Statistics / Football / Germany. 2. Bundesliga / SV Elversberg vs Preußen Münster

SV Elversberg vs Preußen Münster Statistics & Analysis

SV Elversberg SV Elversberg
May 17, 2026 - 13:30
3 2.40
0 0.78
xG Accuracy: 68%
Preußen Münster Preußen Münster
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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 (3 goals) ✖ Incorrect
  • Both Teams To Score BTTS No No ✔ Correct
  • 1X2 SV Elversberg SV Elversberg ✔ Correct
  • Correct Score Insights 2-0 3-0 ✖ Incorrect

AI match briefing

AI Match Summary

Pre-match snapshot for this fixture.

  • League: 2. Bundesliga
  • Fixture: SV Elversberg vs Preußen Münster
  • Kickoff: 2026-05-17 13:30:00
  • 1X2 (model): Home 72.3% · Draw 18.4% · Away 9.2%
  • xG (showing): SV Elversberg 2.4 — Preußen Münster 0.78 (total xG ≈ 3.18)
  • Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
  • Model: 38.4% · Implied: 30.8% · Probability edge: +7.6 pts · Est. EV: +19.0%
  • BTTS (model): Yes 50.3% · No 49.7%
  • Correct score (top bin): 2-0 (12.0%)

Totals and BTTS are evaluated against current market prices where available.

1X2 can look balanced even when side markets show clearer structure.

Best Bet + Reason

Primary angle highlighted on the page: 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

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.

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.

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.

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
2. Bundesliga 2. Bundesliga — Standings
# TEAM MP W D L PTS
1 FC Schalke 04 33 20 7 6 67
2 SV Elversberg 33 17 8 8 59
3 Hannover 96 33 16 11 6 59
4 SC Paderborn 07 33 17 8 8 59
5 SV Darmstadt 98 33 13 13 7 52
6 Hertha BSC 33 14 9 10 51
7 1. FC Kaiserslautern 33 15 4 14 49
8 1. FC Nürnberg 33 12 9 12 45
9 Karlsruher SC 33 12 8 13 44
10 VfL Bochum 33 10 11 12 41
11 Holstein Kiel 33 11 8 14 41
12 1. FC Magdeburg 33 12 3 18 39
13 Dynamo Dresden 33 10 8 15 38
14 Eintracht Braunschweig 33 10 7 16 37
15 Fortuna Düsseldorf 33 11 4 18 37
16 Arminia Bielefeld 33 9 9 15 36
17 SpVgg Greuther Fürth 33 9 7 17 34
18 Preußen Münster 33 6 12 15 30
# TEAM MP GS GC +/- PTS
1 SV Elversberg 33 61 39 +22 59
2 Hannover 96 33 57 41 +16 59
3 SV Darmstadt 98 33 57 43 +14 52
4 SC Paderborn 07 33 57 45 +12 59
5 Dynamo Dresden 33 52 52 0 38
6 1. FC Magdeburg 33 52 57 -5 39
7 Karlsruher SC 33 52 62 -10 44
8 1. FC Kaiserslautern 33 51 47 +4 49
9 FC Schalke 04 33 49 31 +18 67
10 VfL Bochum 33 47 46 +1 41
11 Arminia Bielefeld 33 47 50 -3 36
12 Hertha BSC 33 46 38 +8 51
13 SpVgg Greuther Fürth 33 46 68 -22 34
14 1. FC Nürnberg 33 44 42 +2 45
15 Holstein Kiel 33 43 46 -3 41
16 Preußen Münster 33 38 58 -20 30
17 Eintracht Braunschweig 33 36 53 -17 37
18 Fortuna Düsseldorf 33 33 50 -17 37
# TEAM MP xG xGC +/- PTS
1 FC Schalke 04 33 51.0 30.4 +20.6 67
2 SC Paderborn 07 33 58.1 38.1 +20.0 59
3 Hannover 96 33 55.4 38.5 +16.9 59
4 SV Elversberg 33 51.6 37.3 +14.3 59
5 1. FC Magdeburg 33 52.6 45.9 +6.7 39
6 Arminia Bielefeld 33 50.3 46.2 +4.1 36
7 VfL Bochum 33 51.9 48.6 +3.3 41
8 1. FC Kaiserslautern 33 46.2 44.1 +2.1 49
9 1. FC Nürnberg 33 46.2 44.2 +2.0 45
10 SV Darmstadt 98 33 52.1 51.8 +0.3 52
11 Dynamo Dresden 33 43.2 43.9 -0.7 38
12 Hertha BSC 33 44.7 51.4 -6.7 51
13 Fortuna Düsseldorf 33 40.3 49.4 -9.1 37
14 Eintracht Braunschweig 33 36.7 48.1 -11.4 37
15 SpVgg Greuther Fürth 33 39.4 51.4 -12.0 34
16 Holstein Kiel 33 41.0 53.1 -12.1 41
17 Preußen Münster 33 36.7 55.9 -19.2 30
18 Karlsruher SC 33 42.5 61.9 -19.4 44
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