVolos NFC
57
Контроль
Лучшие плеймейкеры
1
Imrân LouzaPanathinaikos
86
2
Anastasios Bakasetas Panathinaikos
82
3
Pedro ChirivellaPanathinaikos
77
4
Tonny VilhenaPanathinaikos
63
5
Adam Gnezda CerinPanathinaikos
59
Хулиганы
Склонны к карточкам
1
Giannis KargasVolos NFC
1
Агрессия 48
2
Hamza MendylVolos NFC
1
Агрессия 52
3
Étienne CamaraPanathinaikos
1
Агрессия 64
4
Pedro ChirivellaPanathinaikos
18
Агрессия 45
5
Maximiliano CombaVolos NFC
20
Агрессия 49
Рейтинг силы
Сильнейшие в целом
1
Georgios CharalampoglouVolos NFC
97
2
Cyriel DessersPanathinaikos
93
3
Adam Gnezda CerinPanathinaikos
68
4
Manolis SiopisPanathinaikos
67
5
Elmin RastoderPanathinaikos
66
Часто задаваемые вопросы
Our Poisson model calculates the probability of every possible scoreline for Volos NFC vs Panathinaikos 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 Volos NFC vs Panathinaikos 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 Volos NFC 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 Volos NFC vs Panathinaikos 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 Volos NFC 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.