Sharks vs. Blues prediction favors St. Louis to win Thursday's NHL matchup [10/8/2026]

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Written by Jason Bevilacqua

Dimers' simulation model projects a 59.3% win probability for the Blues at Enterprise Center.

The San Jose Sharks take on the St. Louis Blues in NHL action.
San Jose Sharks vs. St. Louis Blues NHL prediction for Thursday, October 8, 2026.

The St. Louis Blues will take on the San Jose Sharks in NHL action at Enterprise Center on Thursday, starting at 8:00 PM ET.

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

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

Sharks vs. Blues prediction and odds

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

Our independent predictive model currently gives the Blues a 59.3% chance of beating the Sharks. According to our model, the Sharks (+1.5) have a 63.2% chance of covering the puck line, while the 5.5-goal over/under has a 55.8% chance of going over.

 

Latest betting odds

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

  • Puck Line: Sharks +1.5 (-190) / Blues -1.5 (+180)
  • Moneyline: Sharks +133, Blues -142
  • Total: Over/Under 5.5 (-124/+110)

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

Model picks and best bet

After matching our probabilities 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: Blues -1.5 @ +180 via DraftKings
  • Moneyline: Blues @ -142 via DraftKings
  • Total: Over 5.5 @ -124 via bet365

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

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

Sharks vs. Blues player props

Who will score a goal in Sharks vs. Blues?

According to our model, Robert Thomas is most likely to score an anytime goal in Sharks vs. Blues.

Our projections give Thomas a 35.6% chance of scoring a goal at Enterprise Center, while the star is a 6.7% chance of netting the first goal the game.

Anytime goal scorer predictions

San Jose Sharks

  • Will Smith: 32.1% probability
  • Igor Chernyshov: 28.0% probability
  • Mason Marchment: 23.7% probability
  • Collin Graf: 23.4% probability
  • Kiefer Sherwood: 22.1% probability

St. Louis Blues

  • Robert Thomas: 35.6% probability
  • Dylan Holloway: 35.3% probability
  • Jimmy Snuggerud: 29.5% probability
  • Mason McTavish: 24.9% probability
  • Pavel Buchnevich: 23.8% probability

First goal scorer predictions

San Jose Sharks

  • Will Smith: 5.6% probability
  • Igor Chernyshov: 5.4% probability
  • Mason Marchment: 4.4% probability
  • Tyler Toffoli: 4.1% probability
  • Collin Graf: 4.1% probability

St. Louis Blues

  • Dylan Holloway: 6.7% probability
  • Robert Thomas: 6.7% probability
  • Jimmy Snuggerud: 5.5% probability
  • Mason McTavish: 4.7% probability
  • Pavel Buchnevich: 4.1% probability

Game info & puck drop details

Thursday's matchup between the Blues and Sharks at Enterprise Center is scheduled to begin at 8:00 PM ET.

Conclusion

We predict the Blues, with a 59.3% win probability, will likely beat the Sharks in Thursday'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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