AS Nancy Lorraine
Stade Laval
AS Nancy Lorraine – Stade Laval
What the model says
Probabilities computed by model v3-20261018-0954, Oct 22, 03:05 AM.
- Predicted result
- 1Home win
- Confidence
- 49 %
- Most likely score
- 1-011 % chance
- Expected goals (xG)
- 1.54 – 1.10
- Over 2.5 goals
- 49 %
- Both teams to score
- 53 %
The score shown is the most likely one among those matching the predicted result, not the rounded xG: with 1.83 – 1.24 expected goals and a home win predicted, that is 1-0. 1-1, the single most frequent score overall, only appears when a draw is predicted.
The short version
The model leans towards AS Nancy Lorraine at home (49 %). The most likely score among those producing that result is 1-0. It expects 1.54 goals for AS Nancy Lorraine and 1.10 for Stade Laval. Bookmakers disagree: they give that outcome 57 %. Over their last five matches, AS Nancy Lorraine took 9 points and Stade Laval 2 points. In recent meetings: 0 wins for AS Nancy Lorraine, 1 draw, 1 win for Stade Laval. Over 2.5 goals: 49 %. Both teams to score: 53 %.
What the market says
Average of one bookmaker, margin removed.
| Predicted result | Odds | Implied probability | Gap |
|---|---|---|---|
| 1AS Nancy Lorraine | 1.64 | 57 % | -20.3 % |
| XDraw | 3.95 | 24 % | +4.8 % |
| 2Stade Laval | 4.80 | 19 % | +19.2 % |
Model probability × odds − 1. Positive: the model gives this outcome a better chance than the odds do. A gap is not betting advice.
Form and statistics
AS Nancy Lorraine
- Last 5 matches
- At home
Stade Laval
- Last 5 matches
Head-to-head
- 0 wins
- AS Nancy Lorraine
- 1 draw
- 1 win
- Stade Laval
- Stade Laval1-1AS Nancy Lorraine
- AS Nancy Lorraine0-2Stade Laval
Frequently asked questions
Who will win AS Nancy Lorraine – Stade Laval?
Our model gives 1 · AS Nancy Lorraine a 49 % probability. The three outcomes split as follows: AS Nancy Lorraine win 49 %, draw 27 %, Stade Laval win 25 %.
What is the most likely score for AS Nancy Lorraine – Stade Laval?
The most likely score is 1-0, at 11 %. It is the most frequent score among those that produce the result the model predicts.