Danish SuperligaCeres Park, Aarhus14°1,7 mm36 km/h

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    AGFAGFPossession & high press
    22
    SilkeborgSilkeborgBalanced & physical
    The model's lean: 1 · AGF (62%)Best value by the model: Under 2,5 @ 2.72 (+11 %)

    AGFSilkeborg · Danish Superliga

    1 · AGF 62%X 20%19% Silkeborg · 2

    Analysis: AGFSilkeborg

    Sofia Andersson · · The model's read on the match · How the predictions work

    AGF Aarhus finds favor in the numbers with a 62% chance of victory, setting a clear expectation for their upcoming clash against Silkeborg. The model's inclination towards the home side is underpinned by superior expected goals (xG) stats, which reflect AGF's dynamic possession and high press style. In contrast, Silkeborg's balanced and physical approach yields a lesser xG, hinting at their challenge in breaking down AGF's setup. This tactical mismatch suggests the hosts have the upper hand at home.

    Despite Silkeborg's current edge in points, AGF's underlying metrics, particularly their xPoints of 8.3 compared to Silkeborg's 7.5, indicate a stronger performance than the standings suggest. AGF's xG of 1.6 per match dwarfs Silkeborg's 0.92, while defensively, they also hold a slight advantage with a lower xGA. These statistics affirm the model's confidence in AGF's potential to secure a result on their home turf.

    The head-to-head history between these sides since 2022 shows AGF unbeaten in four matches, with two wins and two draws. While this is a small sample, it adds weight to the argument that AGF knows how to handle Silkeborg. The model's most likely outcomes, such as a 2-1 or 2-0 victory for AGF, align with this narrative. The expectation of goals is further supported by a 59% chance of over 2.5 goals, suggesting an open game despite the weather forecasts indicating rain, which traditionally tempers goalmouth action.

    Curiously, there’s notable value in the market for a low-scoring draw, specifically under 0.5 goals at attractive odds of 23.0 with Betsafe, Betsson, and Nordicbet. The model rates this outcome at 5%, translating to a sizeable 14% edge. While such an event remains a long shot, the discrepancy presents an intriguing angle for those looking to capitalize on market inefficiencies.

    AGFAGF
    Form DLDLL
    League ranking xG #3xGA #7xT #6xP/match #6Points #11
    Key players
    • Gift LinksRWxG 0.18 · xT 0.23
    • Kristian ArnstadFxG 0.39 · xT 0.09
    • Sebastian JorgensenRWxG 0.26 · xT 0.26
    SilkeborgSilkeborg
    Form LWLLD
    League ranking xG #9xGA #10xT #11xP/match #9Points #9
    Key players
    • Mads LarsenCDMxG 0.02 · xT 0.03
    • Callum MccowattCMxG 0.16 · xT 0.15
    • Robin OstromCBxG 0.00 · xT 0.03
    Final score
    22
    The model missed the outcome
    Predicted probabilities: AGF 62% · Draw 20% · Silkeborg 19%

    How the bookmaker bet builders went

    The verdict on the pre-built bet builders for this match, priced against the model’s score matrix before kickoff. Graded on the 90-minute result.

    Matchresultat - SilkeborgBåda lagen gör mål - JaTotalt antal mål - Över 3.5
    odds 12,61fair 16,35-23 %
    Miss
    Matchresultat - AGFBåda lagen gör mål - JaTotalt antal mål - Över 3.5
    odds 3,61fair 5,36-33 %
    Miss

    Match prediction: AGFSilkeborg

    Predicted score matrix

    Silkeborg
    AGF
    0
    1
    2
    3
    4+
    0
    0–05.0%
    0–14.9%
    0–22.9%
    0–31.1%
    0–4+0.4%
    1
    1–08.8%
    1–110.4%
    1–25.7%
    1–32.1%
    1–4+0.7%
    2
    2–08.9%
    2–19.9%
    2–25.5%
    2–32.1%
    2–4+0.7%
    3
    3–05.8%
    3–16.4%
    3–23.6%
    3–31.3%
    3–4+0.5%
    4+
    4+–04.4%
    4+–14.9%
    4+–22.7%
    4+–31.0%
    4+–4+0.4%
    Probability per final score. Rows = home goals, columns = away goals.

    Most likely scorelines

    1. 1.1-110%
    2. 2.2-110%
    3. 3.2-09%
    4. 4.1-09%
    5. 5.3-16%
    Expected goals
    1,951,12
    Both teams to score
    58%

    Over/under goals

    Expected goals: 3,1
    Under 2,541%
    @2.72EV+11%
    Over 2,559%
    @1.44

    Ready-made bet suggestions

    More combos & build your own

    01 · 2 legs

    Safe

    • AGF to win62%
    • Over 1.5 goals81%

    Combined probability

    48%

    Fair odds

    2.10

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

    02 · 3 legs

    Balanced

    • AGF to win62%
    • Over 2.5 goals59%
    • Jens Jønsson to score33%

    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 score58%
    • Over 2.5 goals59%
    • Jens Jønsson to score33%
    • Mads Larsen to be booked19%

    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+).

    • Under 2.541% probability@2.72BetsafeEV+11%reference odds
    • Under 2.541% probability@2.72BetssonEV+11%reference odds
    • Under 2.541% probability@2.72NordicbetEV+11%reference odds
    Bookmaker1 · AGFX2 · Silkeborg
    Betfair Exchange1.101.101.10
    Betsafe1.524.30
    EV+ 7%
    5.80
    Betsson1.524.35
    EV+ 9%
    5.90
    Nordicbet1.524.35
    EV+ 9%
    5.90
    Pinnacle1.514.83
    EV+ 6%
    5.75

    Odds updated 5 Sept, 10:32

    Odds movement

    10 %
    66 %61 %

    1.511.51

    X+1 %
    22 %17 %

    4.794.83

    2+8 %
    20 %15 %

    5.305.75

    Pinnacle · 11 recorded price levels · 03/09/2026 → 05/09/2026 · fixed scale 5 percentage points

    Statistics

    Head-to-head

    4 matches
    AGF 2Draw 2Silkeborg 0
    Goals: 73 (⌀ 2,5)

    Head-to-head based on Danish Superliga data since 2022.

    xG & xGA per match — 3-game rolling average

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

    AGF
    Silkeborg
    When goals are scored and conceded 2026
    AGF (711)
    Silkeborg (59)
    Pass networks

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

    AGF
    Eric Kahl: 260 passningar, xT 0.53KahlMouhammade Camara: 237 passningar, xT 0.26CamaraMagnus Nordengen Knudsen: 175 passningar, xT 0.38KnudsenKristian Arnstad: 169 passningar, xT 0.28ArnstadJacob Andersen: 148 passningar, xT 0.11AndersenLuka Callo: 136 passningar, xT 0.15CalloGift Links: 134 passningar, xT 0.48LinksMarkus Solbakken: 129 passningar, xT 0.15SolbakkenSebastian Jorgensen: 123 passningar, xT 0.55JorgensenColin Rösler: 118 passningar, xT 0.41RöslerMads Hedenstad Christiansen: 104 passningar, xT 0.05Christiansen
    Silkeborg
    Mads Larsen: 274 passningar, xT 0.15LarsenRobin Ostrom: 257 passningar, xT 0.18OstromVillads Westh: 196 passningar, xT 0.15WesthAlexander Madsen: 174 passningar, xT 0.31MadsenWilliam Lokke Moller: 144 passningar, xT 0.1MollerCallum Mccowatt: 140 passningar, xT 0.52MccowattRami Al Hajj: 125 passningar, xT 0.5HajjJens Martin Gammelby: 121 passningar, xT 0.06GammelbyAske Andresen: 115 passningar, xT 0.01AndresenYounes Bakiz: 62 passningar, xT -0.15BakizOliver Ross: 60 passningar, xT 0.05Ross
    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.

    AGF
    Eric Kahl: 260 passningar, xT 0.53KahlMouhammade Camara: 237 passningar, xT 0.26CamaraMagnus Nordengen Knudsen: 175 passningar, xT 0.38KnudsenKristian Arnstad: 169 passningar, xT 0.28ArnstadJacob Andersen: 148 passningar, xT 0.11AndersenLuka Callo: 136 passningar, xT 0.15CalloGift Links: 134 passningar, xT 0.48LinksMarkus Solbakken: 129 passningar, xT 0.15SolbakkenSebastian Jorgensen: 123 passningar, xT 0.55JorgensenColin Rösler: 118 passningar, xT 0.41RöslerMads Hedenstad Christiansen: 104 passningar, xT 0.05Christiansen
    Silkeborg
    Mads Larsen: 274 passningar, xT 0.15LarsenRobin Ostrom: 257 passningar, xT 0.18OstromVillads Westh: 196 passningar, xT 0.15WesthAlexander Madsen: 174 passningar, xT 0.31MadsenWilliam Lokke Moller: 144 passningar, xT 0.1MollerCallum Mccowatt: 140 passningar, xT 0.52MccowattRami Al Hajj: 125 passningar, xT 0.5HajjJens Martin Gammelby: 121 passningar, xT 0.06GammelbyAske Andresen: 115 passningar, xT 0.01AndresenYounes Bakiz: 62 passningar, xT -0.15BakizOliver Ross: 60 passningar, xT 0.05Ross

    The teams in numbers

    PerformanceAGFSilkeborg
    Points35
    xPoints8.37.5
    xG per match1.60.92
    xGA per match1.41.6
    xG within 8s of winning the ball0.280.2
    xGA within 8s of losing the ball0.190.36
    Playing styleAGFSilkeborg
    Build-up efficiency0.30.32
    Field tilt0.60.39
    xT per match1.20.64
    xTA per match0.871.4
    Won balls, offensive half2418
    Pressing intensity0.240.25
    Pressing efficiency0.30.25
    Pressing efficiency, offensive half0.260.19
    Entries into the box per match1713
    Entries into the box against1216
    Pass completion %0.780.8
    Pass completion % under pressure0.70.76
    Passes per match421416
    Passes against per match276468
    Switches of play per match25.815.6
    Long balls per match2920
    Set piecesAGFSilkeborg
    xG from free kicks0.070.05
    Corners per match8.35
    Corners against per match3.26
    xG per corner0.020.01
    xGA per corner against0.040.02
    First touch, offensive corners %0.30.63
    First touch, defensive corners %0.580.64
    OtherAGFSilkeborg
    Throw-in control0.70.7
    The goalkeepersMads Hedenstad Christiansen (AGF)Aske Andresen (Silkeborg)
    Saves1319
    Save %62%68%
    xG prevented51%43%
    Claims0 (0%)0 (0%)

    Teams & more matches this round

    Frequently asked questions

    Who wins AGF vs Silkeborg according to our model?

    The model gives AGF a 62% win probability. Full 1X2 picture: AGF 62%, draw 20%, Silkeborg 19%.

    What is the most likely scoreline?

    The model's most likely final score is 1-1 at 10% probability.

    Over or under 2.5 goals?

    The model rates over 2.5 goals at 59% and under at 41%.

    Will both teams score?

    The probability of both teams scoring is 58% 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.