Statistics / Football / Spain. La Liga / Sevilla vs Real Madrid

Sevilla vs Real Madrid Statistics & Analysis

Sevilla Sevilla
May 17, 2026 - 17:00
0 0.97
1 1.38
xG Accuracy: 69%
Real Madrid Real Madrid
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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 Under 2.5 (1 goals) ✔ Correct
  • Both Teams To Score BTTS No No ✔ Correct
  • 1X2 Real Madrid Real Madrid ✔ Correct
  • Correct Score Insights 0-1 0-1 ✔ Correct

AI match briefing

AI Match Summary

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

  • League: La Liga
  • Fixture: Sevilla vs Real Madrid
  • Kickoff: 2026-05-17 16:00:00
  • 1X2 (model): Home 24.8% · Draw 30.6% · Away 44.6%
  • xG (showing): Sevilla 0.97 — Real Madrid 1.38 (total xG ≈ 2.35)
  • Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
  • Model: 58.3% · Implied: 44.9% · Probability edge: +13.4 pts · Est. EV: +27.1%
  • BTTS (model): Yes 48.1% · No 51.9%
  • Correct score (top bin): 0-1 (13.2%)

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

Primary pick from the decision engine: 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).

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.

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.

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.

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 19, 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
La Liga La Liga — Standings
# TEAM MP W D L PTS
1 Barcelona 37 31 1 5 94
2 Real Madrid 37 26 5 6 83
3 Villarreal 37 21 6 10 69
4 Atletico Madrid 37 21 6 10 69
5 Real Betis 37 14 15 8 57
6 Celta Vigo 37 13 12 12 51
7 Getafe 37 14 6 17 48
8 Rayo Vallecano 37 11 14 12 47
9 Valencia 37 12 10 15 46
10 Real Sociedad 37 11 12 14 45
11 Espanyol 37 12 9 16 45
12 Athletic Club 37 13 6 18 45
13 Sevilla 37 12 7 18 43
14 Alaves 37 11 10 16 43
15 Levante 37 11 9 17 42
16 Osasuna 37 11 9 17 42
17 Elche 37 10 12 15 42
18 Girona 37 9 13 15 40
19 Mallorca 37 10 9 18 39
20 Oviedo 37 6 11 20 29
# TEAM MP GS GC +/- PTS
1 Barcelona 37 94 33 +61 94
2 Real Madrid 37 73 33 +40 83
3 Villarreal 37 67 45 +22 69
4 Atletico Madrid 37 61 39 +22 69
5 Real Sociedad 37 58 60 -2 45
6 Real Betis 37 57 47 +10 57
7 Celta Vigo 37 52 48 +4 51
8 Elche 37 48 56 -8 42
9 Sevilla 37 46 59 -13 43
10 Levante 37 46 59 -13 42
11 Osasuna 37 44 49 -5 42
12 Mallorca 37 44 57 -13 39
13 Valencia 37 43 54 -11 46
14 Alaves 37 43 54 -11 43
15 Espanyol 37 42 54 -12 45
16 Athletic Club 37 41 54 -13 45
17 Rayo Vallecano 37 39 43 -4 47
18 Girona 37 38 54 -16 40
19 Getafe 37 31 38 -7 48
20 Oviedo 37 26 57 -31 29
# TEAM MP xG xGC +/- PTS
1 Barcelona 37 85.2 44.1 +41.1 94
2 Real Madrid 37 72.9 39.3 +33.6 83
3 Villarreal 37 55.3 43.8 +11.5 69
4 Atletico Madrid 37 56.8 46.1 +10.7 69
5 Real Betis 37 53.2 43.2 +10.0 57
6 Athletic Club 37 48.6 38.6 +10.0 45
7 Valencia 37 47.8 44.8 +3.0 46
8 Rayo Vallecano 37 48.8 46.4 +2.4 47
9 Celta Vigo 37 48.1 48.5 -0.4 51
10 Alaves 37 44.5 46.0 -1.5 43
11 Osasuna 37 45.0 48.9 -3.9 42
12 Real Sociedad 37 47.6 52.3 -4.7 45
13 Espanyol 37 46.0 52.4 -6.4 45
14 Levante 37 49.3 57.8 -8.5 42
15 Girona 37 45.0 55.2 -10.2 40
16 Getafe 37 29.9 40.3 -10.4 48
17 Sevilla 37 37.2 52.4 -15.2 43
18 Elche 37 40.3 59.5 -19.2 42
19 Oviedo 37 34.0 54.7 -20.7 29
20 Mallorca 37 40.4 61.6 -21.2 39
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