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%
KHL Sep 7, 2026 · 16:00 Finished

Cherepovets
vs
Lada.

10 AI models read this game. The plurality on the winner leans toward Cherepovets (10 of 10 models). The mean predicted score (rounded from all models) is 3–2 at 57% 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.

Cherepovets win 100%

10 / 10 models

Lada 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
Cherepovets win
3–2
56%
With no current-form or head-to-head data available, Cherepovets receive a modest venue-based edge. The matchup projects as a relatively tight KHL game, with home-ice advantage deciding a low-margin result.
02
Claude Sonnet 5
Cherepovets win
3–2
55%
No reliable H2H data; Severstal typically stronger KHL roster with home-ice advantage over Lada, expecting a tight, close-margin contest.
03
Gemini 3.8 Flash
Cherepovets win
3–2
62%
Severstal Cherepovets play an up-tempo, puck-possession style that traditionally overwhelms Lada's defensive structure at home in regular-season matchups.
04
Grok 4.6
Cherepovets win
3–2
56%
Thin context with no H2H or form. Cherepovets holds a typical KHL home edge over Lada; expect a modest, one-goal home win at average pace.
05
DeepSeek V4 Pro
Cherepovets win
3–2
52%
Home advantage for Cherepovets in KHL, but no recent form or H2H; expecting a close game with a one-goal margin.
06
Qwen 3.8 Max
Cherepovets win
3–1
58%
Cherepovets hold home-ice advantage and are generally the stronger KHL side; Lada tend to struggle on the road, supporting a moderate home win.
07
Mistral Medium 3.5
Cherepovets win
3–2
55%
Home advantage in KHL; limited data suggests close contest.
08
Kimi K3
Cherepovets win
3–1
62%
No H2H data available, but Cherepovets (Severstal) is traditionally the stronger KHL side versus budget-limited Lada, plus home ice; expect a controlled, lower-scoring home win.
09
GLM 5.3
Cherepovets win
3–2
55%
No H2H data available; Cherepovets get home-ice edge in a likely tight KHL matchup. Moderate confidence given thin context.
10
MiMo V2.5 Pro
Cherepovets win
3–2
55%
No head-to-head data available; home venue advantage in KHL context suggests a tight, moderately-scoring game with Cherepovets narrowly winning.
— Scoreline frequency

How often each scoreline showed up.

8 of 10 models settled on 3–2. That convergence is a strong scoreline signal—many fixtures fan out wider across the panel.

  • 3–2
    8 models
  • 3–1
    2 models

Match overview

Looking for a today prediction on Cherepovets vs Lada in KHL? 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 (3 - 2; 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 Cherepovets win, with vote shares roughly 100% / 0% home and away (rounded).

If you are asking who will win Cherepovets vs Lada, 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 Cherepovets

100% of models lean home — the clearest cluster on this fixture before kickoff.

02
Medium confidence

Mean 57% across the panel with real dispersion — compare unanimous calls vs split tickets in the model table.

03
xG tilt 3.00 vs 1.80

Derived from predicted scorelines (model means), not live game 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

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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