Statistics / Football / Italy. Serie A / Torino vs Juventus

Torino vs Juventus Statistics & Analysis

Torino Torino
May 24, 2026 - 18:45
2 1.07
2 1.91
xG Accuracy: 76%
Juventus Juventus
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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 Over 2.5 (4 goals) ✔ Correct
  • Both Teams To Score BTTS Yes Yes ✔ Correct
  • 1X2 Juventus Draw ✖ Incorrect
  • Correct Score Insights 1-1, 1-2, 0-1, 0-2, 1-3 2-2 ✖ Incorrect

AI match briefing

AI Match Summary

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

  • League: Serie A
  • Fixture: Torino vs Juventus
  • Kickoff: 2026-05-24 13:00:00
  • 1X2 (model): Home 19.6% · Draw 24.8% · Away 55.6%
  • xG (showing): Torino 1.07 — Juventus 1.91 (total xG ≈ 2.98)
  • Primary / headline line (Betting Primary Pick when shown): BTTS Yes
  • Model: 57.3% · Implied: 50.7% · Probability edge: +6.6 pts · Est. EV: +10.0%
  • BTTS (model): Yes 57.3% · No 42.7%
  • Correct score (top bin): 1-1 (10.4%)

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

The engine’s headline primary is: BTTS Yes.

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

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.

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.

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.

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

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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