Beginning July 1, I will be previewing one conference every two weeks in advance of the 2026-27 NCAA Hockey season getting underway in October.

My previously developed predictions model serves as the basis for these roster outlooks, which identifies each team’s top 15 skaters’ projected point-per-game production, top-rated goaltender, and composite, forwards, defensemen, goalie, and depth ratings.

Previous projections:

NCHC

Big Ten

ECAC Hockey

Hockey East

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Michigan Tech enters 2026-27 as one of the favorites to win the CCHA, posting a composite score of 18.78 while returning 74% of last year’s roster centered around a forward tandem that ranks among the best in the country.

Lindenwood transfer Giovanni Morneau leads the Huskies at a projected 1.14 PPG. Max Koskipirtti (0.87 PPG) makes it a formidable one-two punch, and Isaac Gordon (0.72 PPG) and Teydon Trembecky (0.70 PPG) give Michigan Tech a true top four. Elias Jansson (0.57 PPG) and Noah Reinhart (0.54 PPG) provide experienced depth further down the lineup.

The freshman class is small but talented. Forward Tristan Delisle (0.52 PPG) projects as the top freshman scorer, with USports standouts Conner Roulette (0.46 PPG) and Lucas Brenton (0.26 PPG) providing good depth as well.

Between the pipes, Owen Bartoszkiewicz returns as the top-rated goaltender, giving Michigan Tech a proven starter behind one of the deepest returning rosters in the CCHA. Freshman Joey Costanzo figures to be worked into the mix as well.

Bowling Green enters 2026-27 with a projected composite score of 17.90, returning 63% of last year’s group and featuring one of the more balanced rosters in the conference.

Ben Doran leads the Falcons at projected 0.85 PPG. Noah Morneau (0.69 PPG), Tyler Hotson (0.68 PPG), and Dominik Rymon (0.67 PPG) round out a returning forward group with proven production through the top six. Defenseman Breck McKinley (0.71 PPG) and Ty Higgins (0.54 PPG) add significant offensive production from the blue line.

The incoming class features QMJHL standout Thomas Verdon (0.75 PPG), Teague Patton (0.46 PPG) and Matthew Soto (0.41 PPG) as the projected top freshman scorers, with defenseman Mazden Leslie (0.38 PPG) adding great depth on the back end.

Between the pipes, returning netminder Jacob Steinman projects as the top-rated goaltender. Freshman Cooper Michaluk and USports standout Tyler Palmer also figure to see time.

Augustana returns 74% of last year’s roster, boasting the highest depth rating (6.85) in the CCHA, while projecting an overall composite score of 17.12.

Lindenwood transfer Jacob Fletcher leads the Vikings at a projected 0.86 PPG. Brett Meerman (0.69 PPG) and Leonid Bulgakov (0.64 PPG) give Augustana a reliable top three, and Hunter Bischoff (0.60 PPG), Owen Bohn (0.58 PPG), Jacob Jastrzebski (0.57 PPG), and Cole Burtch (0.49 PPG) round out a returning forward group with experience across all four lines. Defenseman Owen Baumgartner (0.62 PPG) adds good offensive production from the blue line as well.

The incoming class features Siamion Marshchanok (0.58 PPG) as the top freshman scorer, with twin brother Matsvei Marshchanok (0.51 PPG) adding further depth up front.

Between the pipes, Miami transfer Mathis Langevin projects as the top-rated goaltender. Christian Manz could see a bigger role this season. Freshman Angelo Zol comes into the program as well after a big year in the BCHL.

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Lake Superior State returns just 40% of last year’s group while welcoming 43% freshmen and 17% via transfer, projecting for an overall composite score of 15.93.

Lindenwood transfer Charles Savoie leads the Lakers at a projected 1.20 PPG. Defenseman Nolan Seed (0.77 PPG) and Calem Mangone (0.72 PPG) provide a reliable core behind him, and Hunter Ramos (0.51 PPG), Casper Nassen (0.48 PPG), and Matheson Mason (0.44 PPG) add returning depth throughout the lineup.

The incoming class is one of the largest in the conference at 43%, headlined by forward Cooper Foster (0.44 PPG) and Ethan Moore (0.38 PPG), with Kazumo Sasaki (0.33 PPG) and defenseman Teo Besnier (0.32 PPG) rounding out a first-year group with multiple contributors ready to step up.

Anders Miller projects to be the top-rated goaltender after a strong year in the WHL. Andrew Oke and Charlie Tritt will also be in the mix for what figures to be a largely improved Lake Superior State squad.

Bemidji State enters 2026-27 with a projected composite score of 14.25, returning just 44% of last year’s group while welcoming 39% freshmen.

Oliver Peer leads the Beavers at a projected 1.12 PPG. Air Force transfer Will Dawson (0.69 PPG) gives Bemidji State a capable second option, and Ferris State transfer Carter Rapalje (0.55 PPG) and Max Namestnikov (0.44 PPG) provide experience at multiple levels. Defenseman Hudson Thornton (0.44 PPG) contributes offensively from the blue line.

The freshman class is one of the largest in the conference, headlined by Brady Birnie (0.43 PPG), Ethan Miedema (0.40 PPG), Jiri Klima (0.40 PPG), and Brayden Dube (0.39 PPG), with defenseman Luca D’Amato (0.35 PPG) and Eddie Revenig (0.34 PPG) adding further depth.

Returning netminder Raythan Robbins projects as the top-rated goaltender. He figures to split time with Max Hildebrand, who made 24 appearances in net last year. Trent Wiemken are also in play here for a larger role.

Minnesota State enters 2026-27 with a projected composite score of 13.78, returning 63% of last year’s roster and building around a proven top-two that gives the Mavericks their most reliable offensive contributors at the top of the lineup.

Up front, Ean Somoza (0.74 PPG) and Luigi Benincasa (0.72 PPG) give Minnesota State a dependable one-two punch, and Liam Watkins (0.53 PPG), Mason LeBel (0.47 PPG), and Alex Zetterberg (0.47 PPG) provide a returning core with proven depth behind them. Finlay Williams (0.38 PPG) and Reid Morich (0.34 PPG) add veteran experience further down the lineup.

The incoming class features Mason Kraft (0.32 PPG) and Sam Kappell (0.32 PPG) as the top freshman scorers. Jack Christ (0.29 PPG) and Cash Carter (0.28) are other names to watch as well.

Alaska transfer Lassi Lehti projects as the top-rated goaltender and figures to carry the load with Alex Tracy moving on. Raiden LeGall and Matthew Syverson could also see time in net.

Northern Michigan enters 2026-27 with a composite score of 12.48 and the highest goalie rating (7.47) in the CCHA, while returning just 44% of last year’s group.

Branden Piku leads the Wildcats at a projected 0.53 PPG, with Peter Cisar (0.41 PPG), Grayden Slipec (0.33 PPG), and Mathew Ward (0.32 PPG) rounding out the returning core up front. The veteran group will be leaning heavily on those pieces as a large freshman class develops.

The incoming class features Owen Pitters (0.42 PPG) and Aapo Katavisto (0.39 PPG) as the top freshman scorers, with Coster Dunn (0.37 PPG), Reilley Kotai (0.36 PPG), and Niklas Gudmundson (0.29 PPG) adding solid depth up front.

Between the pipes, returning goaltender Oliver Auyeung-Ashton projects as the top-rated goaltender after an outstanding freshman campaign a season ago. Max Weilandt could see a larger role after coming in as a mid-season addition last year. Jackson Fuller is also in play here.

Ferris State enters 2026-27 projects to post a composite score of 11.86 while returning just 22% of last year’s group and rebuilding almost entirely through a 43% freshman class and 35% via the transfer portal.

Nathan Leek headlines the incoming class at 0.69 PPG and projects to step in as the team’s top scorer. Akseli Pulkkanen (0.49 PPG) is another freshman who arrives ready to contribute immediately, and transfer forwards Owen Mehlenbacher (0.34 PPG) and Ryan Kusler (0.33 PPG) bring valuable experience to the group. Defensemen Christopher Lie (0.42 PPG) and Urban Podrekar (0.40 PPG) add solid offensive production from the blue line.

The freshman class will define Ferris State’s identity, with Hudson Darby (0.33 PPG), Nicholas Johnson (0.30 PPG), Reggie Newman (0.30 PPG), and Gavin Garland (0.28 PPG) in depth roles.

Freshman goaltender Alan Lendak projects as the top-rated goaltender for a program that will rely on strong goaltending and freshman development to find its footing in 2026-27.

Beginning July 1, I will be previewing one conference every two weeks in advance of the 2026-27 NCAA Hockey season getting underway in October.

My previously developed predictions model serves as the basis for these roster outlooks, which identifies each team’s top 15 skaters’ projected point-per-game production, top-rated goaltender, and composite, forwards, defensemen, goalie, and depth ratings.

Previous projections:

NCHC

Big Ten

ECAC Hockey

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Merrimack looks like a loaded team in Hockey East, posting a projected composite score of 19.71 while returning 70% of last year’s roster centered around one of the deepest and most productive forward groups in the conference and welcoming in a very talented 30% freshman class.

Trevor Hoskin (1.01 PPG) and Parker Lalonde (1.01 PPG) give the Warriors a devastating one-two punch at the top of the lineup, and Justin Gill (0.96 PPG) makes it a true point-per-game top three. Caelan Fitzpatrick (0.75 PPG) and Nick Pierre (0.64 PPG) provide proven depth behind them, and Caden Cranston (0.57 PPG) and Nolan Flamand (0.50 PPG) round out a returning forward group with experience throughout the lineup.

The incoming class features forwards Mavrick Lachance (0.38 PPG) and Connor Dale (0.38 PPG) as the projected top freshman scorers, with Alex Mercier (0.35 PPG) adding good depth.

Despite Max Lundgren moving on, incoming freshman Linards Feldbergs projects to be one of the best goaltenders in the country, giving Merrimack a capable backstop as the Warriors look to claim back-to-back Hockey East titles.

UMass enters 2026-27 with a projected composite score of 19.38, returning 56% of last year’s roster and building around a lethal top-two heading into the season.

Jack Musa (1.03 PPG) and Vaclav Nestrasil (1.01 PPG) give UMass one of the more dangerous one-two combinations in Hockey East, and Jack Galanek (0.61 PPG) and Daniel Jencko (0.46 PPG) provide a reliable returning core behind them. Defenseman Francesco Dell’Elce (0.63 PPG) and Larry Keenan (0.53 PPG) add significant offensive production from the blue line.

The incoming class is incredibly talented. Headlined by Maxim Masse (0.81 PPG) with Jasper Kuhta (0.58 PPG), Max Curran (0.58 PPG), and Egor Barabanov (0.50 PPG), UMass has a freshman group loaded with big-time contributors.

Between the pipes, incoming freshman Tobias Trejbal projects as the top-rated goaltender. Senior Jackson Irving and freshman Ethan Eskit are also in play for time here.

Providence enters 2026-27 with a composite score of 19.11, returning 70% of last year’s roster and featuring one of the more balanced forward groups in Hockey East from top to bottom.

John Mustard (0.90 PPG) and Logan Sawyer (0.84 PPG) give the Friars a proven one-two punch, and Tanner Adams (0.66 PPG) and Will Elger (0.63 PPG) add great depth through the top four. Julius Sumpf (0.56 PPG), Jonathan Fauchon (0.53 PPG), Graham Gamache (0.47 PPG), and Aleksi Kivioja (0.45 PPG) round out one of the more experienced rosters in the conference.

The freshman class features Thomas Vandenberg (0.51 PPG) as the projected top incoming scorer, with Ryder Cali (0.39 PPG) and Carter Klippenstein (0.32 PPG) adding additional depth up front. Blake Zielinksi (0.32 PPG) is another freshman to watch closely.

Between the pipes, Jack Parsons projects as the top-rated goaltender. North Dakota transfer Gibson Homer comes in as a veteran netminder.

Boston College enters 2026-27 with a composite score of 18.31 and one of the largest freshman classes in Hockey East at 41%, giving the Eagles a mix of experienced returners and young talent across the roster.

Up front, Dean Letourneau leads the Eagles at a projected 1.07 PPG and returns as one of the premier forwards in the conference. Ryan Conmy (0.84 PPG) gives Boston College a capable second option, and Teddy Stiga (0.75 PPG) and Jake Sondreal (0.61 PPG) make up a sound returning core behind them. Will Vote (0.57 PPG) and Oscar Hemming (0.42 PPG) add veteran depth further down the lineup, and defenseman Luka Radivojevic (0.54 PPG) contributes offensively from the blue line.

The freshman class is headlined by Teddy Mutryn (0.71 PPG) and Matias Vanhanen (0.63 PPG), two of the stronger freshman projections in Hockey East, with Alex Pelletier (0.43 PPG) and defensemen Carson Woodall (0.52 PPG) and Quinn Beauchesne (0.36 PPG) rounding out a good first-year group.

Incoming freshman Leo Henriquez projects as the top-rated goaltender for the Eagles, though he is likely to split time with returning goaltender Louka Cloutier. Jan Korec is also in play here for time in net.

Northeastern enters 2026-27 with a composite score of 17.40, returning 57% of last year’s group and featuring the highest goalie rating (7.60) in Hockey East.

Jacob Mathieu leads the Huskies at 0.91 PPG and returns as one of the better individual scorers in the conference. Joe Connor (0.77 PPG), Amine Hajibi (0.75 PPG), and Maine transfer Miguel Marques (0.73 PPG) give Northeastern a deep, experienced top four, and Giacomo Martino (0.62 PPG) and Tyler Fukakusa (0.56 PPG) add further depth behind them.

The freshman class features forward Dawson Sharkey (0.38 PPG) as the projected top incoming scorer, with defensemen Jordan Tourigny (0.34 PPG), Noah Laberge (0.33 PPG), and Marcus Kearsey (0.32 PPG) adding depth on the back end.

Lawton Zacher returns as the top-rated goaltender in the conference and gives Northeastern a genuine backbone in net to carry the team through a competitive Hockey East schedule.

Boston University enters 2026-27 with a projected composite score of 16.59, returning 68% of last year’s roster and building around a sophomore who arrives as one of the more anticipated returning forwards in Tynan Lawrence (1.05 PPG).

In addition to Lawrence, Jack Harvey (0.68 PPG) gives Boston University a capable second option, and Ryder Ritchie (0.63 PPG), Conrad Fondrk (0.59 PPG), and Kamil Bednarik (0.55 PPG) round out a returning forward group with good depth through the lineup. Ben Merrill (0.47 PPG), Jonathan Morello (0.44 PPG), and Brandon Svoboda (0.43 PPG) give the Terriers additional veteran experience across the bottom six.

The incoming class features Niklas Aaram-Olsen (0.59 PPG) and Caleb Malhotra (0.52 PPG) as the top freshman forwards. Historical SHL translations are limited, but Aaram-Olsen profiles as a big-time shooter who could push well past his projection. Additionally, defenseman Xavier Villeneuve (0.38 PPG) adds offensive upside on the back end.

After a down 2025-26 campaign, Mikhail Yegorov projects as the top-rated goaltender for a Boston University squad that will need their sophomore class to take major developmental steps forward this year.

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Maine enters 2026-27 with a projected composite score of 16.58, featuring significant roster turnover — returning just 52% of last year’s group while adding 22% via the transfer portal — but boasting the second-highest depth rating (7.25) in Hockey East.

Up front, Ross Campbell (0.68 PPG), Max Scott (0.65 PPG), Owen Fowler (0.63 PPG), and Tanner Klimpke (0.62 PPG) give the Black Bears four forwards projecting in a similar range, and Sully Scholle (0.55 PPG), Sam Alfano (0.50 PPG), Jaden Lipinski (0.44 PPG), and Thomas Pichette (0.33 PPG) provide an experienced core behind them.

The freshman class features forward Rowan Calvert (0.34 PPG) as the top incoming scorer. On the back end, Ludvig Lafton (0.31 PPG) is a player to watch as well.

Between the pipes, Canisius transfer Petter Wickstrom Stumer projects as the top-rated goaltender, but could play more of a secondary role to returning senior netminder Albin Boija.

UConn enters 2026-27 with a composite score of 16.34, returning 59% of last year’s group while welcoming a 36% freshman class that brings projectable scoring to the lineup.

Joey Muldowney leads the Huskies at 0.89 PPG and returns as one of the more dangerous forwards in Hockey East. Jeremy Loranger (0.76 PPG) gives UConn a capable second option, and Alex Blais (0.55 PPG), Kaden Shahan (0.53 PPG), Mike Murtagh (0.52 PPG), Ethan Gardula (0.48 PPG), and Ethan Whitcomb (0.47 PPG) give the Huskies significant depth throughout the forward group. Defenseman Trey Scott (0.52 PPG) adds offensive value from the blue line as well.

The freshman class features Lirim Amidovski (0.42 PPG) as the projected top incoming forward, with Cole Davis (0.38 PPG), Jesse Heslop (0.35 PPG), and Brittan Alstead (0.32 PPG) adding forward depth, while defenseman William McIsaac (0.35 PPG) rounds out a first-year group with multiple contributors ready to step in.

Between the pipes, freshman Veeti Louhivaara projects as the top-rated goaltender. Sophomore Kam Hendrickson is also in play here.

New Hampshire enters 2026-27 with a composite score of 15.44, returning 57% of last year’s group and boasting the third-highest depth rating (6.82) in the conference.

Up front, St. Lawrence transfer Rasmus Svartstrom (0.67 PPG) and returning forward Nick Ring (0.66 PPG) give the Wildcats a dual-threat at the top of the lineup. Ryan Johnson (0.51 PPG), Martins Lavins (0.49 PPG), Jacob Newcombe (0.45 PPG), Mason Zebeski (0.44 PPG), and Cam MacDonald (0.43 PPG) form one of the deepest returning forward groups in Hockey East. Defenseman Parker Von Richter (0.43 PPG) brings offensive instincts from the blue line as well.

The freshman class features defenseman Parker Von Richter (0.43 PPG) as the projected top incoming scorer, with Emile Guite (0.39 PPG) among the more impactful freshman players to keep close tabs on.

Sacred Heart transfer Teagan Kendrick projects as the top-rated goaltender, while Providence transfer Michael Simpson projects well, giving UNH a formidable one-two punch in net.

Vermont enters 2026-27 with a projected composite score of 13.99, returning 57% of last year’s roster and building around a proven top scorer in Colin Kessler (0.90 PPG), who gives the Catamounts a reliable offensive centerpiece.

In addition to Kessler, Cameron Garvey (0.50 PPG), Cedrick Guindon (0.49 PPG), and Jonah Aegerter (0.44 PPG) form a returning core with good depth behind him. Jack Malinski (0.41 PPG) and Sebastian Tornqvist (0.41 PPG) offer great value from the blue line as well.

The incoming class features Felix Lacerte (0.69 PPG) as the projected top freshman scorer, with Sam McCue (0.40 PPG) adding additional forward depth. Mathieu Plante (0.24 PPG) is a player to watch along the blue line.

Between the pipes, Quinnipiac transfer Dylan Silverstein projects as the top-rated goaltender, while sophomores Aiden Wright and Jacob Oster are in play for time as well.

UMass-Lowell enters 2026-27 with a composite score of 12.55, returning 69% of last year’s roster in one of the more experienced groups in Hockey East.

Chris Delaney leads the River Hawks at a projected 0.65 PPG, with Jak Vaarwerk (0.58 PPG) and Dalyn Wakely (0.55 PPG) rounding out a reliable top three. Libor Nemec (0.45 PPG), Tobias Pitka (0.45 PPG), and Kuzma Voronin (0.41 PPG) provide additional returning depth, and Misha Volotovskii (0.37 PPG) rounds out a veteran forward group with experience.

Freshmen forwards Kolin Sisson (0.32 PPG) and Joseph McGraw (0.31 PPG) headline the incoming group and project to contribute in supporting roles.

Between the pipes, Bowling Green transfer Cole Moore projects as the top-rated goaltender. Austin Elliott and Niko Goich could see time in net as well.

Beginning July 1, I will be previewing one conference every two weeks in advance of the 2026-27 NCAA Hockey season getting underway in October.

My previously developed predictions model serves as the basis for these roster outlooks, which identifies each team’s top 15 skaters’ projected point-per-game production, top-rated goaltender, and composite, forwards, defensemen, goalie, and depth ratings.

Previous projections:

NCHC

Big Ten

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Quinnipiac enters 2026-27 as the clear favorite to win the ECAC, posting a composite score of 25.41 while returning 65% of last year’s roster built around one of the most dominant individual performers in the country in Ethan Wyttenbach (1.57 PPG).

In addition to Wyttenbach, Chris Pelosi (1.00 PPG) and Antonin Verreault (1.00 PPG) give Quinnipiac a true point-per-game trio at the top of the lineup, and Markus Vidicek (0.93 PPG), Mason Marcellus (0.92 PPG), Holy Cross transfer Jack Stockfish (0.78 PPG), and Aaron Schwartz (0.77 PPG) form as deep a forward group as anyone in the conference.

The incoming class features forward Alex Kostov (0.67 PPG) as the top freshman scorer, with Dylan Edwards (0.52 PPG) and Gabriel Frasca (0.47 PPG) adding further depth down the lineup.

Between the pipes, Lake Superior State transfer Rorke Applebee projects as the top-rated goaltender for a Quinnipiac program built from top to bottom that can compete for a national championship.

Dartmouth enters 2026-27 with one of the most explosive forward groups in the ECAC, posting a composite score of 19.49 while returning 79% of last year’s roster.

Up front, Hayden Stavroff leads the Big Green at a projected 1.37 PPG and returns as one of the premier scorers in the country. Hank Cleaves (1.15 PPG) makes it a lethal one-two punch, and Nikita Nikora (0.75 PPG) adds a capable third option. Defenseman C.J. Foley (0.72 PPG) and Colin Grable (0.56 PPG) contribute terrific offensive production from the blue line, and Jason Stefanek (0.48 PPG), Nathan Morin (0.45 PPG), and Jack Silverberg (0.44 PPG) provide experienced depth throughout the forward group.

With only 17% freshmen, the incoming class is slim but talented. Forward Jesse Allecia (0.38 PPG) headlines the group and projects to contribute early. Drew Stewart (0.34 PPG) is another name to watch here.

Between the pipes, Emmett Croteau returns as the starter and top-rate goaltender, giving Dartmouth a reliable backstop behind a roster built to make some noise in March and April.

Cornell enters 2026-27 with one of the deeper forward groups in the ECAC, posting an overall composite score of 19.25 while returning 66% of last year’s roster and adding a 30% freshman class with immediate contributors.

Up front, Ryan Walsh (0.98 PPG) and Charlie Major (0.97 PPG) give the Big Red a formidable one-two punch, and Caton Ryan (0.90 PPG) makes it a true top-line threat with all three projecting as legitimate point-per-game options. Defenseman Xavier Veilleux (0.82 PPG) adds good offensive production from the blue line, and Ivan Zadvernyuk (0.72 PPG), Aiden Long (0.60 PPG), and Jacob Kraft (0.59 PPG) form a forward group with great experience.

The incoming class is headlined by forward Bradley Gardiner (0.58 PPG) — one of the stronger freshman projections in the conference — with Giovanni DiGiulian (0.46 PPG) adding depth up front.

Between the pipes, transfer Mathis Rousseau projects as the top-rated goaltender, giving Cornell a proven starter as the Big Red look to contend for an ECAC title.

Clarkson enters 2026-27 with a composite score of 17.15, returning 61% of last year’s roster and boasting one of the highest depth ratings (7.80) in the ECAC.

Up front, Adrian Misaljevic leads the Golden Knights at 0.70 PPG, with Owen Van Steensel (0.66 PPG) and Remi Gelinas (0.63 PPG) giving Clarkson a reliable top three. Justin Cote (0.56 PPG), Jared Mangan (0.55 PPG), Talon Sigurdson (0.52 PPG), and Luka Sukovic (0.52 PPG) form one of the deeper returning forward rosters in the conference, and defenseman Tate Taylor (0.59 PPG) adds significant offensive production from the blue line.

The incoming class features forward Alex Pharand (0.40 PPG) as the top incoming scorer, with Sebastien Gervais (0.37 PPG) and Hayden Reid (0.36 PPG) adding forward depth.

Between the pipes, Shane Soderwall returns as the top-rated goaltender, anchoring a Clarkson team looking to contend in the ECAC.

Under first-year head coach Rob Rassey, Harvard enters 2026-27 with a composite score of 16.09, returning 78% of last year’s roster and featuring one of the more experienced groups in the ECAC heading into the season.

Up front, Mick Thompson leads the Crimson at a projected 0.88 PPG. Heikki Ruohonen (0.66 PPG) gives Harvard a reliable second option, and Richard Gallant (0.53 PPG), Ryan Fine (0.50 PPG), and Justin Solovey (0.43 PPG) round out a returning core with proven contributors throughout the lineup. Defenseman Matthew Morden (0.55 PPG) adds meaningful offensive production from the blue line.

The incoming class features forward Joseph Salandra (0.40 PPG) as the top freshman scorer, with defenseman Alex Huang (0.39 PPG) and Justin Graf (0.35 PPG) adding depth across the roster.

Between the pipes, Detroit Red Wings draft pick Rudy Guimond projects very well as a freshman. Ben Charette and Evan Zhang are also in play here.

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Union enters 2026-27 with a composite score of 14.45, returning 61% of last year’s group and building around a versatile offensive core that features proven production at both forward and the blue line.

Up front, Alaska transfer Tommy Cronin leads the team at a projected 0.88 PPG. Lucas Buzziol (0.71 PPG) gives Union a capable second option, with Alex Laurenza (0.61 PPG) and Riley Brueck (0.46 PPG) providing experienced depth behind them. Defenseman Tyler Dunbar (0.82 PPG), Etienne Lessard (0.62 PPG), and Will Felicio (0.44 PPG) bring good offensive production from the blue line, a group that ranks among the more offensively productive in the conference.

The incoming class features Nathan Free (0.35 PPG) as the top freshman scorer, with Troy Pelton (0.33 PPG) adding forward depth.

Between the pipes, Brayden Gillespie projects as the top-rated goaltender for Union. Cameron Korpi figures to see a good amount of time in net as well.

Colgate enters 2026-27 with a composite score of 13.80, returning 61% of last year’s roster and building around a proven top-end scorer in Max Nagel (1.08 PPG), who gives the Raiders a legitimate offensive centerpiece.

In addition to Nagel, Ryan Sullivan (0.72 PPG) gives the Raiders a capable second option, and Jack Brandt (0.53 PPG) rounds out a returning forward core with proven contributors at the top of the lineup. Defenseman Isaiah Norlin (0.67 PPG) adds significant offensive production from the blue line.

The incoming class features defenseman Nathan Larioza (0.42 PPG) and forward Braiden Clark (0.39 PPG) as the top freshman contributors, with Porter Byrd-Leitner (0.28 PPG) and Tobias Ohman (0.27 PPG) adding further depth up front.

Between the pipes, incoming freshman Matthew Minchak projects as the top-rated goaltender following a strong season in the OHL. Reid Dyck, who played in 29 games as a freshman last year, will likely also see time.

St. Lawrence enters 2026-27 with a composite score of 13.11, returning 74% of last year’s roster and building around a top-end scorer in Tyler Cristall (0.87 PPG) who gives the Saints a legitimate offensive focal point in the ECAC.

In addition to Cristall, Cayden Casey (0.72 PPG) gives the Saints a capable second option, and Filip Juricek (0.59 PPG), Cooper Pierson (0.57 PPG), and Sam LeDrew (0.57 PPG) round out a returning forward group. Isaac Tremblay (0.50 PPG) also provides additional veteran presence further down the lineup.

The incoming class is headlined by forward Luke Posthumus (0.51 PPG), with Ihnat Pazii (0.35 PPG) and Owen Tylec (0.25 PPG) adding further depth up front.

Between the pipes, Cam Smith projects as the top-rated goaltender for St. Lawrence.

RPI enters 2026-27 with a composite score of 12.68 and significant roster turnover, returning just 48% of last year’s group while welcoming 39% freshmen, one of the youngest rosters in the conference.

Up front, Filip Sitar leads the Engineers at 0.49 PPG, with Matthew Buckley (0.38 PPG) and Jagger Tapper (0.38 PPG) providing experienced depth behind him. Rainers Rullers (0.42 PPG) adds to the returning core, and defenseman Thomas Klassek (0.46 PPG) contributes offensively from the blue line.

The incoming class is the largest in the conference at 35% and features defenseman Grayson Burzynski (0.39 PPG) as the top freshman scorer and James Guo (0.35 PPG) adding depth on the back end. Jordan Gavin (0.34 PPG) is another name to watch.

Between the pipes, Nathan Krawchuk, who appeared in 24 games last season, projects as the top-rated goaltender.

Yale enters 2026-27 under new leadership, with Jeff Hamilton taking over bench boss duties. The Bulldogs’ roster is projected to post a composite score of 11.98 while returning 78% of last year’s group and welcoming a 22% freshman class.

Up front, Ronan O’Donnell leads Yale at 0.84 PPG and returns as one of the steadier offensive producers in the ECAC. Donovan Frias (0.69 PPG) gives Yale a capable second option, and Micah Berger (0.60 PPG) rounds out a reliable top three. David Andreychuk (0.44 PPG), Iisai Pesonen (0.44 PPG), Zachary Wagnon (0.37 PPG), and James Shannon (0.36 PPG) make up a solid returning group with experience across all four lines.

The incoming class is headlined by forward Alex Assadourian (0.35 PPG), with Kyle O’Leary (0.19 PPG) and Drew Waterfield (0.19 PPG) providing depth behind him.

Between the pipes, Noah Pak projects as the top-rate netminder for Yale. Jack Stark and Jeth Fogg are also in play here.

Princeton enters 2026-27 with a composite score of 11.26, returning 74% of last year’s roster and building around a top-end forward whose production stands well above the rest of the group.

Up front, Kai Daniells leads the Tigers at 1.09 PPG and returns as one of the more dominant individual scorers in the ECAC. Jake Manfre (0.78 PPG) gives Princeton a capable second option, but the drop-off after the top two is significant — Julian Facchinelli (0.33 PPG) and Josh Karnish (0.29 PPG) making up a returning core that will lean heavily on Daniells and Manfre to generate offense.

The incoming class features forward Shaan Kingwell (0.34 PPG) as the top freshman contributor, with forward Jake Gudelj (0.25 PPG) adding depth down the lineup.

Between the pipes, Arthur Smith returns as the top-rated goaltender. Conor Callaghan, who played in 12 games last season, could see increased time.

Brown enters 2026-27 with a composite score of 9.45, returning 57% of last year’s group while adding 17% via the transfer portal and welcoming in a 26% freshman class.

Up front, Benjamin Poitras leads the Bears at 0.56 PPG, with Andrew King (0.52 PPG) and Brian Nicholas (0.47 PPG) giving Brown a reliable top three. Griffin Erdman (0.44 PPG), Charlie Gollob (0.41 PPG), and Michael Salandra (0.40 PPG) round out a returning forward group that provides genuine depth despite the program’s modest composite score.

The incoming class features Matteo Danis (0.34 PPG) as the top freshman scorer, with defenseman Marco Senerchia (0.22 PPG) adding depth on the back end.

Between the pipes, incoming freshman Devin Shakar projects as the top-rated goaltender for the Bears.

Beginning July 1, I will be previewing one conference every two weeks in advance of the 2026-27 NCAA Hockey season getting underway in October.

My previously developed predictions model serves as the basis for these roster outlooks, which identifies each team’s top 15 skaters’ projected point-per-game production, top-rated goaltender, and composite, forwards, defensemen, goalie, and depth ratings.

Previous projections:

NCHC

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Michigan enters 2026-27 as the clear favorite to win the Big Ten, posting the highest composite score in the conference (and the country) at 25.60 while returning 70% of last year’s roster and bringing in a 30% freshman class that could be the most talented in the country.

Junior forward Michael Hage leads the Wolverines at a projected 1.39 PPG, one of the most dominant individual projections in college hockey. Jayden Perron (1.15 PPG) and Will Horcoff (0.97 PPG) give Michigan a devastating top three, and the depth doesn’t stop there. Adam Valentini (0.76 PPG), Nick Moldenhauer (0.76 PPG), Malcolm Spence (0.68 PPG), and Garrett Schifsky (0.65 PPG) form one of the deepest forward groups in the country. Defenseman Henry Mews (1.02 PPG), who was limited to just ten games as a freshman, could be a genuine point-per-game threat from the blue line.

The incoming class is headlined by forwards Jack Nesbitt (0.64 PPG) and J.P. Hurlbert (0.62 PPG), with defenseman Cam Reid (0.55 PPG) and freshman blueliner Landon Dupont (0.51 PPG) adding immediate impact on the back end.

Between the pipes, sophomore Jack Ivankovic returns as one of the more reliable starters in the Big Ten, giving Michigan a proven backstop behind a roster that looks poised to make a deep NCAA Tournament run.

After coming up short in the title game last season, Wisconsin enters 2026-27 with a composite score of 18.84, returning 64% of last year’s roster and featuring one of the most complete forward groups in the Big Ten.

Gavin Morrissey leads the Badgers at an even 1.00 PPG, giving Wisconsin a legitimate point-per-game producer at the top of the lineup. Penn State transfer J.J. Wiebusch (0.94 PPG) makes it a formidable one-two punch, and Vasily Zelenov (0.77 PPG) adds a capable third option. Sophomore defenseman Luke Osburn (0.69 PPG) contributes elite offensive production from the blue line, and Oliver Tulk (0.60 PPG), Ryan Botterill (0.60 PPG), and Finn Brink (0.49 PPG) round out a returning forward group with proven depth throughout the lineup.

The freshman class features forward and NAHL standout Talan Blanck (0.50 PPG) as the top incoming scorer, although historical NAHL data has been limited, which is important to note for Blanck’s projected numbers. Chase Jette (0.36 PPG) and Zach Wooten (0.33 PPG) add complementary pieces and could very well outperform their projections.

Between the pipes, transfer netminder Alexis Cournoyer projects as the top-rated goaltender for the Badgers. Returning goaltender Daniel Hauser figures to have a say as well.

Penn State enters 2026-27 with a composite score of 19.19, returning 52% of last year’s group while adding a 38% freshman class that injects immediate scoring upside at both forward and the blue line.

Transfer forward Justin Poirier leads the Nittany Lions at 1.09 PPG. Reese Laubach (0.77 PPG) and Dane Dowiak (0.65 PPG) give Penn State a reliable returning core behind him, while defenseman Jackson Smith (0.86 PPG) will look to build off an impressive freshman campaign. Luke Misa (0.56 PPG) provides additional depth up front.

The freshman class is headlined by forward Egor Shilov (0.73 PPG), with Justin Larose (0.67 PPG), Marco Mignosa (0.58 PPG), Kale Dach (0.47 PPG), Kaden Pitre (0.46 PPG), and Shea Busch (0.45 PPG) giving the Nittany Lions a strong group of first-year players.

Sophomore netminder Josh Fleming returns as the top-rated goaltender. Freshman netminder Ryder Fetterolf could also see a good amount of time in net.

Michigan State will feature one of the more exciting young rosters in the Big Ten, posting a composite score of 18.56 while returning 61% of last year’s group and welcoming a 30% freshman class loaded with high-end talent.

Up front, transfer forward Cullen Potter leads the Spartans at a projected 1.14 PPG. Meanwhile, sophomores Ryker Lee (0.94 PPG) and Anthony Romani (0.82 PPG) make Michigan State’s top three one of the more productive trios in the Big Ten, and Tommi Mannisto (0.57 PPG) and Gavin O’Connell (0.46 PPG) provide veteran depth behind them.

The freshman class is exceptional. Defenseman Tommy Bleyl (0.67 PPG) headlines the group as one of the more offensively dynamic freshman blueliners in the country, while forward Ethan Belchetz (0.61 PPG), defenseman Chase Reid (0.59 PPG), and forward Jack Hextall (0.31 PPG) give the Spartans a first-year group capable of contributing immediately.

Between the pipes, transfer goaltender Quentin Sigurdson posted terrific underlying metrics in a limited role with Northeastern last season. Freshman goaltender Joshua Ravensbergen is also in play here for significant time and projects to be an above average goaltender.

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After a down year in 2025-26, Minnesota is firmly back in the mix, posting a composite score of 17.78 while returning 53% of last year’s roster and adding 17% via the transfer portal to supplement a strong returning core.

Senior forward Brodie Ziemer (1.07 PPG) and St. Cloud State transfer Austin Burnevik (1.03 PPG) give the Gophers one of the better one-two punches in the conference, and sophomore L.J. Mooney (0.97 PPG) makes it a true top-line threat that ranks among the best trios in the Big Ten. Javon Moore (0.68 PPG), Beckett Hendrickson (0.53 PPG), and Tanner Ludtke (0.48 PPG) provide veteran depth across the forward group, while Minnesota State transfer defenseman Evan Murr (0.67 PPG) adds significant offensive punch from the blue line.

The incoming class features forward Jacob Kvasnicka (0.56 PPG) as the top freshman scorer, with OHL standout Christian Humphreys (0.53 PPG) adding further upside up front and defenseman Tarin Smith (0.44 PPG) providing good offense from the back end. Wyatt Cullen (0.47 PPG) feels like one to exceed his median projections.

Transfer goaltender Melvin Strahl projects as the top-rated goaltender for the Gophers, although it’s important to note that his projections are based on a three-game sample size with Michigan State last year. Senior Luca DiPasquo is also in play here, as is freshman netminder Alex Sabev.

Ohio State enters 2026-27 as a fringe top 25 program, with a composite score of 15.99, returning 61% of last year’s roster and building around a proven top-end forward.

Up front, Jake Karabela leads Ohio State at 1.00 PPG and returns as one of the more dangerous forwards in the Big Ten. Felix Caron (0.86 PPG) gives the Buckeyes a capable second option, and Max Montes (0.70 PPG) rounds out a reliable top three. Ryan Gordon (0.55 PPG) and Minnesota transfer Erik Pahlsson (0.43 PPG) provide good depth behind them.

The freshman class features forward Ben Wilmott (0.61 PPG) as the top incoming scorer, with defenseman Jakub Fibigr (0.41 PPG), and forwards Evan Jardine (0.37 PPG) and Ryan Rucinski (0.30 PPG) adding quality first-year depth across the lineup.

Between the pipes, freshman Jakub Milota projects as the top-rated goaltender, though that projection is based on his 2024-25 numbers in the QMJHL, given he was limited to just three games there last year. Returning goaltender Kris Eberly and Bentley transfer Jack Erickson are also in play here.

Notre Dame enters 2026-27 with a composite score of 12.82, returning 70% of last year’s roster and will rely on a veteran-heavy core to carry the load, with just 13% of the roster comprised of freshmen.

Up front, Evan Werner leads the Fighting Irish at 0.89 PPG. Danny Nelson (0.83 PPG) gives Notre Dame a reliable second option, and defenseman Paul Fischer (0.70 PPG) provides significant offensive production from the blue line. Cole Brown (0.56 PPG), transfer Michael Quinn (0.53 PPG), and Pano Fimis (0.51 PPG) round out an experienced forward group.

With only 13% freshmen, the incoming class is small but very talented. Forward Beckham Edwards (0.42 PPG) headlines the group, with Danny Klaers (0.25 PPG) adding depth on the blue line. Sammy Nelson (0.22 PPG) will look to outperform his projection as one of the top 2027 NHL Draft-eligible prospects.

Between the pipes, junior Nick Kempf returns as the top-rated goaltender. Air Force transfer Toby Hopp could help take the load off Kempf. As will incoming freshman Patrick Quinlan, who posted modest numbers in the OHL last year.

Beginning July 1, I will be previewing one conference every two weeks in advance of the 2026-27 NCAA Hockey season getting underway in October.

My previously developed predictions model serves as the basis for these roster outlooks, which identifies each team’s top 15 skaters’ projected point-per-game production, top-rated goaltender, and composite, forwards, defensemen, goalie, and depth ratings.

Up first, the NCHC.

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Denver enters 2026-27 as one of the most balanced rosters in the NCHC, posting a composite score of 22.55, while returning 65% of last year’s championship roster.

Up front, senior Sam Harris projects as Denver’s top offensive weapon at a projected 0.92 points per game. James Reeder (0.82 PPG) and Clarke Caswell (0.81 PPG) give Denver a true top-three punch, with all three projecting as legitimate point-per-game threats. Kristian Epperson (0.63 PPG) and Kyle Chyzowski (0.62 PPG) round out a deep, experienced returning forward group.

Forward Jack Pridham headlines Denver’s incoming class with a projected 0.56 PPG, while defensemen Daxon Rudolph (0.50 PPG) and Ryan Lin (0.44 PPG) bring offensive intrigue to the blue line. Forward Ryan Miller (0.47 PPG) is another freshman to keep tabs on.

Between the pipes, sophomore Johnny Hicks projects as the top goaltender in the country, not too difficult to imagine following last year’s historic run, albeit it’s probably fair to expect his numbers to come down a bit over the course of a full season.

North Dakota projects to have one of the most dynamic rosters in the NCHC, posting a composite score of 21.38, while returning just 48% of last year’s group, with 35% freshmen and 17% transfers injecting new energy into the program.

Up front, sophomore Cole Reschny is the headliner, projecting at 1.03 PPG to lead the Fighting Hawks. Will Zellers (0.98 PPG) gives North Dakota a lethal one-two punch at the top of the lineup, and Mac Swanson (0.80 PPG) adds a reliable third option. Transfers Kasper Magnussen (0.71 PPG) and Gavin Lindberg (0.64 PPG) and returning sophomore Jack Kernan (0.65 PPG) give Dane Jackson plenty of depth to work with, making UND’s forward unit one of the deepest in the conference.

Freshman defenseman Carson Carels headlines the incoming class at a projected 0.49 PPG, while forward Cooper Simpson (0.39 PPG) and defenseman Ethan Mackenzie (0.38 PPG) bring additional upside to the table.

Between the pipes, Jan Spunar returns after posting strong numbers last season and figures to once again be a wall in net for North Dakota. Freshman Caleb Heil will likely also see time in net.

Western Michigan figures to ice one of the most complete rosters in the NCHC, posting a composite score of 19.43 while returning 66% of last year’s group, a deep and experienced core that gives head coach Pat Ferschweiler plenty to build around.

Returning forward William Whitelaw leads the Broncos at a projected 0.92 PPG. Zaccharya Wisdom (0.78 PPG) gives Western Michigan a capable second option, and defenseman Joona Vaisanen (0.74 PPG) — after missing most of last year with an injury — will look to once again provide elite offensive production from the blue line. Ty Henricks (0.65 PPG), Bobby Cowan (0.62 PPG), Cole Spicer (0.55 PPG), and Zach Nehring (0.55 PPG) round out a returning forward group with terrific depth.

The incoming class is headlined by Minnesota Wild draft pick Adam Benak (0.58 PPG), with Tommy Lafreniere (0.46 PPG), Nic Skykora (0.39), and defenseman Edison Engle (0.30 PPG) adding complementary pieces to an already experienced roster.

Between the pipes, Quentin Miller stays within the conference as a transfer addition and provides Western Michigan a proven starter behind a roster built to compete deep into the year.

Minnesota Duluth features perhaps the most lethal forward group in the NCHC, posting a composite roster score of 18.74 while returning 74% of last year’s group.

Hobey Baker-winner Max Plante leads the way at a projected 1.32 PPG, followed closely by Zam Plante (1.19 PPG) and Jayson Shaugabay (1.12 PPG), forming arguably the best top line in college hockey. Defenseman Ty Hanson (0.86 PPG) adds elite punch from the blue line, and transfers Barrett Hall (0.80 PPG) and Peyton Platter (0.53 PPG) give Duluth a deep, experienced supporting cast up front.

The incoming class is headlined by former NTDP forward Victor Plante (0.43 PPG), with defenseman Keith McInnis (0.31 PPG) and forward Kade Kohanski (0.30 PPG) adding depth to the mix.

Between the pipes, Adam Gajan figures to be the starter on Opening Night, but look for incoming freshman Carl Axelsson to hold his own and carve out a meaningful role after posting strong numbers in the USHL this past season.

Colorado College will look to make some noise in the NCHC, posting a composite score of 17.41 while returning a remarkable 83% of last year’s group.

Up front, Owen Beckner leads the Tigers at a projected 0.82 PPG. Klavs Veinbergs (0.59 PPG), Connor Hvidson (0.58 PPG), Bret Link (0.56 PPG), and Wilson Bjorck (0.55 PPG) give Colorado College one of the deeper returning forward groups in the league. Defenseman Mats Lindgren (0.59 PPG) and Max Burkholder (0.53 PPG), who missed most of last season due to injury, add significant offensive production from the blue line.

With only 13% freshmen, the incoming class is slim but includes WHL standout Zac Olsen (0.39 PPG), who brings immediate scoring upside, and Ty Halaburda (0.36 PPG).

Between the pipes, Jackson Unger projects as the top-rated goaltender for the Tigers and will look to take over full-time duties after Kaiden Mbereko turned pro. Beckham Dempsey could also see time in net.

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Miami will look to take the next step after an 18-win season firmly put the RedHawks back on the map. Anthony Noreen’s group projects as a well-rounded unit, posting a composite score of 15.64 while returning 61% of last year’s group and welcoming a 30% freshman class.

Matteo Giampa leads the RedHawks with a projected 0.85 PPG, giving Miami a legitimate offensive focal point. David Deputy (0.73 PPG) and Kocha Delic (0.72 PPG) form a strong second and third option, and Ilia Morozov (0.64 PPG) brings additional punch as one of the team’s younger contributors. Ryan Smith (0.55 PPG) and Doug Grimes (0.50 PPG) round out a returning forward group with solid experience.

Forward Ryan Seelinger (0.38 PPG) headlines Miami’s incoming class, with Layne Loomer (0.36 PPG) and Charles Blanchard (0.32 PPG) adding forward depth, while defenseman Timofei Runtso (0.29 PPG) and forward Matous Kucharcik (0.28 PPG) give the RedHawks a first-year group with multiple contributors ready to step up.

Between the pipes, Matteo Drobac returns to the Miami net and projects as an above-average netminder after recording solid numbers a season ago. Russian netminder Yegor Yegorov could also carve out a role as a freshman.

St. Thomas makes the jump to the NCHC and features a roster with a composite score of 15.15, returning just 39% of last year’s group while adding 26% via the transfer portal.

Lucas Van Vliet is the engine for the Tommies, projecting at 1.03 PPG. Nathan Pilling (0.72 PPG) gives St. Thomas a capable second option, and defenseman Chase Cheslock (0.66 PPG) adds offensive production from the blue line. Nick Williams (0.51 PPG) and Dylan Godbout (0.50 PPG) round out a returning group with proven contributors at every level.

The freshman class brings immediate contributors in David Rozsival (0.34 PPG) and Aiden Grossklaus (0.33 PPG) up front, with Jackson Potulny (0.30 PPG) and Kade Stengrim (0.29 PPG) adding further depth. Former North Dakota commit Andrew O’Neill (0.25 PPG) is a name to watch as a potential freshman breakout candidate.

Between the pipes, freshman Ajay White projects as the top goaltender after posting elite numbers in the USHL this past season, with Carsen Musser returning to provide experience and depth in the goaltending room.

After an underwhelming 2025-26 season, Arizona State features one of the most heavily rebuilt rosters in the NCHC, posting a composite score of 15.07 with only 26% returning and 39% of the roster coming via transfer.

Transfers Ben Muthersbaugh (0.91 PPG) and Olivier Houde (0.76 PPG) and returning sophomore Jack Beck (0.90 PPG) give the Sun Devils a dangerous top-line punch. Kyle Smolen (0.60 PPG) and Logan Morrell (0.45 PPG) provide a steady returning base behind them, and defenseman Matthew Mayich (0.40 PPG) contributes offensively from the back end.

Freshman forward Nathan Behm (0.56 PPG) headlines an exceptional incoming class that also includes Lukas Sawchyn (0.54 PPG) and defenseman Jonas Woo (0.63 PPG), with the latter projecting as one of the better freshman blueliners in the country.

Between the pipes, Samuel Urban will look to bounce back after a tough freshman campaign. Transfer Sam Hillebrandt figures to see time in net as well.

Nebraska-Omaha enters 2026-27 with a composite score of 14.31, returning 57% of last year’s roster and boasting an impressive 7.09 depth rating.

Up front, Maxime Pellerin and Luke Woodworth share top point projections at 0.65 PPG each, giving the Mavericks a reliable dual-threat at the top of the lineup. Brett Hyland (0.58 PPG) and Trevor Wong (0.58 PPG) make Omaha’s top four one of the more evenly distributed forward groups in the NCHC. Marcus Nguyen (0.44 PPG) and Sean Tschigerl (0.38 PPG) add depth further down the lineup.

Freshman forward Gavin Hodnett (0.49 PPG) headlines a fun incoming class that projects to contribute immediately, with Aiden Oiring (0.44 PPG), Carter Sotheran (0.44 PPG), and Vincent Desjardins (0.42 PPG) giving the Mavericks a deep first-year group.

Between the pipes, freshman William Lacelle projects as a high-quality starter after posting outstanding numbers in the QMJHL this past season.

The 2026-27 season will mark Nick Oliver’s first as head coach at St. Cloud State, as the Huskies project a composite score of 13.11 while returning just 31% of last year’s roster and welcoming 43% freshmen and 26% via the transfer portal.

Up front, Jake Southgate leads the Huskies at 0.65 PPG, with William Ahlrik (0.60 PPG) and Gavyn Thoreson (0.58 PPG) giving St. Cloud a reliable top three. Nolan Roed (0.50 PPG) and Noah Urness (0.49 PPG) add depth and experience that will be valuable as the younger pieces develop.

The freshman class features Brock Souch (0.47 PPG) and EJ Paddington (0.47 PPG) as the top incoming forwards, while Adam Vigfusson (0.37 PPG), Will Sharpe (0.36 PPG), and defenseman Darels Uljanskis (0.37 PPG) round out a group with genuine upside. Carson Pilgrim (0.23 PPG) is a name to watch as a potential breakout candidate.

Between the pipes, Patriks Berzins projects as the top-rated goaltender for the Huskies, while sophomore Yan Shostak and freshman Mason McElroy via the NAHL also figure to be in the mix for playing time.

How many points will a player produce next season? How will a freshmen goaltender perform? How does a projected roster stack up?

With college hockey gaining increasing popularity, I wanted to introduce something new. Something useful that can help us answer these questions. Rarely is there a systematic, reproducible answer that covers every player on every roster at this level—freshmen, veterans, transfers, and goalies alike.

That is what I have spent the last several months building.

Before projecting anything, there is a more important question to answer first: does any of this actually work?

Before pointing the model at 2026-27 rosters, the entire framework was built and validated using 2024-25 end-of-season data to project 2025-26 production. Those results were then checked against what players actually did. Every skater. Every goalie.

That audit validated the translation logic, the age curves, and the model framework before a single future projection was generated.

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The Junior Translation

A 70-point junior season sounds impressive, but 70 points in the USHL is not the same as 70 points in the OHL, which is not the same as 70 points in the NCAA. Context matters.

Before any junior player enters the model, his production is run through a league-specific translation formula. As I’ve noted in previous studies, scoring rates translate differently depending on the source league.

Since those initial studies, I have received numerous inquiries regarding NAHL data. Unfortunately, my data source, InStat, lacks recent data for the league. To bridge this, I used 2022-23 NAHL data, observed how those rates translated to the NCAA for 2023-24, and incorporated those findings into my model.

Of note, until I have more of a sample size to work with, ECHL players have been omitted from the model.

For returning NCAA players, the translation step is skipped entirely. Their college production history becomes the direct input, adjusted forward based on role and development arc. The effects of the transfer portal were also factored into these projections.

The Age Development Curve

An 18-year-old freshman and a 21-year-old junior are fundamentally different prospects; treating them identically would be a modeling error. After studying four seasons of NCAA data on how production evolves with age, a clear pattern emerged: forwards peak early, defensemen peak late.

The 18-to-19 transition is when most skilled forwards make their biggest offensive jump. By 20 or 21, that spike has usually already occurred; future gains come from expanded roles, not raw development. Defensemen work on a longer runway. Offensive upside at the blue line often does not fully emerge until a player is logging top-four minutes and regular power-play time as an upperclassman.

Both the player’s age at the start of the season and its squared value are included as model features, allowing the framework to capture the nonlinear shape of that development curve rather than assuming a straight-line progression.

The transfer portal adds another layer. A young forward moving to a stronger program or leaving for more playing time can see an immediate scoring jump, while a young defenseman changing systems often struggles before settling in. By the time a player crosses age 21, those effects largely disappear, as the data revealed that experienced transfers generally carry their production with them directly into a new situation.

What the Box Score Misses

Points alone do not paint the full picture.

To measure actual efficiency, the model considers additional average-per-game data, ensuring a fourth-liner producing in limited minutes receives a fair comparison against a top-line forward playing 22 minutes a night.

The model incorporates six underlying rate metrics that capture what the box score cannot: expected goals, slot passes, puck touches, scoring chances, takeaways, and zone entries. Rounding out the twelve features are raw PPG from the prior season, age, age squared, transfer status, CORSI percentage, and puck battles won percentage.

Together, these inputs form the model’s baseline vision.

How the Skater Model Works

Once features are cleaned, translated, and rate-scaled, they are processed through a hierarchical Bayesian regression model that accounts for the environment in which each player competes.

The core idea is that a point scored in the Big Ten and a point scored in the AHA do not carry the same weight. Therefore, conference strength is baked into the model as a structural component.

For incoming freshmen with no NCAA track record, the model applies a conservative approach to the adjustment required to compete at this level. First-year players are volatile by nature, and a model that overpromises on freshmen is a model that cannot be trusted.

Furthermore, the Bayesian approach produces something a traditional model cannot: uncertainty. Every skater is assigned not just a projected PPG, but a realistic range of outcomes, reflecting the model’s confidence in that specific projection.

What About Goalies?

Projecting goaltenders means throwing the points framework out entirely. Goalies don’t score. So what do you measure?

The answer is GSAx, or Goals Saved Above Expected). Rather than counting goals allowed, we measure how many goals a goalie allowed compared to the quality of shots he faced.

The goalie model uses a two-stage approach. First, a Random Forest is trained on 2025-26 NCAA goaltender data, using age, save percentage, and league translation factors, to produce a “prior” projection for each goalie in the 2026-27 pool. Then, a Bayesian update combines that prior with the goalie’s observed performance, weighted by ice time. The result balances the model’s expectation with the goalie’s actual show of form.

This estimate is then scaled into a 0–10 rating centered at 5.0, where anything above 5.0 implies a strong goaltender and anything below indicates an underperformer. Conference strength multipliers are applied here as well.

For junior goalies entering the college game, I applied a similar translation logic to observe how junior goaltending rates convert to the NCAA level.

(If you’d like to see a separate write-up on the goaltending rate translations, I’d be happy to share that. If you’re more interested in the TL;DR version: USHL, OHL: Good, WHL: Stable, QMJHL/BCHL: Adjustments Needed)

What the Audit Actually Found

This is where the model earns its credibility.

For skaters, the results were strong. Across more than 1,400 players, the model explained 79% of scoring variance league-wide. Put plainly: nearly four-fifths of the difference in how players scored across an entire season was captured by factors the model accounted for before the puck dropped. The linear correlation between projected tiers and actual production sat at 90%.

On a per-player basis, nearly two-thirds of skaters were projected within 0.10 PPG of their actual output. Nearly 80% landed within 0.15 PPG, and over 90% were within 0.20. The average error across the entire league was 0.097 PPG—about 3.3 points per season.

For goaltenders, the story is different. Across 102 qualifying goaltenders in 2025-26, the model drew a clear line between the best and worst in the country, revealing a gap of more than a full goal per 60 minutes between the top and bottom quartiles. Among full-season starters, the elite tier clustered tightly at the top, while struggling starters separated themselves just as cleanly at the bottom.

Denver’s Johnny Hicks was the standout, saving roughly half a goal more per game than expected, the best mark in the qualifying pool. The correlation between the model’s expectation and what goalies actually allowed confirms the signal is real, even if factors like team defense and sample size account for the remaining gap.

Putting It All Together: Composite Team Ratings

Individual player projections are useful, but team ratings reveal how programs stack up entering the year.

The composite team rating is the sum of three components: Forward Rating, Defense Rating, and Goalie Rating. The forward and defense ratings aggregate projected PPG from each position group, using Bayesian distributions to produce a mean team rating and a full uncertainty range (the lower bound at the 5th percentile and the upper bound at the 95th).

The goalie component uses the top-rated qualifying goalie per program. Goalie uncertainty—expressed as a confidence interval from the Bayesian posterior—is incorporated into the team’s total range: a goalie with a tight distribution compresses the team’s uncertainty, while a wide distribution expands it. A team with an elite goalie and a narrow confidence interval gets credit for both the rating and the reliability.

The end result is a figure that looks like this (note the roster is subject to change and only used for the purpose of this illustration):

If you are trying to interpret this graphic, please note that it does not suggest Michigan is the 25th-ranked team. In fact, when compiling preliminary rosters—including outgoing seniors, incoming transfers (which do not apply to Michigan for this particular scenario), and incoming freshmen—the Wolverines are frequently rated as a Top-3 team in the country.

Instead, the graphic indicates that higher ratings for Forwards, Defensemen, and Goalies—and therefore the Composite Team Rating—correlate to better performance.

Regarding player projections, I have listed the Top 15 scorers, their projected age at the start of the 2026–27 season, their mean predicted Points Per Game (PPG), and their PPG range based on the confidence interval (CI).

For example, Michael Hage, who is entering his junior season at Michigan, has a PPG range of 1.25–1.53, resulting in a mean projected PPG of 1.39, which would build slightly off his 1.33 PPG season as a sophomore. Conversely, for an incoming freshman like J.P. Hurlbert, who has a PPG range of 0.62-0.94, the junior translation factors are applied plus the Bayesian performance.

What Comes Next

This model is not a crystal ball. It does not account for undisclosed injuries, line shuffles, or the random bounces that shape an individual season. What it does is give every skater and every goalie in college hockey a defensible starting point.

With 2026-27 rosters taking shape, projections will begin to roll out this summer, both here and on Twitter/X. Team and conference breakdowns will follow, covering the full picture from incoming freshmen to proven returners, portal transfers, and the goalies behind them.

More to come. Thanks for reading.

— Ryan

With the transfer portal now closed, we can start to piece together some of the real difference-makers set to skate with new teams next season. To identify the 10 best additions of this cycle, I leaned on my recently developed Skater and Goaltender Impact Ratings alongside advanced statistical data provided by InStat, providing a comprehensive look at how each player performed at their previous program and what they might contribute at their new one.

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Evan Murr (D) | Minnesota State → Minnesota | Impact Rating: 1.68

Murr was one of the most explosive offensive defensemen in the transfer portal, arriving at Minnesota with a 1.68 Impact Rating that ranks among the very best of any player in this entire class. His 1.10 Offensive Impact is the defining feature of his profile, an elite mark that places him in rarefied company among college hockey blueliners and signals a player who fundamentally transforms his team’s offensive structure when he steps on the ice.

He averaged 22:43 of ice time per game at Minnesota State over 40 games, one of the highest marks of any skater in the dataset. Murr’s 0.28 goals and 0.50 assists per game produced a 0.77 points-per-game pace from the blue line, and his 136.7 puck touches per game was among the highest in the country for a defenseman, illustrating how central he was to everything Minnesota State did from the back end.

His 0.63 Production score and 0.15 Misc rating round out a portfolio that goes well beyond just raw offensive numbers. The one area to manage is his -0.28 Defensive Impact, which suggests he is best utilized in an offensive-zone-heavy deployment rather than as a shutdown pairing defenseman.

Murr’s arrival to a Minnesota program led by first-year head coach Brett Larson comes at a great time, as he will provide an immediate upgrade to the Gophers’ power play and their entire offensive identity from the back end.

Justin Poirier (F) | Maine → Penn State | Impact Rating: 1.67

Poirier was perhaps the best goal scorer available in this transfer class, and gives Penn State a solid replacement after Aiden Fink turned pro. His 1.67 Impact Rating is anchored by a remarkable 0.98 Production score — one of the highest in the entire dataset — indicating that he consistently converts offensive opportunity into real results at an elite rate.

What makes his profile particularly impressive is that he accomplished this over just 27 games, posting 0.68 goals and 0.39 assists per game at Maine. He is a shot-generation machine, averaging 7.3 shot attempts per game, and his 3.64 shots on goal per game reflects a player who hunts high-quality looks. His 0.14 Defensive Impact keeps him in positive territory on both sides of the puck, meaning he’s not a liability away from the offensive zone.

He averaged over 16 minutes of ice time per game and could see a bump in TOI with the Nittany Lions. On paper, Poirier has every analytical indicator of a player who will put up top-tier numbers in 2026-27.

Tommy Cronin (F) | Alaska → Union | Impact Rating: 1.42

Cronin is arguably the most underrated name in this entire portal class, a forward whose 1.42 Impact Rating was quietly built outside of the bright lights of college hockey. At Alaska, he posted 0.35 goals and 0.55 assists per game over 18 games, generating real offense against legitimately difficult competition on a nightly basis.

His 0.36 Defensive Impact is outstanding, placing him among the most complete two-way forwards in the portal, while his 0.72 Production rating rounds out a profile that screams high-floor contributor with room to grow in a larger role. Union gets a player who was doing his damage with a minimal supporting cast in Fairbanks, and the upgrade in teammates and conference caliber should only unlock more from him.

He averaged just 13:56 of ice time per game in a limited role that undersells his true capability. It’s probably fair to expect Cronin to be one of the ECAC’s more productive forwards in 2026-27 as a direct result of this move.

Ben Muthersbaugh (F) | Union → Arizona State | Impact Rating: 1.33

Muthersbaugh leaves Union as one of the more polished forwards in the transfer market, bringing a 1.33 Impact Rating from a balanced offensive profile. He averaged 0.53 goals and 0.47 assists per game at Union, contributing a 1.00 points-per-game pace that indicates a reliable, consistent scorer rather than a boom-or-bust offensive player.

Muthersbaugh’s 0.86 Production score suggests a player whose puck work translates into actual goal-scoring contributions for his team. He led Union in shots attempted per game at 7.3 and his 4.19 shots on goal per game ranked among the highest of any forward in the portal class. His 0.24 Offensive Impact and steady 0.03 Defensive Impact paint the picture of a forward who is a net positive in every situation he’s deployed in.

Arizona State is a program looking to bounce back from a tough 2025-26, and Muthersbaugh gives them exactly the type of player to help them achieve that. He is a high-usage, low-risk addition who should immediately slot into a top-six role and produce at a point-per-game pace.

Austin Burnevik (F) | St. Cloud State → Minnesota | Impact Rating: 1.32

Burnevik’s move from St. Cloud State to Minnesota is one of the more interesting in-state transfers of the cycle, and his analytical profile suggests the Gophers are getting a quietly elite offensive contributor. His 1.32 Impact Rating was established despite playing on a St. Cloud State team that finished in the middle of the pack of the NCHC.

He averaged 0.53 goals and 0.47 assists per game over 36 games, racking up 7.2 shots attempted per game that indicates a player who is constantly generating offense. His 0.87 Production score is one of the stronger marks in this class, and his 0.14 Misc score adds additional context to a well-rounded analytical portfolio. The one flag in his profile is a -0.10 Defensive Impact, suggesting he is most effective when deployed in offensive-zone-heavy situations rather than as a matchup forward.

Burnevik joins his St. Cloud head coach Brett Larson at Minnesota, and he is a proven NCHC-level producer who should thrive in a system that figures to give him every opportunity to be a major producer in the Big Ten.

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Quentin Sigurdson (G) | Northeastern → Michigan State | Impact Rating: 1.24

Sigurdson arrives at Michigan State as one of the most analytically validated goaltenders in this transfer cycle, posting a 1.24 Impact Rating that places him firmly in the elite tier of all NCAA netminders. He spent his time at Northeastern largely working behind Lawton Zacher, one of the best goalies in the country, which means his strong numbers came in a backup-to-spot-start capacity.

His advanced metrics show exceptional rebound control and an ability to suppress high-danger scoring chances, consistently outperforming his expected goals conceded. The Spartans needed a legitimate answer in net following the departure of Trey Augustine, and Sigurdson provides exactly that. He’s a ready-made starter with elite underlying metrics.

His save percentage on high-danger, unblocked slot attempts is among the top tier of all goaltenders tracked in the dataset. Sigurdson is technically precise and composed, and at 6-foot-2, he provides a big frame in net for a Michigan State team looking to keep their foot to the pedal.

Kasper Magnussen (F) | Bemidji State → North Dakota | Impact Rating: 1.20

Magnussen brings North Dakota a proven, well-rounded forward whose 1.20 Impact Rating is one of the more complete profiles in this portal class. He averaged 0.30 goals and 0.57 assists per game at Bemidji State over 36 games, good for a 0.86 points-per-game pace.

His 0.75 Production score confirms the offensive output is backed by legitimate underlying efficiency, and his 0.09 Defensive Impact keeps him squarely in positive territory as a two-way player. His 2.24 hits per game at Bemidji State speaks to a physical element that will translate well to the NCHC and identity of North Dakota.

Magnussen is a natural fit for the Fighting Hawks’ middle-six, where his versatility and work rate will allow the coaching staff to deploy him in a variety of situations. His profile shows consistent production, defensive awareness, and physical play, all signs that he will be able to hold his own in one of the toughest conferences in college hockey.

David Klee (F) | North Dakota → Notre Dame | Impact Rating: 1.15

Klee departs North Dakota with a 1.15 Impact Rating and heads to Notre Dame as a player whose value is rooted in two-way reliability and defensive competence. His 0.55 Defensive Impact is exceptional for a forward, ranking among the top defensive-specific scores for skaters in this entire portal class, and speaks to a player who was a trusted piece of the Fighting Hawks' defensive structure.

He averaged just 10:14 of ice time last season on a North Dakota team that was loaded at forward, which means his efficiency numbers were generated in a condensed role. His 0.15 goals and 0.15 assists per game are modest on the surface, but his 0.32 Production score reflects a player who contributes beyond the scoresheet through other areas of his game.

Klee is likely to see an increased workload at Notre Dame, and the underlying metrics suggest he has the two-way game to handle it. The Irish get a forward with a winning pedigree and legitimate offensive upside. I would not be at all surprised to see him emerge as one of the Big Ten's most productive forwards in 2026-27.

Cullen Potter (F) | Arizona State → Michigan State | Impact Rating: 1.10

Potter is a high-usage offensive forward whose move to Michigan State gives the Spartans a legitimate scoring threat with one of the more intriguing analytical profiles in the portal. His 1.10 Impact Rating at Arizona State was highlighted by a strong 0.93 Production score, aided by 12 goals and 26 points in an injury-shortened sophomore season.

His 5.3 shot attempts per game indicates consistent puck pursuit and an aggressive offensive mindset, while his 20:08 average ice time per game at Arizona State was among the highest of any forward in the dataset. Potter is a natural power play weapon whose quick release and ability to find space in the offensive zone make him difficult to contain for defenders.

Stepping into a Michigan State program looking to repeat as Big Ten regular season champions, at minimum, the environment appears to be favorable for him to take a significant step forward in his production. With projected time at 5v5 and on the power play, Potter has every indicator of being a top-six producer in the Big Ten.

Gavin Lindberg (F) | Colorado College → North Dakota | Impact Rating: 0.96

Lindberg arrives at North Dakota as one of the more polished two-way forwards in this portal class, bringing a 0.96 Impact Rating developed from high-volume production at Colorado College. He averaged 0.22 goals and 0.43 assists per game over 36 games while logging 18:43 of ice time per game, and those numbers underscore his identity as more of a facilitator than a finisher.

His 0.58 Production score confirms the underlying efficiency behind those numbers, and his 0.15 Offensive Impact reflects a player who moves the puck cleanly and generates quality looks rather than just volume. His 3.08 hits per game was one of the higher marks among forwards in the dataset, adding a physical dimension that complements North Dakota’s style of play.

North Dakota adds a versatile, experienced forward whose profile gives every indication he can handle the jump to the Fighting Hawks and be a leader both in terms of presence and on-ice production.

Honorable Mentions: Jimmy Clark (Minnesota → Michigan State), Olivier Houde (Lindenwood → Arizona State), Jake Southgate (Lindenwood → St. Cloud State), Jack Stockfish (Holy Cross → Quinnipiac), Hudson Malinoski (Providence → North Dakota)

Paired with our Skater Impact Ratings Tool — and a much shorter explanation — I present to you my Goaltender Ratings Tool.

These ratings quantify performance into a per-game average, utilizing a weighted formula of saves and goals allowed to isolate the true efficiency of goaltenders.

Just like the skaters, these ratings include the 2025-26 campaign and historical data dating back to 2021-22. Use the buttons in the table below to navigate between seasons, or use the search function to locate specific players or players in a program (e.g., “Denver”).

Moving forward, this post will serve as the hub for all 2026-27 data and beyond, with updates provided weekly throughout the year. Full access to this database, including 2026-27 updates and historical data back to 2021-22, is a paid feature.

Your subscription, just $5 a month, gets you access to both Skater and Goaltender Impact Ratings and provides the resources necessary to maintain this modeling. Your support means the world to me.

If you have any questions about this tool (or anything else), feel free to send me an email or reach out to me on Twitter/X!

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Glossary

Impact Rating: Weighted formula that factors saves and goals allowed into a single score and applies a conference-specific multiplier to normalize performance across different levels of competition.

Minimum 5 games played

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What does “impact” actually look like in college hockey?

To find out, I built a historical database using a modified version of Dom Luszczyszyn’s Game Score model. These Player Impact Ratings quantify performance into a per-game average by synthesizing four essential statistical categories.

This database includes comprehensive ratings for the 2025-26 campaign and historical data dating back to 2021-22. Use the buttons in the table below to navigate between seasons, or use the search function to locate specific players or players in a program (e.g., “Denver”).

Moving forward, this post will serve as the central hub for all 2026-27 data and beyond, with updates provided weekly throughout the year.

Full access to this database, including 2026-27 updates and historical data back to 2021-22, is will be paid feature, the first of several analytical tools being added to my Substack. Your subscription provides the resources necessary to maintain this modeling and your support means the world to me.

If you have any questions about this tool (or anything else), feel free to send me an email or reach out to me on Twitter/X!

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Glossary

Off Impact (Offensive Impact): Measures how much a player’s on-ice 5v5 offensive performance—driven by expected goals and plus-rating—exceeds the team and national average.

Def Impact (Offensive Impact): Evaluates a player’s ability to suppress opponent scoring by comparing their on-ice expected goals against and minus-rating to national baselines at 5v5.

Misc (Miscellaneous): Accounts for secondary contributions like blocked shots, penalties drawn, and faceoff efficiency to capture specialized value not seen in scoring stats.

Prod (Production): Quantifies individual offensive output by weighting goals, primary and secondary assists, and personal expected goals.

Impact Rating: Combines all four metrics into a single score and applies a conference-specific multiplier to normalize performance across different levels of competition.

Minimum 10 games played

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The 2025-26 NCAA hockey season marked an important shift in the landscape. With the lifting of eligibility restrictions on players from the Canadian Hockey League (OHL, WHL, and QMJHL), the talent pool for college hockey expanded overnight.

This “Great Migration” — for lack of a better phrase — provided a built-in experiment for hockey analytics and answered a lot of questions. Namely, how will CHL scoring rates translate to the NCAA game? And how do they compare to the USHL and BCHL?

This report provides an in-depth deep-dive into the inaugural CHL translation data, utilizes a tiered production model to account for role compression, and contextualizes these findings against the established three-year weighted averages of the USHL and BCHL.

(Note: All data was derived from InStat and the point averages may differ slightly from other media outlets)

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Junior Hockey Translation

Comparing the 2024-25 junior production of more than 150 CHL players to their 2025-26 NCAA freshman totals reveals a distinct hierarchy of league strength. The data shows that the jump to the NCAA carries a significant “production tax” or drop-off in scoring that fluctuates depending on which league a player is coming from.

The 2025-26 data reveals a clear divide in how junior production carries over to the NCAA, with the OHL and USHL emerging as the most stable pathways for offensive transition. These two leagues lead the group with median point factors of 0.58 and 0.56, respectively, suggesting that players retained over 55% of their junior scoring pace upon entering the college game. In contrast, a notable gap exists within junior hockey itself. While the OHL shows high stability, the WHL and QMJHL faced a much steeper drop-off, where freshman typically saw their scoring totals cut by more than half.

Specific metrics further highlight the OHL’s unique position, particularly in goal-scoring retention, where its 0.61 factor significantly outperformed the QMJHL’s 0.40. On the playmaking side, the USHL leads the field with an assist translation factor of 0.59, indicating that its distributors find the most consistent success in the collegiate environment. Meanwhile, the BCHL remains the most difficult league to project, carrying the highest drop-off in the study with a median point factor of 0.35.

The Tiered Approach

The tiered analysis of the OHL uncovers a non-linear relationship between a player’s junior role and their eventual collegiate output. Rather than a flat conversion rate across the board, the data indicates that scoring retention is heavily influenced by the specific “tier” of production a player occupied before entering the NCAA. This breakdown suggests that the jump to college hockey affects high-volume scorers and bottom-six contributors in fundamentally different ways, challenging the assumption that elite junior numbers automatically lead to elite freshman totals.

The data highlights a unique statistical trend where depth producers from the OHL maintained the highest level of scoring stability. Players averaging under 0.6 points per game in junior hockey retained 73% of their production at the NCAA level, likely because the physical, defensive-minded roles they played in the OHL naturally carried over into the same style of play at the college level.

In contrast, elite producers faced a steeper drop-off — 54.0% retention rate — largely due to role compression; these high-volume scorers couldn’t replicate the same success against bigger and older competition, creating a temporary ceiling on their freshman stat lines. Despite this compression at the top, the OHL remains a premier developmental path, with even its highest-tier talent retaining more than half of its statistical value during the 2025-26 NCAA season.

OHL Case Studies

Michigan State’s Porter Martone stands out as a clear outlier, as his prolific 1.65 PPG junior pace translated to a 1.42 PPG freshman campaign for a remarkable 86.0% retention rate. Michigan’s Malcolm Spence provided a more representative sample of the top tier, seeing his 1.09 PPG transition into 0.63 PPG and aligning almost perfectly with the OHL elite median at 58.0%. Conversely, Kristian Epperson experienced a steeper developmental curve in his first season at Denver, with a 1.33 PPG junior season resulting in 0.55 PPG at the collegiate level, or a 41.4% retention rate.

What about the WHL and QMJHL?

A tiered examination of the WHL and QMJHL reveals how league-specific scoring environments and role compression dictated the recalibration of points in the NCAA. These findings suggest that the transition from Western and Atlantic Canadian junior systems is not uniform, as different levels of production faced varying production hits when moving to the college game. Understanding these variations is essential for determining which junior scoring rates are likely to persist and which are products of a more “open-style” junior game.

The WHL data highlights a significant recalibration for top-tier scorers, with the elite tier showing a points factor of 37%, which was the steepest adjustment among all the CHL leagues. This trend suggests that while the WHL allows for high-volume offensive output, those totals were difficult to replicate against more disciplined collegiate defensive systems.

While secondary producers show slightly improved retention at 44%, the league follows a broader inverse trend where depth players maintain the highest stability at 56%. This indicates that players in lower-line roles often possess the physical and desirable baseline that translated most effectively to the NCAA. Ultimately, the data shows that as production volume increases in the WHL, the likelihood of that production carrying over decreases, making role-specific evaluation a necessity for accurate forecasting.

Case Studies

Penn State’s Gavin McKenna serves as a notable example of high-end talent defying the league median. Despite an exceptional 2.28 PPG in the WHL, he maintained a 1.45 PPG pace in the NCAA for a impressive 63.6% retention rate. North Dakota’s Cole Reschny followed a similar trajectory, transitioning from 1.53 PPG in junior to 0.92 PPG as a freshman, which represents a solid 60% translation. In contrast, Denver’s Kyle Chyzowski’s transition mirrored the broader league trends for elite producers, as his 1.61 PPG junior output shifted to 0.59 PPG at the collegiate level, aligning perfectly with the 37.0% tier median.

The QMJHL data reveals an almost “V-shaped” translation curve, distinguishing it from the linear or inverse trends seen in other junior hockey leagues. The statistical profile suggests that the transition from the “Q” to the NCAA was highly dependent on a player’s specific role and skill set. While top-tier finishers show a surprising level of readiness, the middle-of-the-roster producers faced a disproportionately high production drop-off, indicating that aggregate point totals in this league can be particularly deceptive without more context.

The QMJHL stands as a league of extremes, where elite scorers retained 49% of their production, a figure that suggests high-end finishing talent in the league translated somewhat effectively to the collegiate level. However, this stability collapses in the secondary tier, which faced a sharp drop to a 31% production retention rate, the lowest mark for mid-level producers across all leagues and tiers.

Conversely, depth players returned to a higher stability of 53% points retention, reinforcing the transferability of physical utility roles. Ultimately, the first year of data confirms that while top-end QMJHL talent is often collegiate-ready, the middle of the roster requires a much higher level of scrutiny.

Case Studies

Maine’s Justin Poirier emerged as a significant outlier, carrying a prolific 1.35 PPG junior pace into a 1.07 PPG freshman season for an impressive 79.0% retention rate. Northeastern’s Jacob Mathieu also showed strong stability, translating 1.22 PPG into a 0.81 PPG collegiate campaign at a 66.0% clip. Meanwhile, Providence’s Julius Sumpf’s performance aligned more closely with the elite tier median, as his 1.03 PPG junior output resulted in 0.53 PPG at the college level, representing a 51.0% translation factor.

3-Year USHL and BCHL Weighted Averages

To truly understand the value of the CHL’s inaugural season, we must compare it to the “traditional” paths. Over a three-year aggregate, the USHL and BCHL provide a stable baseline for comparison.

The USHL Benchmark

The USHL remains the “closest relative” to the OHL. The three-year weighted analysis shows that USHL players retained 56% of their production going from junior hockey to the NCAA.

The USHL demonstrates high stability for depth players, who have shown to retain 64% of their scoring. Elite USHL producers showed a factor of 48% retention. These numbers are based on a significant sample size, confirming that the physical habits and defensive responsibility emphasized in the USHL translate effectively to the collegiate game.

The BCHL Outlier

The BCHL continues to be the league requiring the most aggressive adjustment. With a three-year median factor of 0.35, a point in the BCHL is worth significantly less than a point in the OHL or USHL, and therefore, the NCAA.

The BCHL shows a flat translation profile. Regardless of the player’s junior tier, scoring recalibrated to approximately 34% to 37%. This lack of variance suggests that the BCHL scoring environment impacts the entire roster similarly, requiring significant downward projections across all production levels.

Projections, Anyone?

Looking ahead to 2026-27, these translation models allow us to project how the next wave of CHL talent might adjust to the college game. A primary example is Nikita Klepov, a highly touted recruit expected to make the jump to college hockey. This season with Saginaw, Klepov averaged 0.55 goals and 0.89 assists for 1.45 points per game.

By applying the OHL’s tiered median factors, Klepov projects to record approximately 32 points over a nice, round numbered 40-game schedule at Michigan State. While this is certainly a conservative baseline, recent performances of high-end outliers like Porter Martone and Malcolm Spence suggest that elite prospects like Klepov often have the ceiling to significantly outpace these median projections.

Conclusion

To put a bow on this … The inaugural 2025-26 CHL data provides a foundational look at how the shifting landscape of college hockey eligibility impacts statistical projections. The results highlight a clear hierarchy in league strength, with the USHL and OHL (so far) serving as the most reliable predictors of NCAA success. These leagues emphasize a “heavy” style of play and defensive structure that mirrors the collegiate game, allowing even depth players to maintain a high degree of their junior production.

In contrast, the WHL and QMJHL demonstrated more volatility in the first year of data. While elite finishers in these leagues showed flashes of high-end transferability, the broader talent gaps and open-ice nature of these environments often lead to a steeper drop-off for mid-tier scorers.

While these findings establish a vital baseline, the CHL-to-NCAA remains an evolving experiment until we have more data to pull from. It will be interesting to see if the production lines stabilize after Year 2 of CHL eligibility. If I decide to do this again next year, I may separate the scoring for defenseman and forwards. Maybe even look at goaltending metrics.

For now, though, this is enough for what I had hoped to achieve. Thank you for reading.

In advance of Saturday’s National Championship game, I wanted to do an analytical analysis of the Top-10 players from the Frozen Four semifinals.

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Clarke Caswell, F (Denver)

Caswell was the most efficient offensive catalyst on Thursday, finishing as the only skater with multiple points (1G, 1A). His impact stretched far beyond the scoresheet, as he led all skaters with eight takeaways, acting as a primary disruptor of Michigan’s transition game. Caswell demonstrated elite puck security, completing 93.8% of his 32 pass attempts, which allowed Denver to sustain long offensive-zone cycles. He was also a physical presence in the trenches, winning 72.2% of his 18 puck battles, the highest efficiency among high-usage forwards. His ability to control the pace was evident in his 9:10 of offensive-zone possession time, consistently forcing defenders into uncomfortable positions. His ability to win net-front positioning was instrumental to his tip-in goal that tied the game with under three minutes left in regulation.

T.J. Hughes, F (Michigan)

The Hobey Baker finalist was the most persistent offensive threat on Thursday, posting a staggering 1.01 expected goals (xG). While Hughes was rewarded with only one goal, his shot volume and high-danger positioning consistently forced the Denver defense into survival mode. Hughes was central to Michigan’s puck-possession style, recording a team-high 24 Corsi+ and contributing six takeaways. He excelled in transition, successfully completing six zone entries and maintaining 29 seconds of direct puck control to help settle the offense. His two-way reliability was further highlighted by five successful breakouts, ensuring Michigan could exit its zone cleanly under pressure. Hughes effectively dictated the flow of play every time he stepped onto the ice.

Johnny Hicks, G (Denver)

Hicks delivered a goaltending masterclass, serving as the backbone for Denver in a game where they were outshot and outchanced. Facing a heavy barrage from Michigan’s elite offense, Hicks made 38 saves on 41 shots, finishing with an impressive 92.7% save percentage and +0.98 GSAx. He faced 3.98 expected goals against (xGA), meaning he effectively stole a goal for Denver to secure the win. His poise under pressure was evident in his 19 puck touches and 89.5% passing accuracy on outlet attempts, helping clear the zone. Hicks remained composed during sustained Michigan pressure, frequently freezing the puck or directing rebounds into safe areas to limit second-chance opportunities. Denver likely would not have survived the Michigan onslaught without his elite tracking and lateral mobility.

Jan Spunar, G (North Dakota)

In a losing effort, Jan Spunar was arguably the most impressive individual performer for North Dakota, keeping his team in a game where it was largely suppressed. Spunar faced 36 shots and stopped 34 of them, resulting in a stellar 94.4% save percentage and +1.08 GSAx, both the best among goaltenders in the semifinal round. He dealt with a high volume of quality chances, as reflected in his 3.08 xGA, significantly outperforming expectations. Spunar was flawless in puck distribution, completing 100% of his passes to help his defenders find exit lanes. His performance featured elite positioning and a refusal to give up on cross-crease plays, keeping the Fighting Hawks within striking distance.

Dakoda Rheaume-Mullen, D (Michigan)

Rheaume-Mullen was a dominant force on Michigan’s blue line, showcasing why his underlying metrics are so highly regarded. He finished with a remarkable 71.9% Corsi For percentage, meaning Michigan controlled nearly three-quarters of shot attempts while he was on the ice. His defensive instincts were elite, as he recorded six takeaways—the most among all defensemen—and consistently neutralized opposing rushes before they reached the slot. Rheaume-Mullen was a significant transition driver, contributing five successful zone entries and five clean breakouts to keep play moving north. His 1.26 net xG was the highest among all skaters, indicating a massive positive tilt in scoring chances during his shifts. He also contributed offensively, registering five shots and generating steady pressure from the point.

Jayden Perron, F (Michigan)

Jayden Perron embodied puck poise, leading all high-impact skaters with 31 seconds of direct puck control. He used that time effectively to survey the ice, completing two passes into the high-danger slot area. Perron’s goal was a product of his patience and skill, supported by an 80% passing accuracy that kept Michigan’s offense fluid. He also held his own physically, winning 68.8% of his 16 puck battles, often against larger defenders. Perron led the group with seven successful zone entries, consistently using his edges to back defenders off the line. His combination of playmaking and scoring touch made him a difficult matchup for Denver.

Ryan Botterill, F (Wisconsin)

Botterill served as the offensive spark plug for Wisconsin, scoring a goal and generating an impressive 0.88 xG. He was highly efficient with the puck, recording a perfect passing percentage and winning 55.6% of his puck battles to extend offensive-zone time. Botterill’s ability to find soft spots in North Dakota’s structure led to four high-quality shots on goal and several dangerous looks from the slot. His transition play was equally effective, as he completed four zone entries to penetrate the UND defensive setup. With over five minutes of offensive-zone possession time, he became the primary target for Wisconsin’s playmakers. Botterill’s combination of shooting ability and precise passing made him the most dangerous Badger in the semifinal win.

Ellis Rickwood, F (North Dakota)

Rickwood carried the offensive load for North Dakota, scoring its only goal and finishing with a strong 0.62 xG. He was the most technically sound skater for the Fighting Hawks, completing 95.2% of his 21 pass attempts under heavy Wisconsin pressure. Rickwood played a key role in transition, logging three zone entries and four breakouts, reinforcing his value as a true 200-foot player. His defensive work was also notable, as he recorded five takeaways to disrupt Badger momentum. Rickwood’s high motor was reflected in his 60% puck battle win rate, often emerging with possession in contested areas. He led his team in offensive-zone possession time (10:07), serving as the focal point of North Dakota’s attack.

Malcolm Spence, F (Michigan)

Spence played a vital role in Michigan’s territorial dominance, finishing with a team-high 24 Corsi+ alongside T.J. Hughes. Although he did not score, his 0.55 xG and three shots on goal reflected his consistent presence in dangerous areas. Spence contributed as a playmaker under pressure, recording an assist and completing 76.5% of his passes (13/17). He also added defensive value with three takeaways and two successful breakouts. His skating helped him sustain over six minutes of offensive-zone possession, applying constant pressure on Denver’s defense. Spence’s value lies in his ability to execute the subtle details that sustain extended offensive sequences.

Kyle Chyzowski, F (Denver)

The glue for Denver’s forward group, Kyle Chyzowski contributed the game’s first goal while playing a highly disciplined game. He excelled in distribution, finishing with a 90.9% passing accuracy that helped Denver exit its zone under pressure. Chyzowski added defensive value with four successful breakouts and three takeaways, easing the burden on his goaltender. He competed effectively in contested areas, winning 50% of his puck battles while maintaining over eight minutes of offensive-zone possession. His 0.47 xG suggests he consistently found scoring areas, even as Michigan controlled much of the shot share. Chyzowski’s mistake-free performance in a high-pressure environment was instrumental in Denver’s run to the finals.

With the 2026 Frozen Four set to begin next Thursday in Las Vegas, I wanted to do another deep-dive, analytically-driven preview. If you missed my last one for the Regional semifinal round, you can find that (here). Just like last time, this preview leans heavily on expected stat data to determine who might have the edge as we narrow our focus on crowning this year’s NCAA champion.

Same rules apply — All data is derived from InStat on a per-game average (unless otherwise stated) and may differ slightly from other media outlets. Additionally, the data referenced in this preview draws from both regular-season baselines and recent performance in the Regional round to highlight how these four teams are trending toward the finish line.

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First, here is the matchup graphic:

Now, let’s get into the preview.

How We Got Here

Denver

Denver enters the Frozen Four as the most clinical team in the field, having turned a relatively even shot share into an 11–2 aggregate blowout across two games. The Pios' success wasn’t built on overwhelming volume, but rather on elite efficiency at both ends of the ice. Denver generated 6.70 total expected goals (xG) while surrendering 4.85 expected goals against (xGA), resulting in a +1.85 aggregate Net xG in two games, the highest mark for any of the Frozen Four representatives. However, the Pioneers’ actual output significantly outperformed the models, as they exploded for 11 goals on those 6.70 xG. Johnny Hicks played a big role in this efficiency, posting a 96.4% save percentage and a +0.86 GSAx, both second-best among the goalies in the Regional rounds.

North Dakota

North Dakota arrives in Las Vegas as the most resilient defensive unit in the field, highlighted by Jan Spunar’s flawless 100% save percentage and a regional-best +3.74 GSAx. While the Fighting Hawks faced 6.80 total expected goals against (xGA)—the third-highest in the tournament field during the Regionals—they managed to keep a clean sheet by leaning on a Regional round-best 72 takeaways in the neutral zone. This high-disruption strategy allowed them to ground opposing transitions early, though they relied heavily on Spunar to essentially "delete" the high-danger looks that found their way into the slot. Offensively, they remained opportunistic, maximizing their looks by converting 4.84 xG into 8 actual goals.

Wisconsin

Wisconsin enters as the most high-octane transition threat in the field, characterized by a relentless and high-energy style of play. The Badgers secured their spot in the Frozen Four with a 4−3 OT thriller against Michigan State, capping a regional performance in which they recorded 9 total goals, the second-most in the tournament field. This offensive surge was remarkably efficient, as Wisconsin converted 5.80 total xG into 9 actual goals. The Badgers dictated play by forcing opponents to defend the entire length of the ice. While they allowed a field-high 0.12 xG per shot in two Regional games, this style was a calculated trade-off. Where teams like Denver focused on shot suppression, Wisconsin relied on a robust +0.78 Net xG per game and superior possession to simply overwhelm opponents.

Michigan

Michigan’s performance in the regional rounds confirms their status as the high-ceiling “wildcard” of the tournament. The Wolverines operated with surgical efficiency, recording 9 actual goals on a regional total of 5.40 xG. This aggressive style was supported by a defensive environment that, while more porous than their peers, remained opportunistic. Michigan allowed 7.10 total xGA across their two games, resulting in a −1.69 aggregate Net xG. With a stellar .923 save percentage from freshman Jack Ivankovic, the Wolverines proved they could win even when the shot-quality battle wasn't in their favor. Their Regional-high 40% power play also made them a real nightmare matchup for Bentley and Minnesota Duluth.

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Frozen Four Preview: By The Numbers

Denver vs. Michigan

The Regional round saw both teams lean into extreme efficiency to punch their tickets to Las Vegas. Denver and Michigan lead the Frozen Four field with nearly identical finishing ratios, with both converting expected chances into actual goals at a rate significantly higher than the expected numbers would suggest.

While Denver has maintained a dominant “process” by tightening its defensive suppression, Michigan has relied on precision over volume. The Wolverines’ expected goal generation dropped in the Regionals, yet their actual scoring remained nearly identical to their season average, which is a testament to their elite shot quality and a lethal power play.

Denver enters the Frozen Four having successfully recalibrated its game model for the postseason. During the Regional round, the Pioneers redefined their identity by averaging 5.50 goals in two games while operating with precision. Their 1.64 finishing ratio was a significant evolution from their 0.94 regular-season baseline, suggesting the Pioneers have transitioned from a volume-based offense to a deadly, high-danger machine.

This scoring surge is the direct result of a deliberate tactical shift. While Denver spent six months during the regular season as a “possession engine”—averaging 14:20 of offensive zone time per game—they successfully pivoted in the postseason, reducing that mark to 10:27 in the Regional round. This 27% decrease in possession is the hallmark of a David Carle-coached team in March and April, consistent with a disciplined commitment to shortening the game and prioritizing a suffocating defensive structure.

By condensing the ice, Denver has effectively restricted interior access, dropping their Opponent xG against from 2.86 per game during the regular season to 2.43 in the postseason and limiting opponents to a 0.08 total xG per shot, the best mark of the Frozen Four teams. This suppression poses a catastrophic threat to Michigan, whose offense relied on a 0.11 total xG/shot quality to reach the Frozen Four. If Denver forces the Wolverines to settle for a lower benchmark, it would effectively neutralize Michigan’s scoring threat.

Michigan’s offensive identity reflects elite playmaking and shooting skill that has remained consistent all year. While Denver has successfully transitioned into a defensive juggernaut mixed with high-danger scoring this postseason, Michigan relies on a proven finishing ability developed over the last six months. Their 1.15 regular-season finishing ratio is a direct reflection of their high-end talent, demonstrating that the Wolverines capitalize on volume with precision.

This Frozen Four matchup becomes a classic clash of strengths. Michigan will look to leverage its superior power play and league-leading slot-entry rate to test the limits of Denver’s disciplined defensive structure. While Michigan’s aggressive, high-event style led to a 7.10 total xGA in the Regionals — the second-highest mark — they trust freshman Jack Ivankovic to provide the stability needed for their mobile defenders to jump into the rush.

Michigan’s path to a win relies on using that established offensive pedigree to penetrate a Denver defense currently playing at its peak. By forcing high-danger breakdowns through their superior passing volume, Michigan aims to let its battle-tested scoring dictate the scoreboard in what has all the makings to be an instant classic.

North Dakota vs. Wisconsin

The Regional data highlights a sharp divergence from regular-season norms for both programs. While both North Dakota and Wisconsin saw a decrease in expected goal generation against higher-quality opponents, they compensated with exceptional finishing metrics.

Wisconsin has fundamentally transformed its defensive profile, cutting its expected goals against to the lowest per-game volume in the Frozen Four field. Conversely, North Dakota’s defensive metrics have trended toward high variance. They are surviving on elite goaltending that has masked a defensive interior that became uncharacteristically porous.

North Dakota’s path to Las Vegas highlights a fascinating “bend-but-don’t-break” model, having allowed zero actual goals despite surrendering a 3.42 expected goals (xGA) per game in the Regional round. This defensive process represents a regression from their 2.58 regular-season average, suggesting their current success is anchored by Jan Spunar’s massive +3.74 GSAx. While they were technically out-chanced during the Regionals, the Fighting Hawks continue to rely on the high-possession model during the regular season, where they maintained a 56% CORSI% and 14:20 of offensive zone time per game.

The Fighting Hawks’ structural DNA remains rooted in disruption and puck retention, recording 23.5 dump-outs and 136.0 takeaways per game — a figure that considers intercepted passes, stripping the opponent of the puck, winning a 50/50 puck battle that results in a change of possession, and picking up a “forced” loose puck (thus different from how the NHL and other leagues capture takeaways) — in the postseason.

While their neutral-zone play acts as a primary filter, their current results are heavily dependent on Spunar bailing out a defense that has allowed more quality looks than their season-long baseline would suggest. To advance to the title game, North Dakota must reconcile their elite puck-retrieval skills with a defensive interior that has been, for lack of a better phrase, leaky.

Wisconsin has been the most improved “process” team, having dropped their xGA from a 2.90 regular-season average down to a stingy 2.14 per game in the Regionals. The Badgers are currently a premier analytical choice in this matchup, carrying a +0.78 Net xG margin fueled by a transition game that is currently operating at 4.50 actual goals per contest.

The Badgers’ offensive DNA relies on pace. After ranking among the nation’s best with 22.0 pass-based entries and 15.8 counter-attacks per game in the regular season, Wisconsin proved in the Regionals that they could maintain that aggressive transition profile even against the country’s top defenses. Wisconsin wins by using superior foot speed to turn defensive zone puck recoveries into immediate odd-man rushes. While they are comfortable in high-event games—averaging an NCAA-best 4.12 xG per game during the regular season—their recent defensive tightening suggests they have found the balanced profile necessary for a championship run.

Daniel Hauser provides the veteran insurance in net, allowing the Badgers to maintain their aggressive carry-in style without fear of a catastrophic breakdown. For Wisconsin, the blueprint to victory is to utilize their elite transition volume to test whether the “Spunar Wall” can be knocked down.

Similar to how I opened the last post … For anyone who follows me on Twitter/X, I have been putting out a graphical preview every Friday for either ranked matchups or, if there is a smaller slate of games, all NCAA hockey matchups.

The graphic, which is color-coded on a 0–100 percentile scale, shows how teams match up by comparing various stats on a per-game average. These include 5v5 goals, expected goals (and subsequent finishing metrics), Corsi (possession), goals allowed, power play, penalty kill, and save percentage.

With the 2026 NCAA men’s regional semifinal round beginning on Thursday, I wanted to share that graphic here while also providing a deep-dive, analytically-driven preview. This preview leans heavily on expected stat data to determine who might have the edge.

Before we jump in, please note that all data is derived from InStat on a per-game average and may differ slightly from other media outlets. Additionally, the data referenced is from the regular season only.

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(Note: Substack is telling me this post is “too long for email” so that means it just may appear as clipped or truncated in your email inbox. You may have to visit my Substack page online to view the post in its entirety. While you’re there, I would also appreciate a Sub while you’re visiting, it’s free. Thanks!)

Here is the aforementioned graphic:

Now, let’s dive into the preview.

Worcester Regional

#1 Michigan State vs #4 UConn

Michigan State controls the ice with a +1.17 net expected goal differential per game. They generate 3.92 expected goals for, while holding opponents to just 2.75 against. UConn enters with a much thinner margin, sitting at a +0.14 differential.

Why Michigan State can win

The Spartans control the ice better than almost anyone, sporting a 59% CORSI and a +1.17 Net xG. Their 29% power play is a primary weapon, and they possess elite goaltending in Trey Augustine, who carries a .926 save percentage and an exceptional 1.45 xG per goal conceded. Porter Martone (1.41 pts/G) and Charlie Stramel (1.27 pts/G) provide a physical, high-skill presence that dominates the offensive zone cycles. Despite a slightly low 0.89 xG conversion rate — 14th among the 16-team tournament field — their sheer volume of high-danger shots usually results in a multi-goal cushion.

Why UConn can win

UConn is a dangerous underdog because they stay close in the possession battle (52% CORSI) and feature solid goaltending from Tyler Muszelik. Muszelik’s .920 save percentage and 2.05 xG conceded per game suggest he can keep the Huskies in games. Joey Muldowney (0.52 goals/G) is an efficient finisher who can turn a single scoring chance into a lead. If UConn can keep the game at 5-on-5 and utilize their 82% penalty kill to neutralize Michigan State’s power play, they have the formula for an upset.

#2 Dartmouth vs #3 Wisconsin

Wisconsin brings one of the most explosive offenses in the field, generating 4.11 expected goals per game. Dartmouth counters with a +0.59 net differential and a roster of highly efficient shooters.

Why Wisconsin can win

Wisconsin is a 3-seed dressed more like a 1-seed. The Badgers are an analytical juggernaut with a +1.22 Net xG and a 56% CORSI, indicating they spend the majority of the game in the offensive zone. They average 4.03 actual goals per game, supported by a 25% power play that punishes undisciplined opponents. Gavin Morrissey (1.03 pts/G) and Quinn Finley (0.88 pts/G) provide the creative spark needed to break down Dartmouth's structure. If Daniel Hauser can stay steady in net and the Badgers continue to generate 4.11 expected goals per game, they could eventually overwhelm the Big Green.

Why Dartmouth can win

Dartmouth is the ultimate "efficiency" team, pairing a tournament field-leading 1.13 xG conversion rate with a stifling 89% penalty kill. Their defense is exceptionally disciplined, allowing only 2.65 expected goals against per game. Hayden Stavroff is a premier sniper, scoring nearly a goal per game (0.91), which makes the Big Green dangerous even when they are being outshot. Between the pipes, Emmett Croteau boasts a .924 save percentage, giving Dartmouth the elite netminding required to survive Wisconsin's high-pressure attack.

Sioux Falls Regional

#2 Providence vs #3 Quinnipiac

Quinnipiac is the analytical darling of the tournament with a staggering +1.70 net expected goal differential. Providence is a formidable opponent but trails significantly with a plus 0.47 margin.

Why Quinnipiac can win

Quinnipiac is a statistical powerhouse, leading the tournament field with a staggering +1.70 Net xG and a dominant 58% CORSI. The Bobcats allow the fewest expected goals against (2.26) in the tournament field, suffocating opponents before they can even reach the slot. Ethan Wyttenbach is a legitimate superstar, averaging 1.53 points per game, while Chris Pelosi (0.52 goals/G) ensures they have scoring depth across lines. If the Bobcats play their standard game, their sheer volume of scoring chances (3.95 xG per game) should eventually break any goaltender.

Why Providence can win

The Friars are a heavy, possession-oriented team with a 54% CORSI and a stout defense that limits opponents to 2.92 expected goals against. Their special teams are reliable, particularly an 84% penalty kill that helps protect narrow leads in the third period. John Mustard (0.46 goals/G) and Logan Sawyer (0.38 goals/G) provide the necessary finishing touch for a team that focuses on volume and zone time. With Jack Parsons in the crease, Providence relies on a goaltender currently posting a .921 save percentage and a very respectable 0.82 GSAx to withstand Quinnipiac’s relentless offensive pressure.

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#1 North Dakota vs #4 Merrimack

North Dakota enters with a +1.18 net expected goal differential. Merrimack sits at a -0.57 differential, but they possess the ultimate equalizer in the crease.

Why North Dakota can win

North Dakota is one of the most balanced teams in the country, featuring a +1.18 Net xG and an oppressive defense that allows only 2.54 expected goals against. Their 57% CORSI rating indicates they rarely lose the possession battle, while an 86% penalty kill provides elite insurance for their aggressive forecheck. Ben Strinden (0.46 goals/G) and Ellis Rickwood (1.03 pts/G) lead a deep forward group that emphasizes puck pursuit and high-danger chances. And with Jan Spunar sporting a .909 save percentage and shining in low-shot environments, the Fighting Hawks are built for a deep run.

Why Merrimack can win

Merrimack thrives on "opportunistic" hockey, evidenced by their 1.03 xG conversion rate —which ranks 6th among the 16 teams in the tournament field — and a .919 save percentage from workhorse goalie Max Lundgren. Though their 46% CORSI suggests they spend significant time in their own zone, they are adept at shot blocking and limiting second-chance opportunities. Parker Lalonde (1.03 pts/G) is a certified game-breaker who can capitalize on the few mistakes North Dakota might make in transition. For Merrimack to win, they need Lundgren to outperform his 2.23 xG conceded and for Trevor Hoskin (1.00 pts/G) to stay hot on offense.

Albany Regional

#1 Michigan vs #4 Bentley

Michigan is a high octane offensive machine that scores 4.62 goals per game. Bentley is a disciplined underdog that carries a plus 0.24 net expected goal differential.

Why Michigan can win

The Wolverines enter the tournament as the No. 1 overall seed and an offensive juggernaut, leading the field with 4.62 goals per game and a high-octane 28% power play conversion rate. Their +0.85 Net xG indicates they consistently out-chance opponents, while a 1.08 xG conversion rate — tied for second with North Dakota among the 16-team field — proves they have the elite finishing talent to punish small mistakes. Between the pipes, Jack Ivankovic provides a safety net with a .925 save percentage, allowing Michigan to take aggressive risks in the offensive zone. T.J. Hughes (1.56 pts/G) and Michael Hage (1.37 pts/G) form a two-headed monster that few teams have the depth to contain for 60 minutes.

Why Bentley can win

Bentley plays a disciplined, low-event style that can frustrate high-possession teams, evidenced by their solid 84% penalty kill and +0.24 Net xG. To pull the upset, they will rely on Lukas Swedin, whose .919 save percentage and steady play in high-leverage situations give them a chance to steal a game. Michael Mesic leads the attack with 1.25 points per game, showing he can generate offense even when the team’s total shots are limited. If Bentley can limit Michigan’s power play opportunities and keep the game at 5-on-5 where they hold a 51% CORSI, they can hang around late into the third period.

#2 Minnesota Duluth vs #3 Penn State

Minnesota Duluth plays a controlled style with 3.38 expected goals for and 2.57 against. Penn State allows 4.01 expected goals per game, the highest mark in the tournament field.

Why Minnesota Duluth can win

The Bulldogs are a statistical darling, boasting a +0.81 Net xG and the field's most lethal special teams units, including a 30% power play and a 90% penalty kill. Their 54% CORSI suggests they control the puck for the majority of the game, limiting opponents' opportunities to just 2.57 expected goals against. Max Plante (1.24 pts/G) and Zam Plante (1.16 pts/G) are elite playmakers who thrive in the "dirty areas" of the offensive zone. If Duluth can draw penalties, their power play is efficient enough to end a game in a single period.

Why Penn State can win

Penn State plays a high-volume, chaotic style that leads the regional with 34.5 shots on goal per game, forcing opposing goalies into constant action. While their −0.57 Net xG is a concern, they have outperformed their expected metrics by scoring 3.93 actual goals per game, led by the dynamic Gavin McKenna (1.47 pts/G). Josh Fleming has been a workhorse in net, maintaining a .918 save percentage despite facing a heavy workload of 4.01 expected goals against. If Penn State can turn the game into a track meet and leverage J.J. Wiebusch’s elite finishing ability, they can overwhelm Duluth’s structured defense.

Loveland Regional

#1 Western Michigan vs #4 Minnesota State

In a rematch of last year’s Fargo regional semifinal, Western Michigan carries a +0.74 expected goal differential with solid offensive production. Minnesota State sits at +0.15 and plays a much lower event game.

Why Western Michigan can win

The Broncos are built for postseason hockey with a heavy 57% CORSI and an elite 88% penalty kill. They generate 3.75 expected goals per game while maintaining a solid +0.74 Net xG, showing balance across all three zones. Grant Slukynsky (1.08 pts/G) and Liam Valente (1.00 pts/G) lead an offense that focuses on high-percentage plays and sustained pressure. With Hampton Slukynsky posting a .916 save percentage, the Broncos have the reliable goaltending needed to begin their national title defense.

Why Minnesota State can win

Minnesota State is a defensive fortress, led by goaltender Alex Tracy, who boasts an elite .926 save percentage and a 1.51 xG per goal conceded. They play a low-risk style (54% CORSI) that prioritizes middle-of-the-ice defense and an 85% penalty kill. While their 2.78 goals per game is lower than other seeds, Tristan Lemyre (0.87 pts/G) provides enough punch to win close, low-scoring contests. If they can drag Western Michigan into a defensive battle, Tracy is talented enough to shut the door completely.

#2 Denver vs #3 Cornell

In a rematch of the 2024 Northeast regional final, this looks like one of the closest matchups on the board. Denver at +1.00 and Cornell at +0.82 in expected goal differential, though they achieve those numbers in opposite ways.

Why Denver can win

Denver remains an offensive powerhouse, averaging 3.82 goals per game with a robust +1.00 Net xG. They are highly effective in transition, using a 55% CORSI to transition quickly from defense to high-danger scoring chances. Eric Pohlkamp (0.95 pts/G) and Sam Harris (0.88 pts/G) provide scoring depth that makes them difficult to match up against over three periods. Johnny Hicks has been elite in goal with a ridiculous .962 save percentage, ensuring that Denver’s high-octane offense isn't undermined by defensive lapses.

Why Cornell can win

Cornell is the "Cinderella" that the numbers actually like, sporting a +0.82 Net xG and a strong 56% CORSI. They are a physical team that excels in puck battles and defensive zone exits, allowing only 2.71 expected goals against. Ryan Walsh (1.03 pts/G) and Jonathan Castagna (1.00 pts/G) lead a balanced attack that doesn't rely on a single superstar. Alexis Cournoyer is the anchor of the team, carrying a .915 save percentage and an elite 1.68 xG per goal conceded, meaning opponents have to work significantly harder to beat him than the average goalie.

If you follow me on Twitter/X, you have probably seen me posting weekly NCAA hockey analytics throughout the season.

Every Monday I update expected-goal data from the previous weekend’s games using InStat and share a handful of charts and leaderboards. The response has typically been positive. A few likes here. A couple retweets there. And the occasional comment from a fan celebrating where their favorite team ranks.

Recently, though, one comment caught my attention on my Goals Scored Above Expected (GSAx) chart.

“Are we able to see where past national champions ranked at this point in this season in this metric? Curious to see how much it correlates.”

In other words, were previous national champions elite finishers during the regular season? And can we use the information to predict this year’s winner?

That question sent me down a rabbit hole.

After digging through seven played seasons of GSAx data — InStat data is only available going back to 2017-18 — a surprisingly clear pattern emerged for the teams that ultimately win the national championship.

Let’s dive in.

Does finishing efficiency—measured by Goals Scored Above Expected—determine NCAA Hockey champions?

Modern hockey analytics have introduced Expected Goals (xG) to judge how a team plays. The idea is that if a team creates enough dangerous chances, the wins will follow.

That’s the theory, anyway.

However, history tells a slightly different story.

Winning a national title ultimately comes down to the ability to put the puck in the net when it matters.

For this study, I looked at Goals Scored Above Expected (GSAx) data from the last eight seasons of NCAA hockey, spanning 2017–18 through 2024–25. The 2019–20 NCAA tournament was cancelled due to the COVID-19 pandemic, leaving seven completed championships within the sample.

While that is a relatively small dataset, the trend among recent champions is remarkably consistent and reveals a clear statistical blueprint for the teams that ultimately lift the national title.

What is GSAx?

Expected Goals (xG) measures the probability that a shot will become a goal based on factors such as location, shot type, and distance.

Goals Scored Above Expected (GSAx) represents the difference between the number of goals a team actually scores and the number of goals their shot quality would predict.

A positive GSAx means a team is finishing more of its chances than expected. A negative GSAx indicates a team is generating quality opportunities but failing to convert them.

(Note: GSAx is commonly used in hockey analytics to describe Goals Saved Above Expected for goaltenders. In this study, I am using this term specifically for Goals Scored Above Expected at the team level.)

This study uses regular-season data from InStat to examine where each eventual NCAA champion ranked nationally in GSAx entering the bracket.

The Historical Blueprint

The results show a remarkably consistent trend.

Every NCAA champion in the last seven completed tournaments finished the regular season with a positive GSAx, meaning they scored more goals than their expected goal totals predicted.

Western Michigan, Denver (x2), Quinnipiac, Minnesota Duluth (x2), and UMass all entered the tournament outperforming their underlying shot quality metrics offensively.

The Top 15 Rule

Across the seven winners included in the dataset, the average GSAx rank for a national champion is eighth in the country.

In most cases, champions enter the tournament ranked comfortably inside the top ten nationally in finishing efficiency. The only clear outlier is the 2021 UMass Minutemen, who ranked 16th in GSAx during the regular season.

The takeaway is simple: creating chances is often enough to get a team into the NCAA tournament, but converting those chances at an elite rate is usually what carries teams through four single-elimination games.

The Runner-Up Threshold

While the champion profile is littered with elite finishing, the runner-up data shows a slightly wider range of candidates.

The average regular-season GSAx rank for national runner-ups is 14th, noticeably lower than the champion average of eighth. This suggests that teams with strong overall play can still reach the championship game even if their finishing efficiency is not quite at the elite level.

However, it’s important to note that across the seven seasons studied, no team has reached the national championship game with a negative GSAx entering during the regular season.

If a team is consistently under-finishing its chances going into the tournament, history suggests it rarely finds the shooting luck required to survive the labyrinth.

Case Studies in Finishing

2024 Denver Pioneers

The 2023–24 Denver Pioneers represent the gold standard for finishing efficiency in the analytics era. They entered the tournament with an unprecedented +43.7 GSAx during the regular season, leading the nation by a wide margin.

This finishing efficiency meant Denver rarely needed a high volume of chances to win games once the postseason inevitably tightened defensively.

While they were known for a high-flying offense that averaged 4.6 goals per game totaling 202 goals overall, their championship run showcased an exceptional defensive pivot. The Pioneers allowed just three goals across four NCAA tournament games en route to the program’s 10th national title.

This transformation was anchored by junior goaltender Matt Davis, who delivered one of the greatest postseason performances in college hockey history. Named the Frozen Four Most Outstanding Player, Davis stopped 68-of-69 shots faced against Boston University and Boston College.

He punctuated the run with a 35-save shutout in the national championship game, which included a record-tying 23 saves in the third period alone and a miraculous diving save on BC’s Ryan Leonard that immediately became an all-time tournament highlight. Davis finished the four-game tournament with a .979 save percentage and a 0.63 goals-against average.

While Davis provided the security, Denver’s elite finishing talent ensured they capitalized on their limited chances in low-scoring affairs. Forward Tristan Broz became the first player in NCAA history to score two overtime game-winning goals in a single tournament, first against UMass in the regional and then against Boston University in the semifinal.

In the final, Jared Wright scored the championship-winning goal, while Rieger Lorenz added the insurance marker to secure the 2–0 victory. Freshman phenom Zeev Buium, who led all NCAA defensemen in scoring during the regular season, provided the primary assist on the Lorenz’s goal, a microcosm of the skill that allowed Denver to outperform their expected goals so consistently throughout the year.

2021 UMass Minutemen

On the other side of the coin, UMass represents the lowest level of regular season finishing efficiency among champions within the studied dataset.

They ranked 16th in the country with a +3.2 GSAx, but they were still fifth nationally in goals per game (3.6), showing that their offense was far from anemic. Their run serves as a blueprint for how a team can overcome modest finishing stats with elite goaltending and a “scoring by committee” approach.

While they were not the most clinical team during the regular season by finishing standards, they became impossible to beat in the postseason by leaning on a 14-game unbeaten streak and defensive depth.

The 2021 postseason run featured standout goaltending from two different players. Filip Lindberg posted a 25-save shutout in the 5-0 national championship game. When Lindberg was unavailable for the Frozen Four semifinal due to COVID protocol, Matt Murray stepped in to make 36 saves in a 3-2 overtime win against Minnesota Duluth.

Forward Bobby Trivigno earned Most Outstanding Player honors for the Frozen Four, but the depth scoring was the difference. Garrett Wait scored the overtime-winning goal in the Frozen Four semifinal, and freshman defenseman Aaron Bohlinger scored his first collegiate goal as the game-winner in the title game.

Defensemen Zac Jones and Matthew Kessel anchored the back end while contributing offensively, and depth players like Philip Lagunov added tallies in the final. This UMass squad proves that despite a modest finishing efficiency from your offense, elite goaltending – and timely scoring – can carry a team the rest of the way.

What it Means for 2025–26

Michigan (+22.0, 1st nationally), Dartmouth (+15.7, 2nd), and North Dakota (+13.8, 3rd) rank among the national leaders in finishing efficiency, each sitting comfortably inside the top five nationally in GSAx.

Others in the projected Field of 16 — including Quinnipiac (+13.2) and Penn State (+10.6) — also sit well on the positive side of the threshold.

These teams most closely resemble the statistical blueprint established by recent champions of strong chance creation paired with efficient finishing.

However, not every highly ranked team fits that mold.

A handful of projected tournament and early projected tournament favorites are currently underperforming or “under-finishing” (to stay on theme) in their expected goal metrics, which historically can be a warning sign heading into the tournament.

Michigan State, currently ranked No. 3 in the NPI, stands out as the most notable example. Despite sitting comfortably inside the projected bracket, the Spartans currently carry a negative finishing profile (-2.6). Teams like the Cornell (-1.2) and Wisconsin (-6.7) fall into a similar category. They are generating chances but failing to convert them at the expected rate.

As previously noted in the study, no team in the last seven completed seasons has made the national championship game with a negative GSAx. However, recent examples have paired their finishing profile with elite goaltending in the tournament.

Which brings me to my final point.

Michigan State’s Trey Augustine feels like a player who could carry his team through a four-game tournament, especially considering that he was part of a No. 1-seeded Michigan State that was stunned by Cornell in the first round last year.

Can the Spartans do it?

Limitations

GSAx is a great tool and it’s fun to look at, but it does not track everything.

1. As referenced several times throughout this study — even within the standout winners — goaltending does matter. A team can score a lot but still lose if their goalie cannot make saves. Conversely, elite goaltenders take over games.

2. The tournament is short. One bad night can ruin a great season.

3. Power plays can inflate these GSAx numbers. The dataset considers all situations which includes even-strength, power-play, and penalty-kill scenarios. A disciplined opponent, one that can stay out of the box, can take away a potent power play.

Final Takeaway

Since 2018, every NCAA champion has entered the tournament with a positive Goals Scored Above Expected value, and most were among the nation’s top finishing teams.

However, the NCAA tournament will always contain chaos. Goaltenders get hot. Underdogs find magic. A bad puck bounce can end a season. Or some combination of the three.

But the data from the past decade suggests that the teams most likely to lift the trophy in April are the ones that consistently make their chances count.

Recapping Day 2 of the USHL Fall Classic

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Omaha 5, Green Bay 4

Thaddeus McMahon’s first USHL goal midway through the third period proved to be the game-winner in the Omaha Lancers’ 5–4 win over Green Bay on Thursday. Omaha jumped out to a 2–0 lead in the first period before the Gamblers responded with four straight goals in the middle frame, including Oliver McKinney’s first career USHL goal. Omaha answered with three consecutive goals to close out the game, including Adam Israilov’s first career USHL marker. Lancers netminder Devin Shakar turned aside 22 of 26 shots in the win.

Chicago 5, Sioux Falls 1

The Chicago Steel scored five unanswered goals in a convincing win over Sioux Falls on Thursday. After a scoreless first period, Logan Renkowski opened the scoring for the Herd less than eight minutes into the second. Miles Burgin responded with his first career USHL goal less than five minutes later to even the score. The Steel then added four goals in the final 20 minutes, including James Scantlebury’s first career USHL tally. Chicago goaltender Louis-Felix Charrois stopped 33 of 34 shots.

Muskegon 3, Sioux City 2 (OT)

Melvin Novotny scored less than two minutes into overtime to lift the Muskegon Lumberjacks over Sioux City. The Musketeers tallied twice in the opening period, including Matej Teply’s first career USHL goal. Novotny brought the Jacks within one in the second period with his first of two goals on the night. Chuck Blanchard tied the game early in the third. Muskegon netminder William Keane made 22 saves, including a big stop on a partial breakaway late in regulation to keep the game tied.

Dubuque 5, Waterloo 2

Five different skaters scored for Dubuque in a 5–2 win over Waterloo on Thursday. The Fighting Saints struck twice in the first period, including Colin Frank’s third goal of the year. Viktor Eriksson brought the Black Hawks within one at the nine-minute mark of the second period. However, the Saints pulled away with three goals in the third, including Teddy Merrill’s third of the season. Owen Crudale made 21 saves on 23 shots to earn the win for Dubuque.

Recapping all the action on Day 1 of the USHL Fall Classic

Dubuque 10, Sioux City 2

Ten different skaters recorded multi-point efforts for the Fighting Saints in their 10-2 victory over Sioux City on Wednesday. Colin Frank, Teddy Merrill, and Eetu Orpana each recorded two goals. Frank opened the scoring at the 7:06 mark of the first period, starting a run of four straight goals for the Saints in less than seven minutes. Masun Fleece picked up three assists in the win. Jake Tavares scored both goals for the Musketeers. Vojtech Hambalek turned aside 18 of 20 shots in net for Dubuque.

Muskegon 4, Waterloo 3 (SO)

In a rematch of last year’s Clark Cup Final, Muskegon outlasted Waterloo in a 12-round shootout. Ty Mason opened the scoring for the Black Hawks late in the first period before Muskegon’s Carter Sanderson responded less than a minute later. After a scoreless second period, the two teams traded goals, including Jack Christ’s first of the season. In the shootout, Ethan Drabicki scored the go-ahead goal in the 12th round before Carl Axelsson made the save at the other end. Axelsson stopped 20 of 23 shots in net.

Scouting Report: Jacob Rombach
Position: Defense | Shoots: Left | Height: 6'6" | Weight: 201 lbs
Hometown: Blaine, Minnesota | Draft Projection: 2nd to Early 3rd Round
College Commitment: University of Minnesota
Current Team: Lincoln Stars (USHL)

Jacob Rombach is a towering, left-shot defenseman with an NHL-caliber frame and a defensive game that continues to mature with each passing season. At 6-foot-6 and 201 pounds, he already brings a pro-ready physical presence to the blue line and has developed into one of the more intriguing shutdown prospects in the 2025 NHL Draft class.

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Defensive Game & Style of Play

Rombach is a classic stay-at-home defenseman, known for his physicality, disciplined positioning, and poise under pressure. While he doesn’t jump off the page offensively, he plays a fundamentally sound game in his own zone. He excels in puck retrieval, is confident under forechecking pressure, and consistently makes smart breakout passes to initiate transition. He rarely overextends himself, often the last to enter the offensive zone and the first to get back, demonstrating a strong awareness of his defensive responsibilities.

In the defensive zone, Rombach thrives in board battles and uses his size effectively to pin opposing forwards against the glass or separate them from the puck. He doesn’t shy away from contact and uses his reach and active stick to disrupt passing lanes and block shots. His skating isn’t elite, but his long, powerful strides and smart angles help compensate, allowing him to cover large areas efficiently. On the penalty kill, he’s particularly effective at the bottom of the diamond, using his frame to clear the crease and challenge opponents trying to establish screens or high-danger chances.

Offensive Contributions

Though defense remains his bread and butter, Rombach has started to show flashes of offensive upside. In the 2024-25 USHL season, he posted three goals and 15 assists for 18 points in 57 games—solid numbers for a primarily defensive player. He’s not afraid to join the rush when the opportunity presents itself and will occasionally skate the puck deep into the offensive zone. However, he typically opts for the safe play—firing hard, low shots from the point or setting up teammates with quick puck movement along the blue line.

His offensive instincts are still developing, but there's room for growth as he gains confidence and adjusts to higher levels of play. As he transitions to the NCAA with the University of Minnesota, expect his offensive reads and execution to improve.

Developmental Outlook

Rombach is a reliable, physically imposing blueliner who projects as an NHL defenseman with penalty-killing upside. While he may never be a power-play quarterback, his ability to shut down opposing top lines, clear the front of the net, and log tough minutes make him a valuable long-term asset. With his combination of size, physicality, and hockey IQ, Jacob Rombach offers teams a safe, high-floor pick with the potential to evolve into a dependable two-way presence at the NHL level.

Des Moines Buccaneers rookie forward Blake Zielinski was my guest on this week's interview!

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• Playing style and NHL player he models his game after

• Assessment of his first USHL season

• Phase I Draft process

• Pre-season plan and if starting with North Jersey was expected

• Conversations that led to his move to Des Moines

• Choice behind jersey number #42

• Progression from bottom-six to top-six role in Des Moines

• Chemistry with frequent linemate Jack Kernan

• Confidence boost from strong finish to the season

• Offseason focus and areas of improvement for next season

The Fargo Force walked away from the 2025 USHL Draft with a deep haul of talent, targeting high-upside forwards and key pieces for the blue line.

In Phase I, the Force selected 11 forwards and four defensemen, building a group they believe can either make an immediate splash or steadily develop into future USHL contributors.

Among the biggest surprises of the day was forward Caden Zasada, a natural scorer with 30 points last season for Hill-Murray. Despite being rated highly by scouts, Zasada fell to the second round, where Fargo eagerly snapped him up.

“He's a kid that can score, you know, which is really hard to teach,” Fargo head coach and general manager Brett Skinner said. “If you come in the door with that ability, there's lots of things that I can work with kids on as far as building their skillset. But if he has that natural ability to put the puck in the net, that was something that was really attractive to us.”

The Force had to wait until the sixth round for their next pick after parting with several earlier selections in recent years. But the wait paid off with the selection of Colton Lien, a 6-foot-3 centerman who racked up a jaw-dropping 73 points (22 goals, 51 assists) for Okanagan HA Colorado’s 15U squad.

“I think [Assistant GM Cary Eades] let out a pretty big celebration when (Lien) was still there,” Skinner added.

Here is the entire Fargo Force’s Phase I Draft class:

• 2.22 - Caden Zasada, RW

• 6.84 - Colton Lien, C

• 7.97 - Trey Wilson, LW

• 8.112 - Nolan Snyder, RW

• 9.127 - Grayden Peterson, C

• 9.134 - Declan Wotton, RW

• 10.142 - Marcus Matyas, C

• 11.157 - Maxmilian Mares, LW

• 12.172 - Finn Spehar, RW

• 13.187 - Gunnar Schock, LD

• 14.201 - Luke Christopherson, LD

• 15.213 - Alex Ferreira, LD

• 15.224 - Eli McKamey, RW

• 15.231 - Caden Clark, RD

• 15.235 - Owen Konrad, LW

In Phase II, Fargo doubled down on offense, selecting 10 forwards, five defensemen, and a goaltender.

The Force wasted no time making noise with the seventh overall pick, landing Boden Sampair, Zasada’s Hill-Murray teammate and leading scorer with 35 goals and 73 points.

Sampair also got a four-game sample in the BCHL, where he had two goals and five points for the Sherwood Park Crusaders. His BCHL experience adds a layer of readiness that caught Fargo’s eye.

“He's a kid that we know for being in Minnesota high school, it's an area that we can get to and scout,” Skinner said. ‘We think his style of play will translate to the USHL. He plays with pace. He's a good skater. He has some grit to his game, so those things are usually things that are gonna help him translate to junior hockey better.”

Just two picks later, the Force selected Swedish blueliner Axel Lofgren, a 6-foot-4, 200-pound defender committed to UMass. With 13 goals and 33 points in 42 BCHL games—plus six more points in the postseason—Lofgren brings both size and skill to Fargo’s blue line pipeline.

Though slightly older—he’s set to turn 20 in June—Lofgren appeared to take a significant step forward in his development during this past BCHL season.

“He’s kind of a hidden gem, we think,” Skinner said of the pick. “He had really good numbers in the BCHL. He can shoot it, but he's a puck mover. He's kind of that classic Swedish style. He's upright, got his head up, can see the ice, can make plays.”

But perhaps the most jaw-dropping pick of the draft came late in the 14th round: Alexander Karmanov, a 7-foot, 283-pound defenseman committed to Penn State.

At just 17, Karmanov is a raw but intriguing talent. He posted 15 goals and 33 points this past season with the WB/Scranton Knights 16U team.

“He is a pretty good hockey player, even for his size,” Skinner said. “If we can get him under our development plan, you never know. It was one of those kind of later Phase II picks where you take a swing a little bit on a kid and you never know what you're gonna get a couple of years from now.”

Here is the entire Fargo Force’s Phase II Draft class:

• 1.7 - Boden Sampair, C

• 1.9 - Axel Lofgren, RD

• 2.24 - Saxton Tess, LW

• 6.94 - Bryce Mattern, LW

• 7.119 - Kash Crawford, RD

• 8.134 - Jacob Sagadin, C

• 9.147 - Grayson Cohen, RD

• 9.149 - Domán Kristóf Szongoth, C

• 10.164 - Jacob Kvasnicka, RW

• 11.179 - Adam Gionta, C

• 11.181 - Patrick Tolan, RW

• 12.194 - Andrew O'Neill, RW

• 12.196 - Jeremy Poirier, RD

• 13.209 - Yaroslav Korostelyov, G

• 14.224 - Alexander Karmanov, LD

• 15.239 - Chase Watts, LW