Statistik / Bola Sepak / Germany. Bundesliga / 1899 Hoffenheim vs VfB Stuttgart

1899 Hoffenheim vs VfB Stuttgart Statistics & Analysis

1899 Hoffenheim 1899 Hoffenheim
May 02, 2026 - 13:30
3 1.83
3 1.60
xG Accuracy: 49%
VfB Stuttgart VfB Stuttgart
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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
  • Lebih / Kurang 2.5 Kurang 2.5 Lebih 2.5 (6 goals) ✖ Incorrect
  • Kedua-dua Pasukan Menjaringkan Gol BTTS Tidak Ya ✖ Incorrect
  • 1X2 1899 Hoffenheim Lukis ✖ Incorrect
  • Cerapan skor tepat 1-1 3-3 ✖ Incorrect

Taklimat perlawanan AI

Ringkasan perlawanan AI

Pre-match snapshot for this fixture.

  • League: Bundesliga
  • Fixture: 1899 Hoffenheim vs VfB Stuttgart
  • Kickoff: 2026-05-02 13:30:00
  • 1X2 (model): Home 42.4% · Draw 24.9% · Away 32.8%
  • xG (showing): 1899 Hoffenheim 1.83 — VfB Stuttgart 1.6 (total xG ≈ 3.43)
  • Best +EV line (same label as hero card when Primary thresholds are not met): Under 2.5 goals
  • Model: 33.4% · Implied: 31.1% · Probability edge: +2.3 pts · Est. EV: +6.9%
  • BTTS (model): Yes 68.2% · No 31.8%
  • Correct score (top bin): 1-1 (9.5%)

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

Early match state can move realised goals away from pre-kick projections.

Taruhan terbaik dan sebab

Strongest +EV line among tracked markets (not shown as a full Primary under current engine thresholds): Under 2.5 goals.

If 1X2 looks tight, the engine may still find clearer structure in totals or BTTS — that is intentional.

No pick is a guarantee; variance is especially large in scoreline markets.

FAQ

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.

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.

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 is the best-supported line in this snapshot?

Match the hero card above: if it says “Betting Primary Pick”, that leg cleared primary rules; if it says “Best +EV (tracked markets)”, it is the strongest +EV line that did not meet stricter Primary thresholds. The bullets below repeat the same model %, implied %, edge (pts), and EV % as that card.

Faktor risiko

  • 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%).

Metodologi

  • Inputs: Same structured facts bundle as the public prediction page (xG / Poisson snapshot, market EV where available, decision engine v2).
  • Narrative: Template sentence library with fixture-stable selection (no per-request LLM for this block).
  • Compliance: Educational framing only; not personalised advice.

Terakhir dikemas kini

May 01, 2026 (UTC)

Bagaimana untuk menggunakan ini
  • Fokus pada baris Utama apabila anda mahukan satu idea yang boleh diambil tindakan.
  • Jangan parlay banyak picks tepi nipis bersama-sama;tepi tidak menambah dengan pasti.
  • Anggap pukulan panjang sebagai permainan pilihan, bersaiz tinggi sahaja.

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Bundesliga Bundesliga — Kedudukan
# PASUKAN MP M S K PTS
1 Bayern München 34 28 5 1 89
2 Borussia Dortmund 34 22 7 5 73
3 RB Leipzig 34 20 5 9 65
4 VfB Stuttgart 34 18 8 8 62
5 1899 Hoffenheim 34 18 7 9 61
6 Bayer Leverkusen 34 17 8 9 59
7 SC Freiburg 34 13 8 13 47
8 Eintracht Frankfurt 34 11 11 12 44
9 FC Augsburg 34 12 7 15 43
10 FSV Mainz 05 34 10 10 14 40
11 Union Berlin 34 10 9 15 39
12 Borussia Mönchengladbach 34 9 11 14 38
13 Hamburger SV 34 9 11 14 38
14 1. FC Köln 34 7 11 16 32
15 Werder Bremen 34 8 8 18 32
16 VfL Wolfsburg 34 7 8 19 29
17 1. FC Heidenheim 34 6 8 20 26
18 FC St. Pauli 34 6 8 20 26
# PASUKAN MP GS GC +/- PTS
1 Bayern München 34 122 36 +86 89
2 VfB Stuttgart 34 71 49 +22 62
3 Borussia Dortmund 34 70 34 +36 73
4 Bayer Leverkusen 34 68 47 +21 59
5 RB Leipzig 34 66 47 +19 65
6 1899 Hoffenheim 34 65 52 +13 61
7 Eintracht Frankfurt 34 61 65 -4 44
8 SC Freiburg 34 51 57 -6 47
9 1. FC Köln 34 49 63 -14 32
10 FC Augsburg 34 45 61 -16 43
11 VfL Wolfsburg 34 45 69 -24 29
12 FSV Mainz 05 34 44 53 -9 40
13 Union Berlin 34 44 58 -14 39
14 Borussia Mönchengladbach 34 42 53 -11 38
15 1. FC Heidenheim 34 41 72 -31 26
16 Hamburger SV 34 40 54 -14 38
17 Werder Bremen 34 37 60 -23 32
18 FC St. Pauli 34 29 60 -31 26
# PASUKAN MP xG xGC +/- PTS
1 Bayern München 34 97.2 37.9 +59.3 89
2 Borussia Dortmund 34 63.4 38.9 +24.5 73
3 Bayer Leverkusen 34 64.0 44.8 +19.2 59
4 RB Leipzig 34 66.3 50.2 +16.1 65
5 VfB Stuttgart 34 60.1 49.8 +10.3 62
6 1899 Hoffenheim 34 55.1 51.4 +3.7 61
7 SC Freiburg 34 48.6 47.8 +0.8 47
8 FSV Mainz 05 34 52.9 54.6 -1.7 40
9 1. FC Köln 34 50.9 54.9 -4.0 32
10 Eintracht Frankfurt 34 45.4 50.1 -4.7 44
11 Union Berlin 34 44.9 52.2 -7.3 39
12 Borussia Mönchengladbach 34 42.7 52.5 -9.8 38
13 Werder Bremen 34 40.4 54.3 -13.9 32
14 VfL Wolfsburg 34 47.1 61.8 -14.7 29
15 1. FC Heidenheim 34 47.5 63.4 -15.9 26
16 FC Augsburg 34 45.2 62.6 -17.4 43
17 Hamburger SV 34 38.3 56.9 -18.6 38
18 FC St. Pauli 34 30.5 56.2 -25.7 26
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