Bruins vs. Sharks prediction favors Boston to win Tuesday's NHL matchup [10/13/2026]

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Written by Jason Bevilacqua

Dimers' simulation model projects a 55.0% win probability for the Bruins at SAP Center at San Jose.

The Boston Bruins take on the San Jose Sharks in NHL action.
Boston Bruins vs. San Jose Sharks NHL prediction for Tuesday, October 13, 2026.

The San Jose Sharks will face the Boston Bruins in NHL action at SAP Center at San Jose on Tuesday, commencing at 11:00 PM ET.

Dimers' advanced NHL model projects the Bruins as the most likely winner in this Bruins vs. Sharks prediction.

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

Bruins vs. Sharks prediction and odds

Using powerful machine learning and data, we have re-simulated the outcome of Tuesday's NHL game between the Bruins and Sharks, forming part of our NHL predictions coverage.

Our independent predictive model currently gives the Bruins a 55.0% chance of beating the Sharks. According to our model, the Sharks (+1.5) have a 66.8% chance of covering the puck line, while the 6.5-goal over/under has a 54.8% chance of staying under.

 

Latest betting odds

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

  • Puck Line: Bruins -1.5 (+210) / Sharks +1.5 (-235)
  • Moneyline: Bruins -117, Sharks +105
  • Total: Over/Under 6.5 (+110/-125)

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

Model picks and best bet

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

  • Puck line: Bruins -1.5 @ +210 via BetRivers
  • Moneyline: Bruins @ -117 via BetRivers
  • Total: Under 6.5 @ -125 via BetRivers

According to our NHL best bets, the top play to make on this game is Bruins to win.

These picks are based on our probabilities matched against the implied probabilities from the current available odds.

Bruins vs. Sharks player props

Who will score a goal in Bruins vs. Sharks?

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

Our projections give Celebrini a 38.8% chance of scoring a goal at SAP Center at San Jose, while the star is an 8.1% chance of netting the first goal the game.

Anytime goal scorer predictions

Boston Bruins

  • Morgan Geekie: 35.6% probability
  • David Pastrnak: 33.4% probability
  • Pavel Zacha: 29.4% probability
  • JJ Peterka: 28.2% probability
  • Elias Lindholm: 18.9% probability

San Jose Sharks

  • Macklin Celebrini: 38.8% probability
  • Will Smith: 28.3% probability
  • Igor Chernyshov: 23.5% probability
  • Kiefer Sherwood: 22.1% probability
  • Mason Marchment: 21.3% probability

First goal scorer predictions

Boston Bruins

  • Morgan Geekie: 7.4% probability
  • David Pastrnak: 6.6% probability
  • Pavel Zacha: 5.5% probability
  • JJ Peterka: 5.3% probability
  • Elias Lindholm: 3.5% probability

San Jose Sharks

  • Macklin Celebrini: 8.1% probability
  • Will Smith: 5.1% probability
  • Igor Chernyshov: 4.0% probability
  • Tyler Toffoli: 3.8% probability
  • Kiefer Sherwood: 3.8% probability

Game info & puck drop details

Tuesday's game between the Sharks and Bruins at SAP Center at San Jose is scheduled to begin at 11:00 PM ET.

Conclusion

We predict the Bruins, with a 55.0% win probability, will likely beat the Sharks in Tuesday's NHL matchup.

Additional tools and information for betting safely can be found in our Responsible Gambling hub.

This article's predictions and probabilities are generated by Dimers' proprietary NHL prediction model. Our editorial team reviews the resulting content for accuracy and presentation before publication.

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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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