Statistics / Football / Germany. 3. Liga / SSV Jahn Regensburg vs Hoffenheim II

SSV Jahn Regensburg vs Hoffenheim II Statistics & Analysis

SSV Jahn Regensburg SSV Jahn Regensburg
May 02, 2026 - 12:00
2 2.39
1 1.50
xG Accuracy: 78%
Hoffenheim II Hoffenheim II
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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 Over 2.5 (3 goals) ✔ Correct
  • Both Teams To Score BTTS Yes Yes ✔ Correct
  • 1X2 SSV Jahn Regensburg SSV Jahn Regensburg ✔ Correct
  • Correct Score Insights 2-1 2-1 ✔ Correct

AI match briefing

AI Match Summary

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

  • League: 3. Liga
  • Fixture: SSV Jahn Regensburg vs Hoffenheim II
  • Kickoff: 2026-05-02 12:00:00
  • 1X2 (model): Home 56.4% · Draw 21.1% · Away 22.4%
  • xG (showing): SSV Jahn Regensburg 2.39 — Hoffenheim II 1.5 (total xG ≈ 3.89)
  • Primary / headline line (Betting Primary Pick when shown): Over 2.5 goals
  • Model: 74.5% · Implied: 64.8% · Probability edge: +9.7 pts · Est. EV: +8.0%
  • BTTS (model): Yes 71.5% · No 28.5%
  • Correct score (top bin): 2-1 (8.8%)

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

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

Best Bet + Reason

Primary pick from the decision engine: Over 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

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.

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.

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.

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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Back to Statistics
3. Liga 3. Liga — Standings
# TEAM MP W D L PTS
1 VfL Osnabrück 38 24 8 6 80
2 Energie Cottbus 38 21 9 8 72
3 Rot-Weiß Essen 38 20 10 8 70
4 MSV Duisburg 38 19 11 8 68
5 Hansa Rostock 38 18 13 7 67
6 Verl 38 18 10 10 64
7 Alemannia Aachen 38 19 7 12 64
8 TSV 1860 München 38 15 11 12 56
9 SV Wehen 38 15 8 15 53
10 Waldhof Mannheim 38 15 7 16 52
11 FC Viktoria Köln 38 15 6 17 51
12 FC Ingolstadt 04 38 13 10 15 49
13 SSV Jahn Regensburg 38 14 7 17 49
14 Stuttgart II 38 13 7 18 46
15 FC Saarbrücken 38 10 14 14 44
16 Hoffenheim II 38 12 7 19 43
17 Havelse 38 9 8 21 35
18 Erzgebirge Aue 38 7 13 18 34
19 SSV Ulm 1846 38 9 6 23 33
20 FC Schweinfurt 05 38 5 6 27 21
# TEAM MP GS GC +/- PTS
1 Verl 38 82 48 +34 64
2 Rot-Weiß Essen 38 78 66 +12 70
3 Alemannia Aachen 38 76 57 +19 64
4 Hansa Rostock 38 74 49 +25 67
5 Energie Cottbus 38 72 51 +21 72
6 VfL Osnabrück 38 66 34 +32 80
7 MSV Duisburg 38 66 49 +17 68
8 FC Ingolstadt 04 38 65 56 +9 49
9 Hoffenheim II 38 65 71 -6 43
10 Waldhof Mannheim 38 59 72 -13 52
11 Stuttgart II 38 57 69 -12 46
12 Havelse 38 57 89 -32 35
13 SV Wehen 38 54 52 +2 53
14 TSV 1860 München 38 54 53 +1 56
15 SSV Jahn Regensburg 38 54 58 -4 49
16 FC Viktoria Köln 38 51 53 -2 51
17 FC Saarbrücken 38 51 57 -6 44
18 Erzgebirge Aue 38 51 70 -19 34
19 SSV Ulm 1846 38 49 78 -29 33
20 FC Schweinfurt 05 38 38 87 -49 21
# TEAM MP xG xGC +/- PTS
1 Hansa Rostock 38 49.0 35.6 +13.4 67
2 Energie Cottbus 38 46.5 33.7 +12.8 72
3 Verl 38 44.0 31.6 +12.4 64
4 VfL Osnabrück 38 41.6 30.4 +11.2 80
5 MSV Duisburg 38 37.4 29.2 +8.2 68
6 FC Ingolstadt 04 38 45.5 38.9 +6.6 49
7 TSV 1860 München 38 45.8 42.6 +3.2 56
8 Rot-Weiß Essen 38 41.9 38.8 +3.1 70
9 Waldhof Mannheim 38 38.3 35.4 +2.9 52
10 SV Wehen 38 36.8 35.3 +1.5 53
11 FC Saarbrücken 38 41.1 40.6 +0.5 44
12 SSV Jahn Regensburg 38 40.8 40.6 +0.2 49
13 FC Viktoria Köln 38 33.4 34.1 -0.7 51
14 Stuttgart II 38 37.1 41.0 -3.9 46
15 SSV Ulm 1846 38 37.4 42.3 -4.9 33
16 Alemannia Aachen 38 37.1 42.9 -5.8 64
17 Hoffenheim II 38 44.5 51.1 -6.6 43
18 Erzgebirge Aue 38 32.1 45.3 -13.2 34
19 Havelse 38 31.9 50.5 -18.6 35
20 FC Schweinfurt 05 38 32.9 55.1 -22.2 21
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