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Golden Knights vs. Sharks prediction: Vegas favored to win Sunday's NHL matchup [1/11/2026]
Updated: | First published:
Written by Jason Bevilacqua, Head of Content and Communities
Get Vegas Golden Knights vs San Jose Sharks predictions for NHL Sunday.
The San Jose Sharks will square off with the Vegas Golden Knights in NHL action at SAP Center at San Jose on Sunday, starting at 8:00 PM ET.
Based on updated simulations, Dimers' proven NHL model predicts the Golden Knights as the most likely winner of the Golden Knights vs. Sharks game.
This article offers further insights into Sunday's matchup, including projected goal scorers, best bets and player prop picks.
Golden Knights vs. Sharks prediction: Who will win and cover?
Using innovative machine learning and data analysis, we have simulated the outcome of Sunday's NHL matchup between the Golden Knights and Sharks 10,000 times as part of our NHL predictions coverage.
Our leading predictive model gives the Golden Knights a 58% chance of beating the Sharks.
According to our model, the Sharks (+1.5) have a 63% chance of covering the puck line, while the 6.5-goal over/under has a 52% chance of staying under.
Golden Knights vs. Sharks odds
We have sourced the best NHL odds in America for this game, which are listed here:
| Bet Type | Golden Knights | Sharks |
|---|---|---|
| Puck Line | -1.5 (+160) | +1.5 (-185) |
| Moneyline | -148 | +130 |
| Total | o6.5 (-120) | u6.5 (+100) |
All odds are correct at the time of publication and are subject to change.
Golden Knights vs. Sharks picks and best bet
After matching our predictions against the best available 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 | Golden Knights -1.5 | +160 |
| Moneyline | Golden Knights | -148 |
| Total | Under 6.5 | +100 |
According to our NHL best bets, the top play to make on this game is Under 6.5 total goals.
Golden Knights vs. Sharks player props
Who will score a goal in Golden Knights vs. Sharks?
According to our model, Macklin Celebrini is most likely to score an anytime goal in Golden Knights vs. Sharks.
Our projections give Celebrini a 38.5% chance of scoring a goal at SAP Center at San Jose, while the star is a 7.9% chance of netting the first goal the game.
Anytime goal scorer predictions
Vegas Golden Knights
| Player | Anytime Goal Probability |
|---|---|
| Jack Eichel | 35.9% |
| Mark Stone | 32.8% |
| Pavel Dorofeyev | 31.5% |
| Tomas Hertl | 28.7% |
| Mitch Marner | 27.9% |
San Jose Sharks
| Player | Anytime Goal Probability |
|---|---|
| Macklin Celebrini | 38.5% |
| Pavol Regenda | 36.9% |
| Tyler Toffoli | 26.7% |
| William Eklund | 22.1% |
| Collin Graf | 20.4% |
First goal scorer predictions
Vegas Golden Knights
| Player | First Goal Probability |
|---|---|
| Jack Eichel | 7.2% |
| Mark Stone | 6.5% |
| Pavel Dorofeyev | 6.2% |
| Tomas Hertl | 5.2% |
| Mitch Marner | 5.1% |
San Jose Sharks
| Player | First Goal Probability |
|---|---|
| Macklin Celebrini | 7.9% |
| Pavol Regenda | 7.2% |
| Tyler Toffoli | 4.8% |
| Collin Graf | 3.5% |
| Adam Gaudette | 3.5% |
Golden Knights vs. Sharks game-day updates and essential details
Sunday's game between the Sharks and Golden Knights at SAP Center at San Jose is scheduled to start at 8:00 PM ET.
- Who: Vegas Golden Knights vs. San Jose Sharks
- Date: Sunday, January 11, 2026
- Time: 8:00 PM ET/5:00PM PT
- Venue: SAP Center at San Jose
Conclusion
We predict the Golden Knights, with a 58% win probability, will likely beat the Sharks on Sunday.
AI and automation have enhanced this article to quickly deliver accurate Golden Knights vs. Sharks insights, with human oversight ensuring high editorial quality. Our predictions are based on the latest data to help you make informed decisions. For additional resources and advice on responsible gambling, please call 1-800-GAMBLER.
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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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