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Rangers vs. Sharks prediction: Tight battle projected in updated analysis for Friday's NHL matchup [1/23/2026]

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Written by Jason Bevilacqua

Get NY Rangers vs San Jose Sharks predictions for NHL Friday.

NY Rangers-San Jose Sharks Predictions and Game Preview.
NY Rangers vs. San Jose Sharks NHL prediction for Friday, January 23, 2026.

The San Jose Sharks and NY Rangers will meet in the NHL at SAP Center at San Jose on Friday, with puck drop at 10:00 PM ET.

Based on updated simulations, Dimers' advanced NHL model gives an equal chance for both both teams to win the Rangers-Sharks matchup.

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

Rangers vs. Sharks prediction: Who will win and cover?

Using trusted machine learning and data, we have simulated the result of Friday's NHL game between the Rangers and Sharks 10,000 times as part of our NHL predictions coverage.

Our leading predictive model currently can't split the two teams, giving both the Rangers and Sharks a 50% chance of winning the game.

According to our model, the Rangers (+1.5) have a 70% chance of covering the puck line, while the over/under total of 6.5 goals has a 52% chance of staying under.

 

Rangers vs. Sharks odds

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

Bet Type Rangers Sharks
Puck Line +1.5 (-230) -1.5 (+195)
Moneyline +102 -120
Total o6.5 (-135) u6.5 (+115)

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

Rangers 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 Rangers +1.5 -230
Moneyline Rangers +102
Total Under 6.5 +115

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

 

Rangers vs. Sharks player props

Who will score a goal in Rangers vs. Sharks?

According to our model, Artemi Panarin is most likely to score an anytime goal in Rangers vs. Sharks.

Our projections give Panarin a 40.4% chance of scoring a goal at SAP Center at San Jose, while the star is an 8.5% chance of netting the first goal the game.

Anytime goal scorer predictions

NY Rangers

Player Anytime Goal Probability
Artemi Panarin 40.4%
Mika Zibanejad 33.7%
J.T. Miller 32.5%
Vincent Trocheck 25.6%
Will Cuylle 21.1%

San Jose Sharks

Player Anytime Goal Probability
Macklin Celebrini 35.8%
Pavol Regenda 32.7%
Will Smith 28.9%
Tyler Toffoli 28.3%
William Eklund 21.9%

First goal scorer predictions

NY Rangers

Player First Goal Probability
Artemi Panarin 8.5%
J.T. Miller 6.8%
Mika Zibanejad 6.5%
Alexis Lafreniere 4.6%
Vincent Trocheck 4.3%

San Jose Sharks

Player First Goal Probability
Macklin Celebrini 7.2%
Pavol Regenda 6.2%
Tyler Toffoli 5.8%
Will Smith 5.4%
William Eklund 4.0%

Rangers vs. Sharks game-day updates and essential details

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

  • Who: NY Rangers vs. San Jose Sharks
  • Date: Friday, January 23, 2026
  • Time: 10:00 PM ET/7:00PM PT
  • Venue: SAP Center at San Jose

Conclusion

We predict a close game between the Rangers and Sharks on Friday, with each team having a 50% chance of winning.

AI and automation have enhanced this article to quickly deliver accurate Rangers 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 on responsible gambling, please call 1-800-GAMBLER.

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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