Predictions / Football / Moldova. Super Liga / Zimbru vs Dacia-Buiucani

Prediction Audit: Zimbru vs Dacia-Buiucani Prediction, Odds & AI Betting Tips

Zimbru Zimbru
Sep 19, 2026 - 16:00
0 1.48
1 1.12
xG Accuracy: 64%
Dacia-Buiucani Dacia-Buiucani

The model missed the final outcome (Dacia-Buiucani win 0–1).

The model had projected Zimbru at 43.8%, 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 Under 2.5 Under 2.5 (1 goals) ✔ Correct
  • Both Teams To Score BTTS Yes No ✖ Incorrect
  • 1X2 Zimbru Dacia-Buiucani ✖ Incorrect
  • Correct Score Insights 1-1, 1-0, 2-1, 0-1, 2-0 0-1 ✔ Correct

Model vs Closing Market

Strong Disagreement

The closing market prices Zimbru higher than the statistical model.

Largest probability gap: Zimbru -23.4 pp

Outcome Model Closing Market Difference Signal
Zimbru 43.8% 67.3% -23.4 pp Market Higher
Draw 29.1% 19.5% +9.6 pp Model Higher
Dacia-Buiucani 27.1% 13.3% +13.8 pp Model Edge

The closing market estimates Zimbru's win probability at 67.3%, compared with the model's estimate of 43.8%, a difference of 23.4 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: PRE5.

After full time, the result was Dacia-Buiucani win 0–1.

Post Match Insights

What worked

  • Under 2.5 goals aligned with the xG profile
  • Exact score 0–1 fell within the model's highlighted bins

What failed

  • Both Teams To Score: model leaned BTTS Yes; match finished BTTS No
  • 1X2: model leaned Zimbru; match finished Dacia-Buiucani

Market lesson

The closing market differed from the model on Zimbru by 23.4 percentage points (67.2% vs model 43.8%) — 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. Sep 19, 2026 · 16:01 UTC Forecast generated
    • Model 1X2 · Zimbru 43.9% · Draw 29.1% · Dacia-Buiucani 27.1%
    • xG · Zimbru 1.48 — Dacia-Buiucani 1.12
  2. Sep 19, 2026 · 15:34 UTC Opening odds snapshot PRE30
    • 1X2 odds · Zimbru 1.32 · Draw 4.56 · Dacia-Buiucani 6.69
    • Implied 1X2 · Zimbru 67.3% · Draw 19.5% · Dacia-Buiucani 13.3%
    • Bookmaker · Pinnacle
  3. Sep 19, 2026 · 16:01 UTC Closing snapshot recorded PRE5
    • 1X2 odds · Zimbru 1.32 · Draw 4.56 · Dacia-Buiucani 6.69
    • Implied 1X2 · Zimbru 67.3% · Draw 19.5% · Dacia-Buiucani 13.3%
    • Bookmaker · Pinnacle
  4. Sep 19, 2026 · 16:00 UTC Kickoff
  5. FT Full-time result Dacia-Buiucani win · 0–1
  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: Cautious / Wait

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

Historical Decision Wait
Outcome Missed
Pre-match metrics (historical context)
Prediction Reliability 51/100 · Moderate
  • Validation: Warning
  • Large market gap (23 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 · 03:13 UTC Snapshot ID: dp-8703939

Closing Odds 1.32
AI Fair Odds —
CLV Pending
Final Result Dacia-Buiucani win · Zimbru 0–1 Dacia-Buiucani
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: Super Liga
  • Fixture: Zimbru vs Dacia-Buiucani
  • Kickoff: 2026-09-19 16:00:00
  • 1X2 (model): Home 43.9% · Draw 29.1% · Away 27.1%
  • xG (showing): Zimbru 1.48 — Dacia-Buiucani 1.12 (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 53.6% · No 46.4%) Value lean (pricing): O/U 2.5 Under 2.5; BTTS Yes
  • BTTS (model): Yes 53.6% · No 46.4%
  • Correct score (top bin): 1-1 (12.3%)

Saying “no value” on a snapshot is a feature, not a bug: it protects readers from forcing a play when the edge is not there.

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

Historical Recommendation

Historical Decision: Wait

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 29, 2026 (UTC)

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Super Liga Super Liga — Standings
# TEAM MP W D L PTS
1 Sheriff Tiraspol 13 8 4 1 28
2 Zimbru 13 8 2 3 26
3 Petrocub 13 7 3 3 24
4 Dacia-Buiucani 13 6 2 5 20
5 Milsami Orhei 13 4 5 4 17
6 Politehnica UTM 13 4 3 6 15
7 Sireți 13 0 7 6 7
8 CSF Bălți 13 1 2 10 5
# TEAM MP GS GC +/- PTS
1 Sheriff Tiraspol 13 32 10 +22 28
2 Zimbru 13 27 12 +15 26
3 Petrocub 13 24 13 +11 24
4 Politehnica UTM 13 18 19 -1 15
5 Dacia-Buiucani 13 17 16 +1 20
6 Milsami Orhei 13 13 17 -4 17
7 CSF Bălți 13 9 42 -33 5
8 Sireți 13 8 19 -11 7