Statistics / Football / Poland. Ekstraklasa / Korona Kielce vs Widzew Łódź

Korona Kielce vs Widzew Łódź Statistics & Analysis

May 15, 2026 - 18:30
1 1.52
0 1.14
xG Accuracy: 63%
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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 Over 2.5 Under 2.5 (1 goals) ✖ Incorrect
  • Both Teams To Score BTTS Yes No ✖ Incorrect
  • 1X2 Korona Kielce Korona Kielce ✔ Correct
  • Correct Score Insights 1-1 1-0 ✖ Incorrect

AI match briefing

AI Match Summary

Pre-match snapshot for this fixture.

  • League: Ekstraklasa
  • Fixture: Korona Kielce vs Widzew Łódź
  • Kickoff: 2026-05-16 16:00:00
  • 1X2 (model): Home 44.5% · Draw 28.7% · Away 26.9%
  • xG (showing): Korona Kielce 1.52 — Widzew Łódź 1.14 (total xG ≈ 2.66)
  • Primary / headline line (Betting Primary Pick when shown): Over 2.5 goals
  • Model: 49.6% · Implied: 40.0% · Probability edge: +9.6 pts · Est. EV: +23.0%
  • BTTS (model): Yes 54.7% · No 45.3%
  • Correct score (top bin): 1-1 (12.1%)

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

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

Best Bet + Reason

Primary pick from the decision engine: Over 2.5 goals.

Model probability is compared to implied probability from odds to highlight a probability edge; EV uses the same model probability with the best decimal price tracked.

When several markets sit near +EV, keep stakes small — correlation means edges do not add cleanly.

FAQ

Safer market than correct score?

Markets with more liquidity and smoother prices (often 1X2 or O/U 2.5 from many books) are usually easier to reason about than long-tail correct-score prices; still read EV on each leg.

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.

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.

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.

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 18, 2026 (UTC)

How to use this
  • Focus on the Primary line when you want one actionable idea.
  • Do not parlay many thin-edge picks together; edges do not add reliably.
  • Treat longshots as optional, high-stake-sizing plays only.

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Back to Statistics
Ekstraklasa EkstraklasaStandings
# TEAM MP W D L PTS
1 Lech Poznan 33 16 11 6 59
2 Jagiellonia 33 14 11 8 53
3 Gornik Zabrze 33 15 8 10 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 32 9 9 14 36
18 Nieciecza 32 7 7 18 28
# 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 Korona Kielce 33 39 39 0 42
13 Widzew Łódź 33 39 40 -1 39
14 Legia Warszawa 33 38 37 +1 46
15 Cracovia Krakow 33 38 41 -3 41
16 Nieciecza 32 37 61 -24 28
17 Wisla Plock 33 32 36 -4 45
18 Arka Gdynia 32 32 55 -23 36
# 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 32 41.1 53.0 -11.9 28
17 Motor Lublin 33 39.5 52.2 -12.7 43
18 Arka Gdynia 32 34.1 47.9 -13.8 36