Statistics / Football / Germany. Bundesliga / VfL Wolfsburg vs Bayern München

VfL Wolfsburg vs Bayern München Statistics & Analysis

VfL Wolfsburg VfL Wolfsburg
May 09, 2026 - 16:30
0 1.32
1 3.12
xG Accuracy: 39%
Bayern München Bayern München
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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 No No ✔ Correct
  • 1X2 Bayern München Bayern München ✔ Correct
  • Correct Score Insights 1-3 0-1 ✖ Incorrect

AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

  • League: Bundesliga
  • Fixture: VfL Wolfsburg vs Bayern München
  • Kickoff: 2026-05-09 13:30:00
  • 1X2 (model): Home 12.2% · Draw 15.3% · Away 72.5%
  • xG (showing): VfL Wolfsburg 1.32 — Bayern München 3.12 (total xG ≈ 4.44)
  • Primary / headline line (Betting Primary Pick when shown): Bayern München
  • Model: 72.5% · Implied: 61.0% · Probability edge: +11.4 pts · Est. EV: +6.1%
  • BTTS (model): Yes 70.7% · No 29.3%
  • Correct score (top bin): 1-3 (7.9%)

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

Early match state can move realised goals away from pre-kick projections.

Best Bet + Reason

Primary angle highlighted on the page: Bayern München.

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.

Only one modest +EV edge is highlighted here; size cautiously and re-check if odds move.

FAQ

What is the best-supported line in this snapshot?

Match the hero card above: if it says “Betting Primary Pick”, that leg cleared primary rules; if it says “Best +EV (tracked markets)”, it is the strongest +EV line that did not meet stricter Primary thresholds. The bullets below repeat the same model %, implied %, edge (pts), and EV % as that card.

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.

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 17, 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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Bundesliga Bundesliga — Standings
# TEAM MP W D L PTS
1 Bayern München 34 28 5 1 89
2 Borussia Dortmund 34 22 7 5 73
3 RB Leipzig 34 20 5 9 65
4 VfB Stuttgart 34 18 8 8 62
5 1899 Hoffenheim 34 18 7 9 61
6 Bayer Leverkusen 34 17 8 9 59
7 SC Freiburg 34 13 8 13 47
8 Eintracht Frankfurt 34 11 11 12 44
9 FC Augsburg 34 12 7 15 43
10 FSV Mainz 05 34 10 10 14 40
11 Union Berlin 34 10 9 15 39
12 Borussia Mönchengladbach 34 9 11 14 38
13 Hamburger SV 34 9 11 14 38
14 1. FC Köln 34 7 11 16 32
15 Werder Bremen 34 8 8 18 32
16 VfL Wolfsburg 34 7 8 19 29
17 1. FC Heidenheim 34 6 8 20 26
18 FC St. Pauli 34 6 8 20 26
# TEAM MP GS GC +/- PTS
1 Bayern München 34 122 36 +86 89
2 VfB Stuttgart 34 71 49 +22 62
3 Borussia Dortmund 34 70 34 +36 73
4 Bayer Leverkusen 34 68 47 +21 59
5 RB Leipzig 34 66 47 +19 65
6 1899 Hoffenheim 34 65 52 +13 61
7 Eintracht Frankfurt 34 61 65 -4 44
8 SC Freiburg 34 51 57 -6 47
9 1. FC Köln 34 49 63 -14 32
10 FC Augsburg 34 45 61 -16 43
11 VfL Wolfsburg 34 45 69 -24 29
12 FSV Mainz 05 34 44 53 -9 40
13 Union Berlin 34 44 58 -14 39
14 Borussia Mönchengladbach 34 42 53 -11 38
15 1. FC Heidenheim 34 41 72 -31 26
16 Hamburger SV 34 40 54 -14 38
17 Werder Bremen 34 37 60 -23 32
18 FC St. Pauli 34 29 60 -31 26
# TEAM MP xG xGC +/- PTS
1 Bayern München 34 94.5 37.0 +57.5 89
2 Borussia Dortmund 34 60.5 38.5 +22.0 73
3 Bayer Leverkusen 34 60.9 43.0 +17.9 59
4 RB Leipzig 34 65.2 48.7 +16.5 65
5 VfB Stuttgart 34 59.0 47.2 +11.8 62
6 1899 Hoffenheim 34 53.4 50.0 +3.4 61
7 SC Freiburg 34 47.0 46.7 +0.3 47
8 1. FC Köln 34 50.0 52.1 -2.1 32
9 FSV Mainz 05 34 49.4 52.0 -2.6 40
10 Eintracht Frankfurt 34 42.8 49.0 -6.2 44
11 Borussia Mönchengladbach 34 41.3 50.8 -9.5 38
12 Union Berlin 34 41.7 51.7 -10.0 39
13 Werder Bremen 34 40.0 51.5 -11.5 32
14 FC Augsburg 34 44.7 59.4 -14.7 43
15 1. FC Heidenheim 34 44.9 59.9 -15.0 26
16 VfL Wolfsburg 34 43.6 60.3 -16.7 29
17 Hamburger SV 34 36.5 53.8 -17.3 38
18 FC St. Pauli 34 29.0 52.8 -23.8 26
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