Statistics / Football / Denmark. Superliga / Silkeborg vs Viborg

Silkeborg vs Viborg Statistics & Analysis

Silkeborg Silkeborg
Sep 13, 2026 - 12:00
1 0.97
1 1.81
xG Accuracy: 79%
Viborg Viborg
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Tracked markets vs full-time result

Each row compares the pre-match model lean to the full-time result.

  • Market Prediction Result Outcome
  • Over / Under 2.5 Under 2.5 Under 2.5 (2 goals) ✔ Correct
  • Both Teams To Score BTTS No Yes ✖ Incorrect
  • 1X2 Viborg Draw ✖ Incorrect
  • Correct Score Insights 0-1, 1-1, 0-2, 1-2, 0-0 1-1 ✔ Correct

Validation Report

Immutable Snapshot

Prediction Time: Sep 07, 2026 · 01:13 UTC Snapshot ID: dp-7428537

Closing Odds 3.72
AI Fair Odds —
CLV +0.0%
Final Result Draw · Silkeborg 1–1 Viborg
Prediction ✖ Missed
Decision Grade F

Model Performance

This prediction contributes to:

  • Primary Bets ROI (180d): -100.0%

Market intelligence

Supporting read on how the prioritized closing feed moved versus the model — use after the Primary pick above.

Market briefing

Market remained largely stable before kickoff. No meaningful late implied-price shift was detected between PRE30 and PRE5 on the prioritized bookmaker snapshot.

Despite limited late movement, the model still prices Draw (1X2), Viborg (1X2), Under 2.5 goals meaningfully above what those closing snapshots implied — that gap is a static “model vs. price” read, not a late steam or chase story.

The model still exceeds closing implied on Under 2.5 goals by about 9.2 percentage points — the clearest mispricing signal summarized on this page.

Model vs. closing implied

Market Model % Closing impl. % Gap (pp)
Silkeborg (1X2) 18.6 26.0 -7.4
Draw (1X2) 25.8 22.6 +3.2
Viborg (1X2) 55.6 51.4 +4.2
Over 2.5 goals 52.6 61.8 -9.2
Under 2.5 goals 47.4 38.2 +9.2
What this means

In plain terms: the model lands near 47.4% on Under 2.5 goals, while the closing snapshot implied about 38.2%. The difference — about 9.2 percentage points — is the largest model-vs.-market gap highlighted on this page.

Quick definitions: “closing implied” is the probability for that outcome implied by the final captured odds (after a simple de-vig). “Gap (pp)” is the model percentage minus that implied value, in percentage points (pp).

Closing-window line move

Single prioritized bookmaker per snapshot (not all books). Capture path: PRE30 → PRE5 · Book: Pinnacle

Column tags in parentheses: Closing uses the first available snapshot in PRE1→PRE5→PRE10→PRE30; Early uses the first available in PRE30→PRE10→PRE5 that is not the same capture as Closing.

Detailed capture odds are folded below — movement was negligible on de-vig implied prices.

View full line-by-line capture table
Market Early (PRE30) Closing (PRE5) Implied Δ (pp)
Silkeborg (1X2) 3.72 3.72 0.0
Draw (1X2) 4.27 4.27 0.0
Viborg (1X2) 1.88 1.88 0.0
Over 2.5 goals 1.56 1.56 0.0
Under 2.5 goals 2.52 2.52 0.0

AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

  • League: Superliga
  • Fixture: Silkeborg vs Viborg
  • Kickoff: 2026-09-13 12:00:00
  • 1X2 (model): Home 18.6% · Draw 25.8% · Away 55.6%
  • xG (showing): Silkeborg 0.97 — Viborg 1.81 (total xG ≈ 2.78)
  • Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
  • Model: 47.4% · Implied: 37.9% · Probability edge: +9.5 pts · Est. EV: +23.2%
  • BTTS (model): Yes N/A · No N/A
  • Correct score (top bin): N/A

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.

Historical Recommendation

Historical Decision: Primary Bet

Outcome: Missed — Pre-match 1X2 lean did not match the full-time result.

Risk Factors Considered Before Kickoff

  • 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%).

Last Updated

September 23, 2026 (UTC)

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Back to Statistics
Superliga Superliga — Standings
# TEAM MP W D L PTS
1 FC Copenhagen 8 7 0 1 21
2 FC Midtjylland 9 5 4 0 19
3 FC Nordsjaelland 8 5 2 1 17
4 Viborg 8 4 2 2 14
5 Brondby 8 4 1 3 13
6 Silkeborg 9 2 4 3 10
7 AC Horsens 8 3 1 4 10
8 Randers FC 8 3 1 4 10
9 Odense 9 2 3 4 9
10 Lyngby 9 1 4 4 7
11 Aarhus 8 0 4 4 4
12 Sonderjyske 8 0 2 6 2
# TEAM MP GS GC +/- PTS
1 FC Copenhagen 8 22 8 +14 21
2 FC Midtjylland 9 17 9 +8 19
3 FC Nordsjaelland 8 13 6 +7 17
4 AC Horsens 8 13 14 -1 10
5 Silkeborg 9 12 12 0 10
6 Brondby 8 12 14 -2 13
7 Viborg 8 11 7 +4 14
8 Lyngby 9 10 16 -6 7
9 Randers FC 8 9 10 -1 10
10 Aarhus 8 9 15 -6 4
11 Odense 9 8 14 -6 9
12 Sonderjyske 8 8 19 -11 2
# TEAM MP xG xGC +/- PTS
1 FC Nordsjaelland 8 15.9 7.0 +8.9 17
2 Lyngby 9 12.2 8.1 +4.1 7
3 Viborg 8 9.7 6.4 +3.3 14
4 FC Midtjylland 9 9.3 6.3 +3.0 19
5 Aarhus 8 8.5 6.3 +2.2 4
6 FC Copenhagen 8 6.6 6.3 +0.3 21
7 Brondby 8 8.9 9.8 -0.9 13
8 Odense 9 8.7 9.7 -1.0 9
9 Silkeborg 9 6.2 9.8 -3.6 10
10 AC Horsens 8 7.3 11.3 -4.0 10
11 Randers FC 8 5.8 11.6 -5.8 10
12 Sonderjyske 8 5.9 12.2 -6.3 2