Statistics / Football / France. Ligue 1 / Metz vs Lorient

Metz vs Lorient Statistics & Analysis

Metz Metz
May 10, 2026 - 19:00
0 1.16
4 1.54
xG Accuracy: 37%
Lorient Lorient
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Tracked markets vs full-time result

Each row compares the model’s highlighted side (or lean) to what happened at full time.

  • Market Prediction Result Outcome
  • Over / Under 2.5 Under 2.5 Over 2.5 (4 goals) ✖ Incorrect
  • Both Teams To Score BTTS No No ✔ Correct
  • 1X2 Lorient Lorient ✔ Correct
  • Correct Score Insights 1-1 0-4 ✖ Incorrect

AI match briefing

AI Match Summary

Below is a compact, numbers-first snapshot aligned with the same engine as the cards above.

  • League: Ligue 1
  • Fixture: Metz vs Lorient
  • Kickoff: 2026-05-09 19:00:00
  • 1X2 (model): Home 27.1% · Draw 28.4% · Away 44.5%
  • xG (showing): Metz 1.16 — Lorient 1.54 (total xG ≈ 2.7)
  • Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
  • Model: 49.4% · Implied: 44.1% · Probability edge: +5.3 pts · Est. EV: +11.2%
  • BTTS (model): Yes 55.5% · No 44.5%
  • Correct score (top bin): 1-1 (12.0%)

Totals and BTTS are evaluated against current market prices where available.

Early match state can move realised goals away from pre-kick projections.

Best Bet + Reason

The engine’s headline primary is: Under 2.5 goals.

We separate probability edge (model minus implied, in points of probability) from estimated EV (economic edge at the best price shown on the page).

When several markets sit near +EV, keep stakes small — correlation means edges do not add cleanly.

FAQ

What changes first if odds move?

Implied probabilities and EV move immediately with price; model probabilities in this snapshot do not update until the pipeline is re-run. Refresh after material line moves.

Is the most likely correct score a good bet?

Usually no as a standalone bet: the “most likely” scoreline is still a low absolute probability tail event (often single digits, sometimes low teens). Use it as context; keep any correct-score stake in the “fun / small” bucket.

How should I read EV versus a probability gap?

Probability edge = model probability minus implied probability (reported here in percentage points). EV ≈ model probability × best tracked decimal odds − 1, shown as return per unit stake. They are related but not interchangeable labels.

Who has the edge in the match-winner market?

Use the 1X2 model percentages in the summary and the 1X2 market card: the side with the highest model % is the model lean, but check EV — a lean can still be -EV after prices.

Risk Factors

  • Price movement: implied probabilities and EV move with odds.
  • Sample / data gaps: low-information leagues widen forecast bands.
  • In-play state: goals and red cards are not modelled here.
  • Scoreline variance: the most likely scoreline is still usually a low absolute probability outcome (often well below 20%).

Methodology

  • Inputs: Same structured facts bundle as the public prediction page (xG / Poisson snapshot, market EV where available, decision engine v2).
  • Compliance: Educational framing only; not personalised advice.

Last Updated

May 17, 2026 (UTC)

How to use this
  • Focus on the Primary line when you want one actionable idea.
  • Do not parlay many thin-edge picks together; edges do not add reliably.
  • Treat longshots as optional, high-stake-sizing plays only.

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Back to Statistics
Ligue 1 Ligue 1 — Standings
# TEAM MP W D L PTS
1 Paris Saint Germain 33 24 4 5 76
2 Lens 33 21 4 8 67
3 Lille 33 18 7 8 61
4 Lyon 33 18 6 9 60
5 Rennes 33 17 8 8 59
6 Marseille 33 17 5 11 56
7 Monaco 33 16 6 11 54
8 Strasbourg 33 14 8 11 50
9 Lorient 33 11 12 10 45
10 Toulouse 33 12 8 13 44
11 Paris FC 33 10 11 12 41
12 Stade Brestois 29 33 10 8 15 38
13 Angers 33 9 8 16 35
14 Le Havre 33 6 14 13 32
15 Auxerre 33 7 10 16 31
16 Nice 33 7 10 16 31
17 Nantes 33 5 8 20 23
18 Metz 33 3 7 23 16
# TEAM MP GS GC +/- PTS
1 Paris Saint Germain 33 73 27 +46 76
2 Lens 33 62 35 +27 67
3 Marseille 33 60 44 +16 56
4 Rennes 33 58 47 +11 59
5 Monaco 33 56 49 +7 54
6 Lyon 33 53 36 +17 60
7 Strasbourg 33 53 43 +10 50
8 Lille 33 52 35 +17 61
9 Lorient 33 48 49 -1 45
10 Toulouse 33 47 46 +1 44
11 Paris FC 33 45 49 -4 41
12 Stade Brestois 29 33 42 54 -12 38
13 Nice 33 37 60 -23 31
14 Auxerre 33 32 44 -12 31
15 Metz 33 32 76 -44 16
16 Le Havre 33 30 44 -14 32
17 Nantes 33 29 52 -23 23
18 Angers 33 28 47 -19 35
# TEAM MP xG xGC +/- PTS
1 Paris Saint Germain 33 63.1 25.3 +37.8 76
2 Lens 33 65.3 40.7 +24.6 67
3 Marseille 33 57.2 39.5 +17.7 56
4 Lille 33 52.8 36.3 +16.5 61
5 Strasbourg 33 49.8 42.6 +7.2 50
6 Monaco 33 55.6 48.5 +7.1 54
7 Lyon 33 49.1 42.3 +6.8 60
8 Rennes 33 51.5 49.8 +1.7 59
9 Lorient 33 44.0 43.6 +0.4 45
10 Toulouse 33 42.9 42.6 +0.3 44
11 Stade Brestois 29 33 43.1 49.2 -6.1 38
12 Nantes 33 33.4 43.5 -10.1 23
13 Auxerre 33 35.1 46.2 -11.1 31
14 Le Havre 33 36.9 49.5 -12.6 32
15 Paris FC 33 42.3 55.4 -13.1 41
16 Nice 33 41.6 58.5 -16.9 31
17 Angers 33 31.6 54.3 -22.7 35
18 Metz 33 33.7 61.2 -27.5 16
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