Statistics / Football / France. Ligue 1 / Toulouse vs Lyon

Toulouse vs Lyon Statistics & Analysis

Toulouse Toulouse
May 10, 2026 - 19:00
2 1.40
1 1.20
xG Accuracy: 80%
Lyon Lyon
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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 (3 goals) ✖ Incorrect
  • Both Teams To Score BTTS No Yes ✖ Incorrect
  • 1X2 Toulouse Toulouse ✔ Correct
  • Correct Score Insights 1-1 2-1 ✖ Incorrect

AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

  • League: Ligue 1
  • Fixture: Toulouse vs Lyon
  • Kickoff: 2026-05-09 19:00:00
  • 1X2 (model): Home 40.0% · Draw 29.5% · Away 30.6%
  • xG (showing): Toulouse 1.4 — Lyon 1.2 (total xG ≈ 2.6)
  • Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
  • Model: 51.8% · Implied: 46.1% · Probability edge: +5.7 pts · Est. EV: +11.4%
  • BTTS (model): Yes 54.3% · No 45.7%
  • Correct score (top bin): 1-1 (12.5%)

Where EV is shown, it is estimated return per unit stake at the best tracked decimal price — not the same thing as a raw probability gap.

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.

Model probability is compared to implied probability from odds to highlight a probability edge; EV uses the same model probability with the best decimal price tracked.

No pick is a guarantee; variance is especially large in scoreline markets.

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.

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.

Safer market than correct score?

Markets with more liquidity and smoother prices (often 1X2 or O/U 2.5 from many books) are usually easier to reason about than long-tail correct-score prices; still read EV on each leg.

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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