EIN 2-1 1.F · 49% LEM 1-2 TOU · 44% FRO 1-1 SAS · 42% DEP 1-1 LEV · 44% EVE 1-2 CHE · 46% ESP 1-2 ATL · 48% VEN 0-2 NAP · 54% BAY 2-1 SCF · 49% UNI 0-2 BOR · 51% BOR 2-1 189 · 45% HAM 1-2 VFB · 47% WER 2-1 SCP · 48% FCS 1-0 FSV · 44% SVE 1-2 FCA · 47% EIN 2-1 1.F · 49% LEM 1-2 TOU · 44% FRO 1-1 SAS · 42% DEP 1-1 LEV · 44% EVE 1-2 CHE · 46% ESP 1-2 ATL · 48% VEN 0-2 NAP · 54% BAY 2-1 SCF · 49% UNI 0-2 BOR · 51% BOR 2-1 189 · 45% HAM 1-2 VFB · 47% WER 2-1 SCP · 48% FCS 1-0 FSV · 44% SVE 1-2 FCA · 47%
NHL Oct 13, 2026 · 23:30 Finished

Ottawa Senators
vs
St. Louis Blues.

Quick answer

Who will win Ottawa Senators vs St. Louis Blues?

Final score: Ottawa Senators 2–7 St. Louis Blues. Before kickoff, 9 of 10 AI models pick St. Louis Blues to win (90%); the consensus was right. The AI predicted score was 3–4.

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

10 AI models read this game. The plurality on the winner leans toward St. Louis Blues (9 of 10 models). The mean predicted score (rounded from all models) is 3–4 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 Ottawa Senators beat St. Louis Blues 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.

Ottawa Senators win 10%

1 / 10 models

St. Louis Blues win 90%

9 / 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
St. Louis Blues win
3–4
57%
St. Louis won all four most recent meetings, including a 2-1 result in Ottawa, but the Senators’ home advantage and limited current-season context point to a close, moderate-scoring game.
02
Claude Sonnet 5
Ottawa Senators win
3–2
53%
Blues have won recent H2H meetings, but home ice and base rates favor Ottawa slightly in a close, evenly contested matchup.
03
Gemini 3.8 Flash
St. Louis Blues win
2–3
56%
St. Louis has maintained strong defensive structure in recent head-to-head clashes against Ottawa, giving them a slight edge in an evenly contested matchup.
04
Grok 4.6
St. Louis Blues win
2–3
56%
Blues took four of the last five meetings, including recent 2-1 and 4-3 wins. Ottawa’s home ice is real but not enough to erase that matchup edge in a modest-scoring game.
05
DeepSeek V4 Pro
St. Louis Blues win
3–4
57%
Blues have won four of the last five meetings, including two in Ottawa, showing a matchup edge despite Senators' home ice.
06
Qwen 3.8 Max
St. Louis Blues win
3–4
56%
St. Louis won 4 of last 5 H2H meetings convincingly; early-season uncertainty and Ottawa's home ice keep confidence moderate.
07
Mistral Medium 3.5
St. Louis Blues win
4–5
68%
St. Louis dominant in recent H2H (3-1 in last 4) and scoring freely; Ottawa’s lone win was an outlier.
08
Kimi K3
St. Louis Blues win
2–3
58%
St. Louis has won four of the last five meetings including three straight, showing a persistent matchup edge despite Ottawa's home ice. Expect a tight, low-scoring game consistent with recent history.
09
GLM 5.3
St. Louis Blues win
3–4
52%
Blues won four of the last five meetings, including both 2025-26 clashes, though rosters have changed. Ottawa's home edge keeps this close; thin context, so modest confidence.
10
MiMo V2.5 Pro
St. Louis Blues win
3–4
58%
St. Louis won four of the last five meetings including two in Ottawa, showing they can match Ottawa's pace; recent H2H scoring suggests a tight, moderate-event game.
— Scoreline frequency

How often each scoreline showed up.

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

  • 3–4
    5 models
  • 2–3
    3 models
  • 3–2
    1 model
  • 4–5
    1 model

Match overview

Looking for a today prediction on Ottawa Senators vs St. Louis Blues in NHL? 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 - 4; 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 St. Louis Blues win, with vote shares roughly 10% / 90% home and away (rounded).

If you are asking who will win Ottawa Senators vs St. Louis Blues, 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 St. Louis Blues

90% of models lean away — 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 2.80 vs 3.60

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.