· Serie A
Atalanta
ATT
5/10
DEF
7/10
vs
Frosinone
5/10
ATT
5/10
DEF
Запланирован· Sun 25 Oct · 14:00

ИИ-анализ матча

Atalanta’s territorial pressure and steady stream of attacking set pieces should force the breakthrough, while Frosinone may have to abandon its cautious shape and respond. That shift points toward an increasingly open contest, with the goals threshold likely surpassed.

Match GoalsOver 2.5
Лучший выбор
Меньше 2.5
53%
Точный счёт
Отличная
1-1
12.1%
8.50
Отличная
1-0
12.1%
13.00
2-1
9.4%
9.00
2-0
9.4%
12.00
0-0
7.8%
21.00
0-1
7.8%
21.00
1-2
6%
15.00
3-0
4.9%
17.00
150%
1.75TOP
X26%
4.00
224%
4.33
1X76%
X250%
1274%

Экспресс

3 выборов

Atalanta’s 2.12 xG gives them the attacking edge, while their defense is 31% stronger than average, making Atalanta or draw the logical result. Frosinone’s 0.84 xG still offers a route to the third goal, and combined expected corners of 9.89 support over 8.5. The match profile points toward a high-tempo game with goals and sustained pressure.

1Double ChanceAtalanta BC or Draw
2Match GoalsOver 2.5
3Corners Over/UnderOver 8.5
Confirmed XI2-5-3
29
Carnesecchi
31
Kristensen
40
Obric
16
Bellanova
47
Bernasconi
70
Gaetano
13
Éderson
99
Elmas
17
Ketelaere
11
Rowe
90
Krstović
Impact Player Hot Head
Atalanta31 игроков
53.7/ 99Средний
Лучшие
1Nikola KrstovićFWD88
2Gianluca ScamaccaFWD75
3Lazar SamardžićMID73
Frosinone22 игроков
59.0/ 99Сильный
Лучшие
1Wisdom Amey DEF92
2Giorgio CittadiniDEF83
3Ilario MonterisiDEF79
Рейтинги игроков
Снайперы
Главные голевые угрозы
1
Nikola KrstovićAtalanta
99
2
Gianluca ScamaccaAtalanta
76
3
Antonio RaimondoFrosinone
76
4
Tomas BobcekFrosinone
68
5
Giacomo RaspadoriAtalanta
66
Стена
Лучшие защитники
1
Wisdom Amey Frosinone
99
2
Giorgio CittadiniFrosinone
77
3
Thomas KristensenAtalanta
76
4
Berat DjimsitiAtalanta
66
5
Ilario MonterisiFrosinone
65
Контроль
Лучшие плеймейкеры
1
Mario PasalićAtalanta
74
2
Marten de Roon Atalanta
73
3
Giacomo CalòFrosinone
73
4
Luis HasaFrosinone
71
5
Gianluca GaetanoAtalanta
66
Хулиганы
Склонны к карточкам
1
Raoul BellanovaAtalanta
1
2
Kamaldeen SulemanaAtalanta
1
3
Gabriele BracagliaFrosinone
5
4
Ibrahim SulemanaAtalanta
14
5
Gabriele CalvaniFrosinone
20
Рейтинг силы
Сильнейшие в целом
1
Wisdom Amey Frosinone
92
2
Nikola KrstovićAtalanta
88
3
Giorgio CittadiniFrosinone
83
4
Ilario MonterisiFrosinone
79
5
Giacomo CalòFrosinone
76

Часто задаваемые вопросы

Our Poisson model calculates the probability of every possible scoreline for Atalanta vs Frosinone 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 Atalanta vs Frosinone 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 Atalanta 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 Atalanta vs Frosinone 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 Atalanta 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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