Ekstraklasa11°1,1 mm19 km/h

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    Zagłębie LubinZagłębie LubinLow block & direct
    vs
    Jagiellonia BiałystokJagiellonia BiałystokPossession control
    The model's lean: 2 · Jagiellonia Białystok (36%)Soft draw

    Zagłębie Lubin vs Jagiellonia Białystok · Ekstraklasa

    1 · Zagłębie Lubin 35%X 29%36% Jagiellonia Białystok · 2

    Prediction summary

    BetBuilders AI model prediction for Zagłębie Lubin vs Jagiellonia Białystok (Ekstraklasa, Saturday, 17 October 2026): Zagłębie Lubin win 35%, draw 29%, Jagiellonia Białystok win 36%. Expected goals (xG): Zagłębie Lubin 1.38, Jagiellonia Białystok 1.35. Most likely score: 1-1 (13%). Over 2.5 goals 51%, both teams to score 56%. Double chance: Zagłębie Lubin or draw 64%, Jagiellonia Białystok or draw 65%. Prediction updated Friday, 9 October 2026.

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    Analysis: Zagłębie Lubin – Jagiellonia Białystok

    Marcus Johansson · · Analysis from the match model · How the predictions work

    A razor-thin margin is evident in the matchup between Zagłębie Lubin and Jagiellonia Białystok. The model gives the visitors a slight edge with a 36% win probability, just nudging ahead of Lubin's 35%. While Zagłębie's direct play contrasts with Jagiellonia's possession-focused strategy, the clash is expected to be closely contested, with expected goals nearly balanced at 1.38 for the hosts and 1.35 for the visitors.

    Though the historical head-to-head is a small sample of just four games (Lubin with a minor deficit), it's the season form and underlying numbers that present a clearer picture. Zagłębie holds an advantage in both points (15 to Jagiellonia's 12) and xPoints (13.7 vs. 9.3), highlighting their efficiency. However, Jagiellonia's higher xG per match at 1.5 suggests they can create better chances, which could be crucial if they manage to dictate possession.

    The most likely scorelines point to a tight affair, with 1-1 at 13% leading the pack. With an over 2.5 goals probability at 51% and both teams to score at 56%, the game leans slightly toward goals but remains finely poised, with Jagiellonia holding a slight model advantage.

    Zagłębie LubinZagłębie Lubin
    Form DDWWL
    League ranking xG #16xGA #5xT #14xP/match #6Points #7
    Key players
    • Marcel RegulaRWxG 0.31 · xT 0.11
    • Michalis KosidisLWxG 0.26 · xT 0.06
    • Mateusz GrzybekRWxG 0.08 · xT 0.17
    Jagiellonia BiałystokJagiellonia Białystok
    Form WLWLL
    League ranking xG #9xGA #11xT #11xP/match #15Points #9
    Key players
    • Guilherme Manuel Serrão MontóiaLBxG 0.13 · xT 0.23
    • Kajetan SzmytLWxG 0.12 · xT 0.11
    • Norbert WojtuszekRBxG 0.01 · xT 0.06

    Match prediction: Zagłębie Lubin – Jagiellonia Białystok

    Predicted score matrix

    Jagiellonia Białystok →
    Zagłębie Lubin →
    0
    1
    2
    3
    4+
    0
    0–06.9%
    0–18.5%
    0–25.9%
    0–32.7%
    0–4+1.2%
    1
    1–08.7%
    1–112.5%
    1–28.2%
    1–33.7%
    1–4+1.7%
    2
    2–06.2%
    2–18.4%
    2–25.6%
    2–32.5%
    2–4+1.1%
    3
    3–02.9%
    3–13.9%
    3–22.6%
    3–31.2%
    3–4+0.5%
    4+
    4+–01.3%
    4+–11.8%
    4+–21.2%
    4+–30.5%
    4+–4+0.2%
    Probability per final score. Rows = home goals, columns = away goals.

    Most likely scorelines

    1. 1.1-113%
    2. 2.1-09%
    3. 3.0-18%
    4. 4.2-18%
    5. 5.1-28%
    Expected goals
    1,38–1,35
    Both teams to score
    56%

    Over/under goals

    Expected goals: 2,7
    Under 2,549%
    –
    Over 2,551%
    –

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    01 · 2 legs

    Safe

    • Double chance Jagiellonia Białystok or draw (X2)62%
    • Over 1.5 goals76%

    Combined probability

    47%

    Fair odds

    2.12

    Two legs pulling the same way — the probability is computed exactly from the score matrix, not multiplied.

    02 · 3 legs

    Balanced

    • Double chance Jagiellonia Białystok or draw (X2)62%
    • Over 2.5 goals51%
    • Nik Prelec to score24%

    Combined probability

    –

    Fair odds

    –

    The whole slip is priced jointly via 20,000 simulated matches — the scorer interacts with result and total goals, never naive multiplication.

    03 · 4 legs

    Bold

    • Both teams to score56%
    • Over 2.5 goals51%
    • Nik Prelec to score24%
    • Bernardo Maria Morais Cardoso Vital to be booked21%

    Combined probability

    –

    Fair odds

    –

    The goal-fest: both teams score with at least three goals, plus the match's most likely scorer and card candidate — all priced jointly via 20,000 simulated matches.

    Odds & value

    No odds available yet.

    Statistics

    Head-to-head

    4 matches
    Zagłębie Lubin 1Draw 1Jagiellonia Białystok 2
    Goals: 4–5 (⌀ 2,2)

    Head-to-head based on Ekstraklasa data since 2023.

    xG & xGA per match — 3-game rolling average

    Solid line = created (xG), dashed = conceded (xGA).

    Zagłębie Lubin
    Jagiellonia Białystok
    When goals are scored and conceded — 2026
    Zagłębie Lubin (11–10)
    Jagiellonia Białystok (12–13)
    Pass networks

    Last 5 matches. Circle size = pass volume, line width = combinations between players. Attacking left to right.

    Zagłębie Lubin
    Aleks Lawniczak: 175 passningar, xT 0.83LawniczakRoman Yakuba: 146 passningar, xT 1.07YakubaFilip Kocaba: 145 passningar, xT 0.29KocabaZlatan Alomerović: 128 passningar, xT 0.06AlomerovićDamian Dabrowski: 116 passningar, xT 0.34DabrowskiDavid Čolina: 93 passningar, xT 0.22ČolinaDamian Michalski: 87 passningar, xT -0.01MichalskiMichal Nalepa: 84 passningar, xT 0.45NalepaMarcel Regula: 81 passningar, xT 0.39RegulaJakub Kolan: 60 passningar, xT 0.59KolanMateusz Grzybek: 51 passningar, xT 0.23Grzybek
    Jagiellonia Białystok
    Bernardo Vital: 251 passningar, xT 0.36VitalNorbert Wojtuszek: 235 passningar, xT 0.17WojtuszekApostolos Konstantopoulos: 195 passningar, xT 0.34KonstantopoulosTaras Romanczuk: 187 passningar, xT 0.12RomanczukGuilherme Manuel Serrão Montóia: 175 passningar, xT 0.6MontóiaSlawomir Abramowicz: 118 passningar, xT -0.08AbramowiczRodrigo Fernandes da Conceição: 92 passningar, xT 0.77ConceiçãoCezary Polak: 92 passningar, xT 0.1PolakEryk Kozłowski: 89 passningar, xT 0.31KozłowskiSergio Lozano: 79 passningar, xT 0.41LozanoYuki Kobayashi: 79 passningar, xT 0.05Kobayashi
    xT networks

    The same network weighted by offensive threat (xT): circle size = threat created by the player, line width = threat of the combinations. Last 5 matches, attacking left to right.

    Zagłębie Lubin
    Aleks Lawniczak: 175 passningar, xT 0.83LawniczakRoman Yakuba: 146 passningar, xT 1.07YakubaFilip Kocaba: 145 passningar, xT 0.29KocabaZlatan Alomerović: 128 passningar, xT 0.06AlomerovićDamian Dabrowski: 116 passningar, xT 0.34DabrowskiDavid Čolina: 93 passningar, xT 0.22ČolinaDamian Michalski: 87 passningar, xT -0.01MichalskiMichal Nalepa: 84 passningar, xT 0.45NalepaMarcel Regula: 81 passningar, xT 0.39RegulaJakub Kolan: 60 passningar, xT 0.59KolanMateusz Grzybek: 51 passningar, xT 0.23Grzybek
    Jagiellonia Białystok
    Bernardo Vital: 251 passningar, xT 0.36VitalNorbert Wojtuszek: 235 passningar, xT 0.17WojtuszekApostolos Konstantopoulos: 195 passningar, xT 0.34KonstantopoulosTaras Romanczuk: 187 passningar, xT 0.12RomanczukGuilherme Manuel Serrão Montóia: 175 passningar, xT 0.6MontóiaSlawomir Abramowicz: 118 passningar, xT -0.08AbramowiczRodrigo Fernandes da Conceição: 92 passningar, xT 0.77ConceiçãoCezary Polak: 92 passningar, xT 0.1PolakEryk Kozłowski: 89 passningar, xT 0.31KozłowskiSergio Lozano: 79 passningar, xT 0.41LozanoYuki Kobayashi: 79 passningar, xT 0.05Kobayashi

    The teams in numbers

    PerformanceZagłębie LubinJagiellonia Białystok
    Points1512
    xPoints13.79.3
    xG per match1.41.5
    xGA per match1.31.6
    xG within 8s of winning the ball0.260.13
    xGA within 8s of losing the ball0.180.35
    Playing styleZagłębie LubinJagiellonia Białystok
    Build-up efficiency0.280.28
    Field tilt0.320.56
    xT per match11.1
    xTA per match0.931
    Won balls, offensive half2722
    Pressing intensity0.250.24
    Pressing efficiency0.290.33
    Pressing efficiency, offensive half0.230.3
    Entries into the box per match1413
    Entries into the box against1513
    Pass completion %0.710.79
    Pass completion % under pressure0.650.74
    Passes per match257405
    Passes against per match358285
    Switches of play per match25.923.6
    Long balls per match3430
    Set piecesZagłębie LubinJagiellonia Białystok
    xG from free kicks0.060.04
    Corners per match3.83.6
    Corners against per match65.6
    xG per corner0.060.04
    xGA per corner against0.020.03
    First touch, offensive corners %0.370.31
    First touch, defensive corners %0.560.56
    OtherZagłębie LubinJagiellonia Białystok
    Throw-in control0.840.76
    The goalkeepersZlatan Alomerović (Zagłębie Lubin)Slawomir Abramowicz (Jagiellonia Białystok)
    Saves2323
    Save %77%66%
    xG prevented33%39%
    Claims0 (0%)0 (0%)

    Teams & more matches this round

    Frequently asked questions

    Who wins Zagłębie Lubin vs Jagiellonia Białystok according to our model?

    The model gives Jagiellonia Białystok a 36% win probability. Full 1X2 picture: Zagłębie Lubin 35%, draw 29%, Jagiellonia Białystok 36%.

    What is the most likely scoreline?

    The model's most likely final score is 1-1 at 13% probability.

    Over or under 2.5 goals?

    The model rates over 2.5 goals at 51% and under at 49%.

    Will both teams score?

    The probability of both teams scoring is 56% according to the model.

    How many goals are expected in Zagłębie Lubin vs Jagiellonia Białystok?

    The model expects 1.38 goals (xG) for Zagłębie Lubin and 1.35 for Jagiellonia Białystok, 2.73 in total.

    What does the model say about double chance?

    Zagłębie Lubin or draw (1X) 64%, Jagiellonia Białystok or draw (X2) 65% and no draw (12) 71%.

    Why are some legs locked in the bet builder?

    The same rules as the bookmakers’ builders: legs that contradict or already follow from your slip cannot be combined — for example correct score 2–1 with under 2.5 goals. The difference is that we don’t look the rules up in a table; we compute them straight from the score matrix.

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    Predictions come from PlaymakerAI's match model and reflect the model's assessment — not betting advice. Gamble responsibly.