Predictions / Football / China. League Two / Xiamen Feilu vs BIT

Prediction Audit: Xiamen Feilu vs BIT Prediction, Odds & AI Betting Tips

⚽
Xiamen Feilu
Sep 19, 2026 - 11:30
3 1.32
0 1.28
xG Accuracy: 44%
BIT BIT

AI correctly predicted the Xiamen Feilu win.

The match finished 3–0, 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 (3 goals) ✖ Incorrect
  • Both Teams To Score BTTS Yes No ✖ Incorrect
  • 1X2 Xiamen Feilu Xiamen Feilu ✔ Correct
  • Correct Score Insights 1-1, 1-0, 0-1, 2-1, 1-2 3-0 ✖ Incorrect

Model vs Closing Market

Strong Disagreement

The closing market prices Xiamen Feilu higher than the statistical model.

Largest probability gap: Xiamen Feilu -23.7 pp

Outcome Model Closing Market Difference Signal
Xiamen Feilu 36.1% 59.8% -23.7 pp Market Higher
Draw 29.6% 24.1% +5.6 pp Model Higher
BIT 34.2% 16.1% +18.1 pp Model Edge

The closing market estimates Xiamen Feilu's win probability at 59.8%, compared with the model's estimate of 36.1%, a difference of 23.7 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 (Xiamen Feilu win 3–0).

Post Match Insights

What worked

  • Expected goals projected a high-scoring match (ΣxG 2.60) — 3 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 (3 goals)

Market lesson

Large model–market gaps do not automatically mean the market is right. Here the closing market priced Xiamen Feilu higher (59.8% vs model 36.1%, 23.7 pp), but the model's lean was validated (Xiamen Feilu win 3–0).

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Prediction Timeline

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

  1. Sep 19, 2026 · 11:32 UTC Forecast generated
    • Model 1X2 · Xiamen Feilu 36.1% · Draw 29.7% · BIT 34.2%
    • xG · Xiamen Feilu 1.32 — BIT 1.28
  2. Sep 19, 2026 · 11:05 UTC Opening odds snapshot PRE30
    • 1X2 odds · Xiamen Feilu 1.51 · Draw 3.75 · BIT 5.59
    • Implied 1X2 · Xiamen Feilu 59.8% · Draw 24.1% · BIT 16.1%
    • Bookmaker · Pinnacle
  3. Sep 19, 2026 · 11:32 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Xiamen Feilu 1.51 · Draw 3.75 · BIT 5.59
    • Implied 1X2 · Xiamen Feilu 59.8% · Draw 24.1% · BIT 16.1%
    • Bookmaker · Pinnacle
  4. Sep 19, 2026 · 11:30 UTC Kickoff
  5. FT Full-time result Xiamen Feilu win · 3–0
  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: Cautious / Wait

Historical label: Originally displayed as "Wait for validation".

Historical Decision Wait
Outcome Validated
Pre-match metrics (historical context)
Prediction Reliability 51/100 · Moderate
  • Validation: Warning
  • Large market gap (24 pp)
Evidence ★★★★★
  • No strong statistical edge
  • Pricing remains divergent
  • Validation warning
Pricing proximity (inverse gap) 0/100
Betting Confidence 33/100

Validation Report

Immutable Snapshot

Prediction Time: Sep 14, 2026 · 01:09 UTC Snapshot ID: dp-8662535

Closing Odds 1.51
AI Fair Odds —
CLV Pending
Final Result Xiamen Feilu win · Xiamen Feilu 3–0 BIT
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: League Two
  • Fixture: Xiamen Feilu vs BIT
  • Kickoff: 2026-09-19 11:30:00
  • 1X2 (model): Home 36.1% · Draw 29.7% · Away 34.2%
  • xG (showing): Xiamen Feilu 1.32 — BIT 1.28 (total xG ≈ 2.6)
  • Value headline: At least one tracked line reaches the headline EV threshold — align with the hero / Primary card if shown.
  • Structural leans (not bets): Structural lean (model): O/U 2.5 Under 2.5 (Under 2.5 51.8% · Over 2.5 48.2%); BTTS Yes (Yes 54.5% · No 45.5%) Value lean (pricing): O/U 2.5 Under 2.5; BTTS Yes
  • BTTS (model): Yes 54.5% · No 45.5%
  • Correct score (top bin): 1-1 (12.5%)

The decision block shows no default Primary: Primary needs consensus EV ≥ +5.0% plus strength/reliability/calibration gates. A separate Lean/tracked gate is +2.0% (selective only). Lean labels are directional only — not bankroll-sized recommendations.

If lines move materially, re-run generation or refresh — implied probabilities and any future EV readouts will change first.

Historical Recommendation

Historical Decision: Wait

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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