BOR 2-1 WER · 52% RBL 2-1 EIN · 51% 189 2-1 HAM · 48% SCF 2-0 FCS · 54% FCA 1-3 BAY · 56% FSV 1-2 BAY · 47% UNI 2-0 SVE · 51% 1.F 2-2 BOR · 43% SCP 1-2 VFB · 45% LIV 1-1 MAN · 44% ARS 3-0 LEE · 60% CHE 2-1 BOU · 47% MAN 2-1 TOT · 45% AST 2-1 BRE · 45% BOR 2-1 WER · 52% RBL 2-1 EIN · 51% 189 2-1 HAM · 48% SCF 2-0 FCS · 54% FCA 1-3 BAY · 56% FSV 1-2 BAY · 47% UNI 2-0 SVE · 51% 1.F 2-2 BOR · 43% SCP 1-2 VFB · 45% LIV 1-1 MAN · 44% ARS 3-0 LEE · 60% CHE 2-1 BOU · 47% MAN 2-1 TOT · 45% AST 2-1 BRE · 45%
KHL Sep 30, 2026 · 14:00 Finished

Yekaterinburg
vs
Lada.

Quick answer

Who will win Yekaterinburg vs Lada?

Final score: Yekaterinburg 3–1 Lada. Before kickoff, 10 of 10 AI models pick Yekaterinburg to win (100%); the consensus was right. The AI predicted score was 4–3.

Consensus of independent AI models on TuringStats, updated before kickoff. Not betting advice.

All 10 AIs agree. When the whole council agrees, it has been right 62% of the time (269 games).

10 AI models read this game. The plurality on the winner leans toward Yekaterinburg (10 of 10 models). The mean predicted score (rounded from all models) is 4–3 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

10 AI models · council in session Live match data · Pro

Still not sure? Ask 10 AIs at once:
“Will Yekaterinburg beat Lada in their upcoming match?”

Every model votes yes or no and defends it in a sentence or two. Attach this match's last-5 form, head-to-head and the picks above so they argue from real data — then keep asking the same council follow-ups.

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— Consensus

Where the panel lands.

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

Yekaterinburg win 100%

10 / 10 models

Lada win 0%

0 / 10 models

— 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
Yekaterinburg win
4–3
62%
Yekaterinburg has won three of the last five meetings, including the most recent, and home ice supports another narrow result. The repeated high-scoring H2H pattern points to an open KHL game rather than a defensive margin.
02
Claude Sonnet 5
Yekaterinburg win
4–3
55%
H2H consistently high-scoring and close; home ice gives Yekaterinburg slight edge in tight, evenly matched, offense-leaning matchup.
03
Gemini 3.8 Flash
Yekaterinburg win
4–2
64%
Yekaterinburg generally holds the edge in offensive depth on home ice against Lada, consistently generating multiple goals in recent head-to-head encounters.
04
Grok 4.6
Yekaterinburg win
4–3
58%
Recent H2H is tight and high-scoring; Yekaterinburg won the last home meeting 4-3 and holds a slight venue edge in KHL, so a one-goal home win fits.
05
DeepSeek V4 Pro
Yekaterinburg win
3–2
58%
Yekaterinburg has won three of the last five H2H meetings at home, including a 4-3 win in December, and holds the venue edge. Lada's recent split suggests a close, one-goal margin.
06
Qwen 3.8 Max
Yekaterinburg win
3–2
62%
Yekaterinburg won 3 of last 5 H2H including two home victories; home venue advantage and slightly stronger recent scoring support a narrow win.
07
Mistral Medium 3.5
Yekaterinburg win
4–3
62%
Recent H2H leans Yekaterinburg at home; both teams score freely but home edge prevails.
08
Kimi K3
Yekaterinburg win
4–3
58%
Yekaterinburg has won three of the last five meetings and holds home ice, but Lada's two recent one-goal wins show they stay competitive in this matchup. Expect another tight, high-scoring game decided by a single goal.
09
GLM 5.3
Yekaterinburg win
4–2
58%
Yekaterinburg won three of the last five H2H meetings, including home wins, and Lada games tend to be open; home edge despite thin current-form data.
10
MiMo V2.5 Pro
Yekaterinburg win
4–3
62%
Recent H2H shows high-scoring, close games; Yekaterinburg's home venue and recent 4-3 win suggest a slight edge in another tight contest.
— Scoreline frequency

How often each scoreline showed up.

6 of 10 models settled on 4–3. That convergence is a strong scoreline signal—many fixtures fan out wider across the panel.

  • 4–3
    6 models
  • 3–2
    2 models
  • 4–2
    2 models

Match overview

Looking for a today prediction on Yekaterinburg 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 (4 - 3; 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 Yekaterinburg win, with vote shares roughly 100% / 0% home and away (rounded).

If you are asking who will win Yekaterinburg 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 Yekaterinburg

100% 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 3.80 vs 2.60

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.

Ask the Council about this match

Pro

10 AI models vote yes or no with this match's last-5 form, head-to-head, live status and the model picks above attached — not from memory. Then keep asking the same council follow-ups.

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.

Ask multiple AIs about this match.

10 models vote yes or no with this match's last-5 form, head-to-head, live status and our model picks attached — not from memory. Then ask follow-ups to the same council.