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

Hertha BSC vs SpVgg Greuther Fürth Prediction, Odds & AI Betting Tips

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, 1-0, 2-0, 3-1 2-1 ✔ Correct

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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 24, 2026 (UTC)

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2. Bundesliga 2. Bundesliga — Standings
# TEAM MP W D L PTS
1 FC Schalke 04 34 21 7 6 70
2 SV Elversberg 34 18 8 8 62
3 SC Paderborn 07 34 18 8 8 62
4 Hannover 96 34 16 12 6 60
5 SV Darmstadt 98 34 13 13 8 52
6 1. FC Kaiserslautern 34 16 4 14 52
7 Hertha BSC 34 14 9 11 51
8 1. FC Nürnberg 34 12 10 12 46
9 VfL Bochum 34 11 11 12 44
10 Karlsruher SC 34 12 8 14 44
11 Dynamo Dresden 34 11 8 15 41
12 Holstein Kiel 34 11 8 15 41
13 Arminia Bielefeld 34 10 9 15 39
14 1. FC Magdeburg 34 12 3 19 39
15 Eintracht Braunschweig 34 10 7 17 37
16 SpVgg Greuther Fürth 34 10 7 17 37
17 Fortuna Düsseldorf 34 11 4 19 37
18 Preußen Münster 34 6 12 16 30
# TEAM MP GS GC +/- PTS
1 SV Elversberg 34 64 39 +25 62
2 Hannover 96 34 60 44 +16 60
3 SC Paderborn 07 34 59 45 +14 62
4 SV Darmstadt 98 34 57 45 +12 52
5 Dynamo Dresden 34 54 53 +1 41
6 Arminia Bielefeld 34 53 51 +2 39
7 Karlsruher SC 34 53 64 -11 44
8 1. FC Kaiserslautern 34 52 47 +5 52
9 1. FC Magdeburg 34 52 58 -6 39
10 FC Schalke 04 34 50 31 +19 70
11 VfL Bochum 34 49 47 +2 44
12 SpVgg Greuther Fürth 34 49 68 -19 37
13 Hertha BSC 34 47 44 +3 51
14 1. FC Nürnberg 34 47 45 +2 46
15 Holstein Kiel 34 44 48 -4 41
16 Preußen Münster 34 38 61 -23 30
17 Eintracht Braunschweig 34 36 54 -18 37
18 Fortuna Düsseldorf 34 33 53 -20 37
# TEAM MP xG xGC +/- PTS
1 FC Schalke 04 34 51.0 30.4 +20.6 70
2 SC Paderborn 07 34 58.1 38.1 +20.0 62
3 Hannover 96 34 55.4 38.5 +16.9 60
4 SV Elversberg 34 51.6 37.3 +14.3 62
5 1. FC Magdeburg 34 52.6 45.9 +6.7 39
6 Arminia Bielefeld 34 50.3 46.2 +4.1 39
7 VfL Bochum 34 51.9 48.6 +3.3 44
8 1. FC Kaiserslautern 34 46.2 44.1 +2.1 52
9 1. FC Nürnberg 34 46.2 44.2 +2.0 46
10 SV Darmstadt 98 34 52.1 51.8 +0.3 52
11 Dynamo Dresden 34 43.2 43.9 -0.7 41
12 Hertha BSC 34 44.7 51.4 -6.7 51
13 Fortuna Düsseldorf 34 40.3 49.4 -9.1 37
14 Eintracht Braunschweig 34 36.7 48.1 -11.4 37
15 SpVgg Greuther Fürth 34 39.4 51.4 -12.0 37
16 Holstein Kiel 34 41.0 53.1 -12.1 41
17 Preußen Münster 34 36.7 55.9 -19.2 30
18 Karlsruher SC 34 42.5 61.9 -19.4 44
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