EFL League OneLamex Stadium, Stevenage6°27 km/h

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    StevenageStevenagePossession control
    vs
    Milton Keynes DonsMilton Keynes DonsLow block & direct
    The model's lean: 1 · Stevenage (48%)

    Stevenage vs Milton Keynes Dons · EFL League One

    1 · Stevenage 48%X 31%21% Milton Keynes Dons · 2

    Prediction summary

    BetBuilders AI model prediction for Stevenage vs Milton Keynes Dons (EFL League One, Saturday, 24 October 2026): Stevenage win 48%, draw 31%, Milton Keynes Dons win 21%. Expected goals (xG): Stevenage 1.58, Milton Keynes Dons 1.01. Most likely score: 1-1 (12%). Over 2.5 goals 48%, both teams to score 51%. Double chance: Stevenage or draw 79%, Milton Keynes Dons or draw 52%. Prediction updated Friday, 16 October 2026.

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    Analysis: Stevenage – Milton Keynes Dons

    Erik Lindberg · · Written from the model's numbers · How the predictions work

    TEXT: Stevenage's 48% win probability, combined with their edge in points and xPoints, underscores their current form superiority over Milton Keynes Dons. The model suggests a controlled game, with the hosts expected to dominate possession. Milton Keynes Dons, meanwhile, are likely to lean on their defensive setup and more direct style, which might struggle against Stevenage's organized defense.

    The notable contrast in expected goals (xG) highlights the tension: Stevenage generates 1.1 xG per match, while the visitors manage a higher 1.6 xG. However, this attacking edge is tempered by Milton Keynes Dons' vulnerable defense, conceding 1.6 xGA per match compared to Stevenage's solid 0.93 xGA. This disparity suggests that while the visitors might create chances, breaking down Stevenage's defense will be a challenge.

    The most likely scorelines, 1-1 and 1-0, reinforce a low-scoring tilt with potential for a narrow home win. With the over 2.5 goals probability at just 48%, leaning towards the under seems prudent. Although the goal market appears tight, the hosts' composure might just tip the scales.

    StevenageStevenage
    Form DWWLW
    League ranking xG #17xGA #2xT #18xP/match #6Points #9
    Key players
    • Carl PiergianniCBxG 0.05 · xT 0.13
    • Luther James WildinRBxG 0.01 · xT 0.07
    • Terence Owen VancootenCBxG 0.03 · xT 0.05
    Milton Keynes DonsMilton Keynes Dons
    Form LDDDL
    League ranking xG #9xGA #17xT #7xP/match #15Points #23
    Key players
    • Aaron Graham John CollinsFxG 0.43 · xT 0.13
    • Callum Thomas Owen PatersonRWxG 0.39 · xT 0.01
    • Daniel Tan BarlaserCDMxG 0.02 · xT 0.18

    Match prediction: Stevenage – Milton Keynes Dons

    Predicted score matrix

    Milton Keynes Dons →
    Stevenage →
    0
    1
    2
    3
    4+
    0
    0–07.8%
    0–17.2%
    0–23.8%
    0–31.3%
    0–4+0.4%
    1
    1–011.5%
    1–112.3%
    1–26.1%
    1–32.0%
    1–4+0.6%
    2
    2–09.3%
    2–19.5%
    2–24.8%
    2–31.6%
    2–4+0.5%
    3
    3–04.9%
    3–15.0%
    3–22.5%
    3–30.9%
    3–4+0.3%
    4+
    4+–02.8%
    4+–12.8%
    4+–21.4%
    4+–30.5%
    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. 3.2-19%
    4. 4.2-09%
    5. 5.0-08%
    Expected goals
    1,58–1,01
    Both teams to score
    51%

    Over/under goals

    Expected goals: 2,6
    Under 2,552%
    @1.73
    Over 2,548%
    @1.98

    Ready-made bet suggestions

    More combos & build your own →

    01 · 2 legs

    Safe

    • Stevenage to win48%
    • Under 3.5 goals74%

    Combined probability

    35%

    Fair odds

    2.84

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

    02 · 3 legs

    Balanced

    • Stevenage to win48%
    • Under 2.5 goals52%
    • Joshua Brendan David Magennis to score28%

    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 score51%
    • Over 2.5 goals48%
    • Joshua Brendan David Magennis to score28%
    • Marvin Akpereogene Paul Edem Ekpiteta 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 · Stevenage48% probability@2.30LeoVegasEV+10%reference odds
    Bookmaker1 · StevenageX2 · Milton Keynes Dons
    LeoVegas
    EV+ 10%
    2.30
    3.152.95

    Odds updated 18 Oct, 16:36

    Odds movement

    10 %
    42 %37 %

    2.30 → 2.30

    X0 %
    32 %27 %

    3.15 → 3.15

    2+2 %
    34 %29 %

    2.90 → 2.95

    LeoVegas · 3 recorded price levels · 17/10/2026 → 18/10/2026 · fixed scale 5 percentage points

    Statistics

    xG & xGA per match — 3-game rolling average

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

    Stevenage
    Milton Keynes Dons
    When goals are scored and conceded — 2026
    Stevenage (12–8)
    Milton Keynes Dons (9–15)
    Pass networks

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

    Stevenage
    Luther James Wildin: 155 passningar, xT 0.38WildinTerence Owen Vancooten: 128 passningar, xT 0.37VancootenCarl Piergianni: 112 passningar, xT 0.8PiergianniDaniel Phillips: 104 passningar, xT 0.2PhillipsLewis Freestone: 98 passningar, xT 0.47FreestoneDan Kemp: 90 passningar, xT 0.31KempTommy Hogan Setford: 66 passningar, xT 0.23SetfordLouis Thompson: 66 passningar, xT -0.01ThompsonOliver Sanderson: 61 passningar, xT 0.11SandersonMatt Smith: 38 passningar, xT 0.21SmithPhoenix Maclaren Patterson: 35 passningar, xT 0.02Patterson
    Milton Keynes Dons
    Ryan Frank Wintle: 168 passningar, xT 0.27WintleCurtis Alexander Nelson: 161 passningar, xT 0.36NelsonCharles James Goode: 144 passningar, xT 0.21GoodeCohen Conrad Bramall: 121 passningar, xT 0.79BramallDaniel Tan Barlaser: 91 passningar, xT 0.33BarlaserDan Crowley: 88 passningar, xT 0.69CrowleyMarvin Akpereogene Paul Edem Ekpiteta: 87 passningar, xT 0.39EkpitetaWilliam Jonathan Dennis: 75 passningar, xT 0.1DennisGethin Wynne Jones: 55 passningar, xT 0.22JonesAlex Gilbey: 54 passningar, xT 0.13GilbeyCraig Macgillivray: 54 passningar, xT 0.1Macgillivray
    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.

    Stevenage
    Luther James Wildin: 155 passningar, xT 0.38WildinTerence Owen Vancooten: 128 passningar, xT 0.37VancootenCarl Piergianni: 112 passningar, xT 0.8PiergianniDaniel Phillips: 104 passningar, xT 0.2PhillipsLewis Freestone: 98 passningar, xT 0.47FreestoneDan Kemp: 90 passningar, xT 0.31KempTommy Hogan Setford: 66 passningar, xT 0.23SetfordLouis Thompson: 66 passningar, xT -0.01ThompsonOliver Sanderson: 61 passningar, xT 0.11SandersonMatt Smith: 38 passningar, xT 0.21SmithPhoenix Maclaren Patterson: 35 passningar, xT 0.02Patterson
    Milton Keynes Dons
    Ryan Frank Wintle: 168 passningar, xT 0.27WintleCurtis Alexander Nelson: 161 passningar, xT 0.36NelsonCharles James Goode: 144 passningar, xT 0.21GoodeCohen Conrad Bramall: 121 passningar, xT 0.79BramallDaniel Tan Barlaser: 91 passningar, xT 0.33BarlaserDan Crowley: 88 passningar, xT 0.69CrowleyMarvin Akpereogene Paul Edem Ekpiteta: 87 passningar, xT 0.39EkpitetaWilliam Jonathan Dennis: 75 passningar, xT 0.1DennisGethin Wynne Jones: 55 passningar, xT 0.22JonesAlex Gilbey: 54 passningar, xT 0.13GilbeyCraig Macgillivray: 54 passningar, xT 0.1Macgillivray

    The teams in numbers

    PerformanceStevenageMilton Keynes Dons
    Points126
    xPoints12.210.9
    xG per match1.11.6
    xGA per match0.931.6
    xG within 8s of winning the ball0.160.24
    xGA within 8s of losing the ball0.230.47
    Playing styleStevenageMilton Keynes Dons
    Build-up efficiency0.310.33
    Field tilt0.460.38
    xT per match0.861.1
    xTA per match0.711.1
    Won balls, offensive half3427
    Pressing intensity0.220.24
    Pressing efficiency0.370.32
    Pressing efficiency, offensive half0.350.32
    Entries into the box per match1312
    Entries into the box against1015
    Pass completion %0.630.67
    Pass completion % under pressure0.590.61
    Passes per match231251
    Passes against per match339333
    Switches of play per match21.924.4
    Long balls per match3939
    Set piecesStevenageMilton Keynes Dons
    xG from free kicks0.040.1
    Corners per match3.95.1
    Corners against per match5.16.4
    xG per corner0.040.06
    xGA per corner against0.010.02
    First touch, offensive corners %0.390.4
    First touch, defensive corners %0.560.47
    OtherStevenageMilton Keynes Dons
    Throw-in control0.750.69
    The goalkeepersCarl Piergianni (Stevenage)Craig Macgillivray (Milton Keynes Dons)
    Saves921
    Save %100%70%
    xG prevented100%62%
    Claims0 (0%)0 (0%)

    Teams & more matches this round

    Frequently asked questions

    Who wins Stevenage vs Milton Keynes Dons according to our model?

    The model gives Stevenage a 48% win probability. Full 1X2 picture: Stevenage 48%, draw 31%, Milton Keynes Dons 21%.

    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 48% and under at 52%.

    Will both teams score?

    The probability of both teams scoring is 51% according to the model.

    How many goals are expected in Stevenage vs Milton Keynes Dons?

    The model expects 1.58 goals (xG) for Stevenage and 1.01 for Milton Keynes Dons, 2.59 in total.

    What does the model say about double chance?

    Stevenage or draw (1X) 79%, Milton Keynes Dons or draw (X2) 52% and no draw (12) 69%.

    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.