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

Korona Kielce vs Widzew Łódź Prediction, Odds & AI Betting Tips

Korona Kielce Korona Kielce
May 15, 2026 - 18:30
1 1.52
0 1.14
xG Accuracy: 63%
Widzew Łódź Widzew Łódź
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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, 2-1, 2-0, 0-1 1-0 ✔ Correct

Next AI Predictions Available Today

View Today's Football Predictions See Upcoming Ekstraklasa Matches Track Korona Kielce Next Match Prediction Track Widzew Łódź Next Match Prediction

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 24, 2026 (UTC)

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Ekstraklasa Ekstraklasa — Standings
# TEAM MP W D L PTS
1 Lech Poznan 34 16 12 6 60
2 Gornik Zabrze 34 16 8 10 56
3 Jagiellonia 34 15 11 8 56
4 Raków Częstochowa 34 16 7 11 55
5 GKS Katowice 34 14 8 12 50
6 Legia Warszawa 34 12 13 9 49
7 Zaglebie Lubin 34 13 9 12 48
8 Wisla Plock 34 12 10 12 46
9 Pogon Szczecin 34 13 6 15 45
10 Radomiak Radom 34 11 11 12 44
11 Korona Kielce 34 11 10 13 43
12 Motor Lublin 34 10 13 11 43
13 Cracovia Krakow 34 9 15 10 42
14 Widzew Łódź 34 12 6 16 42
15 Piast Gliwice 34 11 8 15 41
16 Lechia Gdansk 34 12 7 15 38
17 Arka Gdynia 34 9 9 16 36
18 Nieciecza 34 9 7 18 34
# TEAM MP GS GC +/- PTS
1 Lech Poznan 34 62 45 +17 60
2 Lechia Gdansk 34 62 65 -3 38
3 Jagiellonia 34 56 41 +15 56
4 Radomiak Radom 34 52 53 -1 44
5 Raków Częstochowa 34 51 40 +11 55
6 GKS Katowice 34 51 45 +6 50
7 Gornik Zabrze 34 50 38 +12 56
8 Pogon Szczecin 34 47 49 -2 45
9 Motor Lublin 34 46 53 -7 43
10 Zaglebie Lubin 34 45 38 +7 48
11 Nieciecza 34 43 65 -22 34
12 Legia Warszawa 34 42 37 +5 49
13 Piast Gliwice 34 42 46 -4 41
14 Widzew Łódź 34 41 41 0 42
15 Korona Kielce 34 40 40 0 43
16 Cracovia Krakow 34 39 42 -3 42
17 Wisla Plock 34 34 38 -4 46
18 Arka Gdynia 34 34 61 -27 36
# TEAM MP xG xGC +/- PTS
1 Lech Poznan 34 59.8 34.6 +25.2 60
2 Legia Warszawa 34 44.3 34.9 +9.4 49
3 Raków Częstochowa 34 52.1 43.1 +9.0 55
4 Piast Gliwice 34 45.4 40.2 +5.2 41
5 Lechia Gdansk 34 48.3 43.9 +4.4 38
6 Pogon Szczecin 34 52.4 48.2 +4.2 45
7 Gornik Zabrze 34 41.5 39.4 +2.1 56
8 Cracovia Krakow 34 38.1 36.2 +1.9 42
9 Widzew Łódź 34 38.2 37.2 +1.0 42
10 Korona Kielce 34 49.6 49.8 -0.2 43
11 Radomiak Radom 34 42.0 43.8 -1.8 44
12 Wisla Plock 34 41.6 44.2 -2.6 46
13 GKS Katowice 34 41.1 45.5 -4.4 50
14 Jagiellonia 34 42.4 48.3 -5.9 56
15 Zaglebie Lubin 34 33.5 42.8 -9.3 48
16 Nieciecza 34 42.4 54.9 -12.5 34
17 Motor Lublin 34 39.5 52.2 -12.7 43
18 Arka Gdynia 34 36.0 49.1 -13.1 36
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