Sharks vs. Stars prediction favors Dallas to win Monday's NHL matchup [10/5/2026]

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Written by Jason Bevilacqua

Dimers' simulation model projects a 63.5% win probability for the Stars at American Airlines Center.

The San Jose Sharks take on the Dallas Stars in NHL action.
San Jose Sharks vs. Dallas Stars NHL prediction for Monday, October 5, 2026.

The Dallas Stars will square off with the San Jose Sharks in NHL action at American Airlines Center on Monday, starting at 8:00 PM ET.

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

This article offers an expanded analysis of Monday's matchup, including projected goal scorers, best bets and player prop picks.

Sharks vs. Stars prediction and odds

Using trusted machine learning and data, we have re-simulated the outcome of Monday's NHL matchup between the Sharks and Stars, forming part of our NHL predictions coverage.

Our famous predictive model currently gives the Stars a 63.5% chance of defeating the Sharks. According to our model, the Sharks (+1.5) have a 59.3% chance of covering the puck line, while the over/under total of 6.5 goals has a 54.1% chance of staying under.

 

Latest betting odds

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

  • Puck Line: Sharks +1.5 (-148) / Stars -1.5 (+132)
  • Moneyline: Sharks +172, Stars -186
  • Total: Over/Under 6.5 (+100/-115)

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 available 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: Sharks +1.5 @ -148 via FanDuel
  • Moneyline: Sharks @ +172 via FanDuel
  • Total: Under 6.5 @ -115 via FanDuel

According to our best NHL bets, the top play to make on this game is Under 6.5 total goals.

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

Sharks vs. Stars player props

Who will score a goal in Sharks vs. Stars?

According to our model, Wyatt Johnston is most likely to score an anytime goal in Sharks vs. Stars.

Our projections give Johnston a 44.5% chance of scoring a goal at American Airlines Center, while the star is a 9.8% chance of netting the first goal the game.

Anytime goal scorer predictions

San Jose Sharks

  • Macklin Celebrini: 34.5% probability
  • Will Smith: 27.0% probability
  • Igor Chernyshov: 21.8% probability
  • Mason Marchment: 18.9% probability
  • Kiefer Sherwood: 18.8% probability

Dallas Stars

  • Wyatt Johnston: 44.5% probability
  • Jason Robertson: 40.8% probability
  • Mikko Rantanen: 36.2% probability
  • Roope Hintz: 26.5% probability
  • Tyler Seguin: 22.3% probability

First goal scorer predictions

San Jose Sharks

  • Macklin Celebrini: 6.7% probability
  • Will Smith: 4.9% probability
  • Igor Chernyshov: 4.2% probability
  • Mason Marchment: 3.8% probability
  • Michael Misa: 3.1% probability

Dallas Stars

  • Wyatt Johnston: 9.8% probability
  • Jason Robertson: 8.2% probability
  • Mikko Rantanen: 7.1% probability
  • Roope Hintz: 4.7% probability
  • Tyler Seguin: 3.7% probability

Game info & puck drop details

Monday's matchup between the Stars and Sharks at American Airlines Center is scheduled to begin at 8:00 PM ET.

Conclusion

We predict the Stars, with a 63.5% win probability, will likely beat the Sharks in Monday'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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