Ligue 1
Retrospective analysis — the model is trained only on matches played before this one.
The model's probabilities for a home win, draw and away win — with reasoning and recommended markets.
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Lille
Monaco
Expected goals, over/under and both teams to score — priced by the model before kick-off.
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The model's top final scores with the best bookmaker odds.
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Half-time score: 2–0
| # | Team | K | V | U | T | +/− | xG +/− | P |
|---|---|---|---|---|---|---|---|---|
| 1 |
Nice
|
10 | 6 | 4 | 0 | +7 | +7.0 | 22 |
| 2 |
Paris SG
|
10 | 6 | 3 | 1 | +14 | +12.6 | 21 |
| 3 |
Monaco
|
9 | 6 | 2 | 1 | +11 | +6.3 | 20 |
| 4 |
Reims
|
10 | 5 | 2 | 3 | +4 | +3.9 | 17 |
| 5 |
Lille
|
9 | 4 | 3 | 2 | +2 | +4.3 | 15 |
| 6 |
Brest
|
10 | 4 | 3 | 3 | +0 | +1.1 | 15 |
| 7 |
Nantes
|
10 | 4 | 2 | 4 | -3 | -7.4 | 14 |
| 8 |
Marseille
|
9 | 3 | 3 | 3 | +0 | +7.4 | 12 |
| 9 |
Lens
|
10 | 3 | 3 | 4 | -1 | +3.5 | 12 |
| 10 |
Rennes
|
9 | 2 | 5 | 2 | +3 | +4.6 | 11 |
| 11 |
Toulouse
|
9 | 2 | 5 | 2 | +1 | -7.8 | 11 |
| 12 |
Le Havre
|
9 | 2 | 4 | 3 | -2 | -5.2 | 10 |
| 13 |
Lorient
|
10 | 2 | 4 | 4 | -5 | -7.1 | 10 |
| 14 |
Strasbourg
|
9 | 3 | 1 | 5 | -6 | -3.0 | 10 |
| 15 |
Montpellier
|
8 | 2 | 3 | 3 | +1 | +1.9 | 9 |
| 16 |
Metz
|
9 | 2 | 2 | 5 | -8 | -8.1 | 8 |
| 17 |
Clermont
|
9 | 1 | 2 | 6 | -7 | -6.3 | 5 |
| 18 |
Lyon
|
9 | 0 | 3 | 6 | -11 | -7.8 | 3 |
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.
L. Chevalier
G
B. Diakité
D
L. Yoro
D
Alexsandro Ribeiro
D
A. Gomes
M
N. Bentaleb
M
E. Zhegrova
M
B. André
M
Y. Yazıcı
F
Coach: Paulo Fonseca
P. Köhn
G
W. Singo
D
C. Matsima
D
I. Jakobs
D
D. Zakaria
M
T. Minamino
M
Y. Fofana
M
A. Golovin
M
F. Balogun
F
Coach: A. Hütter
Green columns are the outcomes that came in.
Lille
Slovan Bratislava
Lille
Brest
Lens
Lille
KI Klaksvik
Lille
Le Havre
Lille
C Turpin · 184 kamper i vår database
🟥 Red cards: 0,20 per match
The analysis is a statistical estimate based on the teams' results and underlying performances (xG) — not a guarantee. See the model's results.