Predictions / Football / Denmark. Superliga / Silkeborg vs Odense

Prediction Audit: Silkeborg vs Odense Prediction, Odds & AI Betting Tips

Silkeborg Silkeborg
Aug 10, 2026 - 17:00
1 1.24
0 1.73
xG Accuracy: 58%
Odense Odense

The model missed the final outcome (Silkeborg win 1–0).

The model had projected Odense at 44.5%, but the full-time result went the other way.

Tracked markets vs full-time result

Prediction grade F

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

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

Model vs Closing Market

Moderate Disagreement

The closing market prices Silkeborg higher than the statistical model.

Largest probability gap: Silkeborg -7.9 pp

Outcome Model Closing Market Difference Signal
Silkeborg 29.0% 37.0% -7.9 pp Market Higher
Draw 26.5% 25.4% +1.1 pp Aligned
Odense 44.5% 37.7% +6.8 pp Model Higher

The closing market estimates Silkeborg's win probability at 37.0%, compared with the model's estimate of 29.0%, a difference of 7.9 percentage points. This highlights a disagreement between the model and market consensus, without indicating which view is ultimately correct.

Model probabilities are generated from the statistical xG model using a Poisson distribution. Closing market probabilities are derived from consensus closing 1X2 odds after margin removal. Values represent implied probabilities rather than betting recommendations. Closing snapshot: PRE1.

After full time, the result was Silkeborg win 1–0.

Market Assessment

The market and model broadly agree on Silkeborg. The remaining divergence may reflect differences in team-strength assumptions rather than a directional disagreement.

  • Monitor line movement before kickoff — not a betting recommendation.

Post Match Insights

What failed

  • Both Teams To Score: model leaned BTTS Yes; match finished BTTS No
  • Over / Under 2.5: model leaned Over 2.5; match finished Under 2.5 (1 goals)

Market lesson

The closing market differed from the model on Odense by 7.9 percentage points (52.4% vs model 44.5%) — in this case the market view proved closer.

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Prediction Timeline

How this prediction moved from forecast to full-time review.

  1. Aug 10, 2026 · 19:10 UTC Forecast generated
    • Model 1X2 · Silkeborg 29.1% · Draw 26.5% · Odense 44.5%
    • xG · Silkeborg 1.24 — Odense 1.73
  2. Aug 10, 2026 · 16:30 UTC Opening odds snapshot PRE30
    • 1X2 odds · Silkeborg 2.62 · Draw 3.82 · Odense 2.57
    • Implied 1X2 · Silkeborg 37.0% · Draw 25.4% · Odense 37.7%
    • Bookmaker · Pinnacle
  3. Aug 10, 2026 · 16:59 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Silkeborg 2.62 · Draw 3.82 · Odense 2.57
    • Implied 1X2 · Silkeborg 37.0% · Draw 25.4% · Odense 37.7%
    • Bookmaker · Pinnacle
  4. Aug 10, 2026 · 17:00 UTC Kickoff
  5. FT Full-time result Silkeborg win · 1–0
  6. FT Prediction missed 1X2 lean did not match full-time result
  7. Archived Prediction review

Historical Snapshot

Frozen at kickoff — the model output as it stood before the match started.

Historical verdict: Observe
Historical Decision No Primary Bet
Outcome Missed
Pre-match metrics (historical context)
Prediction Reliability 75/100 · High
  • Validation: Pass
Evidence ★★★★★
  • No strong statistical edge
  • Direction agrees with model lean
  • Validation passed
Pricing proximity (inverse gap) 60/100
Betting Confidence 78/100

Validation Report

Immutable Snapshot

Prediction Time: Aug 04, 2026 · 02:18 UTC Snapshot ID: dp-2972389

Closing Odds 2.62
AI Fair Odds —
CLV Pending
Final Result Silkeborg win · Silkeborg 1–0 Odense
Prediction ✖ Missed
Decision Grade F

Model Performance

This prediction contributes to:

  • Primary Bets ROI (180d): -100.0%

Review FAQ

How accurate was the prediction?
This page grades directional markets (1X2, Over/Under 2.5, BTTS) against the full-time result. The prediction grade reflects how many of those tracked markets matched reality.
What does xG Accuracy measure?
xG Accuracy compares the model's pre-match expected-goals profile to the actual scoreline — not whether every market hit. A strong directional review can coexist with a moderate xG accuracy score.
Why wasn't the exact score predicted?
Correct-score outcomes are low-probability tails even when the model reads the match profile well. We highlight top score bins for context; missing the exact line does not invalidate a directional review.
Does this improve the AI record?
Each finished match is logged in our validation pipeline. Aggregated hit rates and CLV studies are published separately — this page is the per-match audit trail.

Predictions are for informational purposes only. Always gamble responsibly and within your limits. Past performance does not guarantee future results.

AI match briefing

AI Match Summary

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

  • League: Superliga
  • Fixture: Silkeborg vs Odense
  • Kickoff: 2026-08-10 17:00:00
  • 1X2 (model): Home 45.0% · Draw 45.0% · Away 10.0%
  • xG (showing): Silkeborg 1.24 — Odense 1.73 (total xG ≈ 2.97)
  • Value headline: None — no positive EV could be estimated on tracked lines at current best prices (missing odds or thin book depth).
  • Structural leans (not bets): See the Over/Under and BTTS cards for any directional lean text.
  • BTTS (model): Yes N/A · No N/A
  • Correct score (top bin): N/A

When book depth is thin or odds are missing, EV may be unavailable even though the model still prefers one side on totals or BTTS — wait for cleaner prices or skip.

Most likely correct score stays a low-probability tail: use it for context, not as a must-bet story.

Historical Recommendation

Historical Decision: No 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 28, 2026 (UTC)

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Superliga Superliga — Standings
# TEAM MP W D L PTS
1 FC Copenhagen 9 8 0 1 24
2 FC Midtjylland 9 5 4 0 19
3 Viborg 9 5 2 2 17
4 FC Nordsjaelland 9 5 2 2 17
5 Brondby 9 4 1 4 13
6 AC Horsens 9 3 2 4 11
7 Silkeborg 9 2 4 3 10
8 Randers FC 9 3 1 5 10
9 Odense 9 2 3 4 9
10 Lyngby 9 1 4 4 7
11 Aarhus 9 0 5 4 5
12 Sonderjyske 9 1 2 6 5
# TEAM MP GS GC +/- PTS
1 FC Copenhagen 9 23 8 +15 24
2 FC Midtjylland 9 17 9 +8 19
3 Viborg 9 15 8 +7 17
4 AC Horsens 9 15 16 -1 11
5 FC Nordsjaelland 9 14 10 +4 17
6 Silkeborg 9 12 12 0 10
7 Brondby 9 12 15 -3 13
8 Sonderjyske 9 12 21 -9 5
9 Randers FC 9 11 14 -3 10
10 Aarhus 9 11 17 -6 5
11 Lyngby 9 10 16 -6 7
12 Odense 9 8 14 -6 9
# TEAM MP xG xGC +/- PTS
1 FC Nordsjaelland 9 15.9 7.0 +8.9 17
2 Lyngby 9 12.2 8.1 +4.1 7
3 Viborg 9 9.7 6.4 +3.3 17
4 FC Midtjylland 9 9.3 6.3 +3.0 19
5 Aarhus 9 8.5 6.3 +2.2 5
6 FC Copenhagen 9 6.6 6.3 +0.3 24
7 Brondby 9 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 9 7.3 11.3 -4.0 11
11 Randers FC 9 5.8 11.6 -5.8 10
12 Sonderjyske 9 5.9 12.2 -6.3 5