Statistics / Football / France. National 1 / Le Puy Foot vs Dijon

Le Puy Foot vs Dijon Statistics & Analysis

Le Puy Foot Le Puy Foot
May 09, 2026 - 17:30
1 1.45
2 1.25
xG Accuracy: 72%
Dijon Dijon
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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 Yes Yes ✔ Correct
  • 1X2 Le Puy Foot Dijon ✖ Incorrect
  • Correct Score Insights 1-1 1-2 ✖ Incorrect

AI match briefing

AI Match Summary

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

  • League: National 1
  • Fixture: Le Puy Foot vs Dijon
  • Kickoff: 2026-05-08 17:30:00
  • 1X2 (model): Home 10.0% · Draw 45.0% · Away 45.0%
  • xG (showing): Le Puy Foot 1.45 — Dijon 1.25 (total xG ≈ 2.7)
  • Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
  • Model: 57.0% · Implied: 50.8% · Probability edge: +6.1 pts · Est. EV: +8.3%
  • BTTS (model): Yes 56.2% · No 43.8%
  • Correct score (top bin): 1-1 (12.9%)

Use the cards for tiering; this text only restates the same inputs in narrative form.

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

Best Bet + Reason

Primary angle highlighted on the page: 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.

Edges shrink quickly if prices move; always re-check the number on your book.

FAQ

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.

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.

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.

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
National 1 National 1 — Standings
# TEAM MP W D L PTS
1 Dijon 32 18 11 3 65
2 Sochaux 32 16 10 6 58
3 Rouen 32 14 13 5 55
4 Fleury 91 32 15 9 8 54
5 Versailles 32 15 8 9 53
6 Orleans 32 14 9 9 51
7 Le Puy Foot 32 12 11 9 47
8 Caen 32 8 16 8 40
9 Concarneau 32 8 14 10 38
10 Valenciennes 32 10 8 14 37
11 Aubagne 32 9 10 13 37
12 Villefranche 32 10 7 15 37
13 Quevilly 32 8 9 15 33
14 Gobelins 32 7 11 14 32
15 Bourg-en-bresse 01 32 8 7 17 31
16 Chateauroux 32 6 13 13 30
17 Stade Briochin 32 5 12 15 27
# TEAM MP GS GC +/- PTS
1 Dijon 32 52 25 +27 65
2 Sochaux 32 51 26 +25 58
3 Fleury 91 32 47 30 +17 54
4 Versailles 32 46 34 +12 53
5 Le Puy Foot 32 45 38 +7 47
6 Rouen 32 43 29 +14 55
7 Orleans 32 42 42 0 51
8 Caen 32 39 34 +5 40
9 Aubagne 32 38 46 -8 37
10 Valenciennes 32 35 44 -9 37
11 Chateauroux 32 35 49 -14 30
12 Stade Briochin 32 35 50 -15 27
13 Villefranche 32 34 45 -11 37
14 Quevilly 32 34 45 -11 33
15 Concarneau 32 32 37 -5 38
16 Gobelins 32 26 41 -15 32
17 Bourg-en-bresse 01 32 25 44 -19 31
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