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Maple Leafs vs. Sharks prediction: San Jose favored to win Thursday's NHL matchup [4/2/2026]

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Written by Jason Bevilacqua

Get Toronto Maple Leafs vs San Jose Sharks predictions for NHL Thursday.

Toronto Maple Leafs-San Jose Sharks Predictions and Game Preview.
Toronto Maple Leafs vs. San Jose Sharks NHL prediction for Thursday, April 2, 2026.

The San Jose Sharks will face the Toronto Maple Leafs, in what is expected to be a close NHL game, at SAP Center at San Jose on Thursday, commencing at 10:00 PM ET.

Based on the latest simulations, Dimers' advanced NHL model gives the Sharks a slight edge with a 52% win probability over the Maple Leafs.

This article offers a closer look at Thursday's Maple Leafs vs. Sharks matchup, including projected goal scorers, best bets and player prop picks.

Maple Leafs vs. Sharks prediction: Who will win and cover?

Using trusted machine learning and data analysis, we have simulated the result of Thursday's NHL matchup between the Maple Leafs and Sharks, forming part of our NHL predictions coverage.

Our independent predictive model gives the Sharks a 52% chance of beating the Maple Leafs.

According to our model, the Maple Leafs (+1.5) have a 69% chance of covering the puck line, while the 6.5-goal over/under has a 52% chance of staying under.

 

Maple Leafs vs. Sharks odds

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

Bet Type Maple Leafs Sharks
Puck Line +1.5 (-220) -1.5 (+180)
Moneyline +115 -130
Total o6.5 (-114) u6.5 (-105)

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

Maple Leafs vs. Sharks picks and best bet

After matching our probabilities 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 Maple Leafs +1.5 -220
Moneyline Maple Leafs +115
Total Under 6.5 -105

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

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

 

Maple Leafs vs. Sharks player props

Who will score a goal in Maple Leafs vs. Sharks?

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

Our projections give Celebrini a 43.5% chance of scoring a goal at SAP Center at San Jose, while the star is a 9.1% chance of netting the first goal the game.

Anytime goal scorer predictions

Toronto Maple Leafs

Player Anytime Goal Probability
William Nylander 43.4%
John Tavares 39.7%
Matthew Knies 35.1%
Matias Maccelli 23.9%
Benoit-Olivier Groulx 21.1%

San Jose Sharks

Player Anytime Goal Probability
Macklin Celebrini 43.5%
Will Smith 32.1%
Kiefer Sherwood 25.7%
Igor Chernyshov 23.9%
Michael Misa 23.0%

First goal scorer predictions

Toronto Maple Leafs

Player First Goal Probability
William Nylander 8.9%
John Tavares 8.3%
Matthew Knies 6.6%
Matias Maccelli 4.5%
Benoit-Olivier Groulx 3.6%

San Jose Sharks

Player First Goal Probability
Macklin Celebrini 9.1%
Will Smith 5.6%
Kiefer Sherwood 4.2%
Igor Chernyshov 4.2%
Tyler Toffoli 3.8%

Maple Leafs vs. Sharks game-day updates and essential details

Thursday's action between the Sharks and Maple Leafs at SAP Center at San Jose is scheduled to begin at 10:00 PM ET.

Conclusion

We predict the Sharks, with a 52% win probability, will likely beat the Maple Leafs on Thursday.

AI and automation have enhanced this article to quickly deliver accurate Maple Leafs vs. Sharks insights, with human oversight ensuring high editorial quality. Our predictions are based on up-to-date data to help you make informed decisions. For additional resources and advice, head to our responsible gambling hub.

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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