EFL ChampionshipSt Mary's Stadium, Southampton17°6,7 mm20 km/h

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    SouthamptonSouthamptonPossession control
    vs
    Swansea CitySwansea CityPossession control
    The model's lean: 1 · Southampton (56%)

    SouthamptonSwansea City · EFL Championship

    1 · Southampton 56%X 23%20% Swansea City · 2

    Analysis: SouthamptonSwansea City

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

    Southampton's match against Swansea City brings forward an intriguing clash of possession-oriented styles. The hosts have shown dominance at home, winning 56% of their matches in their history, though this is tempered by the visitors' current unbeaten streak in the league. Swansea's resilience on the road is matched by their superior defensive metrics, conceding only 0.93 xGA per match compared to the home side's 1.4.

    However, the model favors Southampton, giving them a 56% chance to win, indicating a significant edge over Swansea's 20%. The expected goals also lean towards the hosts, with a projection of 1.86 compared to the Welsh side's 1.04. While Swansea's recent form might suggest a closer contest, the analytics underscore Southampton's capacity to capitalize on their scoring opportunities, especially in front of their supporters.

    Although both teams embrace a possession-based approach, the forecasted rain could disrupt fluid play, potentially suppressing goals. Despite this, the model suggests a moderate 55% chance of over 2.5 goals, aligning with the tightly contested scorelines of 1-1 and 2-1 being the most probable. Southampton's ability to break through Swansea's defense will be crucial, further emphasized by their slight edge in xG per match at 1.7.

    With a small head-to-head sample since 2023, it's Southampton's consistent home form that stands out, enhanced by the betting market's mispricing. The home win at odds 1.88 with Betsafe, Betsson, and Nordicbet holds a 6% value edge. This suggests an opportunity for bettors looking to capitalize on the forecasted home advantage. While Swansea's streak is commendable, the numbers tip toward a narrow victory for the Saints, potentially ending in a 2-1 result.

    SouthamptonSouthampton
    Form LWWDD
    League ranking xG #8xGA #11xT #12xP/match #15Points #8
    Key players
    • Cyle Christopher LarinFxG 0.58 · xT 0.04
    • Ryan Phelim ManningLBxG 0.24 · xT 0.17
    • Leonardo Weschenfelder ScienzaLWxG 0.29 · xT 0.07
    Swansea CitySwansea City
    Form WDWWD
    League ranking xG #9xGA #2xT #11xP/match #9Points #1
    Key players
    • Josh KeyRWBxG 0.00 · xT 0.13
    • Josh TymonLWBxG 0.01 · xT 0.13
    • Filip LissahCBxG 0.00 · xT 0.04

    Match prediction: SouthamptonSwansea City

    Predicted score matrix

    Swansea City
    Southampton
    0
    1
    2
    3
    4+
    0
    0–05.8%
    0–15.4%
    0–23.0%
    0–31.0%
    0–4+0.3%
    1
    1–09.9%
    1–111.0%
    1–25.6%
    1–31.9%
    1–4+0.6%
    2
    2–09.5%
    2–19.9%
    2–25.2%
    2–31.8%
    2–4+0.6%
    3
    3–05.9%
    3–16.1%
    3–23.2%
    3–31.1%
    3–4+0.4%
    4+
    4+–04.2%
    4+–14.3%
    4+–22.3%
    4+–30.8%
    4+–4+0.3%
    Probability per final score. Rows = home goals, columns = away goals.

    Most likely scorelines

    1. 1.1-111%
    2. 2.2-110%
    3. 3.1-010%
    4. 4.2-09%
    5. 5.3-16%
    Expected goals
    1,861,04
    Both teams to score
    55%

    Over/under goals

    Expected goals: 2,9
    Under 2,545%
    @2.09
    Over 2,555%
    @1.76

    Ready-made bet suggestions

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    01 · 2 legs

    Safe

    • Southampton to win56%
    • Over 1.5 goals79%

    Combined probability

    46%

    Fair odds

    2.17

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

    02 · 3 legs

    Balanced

    • Southampton to win56%
    • Over 2.5 goals55%
    • Finn Isaac Azaz to score34%

    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 score55%
    • Over 2.5 goals55%
    • Finn Isaac Azaz to score34%
    • Jay Fulton to be booked27%

    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 · Southampton56% probability@1.88BetsafeEV+6%reference odds
    • 1 · Southampton56% probability@1.88BetssonEV+6%reference odds
    • 1 · Southampton56% probability@1.88NordicbetEV+6%reference odds
    Bookmaker1 · SouthamptonX2 · Swansea City
    Betsafe
    EV+ 6%
    1.88
    3.603.90
    Betsson
    EV+ 6%
    1.88
    3.603.90
    Nordicbet
    EV+ 6%
    1.88
    3.603.90
    Pinnacle
    EV+ 5%
    1.86
    3.724.00

    Odds updated 6 Sept, 21:35

    Statistics

    Head-to-head

    2 matches
    Southampton 1Draw 1Swansea City 0
    Goals: 21 (⌀ 1,5)

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

    Key facts

    • Swansea City are unbeaten in their last 8 league matches.
    • Southampton win 56% of their home matches all-time (25 played).
    xG & xGA per match — 3-game rolling average

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

    Southampton
    Swansea City
    When goals are scored and conceded 2026
    Southampton (116)
    Swansea City (71)
    Pass networks

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

    Southampton
    Flynn Downes: 236 passningar, xT 0.09DownesRyan Phelim Manning: 203 passningar, xT 0.68ManningJames Bree: 197 passningar, xT 0.49BreeJack Stephens: 180 passningar, xT 0.18StephensFinn Azaz: 143 passningar, xT 1.05AzazCameron Roger Bragg: 142 passningar, xT 0.16BraggKuryu Matsuki: 138 passningar, xT 0.07MatsukiKeven Matteo Schlotterbeck: 134 passningar, xT 0.21SchlotterbeckDaniel Peretz: 127 passningar, xT 0.06PeretzLeonardo Weschenfelder Scienza: 123 passningar, xT 0.29ScienzaJames Michael Edward Ward-Prowse: 119 passningar, xT 0.12Ward-Prowse
    Swansea City
    Stephen Welsh: 293 passningar, xT 0.89WelshFilip Lissah: 255 passningar, xT 0.18LissahJosh Tymon: 246 passningar, xT 0.64TymonJay Fulton: 186 passningar, xT 0.09FultonJosh Key: 176 passningar, xT 0.41KeyLawrence Vigouroux: 166 passningar, xT 0.23VigourouxMarko Seufatu Nikola Stamenić: 148 passningar, xT 0.1StamenićStephen Antunes Eustaquio: 126 passningar, xT 0.02EustaquioBen Cabango: 119 passningar, xT 0.08CabangoElijah Henry Just: 92 passningar, xT 0.2JustMoussa Kounfolo Yeo: 87 passningar, xT 0.59Yeo
    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.

    Southampton
    Flynn Downes: 236 passningar, xT 0.09DownesRyan Phelim Manning: 203 passningar, xT 0.68ManningJames Bree: 197 passningar, xT 0.49BreeJack Stephens: 180 passningar, xT 0.18StephensFinn Azaz: 143 passningar, xT 1.05AzazCameron Roger Bragg: 142 passningar, xT 0.16BraggKuryu Matsuki: 138 passningar, xT 0.07MatsukiKeven Matteo Schlotterbeck: 134 passningar, xT 0.21SchlotterbeckDaniel Peretz: 127 passningar, xT 0.06PeretzLeonardo Weschenfelder Scienza: 123 passningar, xT 0.29ScienzaJames Michael Edward Ward-Prowse: 119 passningar, xT 0.12Ward-Prowse
    Swansea City
    Stephen Welsh: 293 passningar, xT 0.89WelshFilip Lissah: 255 passningar, xT 0.18LissahJosh Tymon: 246 passningar, xT 0.64TymonJay Fulton: 186 passningar, xT 0.09FultonJosh Key: 176 passningar, xT 0.41KeyLawrence Vigouroux: 166 passningar, xT 0.23VigourouxMarko Seufatu Nikola Stamenić: 148 passningar, xT 0.1StamenićStephen Antunes Eustaquio: 126 passningar, xT 0.02EustaquioBen Cabango: 119 passningar, xT 0.08CabangoElijah Henry Just: 92 passningar, xT 0.2JustMoussa Kounfolo Yeo: 87 passningar, xT 0.59Yeo

    The teams in numbers

    PerformanceSouthamptonSwansea City
    Points811
    xPoints6.67.8
    xG per match1.71.7
    xGA per match1.40.93
    xG within 8s of winning the ball0.440.29
    xGA within 8s of losing the ball0.370.14
    Playing styleSouthamptonSwansea City
    Build-up efficiency0.30.27
    Field tilt0.660.64
    xT per match0.970.97
    xTA per match0.850.67
    Won balls, offensive half2431
    Pressing intensity0.210.21
    Pressing efficiency0.30.38
    Pressing efficiency, offensive half0.190.39
    Entries into the box per match1213
    Entries into the box against1311
    Pass completion %0.80.77
    Pass completion % under pressure0.780.73
    Passes per match423447
    Passes against per match246260
    Switches of play per match23.525.2
    Long balls per match2931
    Set piecesSouthamptonSwansea City
    xG from free kicks0.090.03
    Corners per match6.25.4
    Corners against per match4.83.4
    xG per corner0.010.05
    xGA per corner against0.010.06
    First touch, offensive corners %0.520.52
    First touch, defensive corners %0.710.59
    OtherSouthamptonSwansea City
    Throw-in control0.670.71
    The goalkeepersDaniel Peretz (Southampton)Lawrence Vigouroux (Swansea City)
    Saves148
    Save %74%89%
    xG prevented51%88%
    Claims0 (0%)0 (0%)

    Frequently asked questions

    Who wins Southampton vs Swansea City according to our model?

    The model gives Southampton a 56% win probability. Full 1X2 picture: Southampton 56%, draw 23%, Swansea City 20%.

    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 55% and under at 45%.

    Will both teams score?

    The probability of both teams scoring is 55% 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.