Over 8.5 corners looks the logical call: Roma win corners frequently, while Real Madrid’s attack is exceptionally strong at creating them and they win them very often. With both sides carrying Champions League pressure and motivation, sustained attacks should keep the ball going behind and the corner count climbing.
Corners Over/UnderOver 8.5
Best Pick
Over 2.5
67%
Correct Score
Top
2-1
9.6%
13.00
Top
1-1
8.7%
8.00
2-0
7.8%
23.00
1-0
7.1%
19.00
3-1
7.1%
23.00
2-2
5.9%
11.00
3-0
5.8%
41.00
1-2
5.4%
9.00
160%
3.50TOP
X20%
3.90
220%
1.95
1X80%
X240%
1280%
Combined Bet Builder
3 legs
Roma’s 1.36 xG and Real Madrid’s 1.72 point to a lively contest, with 3.08 combined xG supporting over 2.5 goals and both teams to score. Their 9.62 combined expected corners, reinforced by Madrid’s +46% corner power, make over 8.5 corners a logical fit.
1Match GoalsOver 2.5
2Both Teams to ScoreYes
3Corners Over/UnderOver 8.5
Confirmed XI4-3-3
95
Gollini
23
Mancini
5
Ndicka
22
Hermoso
20
Molina
15
Roon
17
Koné
43
Wesley
21
Dybala
18
Soulé
14
Malen
Impact Player Hot Head
AS Roma28 players
54.6/ 99Average
Top Rated
1Bryan CristanteMID91
2Donyell MalenFWD77
3Artem DovbykFWD74
Real Madrid31 players
49.4/ 99Average
Top Rated
1Carlos EspíFWD87
2Eduardo Camavinga MID83
3Kylian Mbappé FWD83
Player Rankings
Snipers
Top goal threats
1
Donyell MalenAS Roma
99
2
Kylian Mbappé Real Madrid
99
3
Carlos EspíReal Madrid
91
4
Artem DovbykAS Roma
89
5
RodrygoReal Madrid
84
Wall
Top defenders
1
Jan ZiolkowskiAS Roma
80
2
Dean HuijsenReal Madrid
62
3
Evan NdickaAS Roma
54
4
Leonardo Balerdi AS Roma
50
5
Daniele GhilardiAS Roma
48
Control
Top playmakers
1
Bryan CristanteAS Roma
95
2
Aurélien TchouaméniReal Madrid
86
3
Arda GülerReal Madrid
84
4
Marten de Roon AS Roma
81
5
Eduardo Camavinga Real Madrid
81
Bad Boys
Most likely to get booked
1
Matías SouléAS Roma
1
Aggression 20
2
Ibrahima KonatéReal Madrid
1
Aggression 48
3
Trent Alexander-ArnoldReal Madrid
1
Aggression 24
4
RodrygoReal Madrid
1
Aggression 70
5
Franco MastantuonoReal Madrid
7
Aggression 66
Power Rankings
Strongest overall
1
Bryan CristanteAS Roma
91
2
Carlos EspíReal Madrid
87
3
Eduardo Camavinga Real Madrid
83
4
Kylian Mbappé Real Madrid
83
5
Arda GülerReal Madrid
80
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for AS Roma vs Real Madrid 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 AS Roma vs Real Madrid 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 AS Roma 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 AS Roma vs Real Madrid 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 AS Roma 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.