Predictions / Football / Poland. I Liga / Wisla Krakow vs Pogoń Siedlce

Prediction Audit: Wisla Krakow vs Pogoń Siedlce Prediction, Odds & AI Betting Tips

Wisla Krakow Wisla Krakow
May 24, 2026 - 14:30
2 1.45
0 1.25
xG Accuracy: 61%
Pogoń Siedlce Pogoń Siedlce

AI correctly predicted the Wisla Krakow win.

The match finished 2–0, validating the model's directional assessment.

Tracked markets vs full-time result

Prediction grade B-

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 (2 goals) ✔ Correct
  • Both Teams To Score BTTS Yes No ✖ Incorrect
  • 1X2 Wisla Krakow Wisla Krakow ✔ Correct
  • Correct Score Insights 1-0, 1-1, 2-0, 2-1, 0-0 2-0 ✔ Correct

Model vs Closing Market

Strong Disagreement

The closing market prices Wisla Krakow higher than the statistical model.

Largest probability gap: Wisla Krakow -28.6 pp

Outcome Model Closing Market Difference Signal
Wisla Krakow 41.8% 70.4% -28.6 pp Market Higher
Draw 25.7% 17.9% +7.8 pp Model Higher
Pogoń Siedlce 32.6% 11.7% +20.8 pp Model Edge

The closing market estimates Wisla Krakow's win probability at 70.4%, compared with the model's estimate of 41.8%, a difference of 28.6 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 model's directional lean matched the result (Wisla Krakow win 2–0).

Post Match Insights

What worked

  • Under 2.5 goals aligned with the xG profile
  • Exact score 2–0 fell within the model's highlighted bins

What failed

  • Both Teams To Score: model leaned BTTS Yes; match finished BTTS No

Market lesson

Large model–market gaps do not automatically mean the market is right. Here the closing market priced Wisla Krakow higher (70.4% vs model 41.8%, 28.6 pp), but the model's lean was validated (Wisla Krakow win 2–0).

Premium betting site stake: New users can use the promo code FHn2uDc1 to receive $100 cash.

Prediction Timeline

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

  1. May 23, 2026 · 19:28 UTC Forecast generated
    • Model 1X2 · Wisla Krakow 41.8% · Draw 25.7% · Pogoń Siedlce 32.6%
    • xG · Wisla Krakow 1.45 — Pogoń Siedlce 1.25
  2. May 23, 2026 · 14:30 UTC Opening odds snapshot PRE30
    • 1X2 odds · Wisla Krakow 1.31 · Draw 5.16 · Pogoń Siedlce 7.86
    • Implied 1X2 · Wisla Krakow 70.4% · Draw 17.9% · Pogoń Siedlce 11.7%
    • Bookmaker · Pinnacle
  3. May 23, 2026 · 14:56 UTC Closing snapshot recorded PRE5
    • 1X2 odds · Wisla Krakow 1.31 · Draw 5.16 · Pogoń Siedlce 7.86
    • Implied 1X2 · Wisla Krakow 70.4% · Draw 17.9% · Pogoń Siedlce 11.7%
    • Bookmaker · Pinnacle
  4. May 24, 2026 · 14:30 UTC Kickoff
  5. FT Full-time result Wisla Krakow win · 2–0
  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 49/100 · Moderate
  • Validation: Warning
  • Large market gap (29 pp)
Evidence ★★★★★
  • No strong statistical edge
  • Market strongly disagrees on price
  • Validation warning
Pricing proximity (inverse gap) 0/100
Betting Confidence 31/100

Validation Report

Immutable Snapshot

Prediction Time: Jul 30, 2026 · 03:18 UTC Snapshot ID: dp-2489136

Closing Odds 1.31
AI Fair Odds —
CLV Pending
Final Result Wisla Krakow win · Wisla Krakow 2–0 Pogoń Siedlce
Prediction ✔ Correct
Decision Grade B-

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: I Liga
  • Fixture: Wisla Krakow vs Pogoń Siedlce
  • Kickoff: 2026-05-23 15:00:00
  • 1X2 (model): Home 41.8% · Draw 25.7% · Away 32.6%
  • xG (showing): Wisla Krakow 1.45 — Pogoń Siedlce 1.25 (total xG ≈ 2.7)
  • 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 50.6% · Over 2.5 49.4%); BTTS Yes (Yes 62.5% · No 37.5%) Value lean (pricing): O/U 2.5 Under 2.5; BTTS Yes
  • BTTS (model): Yes 62.5% · No 37.5%
  • Correct score (top bin): 1-0 (12.0%)

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.

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

October 02, 2026 (UTC)

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I Liga I Liga — Standings
# TEAM MP W D L PTS
1 Wisla Krakow 34 20 11 3 71
2 Slask Wroclaw 34 17 11 6 62
3 Wieczysta Kraków 34 16 9 9 57
4 Chrobry Głogów 34 16 7 11 55
5 ŁKS Łódź 34 15 9 10 54
6 Polonia Warszawa 34 15 8 11 53
7 Ruch Chorzów 34 14 11 9 53
8 Miedz Legnica 34 15 7 12 52
9 Puszcza Niepołomice 34 12 13 9 49
10 Polonia Bytom 34 13 8 13 47
11 Pogoń Grod. Mazowiecki 34 11 12 11 45
12 Odra Opole 34 11 11 12 44
13 Stal Rzeszów 34 12 7 15 43
14 Stal Mielec 34 10 6 18 36
15 Pogoń Siedlce 34 9 9 16 36
16 Znicz Pruszków 34 7 7 20 28
17 Górnik Łęczna 34 5 12 17 27
18 Tychy 71 34 5 8 21 23
# TEAM MP GS GC +/- PTS
1 Wisla Krakow 34 72 32 +40 71
2 Wieczysta Kraków 34 70 47 +23 57
3 Slask Wroclaw 34 69 47 +22 62
4 ŁKS Łódź 34 56 48 +8 54
5 Polonia Bytom 34 56 50 +6 47
6 Ruch Chorzów 34 54 46 +8 53
7 Polonia Warszawa 34 52 49 +3 53
8 Miedz Legnica 34 52 53 -1 52
9 Pogoń Grod. Mazowiecki 34 51 54 -3 45
10 Stal Mielec 34 51 62 -11 36
11 Stal Rzeszów 34 49 60 -11 43
12 Chrobry Głogów 34 48 36 +12 55
13 Puszcza Niepołomice 34 45 40 +5 49
14 Znicz Pruszków 34 40 68 -28 28
15 Tychy 71 34 40 74 -34 23
16 Górnik Łęczna 34 39 62 -23 27
17 Odra Opole 34 34 40 -6 44
18 Pogoń Siedlce 34 33 43 -10 36