Statistics / Football / Germany. 2. Bundesliga / Hertha BSC vs SpVgg Greuther Fürth

Hertha BSC vs SpVgg Greuther Fürth Statistics & Analysis

Hertha BSC Hertha BSC
May 10, 2026 - 11:30
2 1.93
1 1.08
xG Accuracy: 96%
SpVgg Greuther Fürth SpVgg Greuther Fürth
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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 Yes ✖ Incorrect
  • 1X2 Hertha BSC Hertha BSC ✔ Correct
  • Correct Score Insights 1-1 2-1 ✖ Incorrect

AI match briefing

AI Match Summary

Pre-match snapshot for this fixture.

  • League: 2. Bundesliga
  • Fixture: Hertha BSC vs SpVgg Greuther Fürth
  • Kickoff: 2026-05-08 11:30:00
  • 1X2 (model): Home 55.9% · Draw 24.6% · Away 19.5%
  • xG (showing): Hertha BSC 1.93 — SpVgg Greuther Fürth 1.08 (total xG ≈ 3.01)
  • Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
  • Model: 42.1% · Implied: 34.4% · Probability edge: +7.7 pts · Est. EV: +21.2%
  • BTTS (model): Yes 57.8% · No 42.2%
  • Correct score (top bin): 1-1 (10.3%)

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

Correct score remains high-variance even when a line is most likely on paper.

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).

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

FAQ

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.

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.

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.

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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