Statistics / Football / Germany. 2. Bundesliga / Preußen Münster vs SV Darmstadt 98

Preußen Münster vs SV Darmstadt 98 Statistics & Analysis

Preußen Münster Preußen Münster
May 10, 2026 - 11:30
1 1.55
1 1.44
xG Accuracy: 76%
SV Darmstadt 98 SV Darmstadt 98
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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 (2 goals) ✔ Correct
  • Both Teams To Score BTTS No Yes ✖ Incorrect
  • 1X2 Preußen Münster Draw ✖ Incorrect
  • Correct Score Insights 1-1 1-1 ✔ Correct

AI match briefing

AI Match Summary

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

  • League: 2. Bundesliga
  • Fixture: Preußen Münster vs SV Darmstadt 98
  • Kickoff: 2026-05-08 11:30:00
  • 1X2 (model): Home 38.8% · Draw 27.2% · Away 34.0%
  • xG (showing): Preußen Münster 1.55 — SV Darmstadt 98 1.44 (total xG ≈ 2.99)
  • Best +EV line (same label as hero card when Primary thresholds are not met): Under 2.5 goals
  • Model: 42.5% · Implied: 40.4% · Probability edge: +2.1 pts · Est. EV: +2.9%
  • BTTS (model): Yes 61.6% · No 38.4%
  • Correct score (top bin): 1-1 (11.2%)

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

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

Best Bet + Reason

Best current value angle on the board — same leg as the “Best +EV” hero when Primary rules are not met: Under 2.5 goals.

If 1X2 looks tight, the engine may still find clearer structure in totals or BTTS — that is intentional.

No pick is a guarantee; variance is especially large in scoreline markets.

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.

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.

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 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.

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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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 SC Paderborn 07 33 56.3 36.5 +19.8 59
2 FC Schalke 04 33 49.1 30.0 +19.1 67
3 Hannover 96 33 52.2 36.8 +15.4 59
4 SV Elversberg 33 49.5 37.0 +12.5 59
5 1. FC Magdeburg 33 51.5 44.5 +7.0 39
6 VfL Bochum 33 50.7 45.7 +5.0 41
7 1. FC Nürnberg 33 44.6 41.1 +3.5 45
8 1. FC Kaiserslautern 33 44.9 43.0 +1.9 49
9 SV Darmstadt 98 33 50.5 50.0 +0.5 52
10 Arminia Bielefeld 33 45.2 44.8 +0.4 36
11 Dynamo Dresden 33 42.4 42.9 -0.5 38
12 Hertha BSC 33 43.4 46.3 -2.9 51
13 Fortuna Düsseldorf 33 39.6 47.4 -7.8 37
14 Eintracht Braunschweig 33 36.4 46.3 -9.9 37
15 Holstein Kiel 33 40.0 52.2 -12.2 41
16 SpVgg Greuther Fürth 33 37.4 50.6 -13.2 34
17 Preußen Münster 33 36.4 53.8 -17.4 30
18 Karlsruher SC 33 39.5 60.6 -21.1 44
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