Danish SuperligaJYSK park, Silkeborg8°2,4 mm13 km/h

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    SilkeborgSilkeborgCounter-attacks & crosses
    vs
    HorsensHorsensPossession control
    The model's lean: 1 · Silkeborg (47%)

    Silkeborg vs Horsens · Danish Superliga

    1 · Silkeborg 47%X 24%28% Horsens · 2

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    Analysis: Silkeborg – Horsens

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

    Silkeborg's 47% edge in the model probabilities hints at a subtle advantage for the hosts, despite trailing Horsens by a point in the standings. Yet, numbers often tell a richer story than the table alone. The expected goals paint a nuanced picture: Silkeborg sit at 1.65 xG while their visitors manage 1.33 xG. This suggests that while Silkeborg might not dominate possession, their counter-attacking style — reliant on swift transitions and crosses — could prove effective against Horsens' possession-heavy approach.

    However, defensive vulnerabilities lurk beneath the surface. Silkeborg's xGA is notably tighter at 1.4 compared to Horsens' leaky 1.9. This disparity in defensive solidity might tip the scales slightly in favor of the home team, especially when combined with their knack for capitalizing on counter-attacks. With a 60% chance of both teams finding the back of the net, it seems likely that goals will be exchanged, adding a layer of intrigue to the expected tactical battle.

    While Silkeborg's xPoints lead of 13.0 to Horsens' 10.1 suggests they’ve been performing below potential, Horsens' slight edge in xG per match at 1.1 indicates a capacity to create — albeit less efficiently than their defensive woes imply. The rain forecast of 2.4 mm could dampen Silkeborg’s crossing game, but this factor is already accounted for in the calculated probabilities.

    The small head-to-head sample between these sides means little; form and expected goals provide a firmer foundation for prediction. With a most likely outcome of 1-1 (11%), followed by a 2-1 victory for Silkeborg (9%), the model leans towards a narrow home win. The over 2.5 goals expectation of 57% also suggests a lean towards a more open game than the rain might suggest.

    The sharpest betting angle lies in backing the home side with LeoVegas offering odds of 2.2, delivering a 4% edge in value over the model's estimation. Silkeborg may just edge this one with their tactical adaptability and slight defensive superiority.

    SilkeborgSilkeborg
    Form LDDDW
    League ranking xG #11xGA #6xT #12xP/match #5Points #7
    Key players
    • Mads LarsenCDMxG 0.02 · xT 0.02
    • Callum MccowattCAMxG 0.16 · xT 0.16
    • Oliver RossFxG 0.21 · xT 0.11
    HorsensHorsens
    Form WLWLD
    League ranking xG #9xGA #11xT #11xP/match #10Points #6
    Key players
    • Kelvin EhibhatiomhanFxG 0.36 · xT 0.03
    • Yamirou OuorouRWxG 0.28 · xT 0.13
    • Karlo LusavecCMxG 0.02 · xT 0.03

    Match prediction: Silkeborg – Horsens

    Predicted score matrix

    Horsens →
    Silkeborg →
    0
    1
    2
    3
    4+
    0
    0–05.4%
    0–16.4%
    0–24.5%
    0–32.0%
    0–4+0.9%
    1
    1–08.1%
    1–111.5%
    1–27.4%
    1–33.3%
    1–4+1.5%
    2
    2–06.9%
    2–19.2%
    2–26.1%
    2–32.7%
    2–4+1.2%
    3
    3–03.8%
    3–15.1%
    3–23.4%
    3–31.5%
    3–4+0.7%
    4+
    4+–02.3%
    4+–13.0%
    4+–22.0%
    4+–30.9%
    4+–4+0.4%
    Probability per final score. Rows = home goals, columns = away goals.

    Most likely scorelines

    1. 1.1-111%
    2. 2.2-19%
    3. 3.1-08%
    4. 4.1-27%
    5. 5.2-07%
    Expected goals
    1,65–1,33
    Both teams to score
    60%

    Over/under goals

    Expected goals: 3,0
    Under 2,543%
    @2.08
    Over 2,557%
    @1.72

    Ready-made bet suggestions

    More combos & build your own →

    01 · 2 legs

    Safe

    • Silkeborg to win47%
    • Over 1.5 goals80%

    Combined probability

    37%

    Fair odds

    2.74

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

    02 · 3 legs

    Balanced

    • Silkeborg to win47%
    • Over 2.5 goals57%
    • Rami Al Hajj 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 score60%
    • Over 2.5 goals57%
    • Kelvin Osemudiamen Ehibhatiomhan to score27%
    • Abdul Salam Moro 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

    The model's value spots

    Bets where the model's probability beats what the odds imply (EV+).

    • 1 · Silkeborg47% probability@2.20LeoVegasEV+4%reference odds
    • 1 · Silkeborg47% probability@2.20NordicbetEV+4%reference odds
    • 1 · Silkeborg47% probability@2.18BetsafeEV+3%reference odds
    Bookmaker1 · SilkeborgX2 · Horsens
    Betsafe
    EV+ 3%
    2.18
    3.653.05
    LeoVegas
    EV+ 4%
    2.20
    3.553.10
    Nordicbet
    EV+ 4%
    2.20
    3.653.10

    Odds updated 3 Oct, 07:36

    Statistics

    xG & xGA per match — 3-game rolling average

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

    Silkeborg
    Horsens
    When goals are scored and conceded — 2026
    Silkeborg (12–12)
    Horsens (15–16)
    Pass networks

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

    Silkeborg
    Robin Østrøm: 256 passningar, xT 0.22ØstrømVillads Westh: 234 passningar, xT 0.52WesthMads Larsen: 233 passningar, xT 0.14LarsenWilliam Møller: 206 passningar, xT 0.14MøllerAlexander Madsen: 196 passningar, xT 0.26MadsenRami Al Hajj: 144 passningar, xT 0.35HajjJens Martin Gammelby: 124 passningar, xT -0.03GammelbyAske Andresen: 108 passningar, xT 0.02AndresenOliver Ross: 75 passningar, xT 0.19RossLucas Riisgaard: 67 passningar, xT 0.94RiisgaardWilliam Kirk: 39 passningar, xT 0.02Kirk
    Horsens
    Alagie Saine: 249 passningar, xT 0.45SaineMikkel Kupijbida: 225 passningar, xT 0.31KupijbidaKarlo Lusavec: 224 passningar, xT 0.15LusavecOle Martin Kolskogen: 211 passningar, xT 0.18KolskogenVictor Pálsson: 202 passningar, xT 0.58PálssonAbdul Moro: 186 passningar, xT 0.1MoroMatej Delac: 135 passningar, xT 0.22DelacJulius Madsen: 132 passningar, xT 0.46MadsenAdam Herdonsson: 92 passningar, xT 0.12HerdonssonYamirou Ouorou: 58 passningar, xT -0.05OuorouIvan Milicevic: 49 passningar, xT 0.2Milicevic
    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.

    Silkeborg
    Robin Østrøm: 256 passningar, xT 0.22ØstrømVillads Westh: 234 passningar, xT 0.52WesthMads Larsen: 233 passningar, xT 0.14LarsenWilliam Møller: 206 passningar, xT 0.14MøllerAlexander Madsen: 196 passningar, xT 0.26MadsenRami Al Hajj: 144 passningar, xT 0.35HajjJens Martin Gammelby: 124 passningar, xT -0.03GammelbyAske Andresen: 108 passningar, xT 0.02AndresenOliver Ross: 75 passningar, xT 0.19RossLucas Riisgaard: 67 passningar, xT 0.94RiisgaardWilliam Kirk: 39 passningar, xT 0.02Kirk
    Horsens
    Alagie Saine: 249 passningar, xT 0.45SaineMikkel Kupijbida: 225 passningar, xT 0.31KupijbidaKarlo Lusavec: 224 passningar, xT 0.15LusavecOle Martin Kolskogen: 211 passningar, xT 0.18KolskogenVictor Pálsson: 202 passningar, xT 0.58PálssonAbdul Moro: 186 passningar, xT 0.1MoroMatej Delac: 135 passningar, xT 0.22DelacJulius Madsen: 132 passningar, xT 0.46MadsenAdam Herdonsson: 92 passningar, xT 0.12HerdonssonYamirou Ouorou: 58 passningar, xT -0.05OuorouIvan Milicevic: 49 passningar, xT 0.2Milicevic

    The teams in numbers

    PerformanceSilkeborgHorsens
    Points1011
    xPoints1310.1
    xG per match11.1
    xGA per match1.41.9
    xG within 8s of winning the ball0.270.31
    xGA within 8s of losing the ball0.290.26
    Playing styleSilkeborgHorsens
    Build-up efficiency0.320.32
    Field tilt0.360.35
    xT per match0.650.66
    xTA per match1.21.5
    Won balls, offensive half1922
    Pressing intensity0.260.24
    Pressing efficiency0.250.26
    Pressing efficiency, offensive half0.190.2
    Entries into the box per match119
    Entries into the box against1616
    Pass completion %0.80.76
    Pass completion % under pressure0.750.72
    Passes per match417352
    Passes against per match458414
    Switches of play per match17.321.8
    Long balls per match2430
    Set piecesSilkeborgHorsens
    xG from free kicks0.050.07
    Corners per match4.22.9
    Corners against per match6.46.6
    xG per corner0.010.02
    xGA per corner against0.020.04
    First touch, offensive corners %0.550.46
    First touch, defensive corners %0.610.7
    OtherSilkeborgHorsens
    Throw-in control0.720.73
    The goalkeepersAske Andresen (Silkeborg)Matej Delac (Horsens)
    Saves2828
    Save %70%67%
    xG prevented44%58%
    Claims0 (0%)0 (0%)

    Teams & more matches this round

    Frequently asked questions

    Who wins Silkeborg vs Horsens according to our model?

    The model gives Silkeborg a 47% win probability. Full 1X2 picture: Silkeborg 47%, draw 24%, Horsens 28%.

    What is the most likely scoreline?

    The model's most likely final score is 1-1 at 11% probability.

    Over or under 2.5 goals?

    The model rates over 2.5 goals at 57% and under at 43%.

    Will both teams score?

    The probability of both teams scoring is 60% according to the model.

    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.