Statistik / Bola Sepak / Germany. 2. Bundesliga / SV Elversberg vs SC Paderborn 07

SV Elversberg vs SC Paderborn 07 Statistics & Analysis

SV Elversberg SV Elversberg
May 03, 2026 - 11:30
5 1.59
1 1.13
xG Accuracy: 38%
SC Paderborn 07 SC Paderborn 07
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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 SV Elversberg SV Elversberg ✔ Correct
  • Cerapan skor tepat 1-1 5-1 ✖ Incorrect

Taklimat perlawanan AI

Ringkasan perlawanan AI

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

  • League: 2. Bundesliga
  • Fixture: SV Elversberg vs SC Paderborn 07
  • Kickoff: 2026-05-02 11:30:00
  • 1X2 (model): Home 46.5% · Draw 28.0% · Away 25.5%
  • xG (showing): SV Elversberg 1.59 — SC Paderborn 07 1.13 (total xG ≈ 2.72)
  • Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
  • Model: 48.9% · Implied: 34.6% · Probability edge: +14.2 pts · Est. EV: +38.4%
  • BTTS (model): Yes 55.4% · No 44.6%
  • Correct score (top bin): 1-1 (11.8%)

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.

Taruhan terbaik dan sebab

Primary pick from the decision engine: Under 2.5 goals.

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

When several markets sit near +EV, keep stakes small — correlation means edges do not add cleanly.

FAQ

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.

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.

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.

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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2. Bundesliga 2. Bundesliga — Kedudukan
# PASUKAN MP M S K PTS
1 FC Schalke 04 33 20 7 6 67
2 SV Elversberg 33 17 8 8 59
3 Hannover 96 33 16 11 6 59
4 SC Paderborn 07 33 17 8 8 59
5 SV Darmstadt 98 33 13 13 7 52
6 Hertha BSC 33 14 9 10 51
7 1. FC Kaiserslautern 33 15 4 14 49
8 1. FC Nürnberg 33 12 9 12 45
9 Karlsruher SC 33 12 8 13 44
10 VfL Bochum 33 10 11 12 41
11 Holstein Kiel 33 11 8 14 41
12 1. FC Magdeburg 33 12 3 18 39
13 Dynamo Dresden 33 10 8 15 38
14 Eintracht Braunschweig 33 10 7 16 37
15 Fortuna Düsseldorf 33 11 4 18 37
16 Arminia Bielefeld 33 9 9 15 36
17 SpVgg Greuther Fürth 33 9 7 17 34
18 Preußen Münster 33 6 12 15 30
# PASUKAN MP GS GC +/- PTS
1 SV Elversberg 33 61 39 +22 59
2 Hannover 96 33 57 41 +16 59
3 SV Darmstadt 98 33 57 43 +14 52
4 SC Paderborn 07 33 57 45 +12 59
5 Dynamo Dresden 33 52 52 0 38
6 1. FC Magdeburg 33 52 57 -5 39
7 Karlsruher SC 33 52 62 -10 44
8 1. FC Kaiserslautern 33 51 47 +4 49
9 FC Schalke 04 33 49 31 +18 67
10 VfL Bochum 33 47 46 +1 41
11 Arminia Bielefeld 33 47 50 -3 36
12 Hertha BSC 33 46 38 +8 51
13 SpVgg Greuther Fürth 33 46 68 -22 34
14 1. FC Nürnberg 33 44 42 +2 45
15 Holstein Kiel 33 43 46 -3 41
16 Preußen Münster 33 38 58 -20 30
17 Eintracht Braunschweig 33 36 53 -17 37
18 Fortuna Düsseldorf 33 33 50 -17 37
# PASUKAN MP xG xGC +/- PTS
1 FC Schalke 04 33 51.0 30.4 +20.6 67
2 SC Paderborn 07 33 58.1 38.1 +20.0 59
3 Hannover 96 33 55.4 38.5 +16.9 59
4 SV Elversberg 33 51.6 37.3 +14.3 59
5 1. FC Magdeburg 33 52.6 45.9 +6.7 39
6 Arminia Bielefeld 33 50.3 46.2 +4.1 36
7 VfL Bochum 33 51.9 48.6 +3.3 41
8 1. FC Kaiserslautern 33 46.2 44.1 +2.1 49
9 1. FC Nürnberg 33 46.2 44.2 +2.0 45
10 SV Darmstadt 98 33 52.1 51.8 +0.3 52
11 Dynamo Dresden 33 43.2 43.9 -0.7 38
12 Hertha BSC 33 44.7 51.4 -6.7 51
13 Fortuna Düsseldorf 33 40.3 49.4 -9.1 37
14 Eintracht Braunschweig 33 36.7 48.1 -11.4 37
15 SpVgg Greuther Fürth 33 39.4 51.4 -12.0 34
16 Holstein Kiel 33 41.0 53.1 -12.1 41
17 Preußen Münster 33 36.7 55.9 -19.2 30
18 Karlsruher SC 33 42.5 61.9 -19.4 44
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