Rennes arrive with league-leading authority and a potent attack, while Nice’s fragile back line offers a clear route to chances. Nice may lack firepower, but their home threat and Rennes’ own defensive weakness leave both sides exposed. The match shape points toward sustained pressure, defensive cracks and enough goals to clear the over.
Match GoalsOver 2.5
Best Pick
BTTS
52%
Correct Score
Top
1-1
12.5%
7.50
Top
0-1
10.8%
12.00
1-2
8.9%
9.50
1-0
8.8%
13.00
0-2
7.7%
15.00
0-0
7.6%
19.00
2-1
7.2%
10.00
2-2
5.1%
11.00
131%
2.63
X26%
3.70
243%
2.30TOP
1X57%
X269%
1274%
Combined Bet Builder
5 legs
Combined xG of 2.83 and 10.70 expected corners point to an open contest, while both defences are rated fragile. Rennes’ +41% attack power and 0.75 first-half xG support an early goal; Nice’s 1.45 xG gives them a route to reply. Over 2.5 goals, both teams to score and over 8.5 corners fit the pattern, with Esteban Lepaul a compelling anytime scorer.
1Match GoalsOver 2.5
2Both Teams to ScoreYes
3First Half Over/UnderOver 0.5
4Corners Over/UnderOver 8.5
5GoalscorersAnytime
Confirmed XI4-4-2
23
Diouf
5
Abdelmonem
55
Ndayishimiye
36
Seidu
27
Nkounkou
92
Clauss
6
Boudaoui
28
Witsel
7
Hein
9
Amoura
11
Wahi
Impact Player Hot Head
OGC Nice38 players
47.3/ 99Average
Top Rated
1Charles VanhoutteMID67
2Tom LouchetMID63
3Everton PereiraMID62
Rennes30 players
56.0/ 99Strong
Top Rated
1Yassir ZabiriFWD97
2Valentin RongierMID75
3Mahamadou NagidaDEF69
Player Rankings
Snipers
Top goal threats
1
Yassir ZabiriRennes
99
2
Estéban LepaulRennes
78
3
Boulaye DiaRennes
69
4
Arnaud NordinRennes
65
5
Nordan MukieleRennes
58
Wall
Top defenders
1
Juma BahOGC Nice
79
2
Anthony RouaultRennes
75
3
Abdelhamid Ait BoudlalRennes
74
4
Charlie CresswellRennes
69
5
Youssouf Ndayishimiye OGC Nice
68
Control
Top playmakers
1
Glen KamaraRennes
77
2
Valentin RongierRennes
73
3
Charles VanhoutteOGC Nice
65
4
Gabin BernardeauOGC Nice
63
5
Laurent Abergel OGC Nice
63
Bad Boys
Most likely to get booked
1
Laurent Abergel OGC Nice
1
Aggression 30
2
Elye WahiOGC Nice
1
Aggression 41
3
Isak JanssonOGC Nice
1
Aggression 67
4
Mohamed AmouraOGC Nice
1
Aggression 67
5
Estéban LepaulRennes
1
Aggression 18
Power Rankings
Strongest overall
1
Yassir ZabiriRennes
97
2
Valentin RongierRennes
75
3
Mahamadou NagidaRennes
69
4
Charles VanhoutteOGC Nice
67
5
Ludovic BlasRennes
67
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for OGC Nice vs Rennes 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 OGC Nice vs Rennes 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 OGC Nice 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 OGC Nice vs Rennes 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 OGC Nice 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.