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Sharks vs. Kraken prediction: Seattle favored to win Saturday's NHL matchup [11/15/2025]

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Written by Jason Bevilacqua

Get San Jose Sharks vs Seattle Kraken predictions for NHL Saturday.

San Jose Sharks-Seattle Kraken Predictions and Game Preview.
San Jose Sharks vs. Seattle Kraken NHL prediction for Saturday, November 15, 2025.

The Seattle Kraken will take on the San Jose Sharks in NHL action at Climate Pledge Arena on Saturday, beginning at 10:00PM ET.

Based on the latest simulations, Dimers' proven NHL model predicts the Kraken as the most likely winner of the Sharks vs. Kraken game.

This article offers further insights into Saturday's matchup, including projected goal scorers, best bets and player prop picks.

Sharks vs. Kraken prediction: Who will win and cover?

As part of Dimers' NHL predictions coverage—and following a review of recent results—our pregame model has generated moneyline, puck line, and over/under probabilities for Saturday's clash between the Sharks and Kraken.

According to our model, the Sharks (+1.5) have a 64% chance of covering the puck line, while the 5.5-goal over/under has a 54% chance of going over.

 

Sharks vs. Kraken odds

We have sourced the best NHL odds in America for this game, which are listed here:

Bet Type Sharks Kraken
Puck Line +1.5 (-192) -1.5 (+170)
Moneyline +130 -148
Total o5.5 (-120) u5.5 (+100)

All odds are correct at the time of publication and are subject to change.

Sharks vs. Kraken picks and best bet

After matching our predictions against the best current odds, we can reveal our picks for each of the major markets. Plus, find our best bet for the game below:

Bet Type Pick Bet Now
Puck Line Kraken -1.5 +170
Moneyline Kraken -148
Total Over 5.5 -120

According to our NHL best bets, the top play to make on this game is Kraken -1.5 at the spread.

 

Sharks vs. Kraken player props

Who will score a goal in Sharks vs. Kraken?

According to our model, Macklin Celebrini is most likely to score an anytime goal in Sharks vs. Kraken.

Our projections give Celebrini a 37.4% chance of scoring a goal at Climate Pledge Arena, while the star is a 7.7% chance of netting the first goal the game.

Anytime goal scorer predictions

San Jose Sharks

Player Anytime Goal Probability
Macklin Celebrini 37.4%
Will Smith 29.9%
Tyler Toffoli 29.6%
William Eklund 26.5%
Philipp Kurashev 20.0%

Seattle Kraken

Player Anytime Goal Probability
Jaden Schwartz 29.6%
Jordan Eberle 28.9%
Mason Marchment 27.5%
Matty Beniers 24.9%
Shane Wright 24.8%

First goal scorer predictions

San Jose Sharks

Player First Goal Probability
Macklin Celebrini 7.7%
Will Smith 6.1%
Tyler Toffoli 5.5%
William Eklund 5.1%
Philipp Kurashev 3.8%

Seattle Kraken

Player First Goal Probability
Jaden Schwartz 6.1%
Mason Marchment 5.2%
Matty Beniers 5.0%
Jordan Eberle 4.9%
Shane Wright 4.6%

Sharks vs. Kraken game-day updates and essential details

Saturday's matchup between the Kraken and Sharks at Climate Pledge Arena is scheduled to commence at 10:00PM ET.

Sports betting deal available for Saturday

To make the most of Saturday's massive day of sports, headlined by college football Saturday games, check out details on the latest Bet365 bonus code "DIMERS" that secures $200 in bonus bets from a $5 bet — win or lose. Don't miss out!

Conclusion

We predict the Kraken, with a 59% win probability, will likely beat the Sharks on Saturday.

AI and automation have enhanced this article to quickly deliver accurate Sharks vs. Kraken insights, with human oversight ensuring high editorial quality. Our predictions are sourced from the latest data to help you make informed decisions. For additional resources and advice on responsible gambling, please call 1-800-GAMBLER.

Note: Clicking links to our partners may earn us a referral fee. Learn about how we review products and services.

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Written by
Jason Bevilacqua
Head of Content and Communities

Jason Bevilacqua, an expert in MLB, NHL, NBA, MLS, and NFL, delivers game previews, best bets, and props. Since 2020, as Head of Social and Community at Dimers, he has contributed insightful articles utilizing data-driven models and simulations.

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