Statistics / Football / Italy. Serie A / Lecce vs Genoa

Lecce vs Genoa Statistics & Analysis

Lecce Lecce
May 24, 2026 - 18:45
1 1.02
0 1.31
xG Accuracy: 69%
Genoa Genoa
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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 Over 2.5 Under 2.5 (1 goals) ✖ Incorrect
  • Both Teams To Score BTTS Yes No ✖ Incorrect
  • 1X2 Genoa Lecce ✖ Incorrect
  • Correct Score Insights 1-1, 0-1, 1-0, 0-0, 1-2 1-0 ✔ Correct

AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

  • League: Serie A
  • Fixture: Lecce vs Genoa
  • Kickoff: 2026-05-24 13:00:00
  • 1X2 (model): Home 27.3% · Draw 31.2% · Away 41.5%
  • xG (showing): Lecce 1.02 — Genoa 1.31 (total xG ≈ 2.33)
  • Primary / headline line (Betting Primary Pick when shown): Genoa
  • Model: 41.5% · Implied: 19.8% · Probability edge: +21.7 pts · Est. EV: +11.0%
  • BTTS (model): Yes 48.4% · No 51.6%
  • Correct score (top bin): 1-1 (13.0%)

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

Correct score remains high-variance even when a line is most likely on paper.

Best Bet + Reason

Primary pick from the decision engine: Genoa.

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

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.

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.

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

June 08, 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
Serie A Serie A — Standings
# TEAM MP W D L PTS
1 Inter 38 27 6 5 87
2 Napoli 38 23 7 8 76
3 AS Roma 38 23 4 11 73
4 Como 38 20 11 7 71
5 AC Milan 38 20 10 8 70
6 Juventus 38 19 12 7 69
7 Atalanta 38 15 14 9 59
8 Bologna 38 16 8 14 56
9 Lazio 38 14 12 12 54
10 Udinese 38 14 8 16 50
11 Sassuolo 38 14 7 17 49
12 Torino 38 12 9 17 45
13 Parma 38 11 12 15 45
14 Cagliari 38 11 10 17 43
15 Fiorentina 38 9 15 14 42
16 Genoa 38 10 11 17 41
17 Lecce 38 10 8 20 38
18 Cremonese 38 8 10 20 34
19 Hellas Verona 38 3 12 23 21
20 Pisa 38 2 12 24 18
# TEAM MP GS GC +/- PTS
1 Inter 38 89 35 +54 87
2 Como 38 65 29 +36 71
3 Juventus 38 61 34 +27 69
4 AS Roma 38 59 31 +28 73
5 Napoli 38 58 36 +22 76
6 AC Milan 38 53 35 +18 70
7 Atalanta 38 51 36 +15 59
8 Bologna 38 49 46 +3 56
9 Sassuolo 38 46 50 -4 49
10 Udinese 38 45 48 -3 50
11 Torino 38 44 63 -19 45
12 Lazio 38 41 40 +1 54
13 Fiorentina 38 41 50 -9 42
14 Genoa 38 41 51 -10 41
15 Cagliari 38 40 53 -13 43
16 Cremonese 38 32 57 -25 34
17 Parma 38 28 46 -18 45
18 Lecce 38 28 50 -22 38
19 Pisa 38 26 71 -45 18
20 Hellas Verona 38 25 61 -36 21
# TEAM MP xG xGC +/- PTS
1 Inter 38 71.5 34.8 +36.7 87
2 Juventus 38 65.6 32.0 +33.6 69
3 Como 38 62.2 33.8 +28.4 71
4 AC Milan 38 59.6 43.3 +16.3 70
5 AS Roma 38 55.4 39.1 +16.3 73
6 Atalanta 38 57.3 42.2 +15.1 59
7 Napoli 38 49.7 36.7 +13.0 76
8 Fiorentina 38 49.8 47.5 +2.3 42
9 Bologna 38 44.0 45.8 -1.8 56
10 Lazio 38 41.0 43.5 -2.5 54
11 Genoa 38 45.1 48.8 -3.7 41
12 Torino 38 44.8 52.8 -8.0 45
13 Udinese 38 42.0 52.2 -10.2 50
14 Sassuolo 38 42.6 55.3 -12.7 49
15 Hellas Verona 38 35.2 48.6 -13.4 21
16 Cagliari 38 36.9 53.3 -16.4 43
17 Pisa 38 39.6 58.9 -19.3 18
18 Cremonese 38 35.0 57.8 -22.8 34
19 Parma 38 32.2 56.6 -24.4 45
20 Lecce 38 30.9 57.4 -26.5 38
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