Predictions / Football / South-Korea. FA Cup / Cheongju vs Gyeongju HNP

Prediction Audit: Cheongju vs Gyeongju HNP Prediction, Odds & AI Betting Tips

Jul 15, 2026 - 10:00
4 1.23
0 1.37
xG Accuracy: 32%

AI correctly predicted the Cheongju win.

The match finished 4–0, validating the model's directional assessment.

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 Under 2.5 Over 2.5 (4 goals) ✖ Incorrect
  • Both Teams To Score BTTS Yes No ✖ Incorrect
  • 1X2 Cheongju Cheongju ✔ Correct
  • Correct Score Insights 1-1, 0-1, 1-0, 1-2, 2-1 4-0 ✖ Incorrect

Model vs Closing Market

Strong Disagreement

The closing market prices Cheongju higher than the statistical model.

Largest probability gap: Cheongju -14.5 pp

Outcome Model Closing Market Difference Signal
Cheongju 38.4% 52.9% -14.5 pp Market Higher
Draw 29.0% 25.7% +3.3 pp Aligned
Gyeongju HNP 32.6% 21.4% +11.2 pp Model Edge

The closing market estimates Cheongju's win probability at 52.9%, compared with the model's estimate of 38.4%, a difference of 14.5 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 model's directional lean matched the result (Cheongju win 4–0).

Market Assessment

The market is materially more optimistic about Cheongju than the current fair estimate.

  • Investors may be incorporating information not fully reflected in the baseline model.
  • Tournament-specific context can shift market pricing.

Post Match Insights

What worked

  • Expected goals projected a high-scoring match (ΣxG 2.60) — 4 goals materialised

What failed

  • Both Teams To Score: model leaned BTTS Yes; match finished BTTS No
  • Over / Under 2.5: model leaned Under 2.5; match finished Over 2.5 (4 goals)

Market lesson

Large model–market gaps do not automatically mean the market is right. Here the closing market priced Cheongju higher (52.9% vs model 38.4%, 14.5 pp), but the model's lean was validated (Cheongju win 4–0).

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Prediction Timeline

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

  1. Jul 15, 2026 · 19:15 UTC Forecast generated
    • Model 1X2 · Cheongju 38.5% · Draw 29.0% · Gyeongju HNP 32.6%
    • xG · Cheongju 1.23 — Gyeongju HNP 1.37
  2. Jul 15, 2026 · 09:30 UTC Opening odds snapshot PRE30
    • 1X2 odds · Cheongju 1.60 · Draw 3.29 · Gyeongju HNP 3.95
    • Implied 1X2 · Cheongju 52.9% · Draw 25.7% · Gyeongju HNP 21.4%
    • Bookmaker · SBO
  3. Jul 15, 2026 · 10:00 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Cheongju 1.60 · Draw 3.29 · Gyeongju HNP 3.95
    • Implied 1X2 · Cheongju 52.9% · Draw 25.7% · Gyeongju HNP 21.4%
    • Bookmaker · SBO
  4. Jul 15, 2026 · 10:00 UTC Kickoff
  5. FT Full-time result Cheongju win · 4–0
  6. FT Prediction validated Directional lean matched full-time result
  7. Archived Prediction review

Historical Snapshot

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

Historical verdict: Monitor
Historical Decision Monitor
Outcome Validated
Pre-match metrics (historical context)
Prediction Reliability 56/100 · Moderate
  • Validation: Warning
  • Large market gap (14 pp)
Evidence ★★★★★
  • No strong statistical edge
  • Market has already priced much of the edge
  • Validation warning
Pricing proximity (inverse gap) 27/100
Betting Confidence 44/100

Validation Report

Immutable Snapshot

Prediction Time: Jul 24, 2026 · 02:53 UTC Snapshot ID: dp-1538713

Closing Odds 1.6
AI Fair Odds —
CLV Pending
Final Result Cheongju win · Cheongju 4–0 Gyeongju HNP
Prediction ✔ Correct
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

Pre-match snapshot for this fixture.

  • League: FA Cup
  • Fixture: Cheongju vs Gyeongju HNP
  • Kickoff: 2026-07-15 10:00:00
  • 1X2 (model): Home 0.0% · Draw 50.0% · Away 50.0%
  • xG (showing): Cheongju 1.23 — Gyeongju HNP 1.37 (total xG ≈ 2.6)
  • 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: Monitor

Outcome: Validated — Pre-match lean validated against 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 30, 2026 (UTC)

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