Predictions / Football / Poland. Ekstraklasa / Wisla Plock vs Motor Lublin

Wisla Plock vs Motor Lublin Prediction, Odds & AI Betting Tips

Wisla Plock Wisla Plock
May 10, 2026 - 12:45
0 1.58
4 1.13
xG Accuracy: 29%
Motor Lublin Motor Lublin
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Tracked markets vs full-time result

Each row compares the model’s highlighted side (or lean) to what happened at full time.

  • Market Prediction Result Outcome
  • Over / Under 2.5 Under 2.5 Over 2.5 (4 goals) ✖ Incorrect
  • Both Teams To Score BTTS No No ✔ Correct
  • 1X2 Wisla Plock Motor Lublin ✖ Incorrect
  • Correct Score Insights 1-1, 1-0, 2-1, 2-0, 0-1 0-4 ✖ Incorrect

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AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

  • League: Ekstraklasa
  • Fixture: Wisla Plock vs Motor Lublin
  • Kickoff: 2026-05-09 16:00:00
  • 1X2 (model): Home 46.2% · Draw 28.1% · Away 25.7%
  • xG (showing): Wisla Plock 1.58 — Motor Lublin 1.13 (total xG ≈ 2.71)
  • Best +EV line (same label as hero card when Primary thresholds are not met): Under 2.5 goals
  • Model: 49.1% · Implied: 46.4% · Probability edge: +2.7 pts · Est. EV: +2.1%
  • BTTS (model): Yes 55.3% · No 44.7%
  • Correct score (top bin): 1-1 (11.9%)

Where EV is shown, it is estimated return per unit stake at the best tracked decimal price — not the same thing as a raw probability gap.

1X2 can look balanced even when side markets show clearer structure.

Best Bet + Reason

Best current value angle on the board — same leg as the “Best +EV” hero when Primary rules are not met: Under 2.5 goals.

If 1X2 looks tight, the engine may still find clearer structure in totals or BTTS — that is intentional.

Edges shrink quickly if prices move; always re-check the number on your book.

FAQ

How should I read EV versus a probability gap?

Probability edge = model probability minus implied probability (reported here in percentage points). EV ≈ model probability × best tracked decimal odds − 1, shown as return per unit stake. They are related but not interchangeable labels.

Why might 1X2 look unattractive while totals do not?

Tight 1X2 prices often embed a fair three-way split, so EV on match-winner can sit negative even when Over/Under or BTTS still diverges from the model — compare the 1X2 row on the market cards to O/U and BTTS.

What changes first if odds move?

Implied probabilities and EV move immediately with price; model probabilities in this snapshot do not update until the pipeline is re-run. Refresh after material line moves.

Who has the edge in the match-winner market?

Use the 1X2 model percentages in the summary and the 1X2 market card: the side with the highest model % is the model lean, but check EV — a lean can still be -EV after prices.

Risk Factors

  • 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%).

Methodology

  • Inputs: Same structured facts bundle as the public prediction page (xG / Poisson snapshot, market EV where available, decision engine v2).
  • Compliance: Educational framing only; not personalised advice.

Last Updated

May 22, 2026 (UTC)

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Ekstraklasa Ekstraklasa — Standings
# TEAM MP W D L PTS
1 Lech Poznan 33 16 11 6 59
2 Gornik Zabrze 33 15 8 10 53
3 Jagiellonia 33 14 11 8 53
4 Raków Częstochowa 33 15 7 11 52
5 GKS Katowice 33 14 7 12 49
6 Zaglebie Lubin 33 13 9 11 48
7 Legia Warszawa 33 11 13 9 46
8 Wisla Plock 33 12 9 12 45
9 Radomiak Radom 33 11 11 11 44
10 Pogon Szczecin 33 13 5 15 44
11 Motor Lublin 33 10 13 10 43
12 Korona Kielce 33 11 9 13 42
13 Piast Gliwice 33 11 8 14 41
14 Cracovia Krakow 33 9 14 10 41
15 Widzew Łódź 33 11 6 16 39
16 Lechia Gdansk 33 12 7 14 38
17 Arka Gdynia 33 9 9 15 36
18 Nieciecza 33 8 7 18 31
# TEAM MP GS GC +/- PTS
1 Lech Poznan 33 60 43 +17 59
2 Lechia Gdansk 33 60 62 -2 38
3 Jagiellonia 33 55 41 +14 53
4 GKS Katowice 33 50 44 +6 49
5 Radomiak Radom 33 50 47 +3 44
6 Raków Częstochowa 33 48 40 +8 52
7 Pogon Szczecin 33 46 48 -2 44
8 Motor Lublin 33 46 49 -3 43
9 Zaglebie Lubin 33 45 37 +8 48
10 Gornik Zabrze 33 44 36 +8 53
11 Piast Gliwice 33 41 44 -3 41
12 Nieciecza 33 40 63 -23 31
13 Korona Kielce 33 39 39 0 42
14 Widzew Łódź 33 39 40 -1 39
15 Legia Warszawa 33 38 37 +1 46
16 Cracovia Krakow 33 38 41 -3 41
17 Arka Gdynia 33 34 58 -24 36
18 Wisla Plock 33 32 36 -4 45
# TEAM MP xG xGC +/- PTS
1 Lech Poznan 33 59.8 34.6 +25.2 59
2 Legia Warszawa 33 44.3 34.9 +9.4 46
3 Raków Częstochowa 33 52.1 43.1 +9.0 52
4 Piast Gliwice 33 45.4 40.2 +5.2 41
5 Lechia Gdansk 33 48.3 43.9 +4.4 38
6 Pogon Szczecin 33 52.4 48.2 +4.2 44
7 Gornik Zabrze 33 41.5 39.4 +2.1 53
8 Cracovia Krakow 33 38.1 36.2 +1.9 41
9 Widzew Łódź 33 38.2 37.2 +1.0 39
10 Korona Kielce 33 49.6 49.8 -0.2 42
11 Radomiak Radom 33 42.0 43.8 -1.8 44
12 Wisla Plock 33 41.6 44.2 -2.6 45
13 GKS Katowice 33 41.1 45.5 -4.4 49
14 Jagiellonia 33 42.4 48.3 -5.9 53
15 Zaglebie Lubin 33 33.5 42.8 -9.3 48
16 Nieciecza 33 42.4 54.9 -12.5 31
17 Motor Lublin 33 39.5 52.2 -12.7 43
18 Arka Gdynia 33 36.0 49.1 -13.1 36
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