Statistics / Football / England. Premier League / Aston Villa vs Liverpool

Aston Villa vs Liverpool Statistics & Analysis

Aston Villa Aston Villa
May 15, 2026 - 19:00
4 1.41
2 1.24
xG Accuracy: 40%
Liverpool Liverpool
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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 (6 goals) ✖ Incorrect
  • Both Teams To Score BTTS No Yes ✖ Incorrect
  • 1X2 Aston Villa Aston Villa ✔ Correct
  • Correct Score Insights 1-1 4-2 ✖ Incorrect

AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

  • League: Premier League
  • Fixture: Aston Villa vs Liverpool
  • Kickoff: 2026-05-17 14:00:00
  • 1X2 (model): Home 39.4% · Draw 29.2% · Away 31.4%
  • xG (showing): Aston Villa 1.41 — Liverpool 1.24 (total xG ≈ 2.65)
  • Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
  • Model: 50.6% · Implied: 46.9% · Probability edge: +3.7 pts · Est. EV: +22.4%
  • BTTS (model): Yes 55.3% · No 44.7%
  • Correct score (top bin): 1-1 (12.4%)

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

The engine’s headline primary is: 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.

When several markets sit near +EV, keep stakes small — correlation means edges do not add cleanly.

FAQ

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.

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.

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.

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 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
Premier League Premier League — Standings
# TEAM MP W D L PTS
1 Arsenal 36 24 7 5 79
2 Manchester City 36 23 8 5 77
3 Manchester United 37 19 11 7 68
4 Aston Villa 37 18 8 11 62
5 Liverpool 37 17 8 12 59
6 Bournemouth 36 13 16 7 55
7 Brighton 37 14 11 12 53
8 Brentford 37 14 10 13 52
9 Sunderland 37 13 12 12 51
10 Chelsea 36 13 10 13 49
11 Newcastle 37 14 7 16 49
12 Everton 37 13 10 14 49
13 Fulham 37 14 7 16 49
14 Leeds 37 11 14 12 47
15 Crystal Palace 37 11 12 14 45
16 Nottingham Forest 37 11 10 16 43
17 Tottenham 36 9 11 16 38
18 West Ham 37 9 9 19 36
19 Burnley 36 4 9 23 21
20 Wolves 37 3 10 24 19
# TEAM MP GS GC +/- PTS
1 Manchester City 36 75 32 +43 77
2 Arsenal 36 68 26 +42 79
3 Manchester United 37 66 50 +16 68
4 Liverpool 37 62 52 +10 59
5 Bournemouth 36 56 52 +4 55
6 Chelsea 36 55 49 +6 49
7 Aston Villa 37 54 48 +6 62
8 Brentford 37 54 51 +3 52
9 Newcastle 37 53 53 0 49
10 Brighton 37 52 43 +9 53
11 Leeds 37 49 53 -4 47
12 Everton 37 47 49 -2 49
13 Nottingham Forest 37 47 50 -3 43
14 Tottenham 36 46 55 -9 38
15 Fulham 37 45 51 -6 49
16 West Ham 37 43 65 -22 36
17 Sunderland 37 40 47 -7 51
18 Crystal Palace 37 40 49 -9 45
19 Burnley 36 37 73 -36 21
20 Wolves 37 26 67 -41 19
# TEAM MP xG xGC +/- PTS
1 Arsenal 36 62.1 27.7 +34.4 79
2 Manchester City 36 65.8 39.0 +26.8 77
3 Manchester United 37 62.8 47.7 +15.1 68
4 Chelsea 36 62.0 48.3 +13.7 49
5 Liverpool 37 57.2 45.7 +11.5 59
6 Crystal Palace 37 56.5 47.8 +8.7 45
7 Brighton 37 56.5 48.1 +8.4 53
8 Newcastle 37 56.9 49.8 +7.1 49
9 Brentford 37 58.1 51.4 +6.7 52
10 Bournemouth 36 59.5 53.2 +6.3 55
11 Leeds 37 52.8 52.4 +0.4 47
12 Fulham 37 46.2 51.9 -5.7 49
13 Aston Villa 37 46.0 53.0 -7.0 62
14 Everton 37 45.5 54.9 -9.4 49
15 Nottingham Forest 37 44.4 55.3 -10.9 43
16 West Ham 37 42.5 55.2 -12.7 36
17 Tottenham 36 38.1 51.2 -13.1 38
18 Sunderland 37 37.2 52.5 -15.3 51
19 Wolves 37 33.5 57.0 -23.5 19
20 Burnley 36 31.2 72.9 -41.7 21
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