Statistics / Football / Germany. Bundesliga / Borussia Mönchengladbach vs Borussia Dortmund

Borussia Mönchengladbach vs Borussia Dortmund Statistics & Analysis

Borussia Mönchengladbach Borussia Mönchengladbach
May 03, 2026 - 15:30
1 1.00
0 1.87
xG Accuracy: 60%
Borussia Dortmund Borussia Dortmund
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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 (1 goals) ✔ Correct
  • Both Teams To Score BTTS No No ✔ Correct
  • 1X2 Borussia Dortmund Borussia Mönchengladbach ✖ Incorrect
  • Correct Score Insights 0-1 1-0 ✖ Incorrect

AI match briefing

AI Match Summary

Pre-match snapshot for this fixture.

  • League: Bundesliga
  • Fixture: Borussia Mönchengladbach vs Borussia Dortmund
  • Kickoff: 2026-05-02 13:30:00
  • 1X2 (model): Home 18.5% · Draw 25.1% · Away 56.4%
  • xG (showing): Borussia Mönchengladbach 1.0 — Borussia Dortmund 1.87 (total xG ≈ 2.87)
  • Primary / headline line (Betting Primary Pick when shown): BTTS No
  • Model: 45.1% · Implied: 38.4% · Probability edge: +6.8 pts · Est. EV: +12.8%
  • BTTS (model): Yes 54.9% · No 45.1%
  • Correct score (top bin): 0-1 (10.6%)

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

The engine’s headline primary is: BTTS No.

Model probability is compared to implied probability from odds to highlight a probability edge; EV uses the same model probability with the best decimal price tracked.

Edges shrink quickly if prices move; always re-check the number on your book.

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.

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.

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.

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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Bundesliga Bundesliga — Standings
# TEAM MP W D L PTS
1 Bayern München 33 27 5 1 86
2 Borussia Dortmund 33 21 7 5 70
3 RB Leipzig 33 20 5 8 65
4 VfB Stuttgart 33 18 7 8 61
5 1899 Hoffenheim 33 18 7 8 61
6 Bayer Leverkusen 33 17 7 9 58
7 SC Freiburg 33 12 8 13 44
8 Eintracht Frankfurt 33 11 10 12 43
9 FC Augsburg 33 12 7 14 43
10 FSV Mainz 05 33 9 10 14 37
11 Hamburger SV 33 9 10 14 37
12 Union Berlin 33 9 9 15 36
13 Borussia Mönchengladbach 33 8 11 14 35
14 1. FC Köln 33 7 11 15 32
15 Werder Bremen 33 8 8 17 32
16 VfL Wolfsburg 33 6 8 19 26
17 1. FC Heidenheim 33 6 8 19 26
18 FC St. Pauli 33 6 8 19 26
# TEAM MP GS GC +/- PTS
1 Bayern München 33 117 35 +82 86
2 VfB Stuttgart 33 69 47 +22 61
3 Borussia Dortmund 33 68 34 +34 70
4 Bayer Leverkusen 33 67 46 +21 58
5 RB Leipzig 33 65 43 +22 65
6 1899 Hoffenheim 33 65 48 +17 61
7 Eintracht Frankfurt 33 59 63 -4 43
8 1. FC Köln 33 48 58 -10 32
9 SC Freiburg 33 47 56 -9 44
10 FC Augsburg 33 45 57 -12 43
11 FSV Mainz 05 33 42 53 -11 37
12 VfL Wolfsburg 33 42 68 -26 26
13 1. FC Heidenheim 33 41 70 -29 26
14 Union Berlin 33 40 58 -18 36
15 Hamburger SV 33 39 53 -14 37
16 Borussia Mönchengladbach 33 38 53 -15 35
17 Werder Bremen 33 37 58 -21 32
18 FC St. Pauli 33 28 57 -29 26
# TEAM MP xG xGC +/- PTS
1 Bayern München 33 94.5 37.0 +57.5 86
2 Borussia Dortmund 33 60.5 38.5 +22.0 70
3 Bayer Leverkusen 33 60.9 43.0 +17.9 58
4 RB Leipzig 33 65.2 48.7 +16.5 65
5 VfB Stuttgart 33 59.0 47.2 +11.8 61
6 1899 Hoffenheim 33 53.4 50.0 +3.4 61
7 SC Freiburg 33 47.0 46.7 +0.3 44
8 1. FC Köln 33 50.0 52.1 -2.1 32
9 FSV Mainz 05 33 49.4 52.0 -2.6 37
10 Eintracht Frankfurt 33 42.8 49.0 -6.2 43
11 Borussia Mönchengladbach 33 41.3 50.8 -9.5 35
12 Union Berlin 33 41.7 51.7 -10.0 36
13 Werder Bremen 33 40.0 51.5 -11.5 32
14 FC Augsburg 33 44.7 59.4 -14.7 43
15 1. FC Heidenheim 33 44.9 59.9 -15.0 26
16 VfL Wolfsburg 33 43.6 60.3 -16.7 26
17 Hamburger SV 33 36.5 53.8 -17.3 37
18 FC St. Pauli 33 29.0 52.8 -23.8 26
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