EFL League OneStadium MK, Milton Keynes17°0,2 mm28 km/h

    Läs på svenska
    Milton Keynes DonsMilton Keynes DonsLow block & direct
    vs
    Peterborough UnitedPeterborough UnitedPossession & high press
    The model's lean: 1 · Milton Keynes Dons (41%)Best value by the model: Under 0,5 @ 17.00 (+22 %)

    Milton Keynes DonsPeterborough United · EFL League One

    1 · Milton Keynes Dons 41%X 27%32% Peterborough United · 2

    Analysis: Milton Keynes DonsPeterborough United

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

    The head-to-head claim is unsupported — no such data appears in the FACTS.

    CORRECTED TEXT:

    The current numbers point toward Milton Keynes Dons at home, with the model leaning towards a home win at 41% probability.

    Milton Keynes Dons showcase a solid xG of 1.8 per match, contrasting sharply with Peterborough's 1.4. This offensive edge, coupled with a slightly better defensive record, suggests their direct style may overwhelm Peterborough's possession game. The model's expected goals reinforce this, predicting a 1.61 to 1.07 edge for the hosts.

    Despite a balanced over/under 2.5 goals probability at 50%, the likelihood of a tight match remains. The market's most probable scorelines—1-1, 1-0, and 2-1—indicate a close contest, aligning with an over 50% chance of both teams finding the net. Yet, the style clash—Dons' low defence versus Peterborough's high press—could result in a tactical stalemate.

    A unique opportunity arises with Betsafe, Betsson, and Nordicbet, offering odds of 17.0 on under 0.5 goals. With the model rating this at a 7% likelihood, there's a notable 22% value edge. While the odds are slim, the numbers suggest a calculated punt for those seeking an unconventional angle in a match that could defy expectations.

    Milton Keynes DonsMilton Keynes Dons
    Form DDDLD
    League ranking xG #6xGA #15xT #4xP/match #8Points #19
    Key players
    • Aaron Graham John CollinsFxG 0.51 · xT 0.24
    • Callum Thomas Owen PatersonLWxG 0.62 · xT 0.01
    • Daniel Tan BarlaserCMxG 0.02 · xT 0.16
    Peterborough UnitedPeterborough United
    Form LWLDL
    League ranking xG #11xGA #20xT #2xP/match #16Points #20
    Key players
    • Owura Nsiah EdwardsFxG 0.06 · xT 0.65
    • Harley MillsLBxG 0.01 · xT 0.20
    • Brandon Singh KhelaRWxG 0.16 · xT 0.03

    Match prediction: Milton Keynes DonsPeterborough United

    Predicted score matrix

    Peterborough United
    Milton Keynes Dons
    0
    1
    2
    3
    4+
    0
    0–07.2%
    0–17.0%
    0–23.9%
    0–31.4%
    0–4+0.5%
    1
    1–010.7%
    1–112.2%
    1–26.3%
    1–32.3%
    1–4+0.8%
    2
    2–08.8%
    2–19.5%
    2–25.1%
    2–31.8%
    2–4+0.6%
    3
    3–04.7%
    3–15.1%
    3–22.7%
    3–31.0%
    3–4+0.3%
    4+
    4+–02.7%
    4+–12.9%
    4+–21.6%
    4+–30.6%
    4+–4+0.2%
    Probability per final score. Rows = home goals, columns = away goals.

    Most likely scorelines

    1. 1.1-112%
    2. 2.1-011%
    3. 3.2-110%
    4. 4.2-09%
    5. 5.0-07%
    Expected goals
    1,611,07
    Both teams to score
    53%

    Over/under goals

    Expected goals: 2,7
    Under 2,550%
    @2.32EV+16%
    Over 2,550%
    @1.55

    Ready-made bet suggestions

    More combos & build your own

    01 · 2 legs

    Safe

    • Double chance Milton Keynes Dons or draw (1X)75%
    • Under 3.5 goals72%

    Combined probability

    53%

    Fair odds

    1.88

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

    02 · 3 legs

    Balanced

    • Double chance Milton Keynes Dons or draw (1X)75%
    • Over 2.5 goals50%
    • Aaron Graham John Collins to score27%

    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 score53%
    • Over 2.5 goals50%
    • Aaron Graham John Collins to score27%
    • Liam Anthony Kelly to be booked22%

    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 0.57% probability@17.00BetsafeEV+22%reference odds
    • Under 0.57% probability@17.00BetssonEV+22%reference odds
    • Under 0.57% probability@17.00NordicbetEV+22%reference odds
    Bookmaker1 · Milton Keynes DonsX2 · Peterborough United
    Betsafe1.983.45
    EV+ 9%
    3.45
    Betsson1.983.45
    EV+ 9%
    3.45
    Nordicbet1.983.45
    EV+ 9%
    3.45
    Pinnacle2.01
    EV+ 3%
    3.81
    EV+ 9%
    3.44

    Odds updated 11 Sept, 16:32

    Odds movement

    1+3 %
    50 %45 %

    1.942.01

    X+3 %
    28 %23 %

    3.713.81

    2-6 %
    30 %25 %

    3.663.44

    Pinnacle · 9 recorded price levels · 06/09/2026 → 11/09/2026 · fixed scale 5 percentage points

    Statistics

    xG & xGA per match — 3-game rolling average

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

    Milton Keynes Dons
    Peterborough United
    When goals are scored and conceded 2026
    Milton Keynes Dons (79)
    Peterborough United (39)
    Pass networks

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

    Milton Keynes Dons
    Daniel Tan Barlaser: 147 passningar, xT 0.54BarlaserCraig Macgillivray: 127 passningar, xT 0.28MacgillivrayCurtis Alexander Nelson: 126 passningar, xT 0.33NelsonRyan Frank Wintle: 116 passningar, xT 0.33WintleMarvin Akpereogene Paul Edem Ekpiteta: 113 passningar, xT 0.14EkpitetaCohen Conrad Bramall: 101 passningar, xT 1.02BramallCharles James Goode: 101 passningar, xT 0.06GoodeGethin Wynne Jones: 72 passningar, xT 0.65JonesBenjamin Jack Wiles: 40 passningar, xT 0.13WilesRyan Barnett: 33 passningar, xT 0.83BarnettSamuel Tshiayima Nombe: 31 passningar, xT 0.34Nombe
    Peterborough United
    Liam Anthony Kelly: 213 passningar, xT 0.36KellyDavid Chukwudubem Okagbue: 205 passningar, xT 0.21OkagbueHarley Mills: 201 passningar, xT 0.8MillsCarl Robert Johnston: 187 passningar, xT 0.25JohnstonAlexander Michael Bass: 168 passningar, xT 0.28BassThomas James O'Connor: 145 passningar, xT 0.2O'ConnorJames Daniel Dornelly: 132 passningar, xT 0.84DornellyBrandon Singh Khela: 121 passningar, xT -0.06KhelaBenjamin Jack Woods: 103 passningar, xT 0.39WoodsHarrison Jones: 98 passningar, xT 0.07JonesJosh Feeney: 93 passningar, xT 0.1Feeney
    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.

    Milton Keynes Dons
    Daniel Tan Barlaser: 147 passningar, xT 0.54BarlaserCraig Macgillivray: 127 passningar, xT 0.28MacgillivrayCurtis Alexander Nelson: 126 passningar, xT 0.33NelsonRyan Frank Wintle: 116 passningar, xT 0.33WintleMarvin Akpereogene Paul Edem Ekpiteta: 113 passningar, xT 0.14EkpitetaCohen Conrad Bramall: 101 passningar, xT 1.02BramallCharles James Goode: 101 passningar, xT 0.06GoodeGethin Wynne Jones: 72 passningar, xT 0.65JonesBenjamin Jack Wiles: 40 passningar, xT 0.13WilesRyan Barnett: 33 passningar, xT 0.83BarnettSamuel Tshiayima Nombe: 31 passningar, xT 0.34Nombe
    Peterborough United
    Liam Anthony Kelly: 213 passningar, xT 0.36KellyDavid Chukwudubem Okagbue: 205 passningar, xT 0.21OkagbueHarley Mills: 201 passningar, xT 0.8MillsCarl Robert Johnston: 187 passningar, xT 0.25JohnstonAlexander Michael Bass: 168 passningar, xT 0.28BassThomas James O'Connor: 145 passningar, xT 0.2O'ConnorJames Daniel Dornelly: 132 passningar, xT 0.84DornellyBrandon Singh Khela: 121 passningar, xT -0.06KhelaBenjamin Jack Woods: 103 passningar, xT 0.39WoodsHarrison Jones: 98 passningar, xT 0.07JonesJosh Feeney: 93 passningar, xT 0.1Feeney

    The teams in numbers

    PerformanceMilton Keynes DonsPeterborough United
    Points44
    xPoints7.66.2
    xG per match1.81.4
    xGA per match1.61.7
    xG within 8s of winning the ball0.320.32
    xGA within 8s of losing the ball0.410.36
    Playing styleMilton Keynes DonsPeterborough United
    Build-up efficiency0.330.29
    Field tilt0.360.55
    xT per match1.31.3
    xTA per match11.1
    Won balls, offensive half3023
    Pressing intensity0.250.24
    Pressing efficiency0.280.39
    Pressing efficiency, offensive half0.260.35
    Entries into the box per match1213
    Entries into the box against1512
    Pass completion %0.650.75
    Pass completion % under pressure0.590.71
    Passes per match230412
    Passes against per match351272
    Switches of play per match23.816.7
    Long balls per match3827
    Set piecesMilton Keynes DonsPeterborough United
    xG from free kicks0.110
    Corners per match5.34.2
    Corners against per match6.35
    xG per corner0.070.04
    xGA per corner against0.030.05
    First touch, offensive corners %0.350.57
    First touch, defensive corners %0.520.52
    OtherMilton Keynes DonsPeterborough United
    Throw-in control0.680.73
    The goalkeepersCraig Macgillivray (Milton Keynes Dons)Alexander Michael Bass (Peterborough United)
    Saves2119
    Save %70%70%
    xG prevented62%31%
    Claims0 (0%)0 (0%)

    Teams & more matches this round

    Frequently asked questions

    Who wins Milton Keynes Dons vs Peterborough United according to our model?

    The model gives Milton Keynes Dons a 41% win probability. Full 1X2 picture: Milton Keynes Dons 41%, draw 27%, Peterborough United 32%.

    What is the most likely scoreline?

    The model's most likely final score is 1-1 at 12% probability.

    Over or under 2.5 goals?

    The model rates over 2.5 goals at 50% and under at 50%.

    Will both teams score?

    The probability of both teams scoring is 53% 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.

    More matches in the league

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