Serie BStadio Druso, Bolzano27°12 km/h

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    SüdtirolSüdtirolCounter-attacks & crosses
    20
    CatanzaroCatanzaroCounter-attacks & crosses
    The model's lean: 1 · Südtirol (37%)Best value by the model: 2 · Catanzaro @ 3.33 (+10 %)

    SüdtirolCatanzaro · Serie B

    1 · Südtirol 37%X 30%33% Catanzaro · 2

    Analysis: SüdtirolCatanzaro

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

    Südtirol's recent unbeaten streak across five league games highlights their current form, despite their lower expected goals tally compared to Catanzaro. The hosts have a slight edge in model probabilities at 37% to secure a win, but the match is finely balanced with a draw at 30% and the visitors at 33%. Both teams employ a similar counter-attacking style with an emphasis on crossing, suggesting a tactical battle rather than a high-scoring affair.

    The head-to-head record is limited to just four matches since 2023, showing an even split with one win each and two draws. While Südtirol has the edge in points and xPoints, Catanzaro surpasses them with a higher expected goals per match at 1.2, indicating a potential threat in front of goal. However, Südtirol's defense could prove robust enough to handle the visitors' attacking attempts given their superior xGA per match.

    Despite Südtirol's slight model lean, bettors might find value in backing Catanzaro at odds of 3.15 with Betsafe, Betsson, or Nordicbet, where there's a 4% edge. A 1-1 draw is the most probable scoreline at 13%, with over 2.5 goals having a modest 44% probability. The numbers suggest a tight contest with both defenses potentially holding firm.

    SüdtirolSüdtirol
    Form DDDWD
    League ranking xG #20xGA #14xT #18xP/match #14Points #6
    Key players
    • Andrea GiorginiCBxG 0.00 · xT 0.04
    • Silvio MerkajFxG 0.11 · xT 0.06
    • Simone TronchinCAMxG 0.03 · xT 0.01
    CatanzaroCatanzaro
    Form LLWLL
    League ranking xG #11xGA #19xT #8xP/match #18Points #18
    Key players
    • Jacopo PetriccioneCMxG 0.01 · xT 0.25
    • N'Dri Philippe KoffiFxG 0.20 · xT 0.07
    • Pietro IemmelloCMxG 0.18 · xT 0.14
    Final score
    20
    The model called the outcome
    Predicted probabilities: Südtirol 37% · Draw 30% · Catanzaro 33%

    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 - CatanzaroBåda lagen gör mål - JaTotalt antal mål - Över 3.5
    odds 12,16fair 11,34+7 %
    Miss
    Matchresultat - SudtirolBåda lagen gör mål - JaTotalt antal mål - Över 3.5
    odds 8,93fair 15,35-42 %
    Miss

    Match prediction: SüdtirolCatanzaro

    Predicted score matrix

    Catanzaro
    Südtirol
    0
    1
    2
    3
    4+
    0
    0–09.0%
    0–111.0%
    0–27.5%
    0–33.3%
    0–4+1.5%
    1
    1–09.3%
    1–113.2%
    1–28.5%
    1–33.7%
    1–4+1.7%
    2
    2–05.5%
    2–17.3%
    2–24.8%
    2–32.1%
    2–4+0.9%
    3
    3–02.1%
    3–12.8%
    3–21.8%
    3–30.8%
    3–4+0.4%
    4+
    4+–00.8%
    4+–11.0%
    4+–20.7%
    4+–30.3%
    4+–4+0.1%
    Probability per final score. Rows = home goals, columns = away goals.

    Most likely scorelines

    1. 1.1-113%
    2. 2.0-111%
    3. 3.1-09%
    4. 4.0-09%
    5. 5.1-29%
    Expected goals
    1,131,32
    Both teams to score
    50%

    Over/under goals

    Expected goals: 2,5
    Under 2,556%
    @1.79
    Over 2,544%
    @2.15

    Ready-made bet suggestions

    More combos & build your own

    01 · 2 legs

    Safe

    • Double chance Südtirol or draw (1X)59%
    • Under 3.5 goals77%

    Combined probability

    46%

    Fair odds

    2.15

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

    02 · 3 legs

    Balanced

    • Double chance Südtirol or draw (1X)59%
    • Under 2.5 goals56%
    • Silvio Merkaj 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 score50%
    • Over 2.5 goals44%
    • Mattia Liberali to score24%
    • Simone Tronchin 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+).

    • 2 · Catanzaro33% probability@3.33PinnacleEV+10%reference odds
    • 2 · Catanzaro33% probability@3.15BetsafeEV+4%reference odds
    • 2 · Catanzaro33% probability@3.15BetssonEV+4%reference odds
    Bookmaker1 · SüdtirolX2 · Catanzaro
    Betsafe2.253.00
    EV+ 4%
    3.15
    Betsson2.253.00
    EV+ 4%
    3.15
    Nordicbet2.253.00
    EV+ 4%
    3.15
    Pinnacle2.413.11
    EV+ 10%
    3.33

    Odds updated 5 Sept, 10:33

    Odds movement

    Market signal

    The market has moved clearly towards over 2.5 goals since 31 August — the odds have shortened from 2.28 to 2.09 (−8%).

    1+6 %
    42 %37 %

    2.282.41

    X-2 %
    34 %29 %

    3.173.11

    20 %
    32 %27 %

    3.343.33

    Over 2,5-8 %
    46 %41 %

    2.282.09

    Pinnacle · 15 recorded price levels · 31/08/2026 → 05/09/2026 · fixed scale 5 percentage points

    Statistics

    Head-to-head

    4 matches
    Südtirol 1Draw 2Catanzaro 1
    Goals: 46 (⌀ 2,5)

    Head-to-head based on Serie B data since 2023.

    Key facts

    • Sudtirol are unbeaten in their last 5 league matches.
    xG & xGA per match — 3-game rolling average

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

    Südtirol
    Catanzaro
    When goals are scored and conceded 2026
    Südtirol (21)
    Catanzaro (35)
    Pass networks

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

    Südtirol
    Andrea Giorgini: 137 passningar, xT 0.11GiorginiRiccardo Stivanello: 87 passningar, xT 0.19StivanelloSimone Tronchin: 85 passningar, xT 0.02TronchinSalvatore Molina: 71 passningar, xT 0MolinaAlessandro Plizzari: 69 passningar, xT 0.04PlizzariFabio Rispoli: 64 passningar, xT 0.11RispoliDavide Veroli: 46 passningar, xT 0.02VeroliGiacomo Stabile: 34 passningar, xT 0.06StabileVasco Rafael Fortes Lopes: 33 passningar, xT 0.01LopesBjarki Steinn Bjarkason: 32 passningar, xT 0.09BjarkasonAljosa Vasic: 24 passningar, xT 0.01Vasic
    Catanzaro
    Jacopo Petriccione: 143 passningar, xT 0.49PetriccioneMatias Antonini: 82 passningar, xT 0.34AntoniniBruno Verrengia: 81 passningar, xT 0.29VerrengiaMarco Ruggero: 71 passningar, xT 0.04RuggeroGabriele Alesi: 52 passningar, xT -0.03AlesiNicola Mosti: 52 passningar, xT 0.08MostiPietro Iemmello: 47 passningar, xT 0.08IemmelloMirko Pigliacelli: 38 passningar, xT 0.11PigliacelliMehdi Dorval: 35 passningar, xT 0.11DorvalAntonio Candela: 32 passningar, xT 0.14CandelaFranck Tchaouna: 27 passningar, xT -0.01Tchaouna
    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.

    Südtirol
    Andrea Giorgini: 137 passningar, xT 0.11GiorginiRiccardo Stivanello: 87 passningar, xT 0.19StivanelloSimone Tronchin: 85 passningar, xT 0.02TronchinSalvatore Molina: 71 passningar, xT 0MolinaAlessandro Plizzari: 69 passningar, xT 0.04PlizzariFabio Rispoli: 64 passningar, xT 0.11RispoliDavide Veroli: 46 passningar, xT 0.02VeroliGiacomo Stabile: 34 passningar, xT 0.06StabileVasco Rafael Fortes Lopes: 33 passningar, xT 0.01LopesBjarki Steinn Bjarkason: 32 passningar, xT 0.09BjarkasonAljosa Vasic: 24 passningar, xT 0.01Vasic
    Catanzaro
    Jacopo Petriccione: 143 passningar, xT 0.49PetriccioneMatias Antonini: 82 passningar, xT 0.34AntoniniBruno Verrengia: 81 passningar, xT 0.29VerrengiaMarco Ruggero: 71 passningar, xT 0.04RuggeroGabriele Alesi: 52 passningar, xT -0.03AlesiNicola Mosti: 52 passningar, xT 0.08MostiPietro Iemmello: 47 passningar, xT 0.08IemmelloMirko Pigliacelli: 38 passningar, xT 0.11PigliacelliMehdi Dorval: 35 passningar, xT 0.11DorvalAntonio Candela: 32 passningar, xT 0.14CandelaFranck Tchaouna: 27 passningar, xT -0.01Tchaouna

    The teams in numbers

    PerformanceSüdtirolCatanzaro
    Points40
    xPoints21.7
    xG per match0.591.2
    xGA per match1.52.2
    xG within 8s of winning the ball0.090.04
    xGA within 8s of losing the ball0.270.36
    Playing styleSüdtirolCatanzaro
    Build-up efficiency0.330.31
    Field tilt0.320.49
    xT per match0.411.1
    xTA per match1.11.9
    Won balls, offensive half1317
    Pressing intensity0.260.24
    Pressing efficiency0.320.24
    Pressing efficiency, offensive half0.30.19
    Entries into the box per match614
    Entries into the box against1620
    Pass completion %0.790.79
    Pass completion % under pressure0.730.77
    Passes per match356378
    Passes against per match360309
    Switches of play per match1819.6
    Long balls per match2424
    Set piecesSüdtirolCatanzaro
    xG from free kicks00.01
    Corners per match0.977
    Corners against per match7.88.1
    xG per corner0.020.06
    xGA per corner against0.020.04
    First touch, offensive corners %0.50.43
    First touch, defensive corners %0.690.62
    OtherSüdtirolCatanzaro
    Throw-in control0.710.67
    The goalkeepersAlessandro Plizzari (Südtirol)Mirko Pigliacelli (Catanzaro)
    Saves612
    Save %86%71%
    xG prevented27%60%
    Claims0 (0%)0 (0%)

    Teams & more matches this round

    Frequently asked questions

    Who wins Südtirol vs Catanzaro according to our model?

    The model gives Südtirol a 37% win probability. Full 1X2 picture: Südtirol 37%, draw 30%, Catanzaro 33%.

    What is the most likely scoreline?

    The model's most likely final score is 1-1 at 13% probability.

    Over or under 2.5 goals?

    The model rates over 2.5 goals at 44% and under at 56%.

    Will both teams score?

    The probability of both teams scoring is 50% 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.