Serie BStadio Euganeo, Padova

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    PadovaPadovaBalanced & physical
    vs
    SüdtirolSüdtirolCounter-attacks & crosses
    The model's lean: 1 · Padova (42%)

    Padova vs Südtirol · Serie B

    1 · Padova 42%X 26%33% Südtirol · 2

    Prediction summary

    BetBuilders AI model prediction for Padova vs Südtirol (Serie B, Tuesday, 27 October 2026): Padova win 42%, draw 26%, Südtirol win 33%. Expected goals (xG): Padova 1.17, Südtirol 1.06. Most likely score: 1-1 (14%). Over 2.5 goals 38%, both teams to score 45%. Double chance: Padova or draw 67%, Südtirol or draw 58%. Prediction updated Monday, 19 October 2026.

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    Analysis: Padova – Südtirol

    Oscar Nilsson · · Model-assisted analysis, fact-checked · How the predictions work

    Calcio Padova are under pressure, having lost their last three matches. This streak is a worrying sign for them, especially when considered against the backdrop of their past encounters with Südtirol. In their small head-to-head sample of just two matches since 2023, Padova has yet to secure a victory, managing only a draw and a loss. Despite these setbacks, the model still leans slightly towards a Padova win with a 42% probability, hinting at potential value overlooked by the market.

    The most likely scorelines — 1-1, 1-0, and 0-0 — suggest that goals will be at a premium. The expected goals (xG) figures, with Padova at 1.17 and Südtirol at 1.06, echo this sentiment, portraying a tight affair where neither side is likely to dominate offensively. Furthermore, with only a 38% chance of the match going over 2.5 goals and both teams to score at 45%, a defensive tussle seems probable.

    Padova’s balanced and physical style contrasts sharply with Südtirol's counter-attacking and crossing strategy. This clash of approaches could see Padova trying to impose themselves physically, while Südtirol waits for opportunities to break. The hosts' higher xG per match suggests they can create chances, but their defensive frailties are exposed with an xGA of 1.7, compared to the visitors' 1.3.

    Considering current form and expected patterns of play, a low-scoring draw or a narrow home win appears most likely. The 1-1 scoreline, which holds a 14% likelihood, stands out as the safest prediction. However, for bettors seeking value, the model's lean towards Padova aligns with the 42% win probability, offering a potential edge. Bookmakers who price Südtirol more favorably might provide odds worth exploring, given the model's subtle preference for the hosts despite their recent form struggles.

    PadovaPadova
    Form LWLLL
    League ranking xG #17xGA #19xT #14xP/match #20Points #16
    Key players
    • Lorenzo CarissoniRWxG 0.01 · xT 0.21
    • Kevin LasagnaFxG 0.26 · xT 0.11
    • Pietro FusiLWxG 0.04 · xT 0.16
    SüdtirolSüdtirol
    Form WDWDL
    League ranking xG #20xGA #15xT #19xP/match #14Points #5
    Key players
    • Simone TronchinCAMxG 0.03 · xT 0.02
    • Fabio RispoliCAMxG 0.10 · xT 0.06
    • Andrea GiorginiCBxG 0.02 · xT 0.05

    Match prediction: Padova – Südtirol

    Predicted score matrix

    Südtirol →
    Padova →
    0
    1
    2
    3
    4+
    0
    0–011.2%
    0–111.0%
    0–26.0%
    0–32.1%
    0–4+0.7%
    1
    1–012.2%
    1–113.7%
    1–27.1%
    1–32.5%
    1–4+0.8%
    2
    2–07.4%
    2–17.8%
    2–24.1%
    2–31.5%
    2–4+0.5%
    3
    3–02.9%
    3–13.0%
    3–21.6%
    3–30.6%
    3–4+0.2%
    4+
    4+–01.1%
    4+–11.1%
    4+–20.6%
    4+–30.2%
    4+–4+0.1%
    Probability per final score. Rows = home goals, columns = away goals.

    Most likely scorelines

    1. 1.1-114%
    2. 2.1-012%
    3. 3.0-011%
    4. 4.0-111%
    5. 5.2-18%
    Expected goals
    1,17–1,06
    Both teams to score
    45%

    Over/under goals

    Expected goals: 2,2
    Under 2,562%
    –
    Over 2,538%
    –

    Ready-made bet suggestions

    More combos & build your own →

    01 · 2 legs

    Safe

    • Double chance Padova or draw (1X)68%
    • Under 3.5 goals81%

    Combined probability

    55%

    Fair odds

    1.81

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

    02 · 3 legs

    Balanced

    • Double chance Padova or draw (1X)68%
    • Under 2.5 goals62%
    • Marco Pompetti to score15%

    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 score45%
    • Over 2.5 goals38%
    • Lorenzo Anghelè to score30%
    • 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

    Bookmaker1 · PadovaX2 · Südtirol
    Betsafe2.253.103.05
    LeoVegas2.253.203.00
    Nordicbet2.253.103.05

    Odds updated 20 Oct, 21:38

    Statistics

    Head-to-head

    2 matches
    Padova 0Draw 1Südtirol 1
    Goals: 1–4 (⌀ 2,5)

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

    Key facts

    • Calcio Padova have lost 3 in a row.
    xG & xGA per match — 3-game rolling average

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

    Padova
    Südtirol
    When goals are scored and conceded — 2026
    Padova (6–10)
    Südtirol (9–6)
    Pass networks

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

    Padova
    Alessandro Dellavalle: 219 passningar, xT 0.35DellavalleEmanuele Zuelli: 209 passningar, xT 0.25ZuelliMatteo Lovato: 185 passningar, xT 0.12LovatoChristian Diego Pastina: 135 passningar, xT 0.17PastinaAlessandro Sorrentino: 116 passningar, xT 0.12SorrentinoLorenzo Carissoni: 101 passningar, xT 0.8CarissoniPietro Fusi: 98 passningar, xT 0.44FusiMatteo Cocchi: 95 passningar, xT 0.48CocchiAlessandro Capelli: 89 passningar, xT 0.34CapelliMarco Pompetti: 62 passningar, xT 0.03PompettiGianluca Caprari: 60 passningar, xT 0.42Caprari
    Südtirol
    Andrea Giorgini: 238 passningar, xT 0.24GiorginiRiccardo Stivanello: 234 passningar, xT 0.26StivanelloFabio Rispoli: 219 passningar, xT 0.16RispoliSimone Tronchin: 208 passningar, xT 0.11TronchinSalvatore Molina: 205 passningar, xT 0.72MolinaAlessandro Plizzari: 186 passningar, xT 0.08PlizzariGiacomo Stabile: 169 passningar, xT 0.14StabileSimone Davi: 85 passningar, xT 0.02DaviVasco Rafael Fortes Lopes: 83 passningar, xT 0.07LopesBjarki Steinn Bjarkason: 60 passningar, xT 0.47BjarkasonAljosa Vasic: 54 passningar, xT 0.15Vasic
    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.

    Padova
    Alessandro Dellavalle: 219 passningar, xT 0.35DellavalleEmanuele Zuelli: 209 passningar, xT 0.25ZuelliMatteo Lovato: 185 passningar, xT 0.12LovatoChristian Diego Pastina: 135 passningar, xT 0.17PastinaAlessandro Sorrentino: 116 passningar, xT 0.12SorrentinoLorenzo Carissoni: 101 passningar, xT 0.8CarissoniPietro Fusi: 98 passningar, xT 0.44FusiMatteo Cocchi: 95 passningar, xT 0.48CocchiAlessandro Capelli: 89 passningar, xT 0.34CapelliMarco Pompetti: 62 passningar, xT 0.03PompettiGianluca Caprari: 60 passningar, xT 0.42Caprari
    Südtirol
    Andrea Giorgini: 238 passningar, xT 0.24GiorginiRiccardo Stivanello: 234 passningar, xT 0.26StivanelloFabio Rispoli: 219 passningar, xT 0.16RispoliSimone Tronchin: 208 passningar, xT 0.11TronchinSalvatore Molina: 205 passningar, xT 0.72MolinaAlessandro Plizzari: 186 passningar, xT 0.08PlizzariGiacomo Stabile: 169 passningar, xT 0.14StabileSimone Davi: 85 passningar, xT 0.02DaviVasco Rafael Fortes Lopes: 83 passningar, xT 0.07LopesBjarki Steinn Bjarkason: 60 passningar, xT 0.47BjarkasonAljosa Vasic: 54 passningar, xT 0.15Vasic

    The teams in numbers

    PerformancePadovaSüdtirol
    Points712
    xPoints6.98.5
    xG per match0.950.77
    xGA per match1.71.3
    xG within 8s of winning the ball0.220.15
    xGA within 8s of losing the ball0.120.28
    Playing stylePadovaSüdtirol
    Build-up efficiency0.290.32
    Field tilt0.430.37
    xT per match0.90.48
    xTA per match1.10.99
    Won balls, offensive half2516
    Pressing intensity0.240.24
    Pressing efficiency0.280.27
    Pressing efficiency, offensive half0.230.23
    Entries into the box per match125
    Entries into the box against1313
    Pass completion %0.750.81
    Pass completion % under pressure0.70.75
    Passes per match328374
    Passes against per match412387
    Switches of play per match27.118.6
    Long balls per match3225
    Set piecesPadovaSüdtirol
    xG from free kicks0.010.03
    Corners per match5.12.7
    Corners against per match5.16.4
    xG per corner0.020.02
    xGA per corner against0.050.01
    First touch, offensive corners %0.490.32
    First touch, defensive corners %0.60.6
    OtherPadovaSüdtirol
    Throw-in control0.70.7
    The goalkeepersAlessandro Sorrentino (Padova)Alessandro Plizzari (Südtirol)
    Saves2825
    Save %74%81%
    xG prevented46%55%
    Claims0 (0%)0 (0%)

    Teams & more matches this round

    Frequently asked questions

    Who wins Padova vs Südtirol according to our model?

    The model gives Padova a 42% win probability. Full 1X2 picture: Padova 42%, draw 26%, Südtirol 33%.

    What is the most likely scoreline?

    The model's most likely final score is 1-1 at 14% probability.

    Over or under 2.5 goals?

    The model rates over 2.5 goals at 38% and under at 62%.

    Will both teams score?

    The probability of both teams scoring is 45% according to the model.

    How many goals are expected in Padova vs Südtirol?

    The model expects 1.17 goals (xG) for Padova and 1.06 for Südtirol, 2.23 in total.

    What does the model say about double chance?

    Padova or draw (1X) 67%, Südtirol or draw (X2) 58% and no draw (12) 74%.

    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.