Statistics / Football / Germany. 3. Liga / FC Viktoria Köln vs Alemannia Aachen

FC Viktoria Köln vs Alemannia Aachen Statistics & Analysis

FC Viktoria Köln FC Viktoria Köln
May 10, 2026 - 14:30
0 1.41
3 1.19
xG Accuracy: 41%
Alemannia Aachen Alemannia Aachen
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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 FC Viktoria Köln Alemannia Aachen ✖ Incorrect
  • Correct Score Insights 1-1 0-3 ✖ Incorrect

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: FC Viktoria Köln vs Alemannia Aachen
  • Kickoff: 2026-05-09 12:00:00
  • 1X2 (model): Home 40.5% · Draw 29.4% · Away 30.1%
  • xG (showing): FC Viktoria Köln 1.41 — Alemannia Aachen 1.19 (total xG ≈ 2.6)
  • Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
  • Model: 51.8% · Implied: 37.2% · Probability edge: +14.6 pts · Est. EV: +38.8%
  • BTTS (model): Yes 54.2% · No 45.8%
  • Correct score (top bin): 1-1 (12.5%)

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 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

Safer market than correct score?

Markets with more liquidity and smoother prices (often 1X2 or O/U 2.5 from many books) are usually easier to reason about than long-tail correct-score prices; still read EV on each leg.

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.

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.

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.

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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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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