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Ligue 1
Referee: Mikael Lesage
Bordeaux
U U S S U
4–0
Full-time
Rennes
U S U S S

Retrospective analysis — the model is trained only on matches played before this one.

🔒 Who wins? Pro

The model's probabilities for a home win, draw and away win — with reasoning and recommended markets.

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Team trends

Bordeaux Rennes
Last 6 — all competitions · 6/6 matches
Win Ø 50%
50%
50%
Points per game
2,0
2,0
Over 2.5 Ø 25%
17%
33%
Both score Ø 59%
50%
67%
Average goals
1,8
2,8
Scored
1,2
1,8
Conceded
0,7
1,0
Goal diff per game
+0,5
+0,8
This season — Ligue 1 · 22/22 matches
Win Ø 34%
32%
36%
Points per game
1,4
1,5
Over 2.5 Ø 39%
41%
36%
Both score Ø 64%
59%
68%
Average goals
2,6
2,6
Scored
1,2
1,5
Conceded
1,4
1,1
Goal diff per game
-0,1
+0,3
🔒 Goal markets Pro

Expected goals, over/under and both teams to score — priced by the model before kick-off.

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🔒 Most likely result Pro

The model's top final scores with the best bookmaker odds.

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Match stats

Half-time score: 1–0

50% Possession 50%
21 Shots 8
7 Shots on target 3
2 Blocked shots 2
3 Goalkeeper saves 3
3 Corners 5
1 Offside 3
0 Free kicks against 0
2 Yellow cards 1
0 Red cards 0
The table before the match Bordeaux 12. · Rennes 6.
# Team K V U T +/− P
1 Paris SG 22 19 3 0 +46 60
2 Monaco 23 10 9 4 +5 39
3 Angers 23 10 7 6 +6 37
4 Nice 23 10 6 7 +9 36
5 Caen 23 11 3 9 -2 36
6 Rennes 22 8 10 4 +7 34
7 St Etienne 22 10 3 9 +0 33
8 Nantes 23 8 8 7 -1 32
9 Marseille 23 7 10 6 +9 31
10 Lyon 23 8 6 9 +1 30
11 Lorient 23 7 9 7 +0 30
12 Bordeaux 22 7 9 6 -3 30
13 Bastia 23 8 4 11 -5 28
14 Guingamp 23 7 6 10 -8 27
15 Lille 23 5 11 7 -1 26
16 Gazelec FC Ajaccio 23 6 8 9 -7 26
17 Montpellier 23 7 4 12 -2 25
18 Reims 23 5 8 10 -9 23
19 Toulouse 23 4 8 11 -16 20
20 Troyes 23 1 8 14 -29 11

Calculated from our results data. “xG +/−” is expected goal difference — a positive number with a low league position suggests an undervalued team. Ties on points are sorted by goal difference; some leagues use other rules.

Recent head-to-head meetings

22. Nov -15 Rennes – Bordeaux 2–2

The analysis is a statistical estimate based on the teams' results and underlying performances (xG) — not a guarantee. See the model's results.