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 · 13:30 Finished

Avangard Omsk
vs
Salavat Ufa.

10 AI models read this game. The plurality on the winner leans toward Avangard Omsk (10 of 10 models). The mean predicted score (rounded from all models) is 3–2 at 56% mean confidence (Medium band) — see the strip and table below.

Who do you think will win?

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— Consensus

Where the panel lands.

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

Avangard Omsk win 100%

10 / 10 models

Salavat Ufa 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
Avangard Omsk win
3–2
55%
With no current form or head-to-head data available, Avangard Omsk receive a modest venue-based edge. Salavat Ufa should keep the game close, pointing to a controlled KHL matchup decided by one goal.
02
Claude Sonnet 5
Avangard Omsk win
3–2
55%
No H2H data available, so relying on home-ice advantage for Avangard; expect a tight, competitive game decided by a single goal.
03
Gemini 3.8 Flash
Avangard Omsk win
3–2
60%
Avangard holds home ice advantage in Omsk, typically giving them a tight edge over a well-matched and dangerous Salavat Yulaev Ufa squad.
04
Grok 4.6
Avangard Omsk win
3–2
56%
Even KHL pair with no usable 2026 H2H; Avangard’s Omsk home ice edges a typical close, mid-scoring game versus Salavat.
05
DeepSeek V4 Pro
Avangard Omsk win
3–2
56%
Limited matchup data; Avangard Omsk's home KHL venue and Salavat Ufa's typical road discipline point to a narrow home win.
06
Qwen 3.8 Max
Avangard Omsk win
3–2
55%
Early-season KHL matchup with limited context; Avangard's home-ice advantage and historically stronger roster depth give a slight edge in what should be a competitive game.
07
Mistral Medium 3.5
Avangard Omsk win
3–2
55%
Home ice advantage in KHL; Avangard’s venue tips a typical close-scoring game.
08
Kimi K3
Avangard Omsk win
3–2
58%
Avangard at home in Omsk are historically the stronger side against Salavat; expect a tight, competitive KHL matchup decided by home-ice edge in a one-goal game.
09
GLM 5.3
Avangard Omsk win
3–2
55%
No H2H or form data available, so leaning on Avangard's home ice in Omsk; Salavat is a strong rival, so a tight one-goal game fits.
10
MiMo V2.5 Pro
Avangard Omsk win
3–2
55%
Home ice advantage for Avangard Omsk in a competitive KHL matchup; expect a close game with both teams scoring.
— Scoreline frequency

How often each scoreline showed up.

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

  • 3–2
    10 models

Match overview

Looking for a today prediction on Avangard Omsk vs Salavat Ufa 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 Avangard Omsk win, with vote shares roughly 100% / 0% home and away (rounded).

If you are asking who will win Avangard Omsk vs Salavat Ufa, 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 Avangard Omsk

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

02
Medium confidence

Mean 56% across the panel with real dispersion — compare unanimous calls vs split tickets in the model table.

03
xG tilt 3.00 vs 2.00

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

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— Journal

Articles linked to these clubs or AI forecasting.

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