Utah's 3-2 win masked something closer to a rout: they generated eight high-danger chances to Boston's two, a 4-to-1 dominance that made the one-goal margin almost arbitrary. The underlying control looked even starker—Utah owned 66% of expected goals at 5v5, 3.96 to 2.08.

This wasn't a case of Utah getting lucky. They outshot Boston on quality, generating 0.067 expected goals per attempt versus Boston's 0.039. Boston created higher-leverage chances than their shot count suggested, but Utah simply generated far more of them and converted at a better rate.

Utah's forward pressure paid off in two areas. They scored twice on 20 rush chances—a 10% rush conversion rate that reflects crisp breakout execution. They also generated six rebound chances and capitalized on one, showing the kind of net-front discipline that wins tight games played in front of the goal line.

Boston managed three rebound opportunities but converted zero, a small but meaningful gap in the dirty-work department. V. Vanecek (UTA) stopped 24 of 26 shots at 0.923 save percentage—solid performance on expected volume. J. Korpisalo (BOS) made 24 saves on 27 attempts, a 0.889 mark, though he faced tougher shot quality overall.

Utah's only blemish: they underperformed their xG by 0.96 goals, meaning the margin could have been wider if finishing had matched their chance creation. The turning point came on Dylan Guenther's slap shot from the high slot at 10:37 of the third, which broke a 2-2 tie.

Three Stars:

• N. Schmaltz (UTA) — 2A in 22:50
• D. Pastrnak (BOS) — 2G in 22:10
• C. Keller (UTA) — 1G, 1A in 19:08

Utah's underlying edges are real. The model projects a 2.4–2.8 game with Utah at 57% win probability, and that lean reflects Utah's actual play advantage.

Utah controls the play at every level. At 52.0% expected goals versus Boston's 47.0%, Utah generates superior scoring chances. That edge extends to possession: Utah's 53.0% Corsi dominates Boston's 49.0%. These aren't massive margins, but they're consistent—Utah is simply the better team.

Boston, however, has been playing above talent. Their 1.023 PDO sits well above sustainable; they're converting chances at an unsustainable rate. Utah's 1.003 hovers at expectation. When Boston's hot hand regresses—and it will—Utah's edges widen.

In net, K. Vejmelka starts on back-to-back rest. A backup start remains possible. Either way, fatigue is Utah's tactical advantage.

Utah's attack hinges on Dylan Guenther staying dangerous. He generates 1.02 expected goals per 60 and has scored 28 goals on 16.8% shooting—elite conversion. His three high-danger goals suggest earned volume, not variance. Daniil But warrants attention too; at 1.01 xG/60 with just two goals on 5.6% shooting, he's been unlucky. Regression favors his next stretch.

Boston's best shot runs through Viktor Arvidsson. He operates at 1.04 xG/60, the team's highest rate, with 18 goals and 24 assists on 13.0% shooting. Mark Kastelic's 0.89 xG/60 and 10 goals provide secondary scoring; his three high-danger goals mean he can hurt you even at lower volume.

Defensively, Boston's Elias Lindholm is exposed; for Utah, Maveric Lamoureux bears watching as a turnover risk. Those mistakes compound.

Utah owns this matchup on talent and timing. Boston's PDO is the ticking clock: when hot play regresses, Utah's edges widen past model expectation.

Three Things to Watch

• Utah dominates 5v5 play at 52.0% expected goals to Boston's 47.0%, controlling territory at 53.0% Corsi.
• Boston's 1.023 PDO signals regression; Utah's 1.003 sits near sustainable rates, validating the model's 57% lean.
• K. Vejmelka faces back-to-back rest—fatigue or a backup start becomes Utah's tactical edge.

Data: MoneyPuck 5v5 models, NHL API