UNI 1-0 FCS · 57% REN 1-2 MAR · 58% VEN 1-1 FIO · 57% GEN 1-0 FRO · 57% BOR 3-1 SCP · 67% 189 1-1 VFB · 57% SCF 2-1 BOR · 59% FCA 1-2 BAY · 60% FSV 1-1 EIN · 57% CHE 2-0 HUL · 67% LIV 2-1 FUL · 65% AST 2-1 NOT · 62% BOU 1-1 BRE · 57% CRY 1-0 IPS · 60% UNI 1-0 FCS · 57% REN 1-2 MAR · 58% VEN 1-1 FIO · 57% GEN 1-0 FRO · 57% BOR 3-1 SCP · 67% 189 1-1 VFB · 57% SCF 2-1 BOR · 59% FCA 1-2 BAY · 60% FSV 1-1 EIN · 57% CHE 2-0 HUL · 67% LIV 2-1 FUL · 65% AST 2-1 NOT · 62% BOU 1-1 BRE · 57% CRY 1-0 IPS · 60%
Serie A · Regular Season - 3 Sep 6, 2026 · 13:00 · UTC Stadio Ennio Tardini, Parma Match Finished · 90'

Parma
vs
Monza.

10 AI models read this match. The plurality on 1X2 leans toward Parma (5 of 10 models). The mean predicted score (rounded from all models) is 1–1 at 58% mean confidence (Medium band) — see the strip and table below.

Who do you think will win?

Vote before kick-off and compare with AI

— Consensus

Where the panel lands.

Vote share across models. Read this strip first, then the line-by-line table.

Parma win 50%

5 / 10 models

Draw 50%

5 / 10 models

Monza win 0%

0 / 10 models

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— Model breakdown

10 picks. Receipts attached.

Line-by-line comparison of each system’s call.

#
Model
Pick
Score
Conf.
Reasoning
01
GPT-5.6 Luna
Draw
1–1
60%
Recent head-to-heads show balanced outcomes; both teams likely to score but struggle to dominate.
02
Claude Sonnet 5
Parma win
2–1
54%
Parma at Tardini historically edge close H2H clashes; Monza has struggled since relegation, favoring a narrow home win.
03
Gemini 3.8 Flash
Draw
1–1
60%
Recent H2H meetings show a strong tendency for tight matches and draws, with three of the last four ending level. A 1-1 scoreline reflects their closely matched abilities early in the season.
04
Grok 4.6
Draw
1–1
45%
Early Serie A round at Tardini; H2H low-scoring pattern and limited 2026 form data favor a tight 1-1.
05
DeepSeek V4 Pro
Parma win
2–1
60%
Parma's home advantage and recent H2H edge suggest a narrow win.
06
Qwen 3.8 Max
Parma win
1–0
60%
Parma's home advantage and recent form suggest a narrow win, while Monza's away record is less convincing.
07
Mistral Medium 3.5
Parma win
1–0
60%
Parma’s home form and Monza’s defensive frailties suggest a narrow win; recent H2H draws lean low-scoring.
08
Llama 3.1 70B Instruct
Draw
1–1
58%
Recent head-to-heads show parity and low-scoring draws; neither team has a strong edge in current form or venue.
09
Cohere Command R+ (08-2024)
Parma win
1–0
60%
Parma's home advantage and recent form should see them edge a tight game; Monza's finishing has been inconsistent, and Parma's defence is solid.
10
MiMo V2.5 Pro
Draw
1–1
60%
H2H shows frequent draws and low-scoring matches; home venue offers Parma slight edge but likely a tight contest.
— Scoreline frequency

How often each scoreline showed up.

5 of 10 models settled on 1–1. That convergence is a strong scoreline signal—many fixtures fan out wider across the panel.

  • 1–1
    5 models
  • 1–0
    3 models
  • 2–1
    2 models

Match overview

Looking for a today prediction on Parma vs Monza in Serie A? TuringStats aggregates multiple AI scorelines into one readable page so you can see who the models favor, the mean predicted score shown in the hero (1 - 1; the frequency chart below lists the most common exact scorelines), and implied splits before kickoff.

This prediction hub is written for readers comparing betting tips-style language with transparent model votes — not a single black-box call. The headline read is Parma win, with vote shares roughly 50% / 50% / 0% home, draw, and away (rounded).

If you are asking who will win Parma vs Monza, start with the consensus strip and model table, then cross-check form and injuries in Match context further down — that order keeps the strongest signals first.

— Aggregated insights

What’s moving the panel.

01
Consensus favors Parma

50% of models lean home — the clearest cluster on this fixture before kickoff.

02
Medium confidence

Mean 58% across the panel with real dispersion — compare unanimous calls vs split tickets in the model table.

03
xG tilt 1.20 vs 0.70

Derived from predicted scorelines (model means), not live match data — useful for pace vs vote-share sanity checks.

04
Match context

Expected-goals tilt and home-field rhythm (see xG on this page) usually explain whether the game stays open or compresses late.

Confidence trend

Cumulative average confidence in table order.

First model Last model
Result check

Actual Draw · AI Parma win

1X2: Miss Score: Exact
— Match context

Form, history, team news.

Parma
Last 5
0 GF · 0 GA
Recent fixtures
  • No recent fixtures in the current window.
Team news

No major injury updates in the current API snapshot.

Monza
Last 5
0 GF · 0 GA
Recent fixtures
  • No recent fixtures in the current window.
Team news

No major injury updates in the current API snapshot.

Betting tips (AI-signal view)

Educational only — not financial advice. We summarize how the AI picks cluster so you can cross-check with your own staking plan.

  • Lean with the plurality: when 50% of models side with Parma, treat that as the default script unless late team news breaks the assumptions.
  • Watch the draw lane at 50% — tight Serie A games often compress toward stalemates when both midfields win the second-ball.
  • If you chase “best bets today” narratives, require alignment between the headline pick and the score-frequency table; conflicting signals usually mean thinner edge.

Odds & analysis (implied probabilities)

Implied fair percentages from the model vote share (normalized to 100%) approximate how a balanced market might price the 1X2 if it mirrored this panel — useful for odds analysis homework even though we do not quote sportsbook ticks here.

Parma
~50%
implied lean
Draw
~50%
implied lean
Monza
~0%
implied lean

Over / under prediction (totals)

Model-derived xG sums to 1.90 goals in expectation. A notional totals line near 2.2 is consistent with that pace (rounded for readability). If your sportsbook posts a similar number, compare juice and live team news before deciding either side of the total.

Handicap prediction (spread-style read)

When Parma is priced as the stronger side in the model vote, a −1 handicap narrative only clears if the most common scorelines include multi-goal wins. Cross-check the score-frequency list: if tight one-goal wins dominate, Asian handicaps near pick’em or −0.5 / −0.75 splits often fit the story better than a full −1.5 sell.

BTTS prediction (both teams to score)

With combined offensive weight near 1.90 xG, a heuristic “both teams score” prior lands around 55% yes before defensive adjustments. If several top models forecast clean-sheet pathways, downgrade BTTS enthusiasm even when the raw xG sum looks juicy.

Best bet framing (consensus-led)

Our headline best bet label follows the consensus recommendation: Parma win. Pair that with the confidence band (Medium) — high dispersion across models usually argues for smaller stake or pass, even when the headline pick looks tempting for a today prediction card on social.

Explore more

Keep browsing today prediction coverage and league hubs.

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— Journal

Articles linked to these clubs or AI forecasting.

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