Oilers vs. Sharks prediction favors Edmonton to win Saturday's NHL matchup [10/10/2026]

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Written by Jason Bevilacqua

Dimers' simulation model projects a 58.3% win probability for the Oilers at SAP Center at San Jose.

The Edmonton Oilers take on the San Jose Sharks in NHL action.
Edmonton Oilers vs. San Jose Sharks NHL prediction for Saturday, October 10, 2026.

The San Jose Sharks and Edmonton Oilers will lock horns in the NHL at SAP Center at San Jose on Saturday, with puck drop at 4:00 PM ET.

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

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

Oilers vs. Sharks prediction and odds

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

Our independent predictive model gives the Oilers a 58.3% chance of defeating the Sharks. According to our model, the Sharks (+1.5) have a 62.7% chance of covering the puck line, while the over/under total of 6.5 goals has a 51.5% chance of going over.

 

Latest betting odds

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

  • Puck Line: Oilers -1.5 (+160) / Sharks +1.5 (-176)
  • Moneyline: Oilers -145, Sharks +130
  • Total: Over/Under 6.5 (-134/+120)

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: Sharks +1.5 @ -176 via FanDuel
  • Moneyline: Oilers @ -145 via ESPN BET
  • Total: Under 6.5 @ +120 via Caesars

According to our NHL best 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.

Oilers vs. Sharks player props

Who will score a goal in Oilers vs. Sharks?

According to our model, Leon Draisaitl is most likely to score an anytime goal in Oilers vs. Sharks.

Our projections give Draisaitl a 49.6% chance of scoring a goal at SAP Center at San Jose, while the star is a 10.9% chance of netting the first goal the game.

Anytime goal scorer predictions

Edmonton Oilers

  • Leon Draisaitl: 49.6% probability
  • Connor McDavid: 47.5% probability
  • Evan Bouchard: 31.4% probability
  • Vasily Podkolzin: 28.7% probability
  • Kasperi Kapanen: 22.5% probability

San Jose Sharks

  • Will Smith: 33.4% probability
  • Igor Chernyshov: 30.6% probability
  • Kiefer Sherwood: 25.5% probability
  • Tyler Toffoli: 24.7% probability
  • Mason Marchment: 24.5% probability

First goal scorer predictions

Edmonton Oilers

  • Leon Draisaitl: 10.9% probability
  • Connor McDavid: 10.4% probability
  • Evan Bouchard: 6.0% probability
  • Vasily Podkolzin: 4.6% probability
  • Owen Michaels: 3.6% probability

San Jose Sharks

  • Will Smith: 6.0% probability
  • Igor Chernyshov: 5.6% probability
  • Kiefer Sherwood: 4.2% probability
  • Tyler Toffoli: 4.1% probability
  • Mason Marchment: 4.0% probability

Game info & puck drop details

Saturday's game between the Sharks and Oilers at SAP Center at San Jose is scheduled to start at 4:00 PM ET.

Conclusion

We predict the Oilers, with a 58.3% win probability, will likely beat the Sharks in Saturday'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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