Predictions / Football / Italy. Serie A / Napoli vs Bologna

Napoli vs Bologna Prediction, Odds & AI Betting Tips

Napoli Napoli
May 11, 2026 - 18:45
2 1.49
3 0.71
xG Accuracy: 46%
Bologna Bologna
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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 (5 goals) ✖ Incorrect
  • Both Teams To Score BTTS No Yes ✖ Incorrect
  • 1X2 Napoli Bologna ✖ Incorrect
  • Correct Score Insights 1-0, 2-0, 1-1, 0-0, 2-1 2-3 ✖ Incorrect

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AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

  • League: Serie A
  • Fixture: Napoli vs Bologna
  • Kickoff: 2026-05-10 13:00:00
  • 1X2 (model): Home 54.4% · Draw 29.3% · Away 16.2%
  • xG (showing): Napoli 1.49 — Bologna 0.71 (total xG ≈ 2.2)
  • Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
  • Model: 62.3% · Implied: 50.5% · Probability edge: +11.8 pts · Est. EV: +20.9%
  • BTTS (model): Yes 40.9% · No 59.1%
  • Correct score (top bin): 1-0 (16.5%)

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.

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

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

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.

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.

Why might 1X2 look unattractive while totals do not?

Tight 1X2 prices often embed a fair three-way split, so EV on match-winner can sit negative even when Over/Under or BTTS still diverges from the model — compare the 1X2 row on the market cards to O/U and BTTS.

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.

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 22, 2026 (UTC)

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Serie A Serie A — Standings
# TEAM MP W D L PTS
1 Inter 37 27 5 5 86
2 Napoli 37 22 7 8 73
3 AC Milan 37 20 10 7 70
4 AS Roma 37 22 4 11 70
5 Como 37 19 11 7 68
6 Juventus 37 19 11 7 68
7 Atalanta 37 15 13 9 58
8 Bologna 37 16 7 14 55
9 Lazio 37 13 12 12 51
10 Udinese 37 14 8 15 50
11 Sassuolo 37 14 7 16 49
12 Torino 37 12 8 17 44
13 Parma 37 10 12 15 42
14 Genoa 37 10 11 16 41
15 Fiorentina 37 9 14 14 41
16 Cagliari 37 10 10 17 40
17 Lecce 37 9 8 20 35
18 Cremonese 37 8 10 19 34
19 Hellas Verona 37 3 12 22 21
20 Pisa 37 2 12 23 18
# TEAM MP GS GC +/- PTS
1 Inter 37 86 32 +54 86
2 Como 37 61 28 +33 68
3 Juventus 37 59 32 +27 68
4 AS Roma 37 57 31 +26 70
5 Napoli 37 57 36 +21 73
6 AC Milan 37 52 33 +19 70
7 Atalanta 37 50 35 +15 58
8 Bologna 37 46 43 +3 55
9 Sassuolo 37 46 49 -3 49
10 Udinese 37 45 47 -2 50
11 Torino 37 42 61 -19 44
12 Genoa 37 41 50 -9 41
13 Fiorentina 37 40 49 -9 41
14 Lazio 37 39 39 0 51
15 Cagliari 37 38 52 -14 40
16 Cremonese 37 31 53 -22 34
17 Parma 37 27 46 -19 42
18 Lecce 37 27 50 -23 35
19 Hellas Verona 37 25 59 -34 21
20 Pisa 37 25 69 -44 18
# TEAM MP xG xGC +/- PTS
1 Inter 37 69.7 33.4 +36.3 86
2 Juventus 37 63.6 31.2 +32.4 68
3 Como 37 59.9 32.7 +27.2 68
4 AC Milan 37 57.9 40.5 +17.4 70
5 Atalanta 37 55.9 41.4 +14.5 58
6 AS Roma 37 52.1 38.3 +13.8 70
7 Napoli 37 47.9 36.2 +11.7 73
8 Fiorentina 37 48.9 46.1 +2.8 41
9 Bologna 37 42.6 44.0 -1.4 55
10 Lazio 37 39.8 42.0 -2.2 51
11 Genoa 37 44.3 47.0 -2.7 41
12 Torino 37 44.0 50.7 -6.7 44
13 Udinese 37 41.5 50.4 -8.9 50
14 Hellas Verona 37 34.5 45.3 -10.8 21
15 Sassuolo 37 41.7 54.1 -12.4 49
16 Cagliari 37 34.1 51.6 -17.5 40
17 Pisa 37 38.1 57.8 -19.7 18
18 Cremonese 37 34.0 55.4 -21.4 34
19 Parma 37 31.0 55.6 -24.6 42
20 Lecce 37 29.1 56.6 -27.5 35
Serie A Italy. Serie A Predictions
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