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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Le Havre
Nice
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 |
Paris SG
|
15 | 11 | 3 | 1 | +26 | +16.0 | 36 |
| 2 |
Nice
|
15 | 9 | 5 | 1 | +10 | +7.3 | 32 |
| 3 |
Monaco
|
16 | 9 | 3 | 4 | +10 | +5.6 | 30 |
| 4 |
Lille
|
15 | 7 | 6 | 2 | +8 | +6.6 | 27 |
| 5 |
Brest
|
15 | 7 | 4 | 4 | +4 | +1.4 | 25 |
| 6 |
Marseille
|
15 | 6 | 5 | 4 | +6 | +10.8 | 23 |
| 7 |
Lens
|
15 | 6 | 5 | 4 | +4 | +3.2 | 23 |
| 8 |
Reims
|
15 | 7 | 2 | 6 | +0 | +2.5 | 23 |
| 9 |
Nantes
|
15 | 5 | 3 | 7 | -6 | -6.6 | 18 |
| 10 |
Strasbourg
|
15 | 4 | 5 | 6 | -6 | -2.9 | 17 |
| 11 |
Le Havre
|
15 | 3 | 7 | 5 | -4 | -6.5 | 16 |
| 12 |
Metz
|
15 | 4 | 4 | 7 | -8 | -10.4 | 16 |
| 13 |
Rennes
|
15 | 3 | 6 | 6 | -1 | +4.2 | 15 |
| 14 |
Montpellier
|
15 | 3 | 6 | 6 | -3 | +3.9 | 15 |
| 15 |
Toulouse
|
15 | 2 | 7 | 6 | -7 | -10.4 | 13 |
| 16 |
Lyon
|
16 | 3 | 4 | 9 | -12 | -7.2 | 13 |
| 17 |
Lorient
|
15 | 2 | 6 | 7 | -9 | -9.2 | 12 |
| 18 |
Clermont
|
15 | 2 | 5 | 8 | -12 | -8.4 | 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.
A. Sangante
D
É. Youté
D
G. Lloris
D
C. Opéri
D
O. Targhalline
M
Y. Kechta
M
J. Casimir
F
M. Bayo
F
Coach: L. Elsner
J. Lotomba
D
J. Todibo
D
M. Bard
D
H. Boudaoui
M
Y. Ndayishimiye
M
P. Rosario
M
E. Guessand
F
J. Boga
F
Coach: F. Farioli
Green columns are the outcomes that came in.
Strasbourg
Le Havre
Le Havre
Paris SG
Nantes
Le Havre
Le Havre
Monaco
Toulouse
Le Havre
J Pignard · 145 kamper i vår database
🟥 Red cards: 0,29 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.