Prediction Audit: Equatorial Guinea vs Sierra Leone Prediction, Odds & AI Betting Tips

Sep 28, 2026 - 16:00
1 1.34
0 1.26
xG Accuracy: 64%

AI correctly predicted the Equatorial Guinea win.

The match finished 1–0, 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 Over 2.5 Under 2.5 (1 goals) ✖ Incorrect
  • Both Teams To Score BTTS Yes No ✖ Incorrect
  • 1X2 Equatorial Guinea Equatorial Guinea ✔ Correct
  • Correct Score Insights 1-1, 1-0, 0-1, 2-1, 1-2 1-0 ✔ Correct

Model vs Closing Market

Moderate Disagreement

The model rates Sierra Leone considerably stronger than the closing betting market.

Largest probability gap: Sierra Leone +8.7 pp

Outcome Model Closing Market Difference Signal
Equatorial Guinea 37.1% 42.8% -5.7 pp Market Higher
Draw 29.6% 32.6% -3.0 pp Aligned
Sierra Leone 33.3% 24.6% +8.7 pp Model Higher

The statistical model estimates Sierra Leone's win probability at 33.3%, compared with the closing market's implied probability of 24.6%, a difference of 8.7 percentage points. This highlights a substantial disagreement between the model's assessment and the market consensus, rather than 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 (Equatorial Guinea win 1–0).

Market Assessment

The fair estimate shows a modest edge over current market pricing on Sierra Leone.

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

Post Match Insights

What worked

  • Exact score 1–0 fell within the model's highlighted bins

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

Large model–market gaps do not automatically mean the market is right. Here the closing market priced Equatorial Guinea more conservatively (28.4% vs model 37.1%, 8.7 pp), but the model's lean was validated (Equatorial Guinea win 1–0).

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Prediction Timeline

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

  1. Sep 28, 2026 · 16:00 UTC Forecast generated
    • Model 1X2 · Equatorial Guinea 37.0% · Draw 29.6% · Sierra Leone 33.4%
    • xG · Equatorial Guinea 1.34 — Sierra Leone 1.26
  2. Sep 28, 2026 · 15:30 UTC Opening odds snapshot PRE30
    • 1X2 odds · Equatorial Guinea 2.17 · Draw 2.85 · Sierra Leone 3.77
    • Implied 1X2 · Equatorial Guinea 42.8% · Draw 32.6% · Sierra Leone 24.6%
    • Bookmaker · Pinnacle
  3. Sep 28, 2026 · 15:59 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Equatorial Guinea 2.17 · Draw 2.85 · Sierra Leone 3.77
    • Implied 1X2 · Equatorial Guinea 42.8% · Draw 32.6% · Sierra Leone 24.6%
    • Bookmaker · Pinnacle
  4. Sep 28, 2026 · 16:00 UTC Kickoff
  5. FT Full-time result Equatorial Guinea win · 1–0
  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: Observe
Historical Decision No Primary Bet
Outcome Validated
Pre-match metrics (historical context)
Prediction Reliability 75/100 · High
  • Validation: Pass
Evidence ★★★★★
  • Statistical edge detected
  • Direction agrees with model lean
  • Validation passed
Pricing proximity (inverse gap) 56/100
Betting Confidence 78/100

Validation Report

Immutable Snapshot

Prediction Time: Sep 22, 2026 · 02:02 UTC Snapshot ID: dp-10250719

Closing Odds 3.77
AI Fair Odds —
CLV Pending
Final Result Equatorial Guinea win · Equatorial Guinea 1–0 Sierra Leone
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

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

  • League: Africa Cup of Nations - Qualification
  • Fixture: Equatorial Guinea vs Sierra Leone
  • Kickoff: 2026-09-28 16:00:00
  • 1X2 (model): Home 37.0% · Draw 29.6% · Away 33.4%
  • xG (showing): Equatorial Guinea 1.34 — Sierra Leone 1.26 (total xG ≈ 2.6)
  • Primary / headline line (Betting Primary Pick when shown): BTTS Yes
  • Model: 54.5% · Implied: 44.0% · Probability edge: +10.5 pts · Est. EV: +19.9%
  • BTTS (model): Yes 54.5% · No 45.5%
  • Correct score (top bin): 1-1 (12.5%)

Use the cards for tiering; this text only restates the same inputs in narrative form.

Correct score remains high-variance even when a line is most likely on paper.

Historical Recommendation

Historical Decision: No Primary Bet

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 29, 2026 (UTC)

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