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FinalU-Power Stadium · Monza
Sassuolo
1–2Final
Monza
Score by period
Team12T
SASS011
MONZ022

Top model pick, graded

Won

Match total over 2.5 55¢

Why Coaches Corner likes it: The confirmed 3-4-2-1 versus 4-3-3 matchup opens wing channels and transition space.

Coaches CornerUp 1% return this season · 961 picks
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How the picks did

Models went 4-8 on this game · −41% return

  1. Sassuolo to win3 models agree
    −100%Lost

    Taken at +150 on FanDuel

    3 modelsCoaches Corner, Low Tide Line +1
    Why 3 models like it
    Coaches Corner
    • 47.3% warehouse away-win model probability versus 40.0% implied by FanDuel +150.
    • Confirmed Aquilani 4-3-3 attacks Monza's Jurić 3-4-2-1 wing-back spaces.
    • Jurić's man-oriented structure can disrupt Sassuolo's buildup and force transition errors.
    Low Tide Line
    • Full feature coverage is present; Dixon-Coles inputs use 10-match form and 9-match minimum xG history.
    • Pinnacle fair value is close to the Kalshi price, so the 10-point projection gap may be model error rather than market error.
    Number Earned
    • Model probability 47.3%; fair line +112 versus DraftKings +145.
    • Opponent-adjusted xG favors Sassuolo 1.65, 1.34; raw away/home xG is 1.63, 1.40.
    • The model uses a 10-match form window and has no prior meetings on file.
  2. Match total over 2.5
    +82%Won

    Taken at 55¢ on Polymarket US

    Coaches CornerDown 8% last 7 days
    Why it likes it
    • The confirmed 3-4-2-1 versus 4-3-3 matchup opens wing channels and transition space.
    • Feature xG rates total 3.03 goals per match: Monza 1.40 and Sassuolo 1.63.
    • The tactical preview cites Monza's porous defense and Sassuolo's ability to score when given space.
    • Jurić can retreat into a compact block after taking a lead, killing the game's tempo.
  3. Sassuolo Calcio first team to score
    −100%Lost

    Taken at 49¢ on Kalshi

    First StrikeUp 2% over 212 picks
    Why it likes it
    • 54.0% race probability after the late-hazard adjustment; 5.0 points above the 49c Kalshi price.
    • Opponent-adjusted attack rates favor Sassuolo: 1.65 xGF versus Monza's 1.52 xGA.
    • Monza's 1.34 xGF versus Sassuolo's 1.44 xGA supports the away-side scoring share.
    • The 9-fixture hazard sample had 11 of 33 goals in minutes 76-90; the +0.8-point No-Goal correction is uncertain.
  4. Sassuolo spread −0.5
    −100%Lost

    Taken at 38¢ on ProphetX

    Deep BookUp 4% last 7 days
    Why it likes it
    • ProphetX board as_of_pt: 2026-09-18 11:11am PT. Feature sample: 10 matches; minimum xG coverage is 9.
  5. Match over 2.5 total goals
    +79%Won

    Taken at 56¢ on Kalshi

    Two NilUp 12% last 7 days
    Why it likes it
    • live Pinnacle is 2.8 goals, Over +100/Under -110; treated as an adjacent-line reference, not as the Kalshi 2.5 rung.
    • The thinner xG side has only 9 matches; the brief's under-12 coverage tier warrants a restrained band.
  6. Match total over 2.5 goals
    +71%Won

    Taken at −140 on BetMGM

    Snapp Decision of the DayDown 8% last 7 days
    Why it likes it
    • Warehouse model projects 3.29 expected goals and a 63.8% over-2.5 probability.
    • BetMGM's -140 requires roughly 58.3%, leaving a 5.5-point model gap.
    • Monza and Sassuolo average 2.78 and 3.10 big chances created in the recent 10-match window.
    • The total depends on a 10-match xG sample and features computed through September 13.
Show all 10 picks
  1. Sassuolo moneyline +160
    −100%Lost

    Taken at +160 on BetMGM

    Snapp Decision of the DayDown 8% last 7 days
    Why it likes it
    • Warehouse model makes Sassuolo a 47.3% winner; +160 implies only 38.5%.
    • Sassuolo leads the PIT club Elo 1,579 to 1,321, a 257.8-point gap.
    • Opponent-adjusted xG favors Sassuolo 1.65 to Monza's 1.34 per match.
    • The model is Dixon-Coles and unvalidated; its inputs are only 10-match samples.
  2. Both Teams To Score NO
    −100%Lost

    Taken at 39¢ on Kalshi

    Both NetsDown 6% last 7 days
    Why it likes it
    • Monza blended scoring probability: 72.3% from 9 xG matches; Sassuolo: 78.3% from 10.
    • Independent BTTS projection is 56.6% YES, 43.4% NO; 39c NO implies 39.0%.
    • Only 9, 10 xG matches per team; adequate coverage, but still a limited sample.
  3. Sassuolo spread over 1.5 goalsLong shot
    −100%Lost

    Taken at 17¢ on Kalshi

    HandicapDown 14% last 7 days
    Why it likes it
    • same-rung Pinnacle devigged probability is 17.0%; lambda cross-check estimates Sassuolo 2+ goals ahead at about 26.6%.
    • Current Serie A season data show Sassuolo-direction 2+ margins in 7 of 40 matches (17.5%), much closer to the market than the model estimate.
  4. Over 2.5 total goals
    +74%Won

    Taken at −135 on DraftKings

    Number EarnedDown 28% last 7 days
    Why it likes it
    • Model probability 63.8%; fair line -176 versus DraftKings -135.
    • Model expected goals total is 3.29 from lambdas of 1.43 for Monza and 1.86 for Sassuolo.
    • The model also projects 64.7% for both teams to score, supporting a higher-scoring game script.
    • The model’s total-goals estimate is built from a limited 10-match form window.

Questions

Who won Sassuolo vs. Monza?

Monza won 2-1 on Friday, Sep 18, 2026.

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