FEN 2-1 SLA · 45% SAB 0-3 BOR · 66% LIV 2-0 VIL · 51% MAN 3-0 AEK · 66% PAR 2-1 BAR · 44% VFB 0-1 ATL · 42% ASR 2-0 SLO · 58% FCP 2-1 PSV · 43% NAP 2-0 BOD · 54% COM 1-2 MAN · 46% LIL 1-0 GAL · 39% LEV 1-2 ATH · 49% BAY 2-1 ARS · 38% REA 2-1 RBL · 52% FEN 2-1 SLA · 45% SAB 0-3 BOR · 66% LIV 2-0 VIL · 51% MAN 3-0 AEK · 66% PAR 2-1 BAR · 44% VFB 0-1 ATL · 42% ASR 2-0 SLO · 58% FCP 2-1 PSV · 43% NAP 2-0 BOD · 54% COM 1-2 MAN · 46% LIL 1-0 GAL · 39% LEV 1-2 ATH · 49% BAY 2-1 ARS · 38% REA 2-1 RBL · 52%
NHL Oct 19, 2026 · 23:30 Finished

Toronto Maple Leafs
vs
San Jose Sharks.

Quick answer

Who will win Toronto Maple Leafs vs San Jose Sharks?

Final score: Toronto Maple Leafs 3–1 San Jose Sharks. Before kickoff, 8 of 10 AI models pick Toronto Maple Leafs to win (80%); the consensus was right. The AI predicted score was 4–2.

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 Toronto Maple Leafs (8 of 10 models). The mean predicted score (rounded from all models) is 4–2 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 Toronto Maple Leafs beat San Jose Sharks 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.

Toronto Maple Leafs win 80%

8 / 10 models

San Jose Sharks win 20%

2 / 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
Toronto Maple Leafs win
4–3
58%
Toronto’s home-ice advantage and stronger overall offensive ceiling support a narrow win, but San Jose has won the last four listed meetings, making a high-scoring, close game more likely than a comfortable margin.
02
Claude Sonnet 5
Toronto Maple Leafs win
4–3
54%
Sharks have won 3 of last 4 meetings, but Toronto's home ice and typically stronger roster depth slightly favor a narrow win in a high-scoring affair.
03
Gemini 3.8 Flash
Toronto Maple Leafs win
4–2
62%
Despite recent head-to-head struggles against San Jose, Toronto's overall offensive depth and home-ice advantage make them favored to control tempo and secure a multi-goal victory.
04
Grok 4.6
Toronto Maple Leafs win
4–2
58%
Leafs are the stronger roster at home despite recent H2H losses to San Jose; Toronto should control pace and win by a couple of goals.
05
DeepSeek V4 Pro
San Jose Sharks win
4–5
58%
Sharks have won four of the last five meetings, including three in Toronto; their speed has troubled the Leafs' defence. Toronto's home edge is offset by San Jose's recent head-to-head dominance.
06
Qwen 3.8 Max
Toronto Maple Leafs win
4–2
58%
Toronto's talent edge and home ice outweigh San Jose's recent H2H success, which likely reflected prior roster matchups now changed in a new season.
07
Mistral Medium 3.5
San Jose Sharks win
4–5
62%
Sharks won 4 of last 5 H2H; Leafs' recent form inconsistent. Away side's attack likely to exploit home defense gaps.
08
Kimi K3
Toronto Maple Leafs win
4–3
58%
Despite San Jose's recent H2H dominance, Toronto's home ice and typically stronger roster depth should prevail in a high-scoring affair consistent with both teams' recent meetings.
09
GLM 5.3
Toronto Maple Leafs win
4–2
58%
Toronto's superior roster and home ice outweigh San Jose's surprising recent H2H edge; Sharks have been competitive, so a modest two-goal margin fits.
10
MiMo V2.5 Pro
Toronto Maple Leafs win
4–2
62%
Leafs have strong home form and offensive depth; Sharks' recent H2H wins are against different rosters and they struggle defensively on the road.
— Scoreline frequency

How often each scoreline showed up.

5 of 10 models settled on 4–2. That convergence is a strong scoreline signal—many fixtures fan out wider across the panel.

  • 4–2
    5 models
  • 4–3
    3 models
  • 4–5
    2 models

Match overview

Looking for a today prediction on Toronto Maple Leafs vs San Jose Sharks 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 (4 - 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 Toronto Maple Leafs win, with vote shares roughly 80% / 20% home and away (rounded).

If you are asking who will win Toronto Maple Leafs vs San Jose Sharks, 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 Toronto Maple Leafs

80% 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 4.00 vs 2.90

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.