Swiss Super LeagueKybunpark, St. Gallen20°14 km/h

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    St. GallenSt. GallenPossession control
    34
    ThunThunCounter-attacks & crosses
    The model's lean: 1 · St. Gallen (59%)Best value by the model: Under 3,5 @ 1.97 (+14 %)

    St. GallenThun · Swiss Super League

    1 · St. Gallen 59%X 24%17% Thun · 2

    Analysis: St. GallenThun

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

    TEXT:

    St. Gallen holds a distinct advantage heading into their matchup against Thun, as underscored by a 59% probability of victory. The model's numbers favor the hosts, reflecting their superior recent form. With an unbeaten streak in their last six league games, St. Gallen's possession-based strategy seems poised to control the flow against Thun's counter-attacking and crossing tactics.

    Despite the small sample size of recent head-to-head meetings — just four since 2023 — these encounters have been tight, with each team securing a single victory and sharing two draws. However, league form and expected goals lean decisively in favor of St. Gallen. They boast a significant edge in both xG and xGA per match, averaging 2.4 to Thun's 1.4 and conceding 1.5 to Thun's 2.6. This statistical superiority suggests that the hosts could find the net more than once.

    The model lists a 2-1 victory for St. Gallen as the most probable outcome, supported by their expected goals of 2.12. Although Thun will look to exploit any defensive lapses through quick transitions, their chances are limited, reflected in their lower xG of 1.19. The probabilities suggest a 64% likelihood of over 2.5 goals, indicating potential for an open game.

    Yet, the sharp betting angle might lie in the contrary value on under 2.5 goals, with an EV+ at odds of 3.1 offered by Betsafe, Betsson, and Nordicbet. Despite the forecasted goal potential, the model rates the under at 36%, representing a notable 11% edge. In a match where possession could dominate over conversion, this contrarian view could yield returns.

    St. GallenSt. Gallen
    Form WDWWD
    League ranking xG #2xGA #7xT #9xP/match #2Points #4
    Key players
    • Lukas GortlerFxG 0.57 · xT 0.25
    • Aliou BaldeLWxG 0.67 · xT -0.02
    • Lukas DaschnerCAMxG 0.38 · xT 0.15
    ThunThun
    Form LDWLL
    League ranking xG #8xGA #12xT #10xP/match #12Points #9
    Key players
    • Marc GutbubFxG 0.46 · xT 0.00
    • Lucien DahlerRWBxG 0.10 · xT 0.12
    • Fabio Saiz PennarossaFxG 0.02 · xT 0.06
    Final score
    34
    The model missed the outcome
    Predicted probabilities: St. Gallen 59% · Draw 24% · Thun 17%

    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 - St. GallenBåda lagen gör mål - JaTotalt antal mål - Över 3.5
    odds 3,28fair 4,55-28 %
    Miss
    Matchresultat - ThunBåda lagen gör mål - JaTotalt antal mål - Över 3.5
    odds 9,01fair 14,78-39 %
    Hit

    Match prediction: St. GallenThun

    Predicted score matrix

    Thun
    St. Gallen
    0
    1
    2
    3
    4+
    0
    0–03.9%
    0–14.1%
    0–22.6%
    0–31.0%
    0–4+0.4%
    1
    1–07.5%
    1–19.5%
    1–25.5%
    1–32.2%
    1–4+0.8%
    2
    2–08.2%
    2–19.8%
    2–25.8%
    2–32.3%
    2–4+0.9%
    3
    3–05.8%
    3–16.9%
    3–24.1%
    3–31.6%
    3–4+0.6%
    4+
    4+–05.1%
    4+–16.0%
    4+–23.6%
    4+–31.4%
    4+–4+0.5%
    Probability per final score. Rows = home goals, columns = away goals.

    Most likely scorelines

    1. 1.2-110%
    2. 2.1-19%
    3. 3.2-08%
    4. 4.1-07%
    5. 5.3-17%
    Expected goals
    2,121,19
    Both teams to score
    61%

    Over/under goals

    Expected goals: 3,3
    Under 2,536%
    @3.00EV+7%
    Over 2,564%
    @1.33

    Ready-made bet suggestions

    More combos & build your own

    01 · 2 legs

    Safe

    • St. Gallen to win59%
    • Over 1.5 goals85%

    Combined probability

    51%

    Fair odds

    1.96

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

    02 · 3 legs

    Balanced

    • St. Gallen to win59%
    • Over 2.5 goals64%
    • Jordi Quintillà Guasch to score23%

    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 score61%
    • Over 2.5 goals64%
    • Jordi Quintillà Guasch to score23%
    • Leonardo Fabrizio Bertone to be booked23%

    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 3.558% probability@1.97PinnacleEV+14%reference odds
    • Under 3.558% probability@1.88BetsafeEV+9%reference odds
    • Under 3.558% probability@1.88BetssonEV+9%reference odds
    Bookmaker1 · St. GallenX2 · Thun
    Betsafe1.60
    EV+ 2%
    4.30
    4.30
    Betsson1.60
    EV+ 2%
    4.30
    4.30
    Nordicbet1.60
    EV+ 2%
    4.30
    4.30
    Pinnacle1.67
    EV+ 3%
    4.35
    4.71

    Odds updated 30 Aug, 10:31

    Odds movement

    1+2 %
    61 %56 %

    1.641.67

    X+3 %
    24 %19 %

    4.214.35

    20 %
    23 %18 %

    4.714.71

    Pinnacle · 15 recorded price levels · 24/08/2026 → 30/08/2026 · fixed scale 5 percentage points

    Statistics

    Head-to-head

    4 matches
    St. Gallen 1Draw 2Thun 1
    Goals: 65 (⌀ 2,8)

    Head-to-head based on Swiss Super League data since 2023.

    Key facts

    • St. Gallen are unbeaten in their last 6 league matches.
    xG & xGA per match — 3-game rolling average

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

    St. Gallen
    Thun
    When goals are scored and conceded 2026
    St. Gallen (75)
    Thun (511)
    Pass networks

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

    St. Gallen
    Lukas Gortler: 87 passningar, xT 0.6GortlerChima Okoroji: 84 passningar, xT 0.34OkorojiLukas Daschner: 70 passningar, xT 0.29DaschnerJozo Stanic: 59 passningar, xT -0.09StanicMihailo Stevanovic: 53 passningar, xT 0.23StevanovicChristian Witzig: 49 passningar, xT 0.22WitzigLawrence Ati Zigi: 49 passningar, xT 0.18ZigiHugo Vandermersch: 45 passningar, xT 0.14VandermerschLeon Frokaj: 43 passningar, xT 0.17FrokajJoel Ruiz: 39 passningar, xT 0.04RuizEnoch Owusu: 36 passningar, xT 0.27Owusu
    Thun
    Nicolas Sandro Bürgy: 141 passningar, xT 0.27BürgyJan Bamert: 122 passningar, xT 0.21BamertNiklas Steffen: 84 passningar, xT 0.04SteffenFabio Saiz Pennarossa: 82 passningar, xT 0.12PennarossaLucien Dahler: 79 passningar, xT 0.36DahlerNassim-Othmane Zoukit: 70 passningar, xT 0.14ZoukitJustin Roth: 53 passningar, xT -0.01RothNico Maier: 41 passningar, xT 0.41MaierDorian Derbaci: 33 passningar, xT 0.21DerbaciMichael Heule: 33 passningar, xT 0.28HeuleAshvin Balaruban: 30 passningar, xT 0.18Balaruban
    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.

    St. Gallen
    Lukas Gortler: 87 passningar, xT 0.6GortlerChima Okoroji: 84 passningar, xT 0.34OkorojiLukas Daschner: 70 passningar, xT 0.29DaschnerJozo Stanic: 59 passningar, xT -0.09StanicMihailo Stevanovic: 53 passningar, xT 0.23StevanovicChristian Witzig: 49 passningar, xT 0.22WitzigLawrence Ati Zigi: 49 passningar, xT 0.18ZigiHugo Vandermersch: 45 passningar, xT 0.14VandermerschLeon Frokaj: 43 passningar, xT 0.17FrokajJoel Ruiz: 39 passningar, xT 0.04RuizEnoch Owusu: 36 passningar, xT 0.27Owusu
    Thun
    Nicolas Sandro Bürgy: 141 passningar, xT 0.27BürgyJan Bamert: 122 passningar, xT 0.21BamertNiklas Steffen: 84 passningar, xT 0.04SteffenFabio Saiz Pennarossa: 82 passningar, xT 0.12PennarossaLucien Dahler: 79 passningar, xT 0.36DahlerNassim-Othmane Zoukit: 70 passningar, xT 0.14ZoukitJustin Roth: 53 passningar, xT -0.01RothNico Maier: 41 passningar, xT 0.41MaierDorian Derbaci: 33 passningar, xT 0.21DerbaciMichael Heule: 33 passningar, xT 0.28HeuleAshvin Balaruban: 30 passningar, xT 0.18Balaruban

    The teams in numbers

    PerformanceSt. GallenThun
    Points73
    xPoints5.12.7
    xG per match2.41.4
    xGA per match1.52.6
    xG within 8s of winning the ball0.680.45
    xGA within 8s of losing the ball0.330.38
    Playing styleSt. GallenThun
    Build-up efficiency0.330.32
    Field tilt0.530.38
    xT per match0.830.81
    xTA per match0.591.7
    Won balls, offensive half4121
    Pressing intensity0.230.25
    Pressing efficiency0.370.32
    Pressing efficiency, offensive half0.430.28
    Entries into the box per match2113
    Entries into the box against1516
    Pass completion %0.640.72
    Pass completion % under pressure0.610.62
    Passes per match273287
    Passes against per match268346
    Switches of play per match19.723.3
    Long balls per match3830
    Set piecesSt. GallenThun
    xG from free kicks0.090.15
    Corners per match7.15
    Corners against per match66
    xG per corner0.030.01
    xGA per corner against0.010.02
    First touch, offensive corners %0.470.4
    First touch, defensive corners %0.620.5
    OtherSt. GallenThun
    Throw-in control0.650.66
    The goalkeepersLawrence Ati Zigi (St. Gallen)Niklas Steffen (Thun)
    Saves315
    Save %50%58%
    xG prevented59%39%
    Claims0 (0%)0 (0%)

    Teams & more matches this round

    Frequently asked questions

    Who wins St. Gallen vs Thun according to our model?

    The model gives St. Gallen a 59% win probability. Full 1X2 picture: St. Gallen 59%, draw 24%, Thun 17%.

    What is the most likely scoreline?

    The model's most likely final score is 2-1 at 10% probability.

    Over or under 2.5 goals?

    The model rates over 2.5 goals at 64% and under at 36%.

    Will both teams score?

    The probability of both teams scoring is 61% 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.