Series result: Carolina are the 2026 Stanley Cup champions. The Hurricanes beat Vegas 4-2, clinching with a 3-0 Game 6 shutout in Las Vegas (Taylor Hall and Jackson Blake scoring, Brandon Bussi in net) for the franchise's first Cup since 2006. The analysis below is preserved as it stood entering that decisive Game 6: the model had Carolina a heavy favorite to close out the series, and the Hurricanes did exactly that. We have left the projection intact rather than rewriting it after the fact, so you can judge the read against the outcome.
How the analysis works in 30 seconds
Every input is real 2025-26 data, not a reconstruction. Team strength comes from expected goals (xG); goaltending from goals-saved-above-expected (GSAx); goalscorer rates from per-player goals and ice time. The league averaged 3.13 expected goals per game this season, and each team is measured against that baseline.
- Carolina attack 1.17x league, defense 0.92x (below 1 is good: they suppress chances). Season xG share 56%.
- Vegas attack 1.03x league, defense 0.88x. Season xG share 54%.
For each game the model sets expected goals for both sides (adjusted for the opposing starter and home ice), turns that into an exact single-game win probability with two Poisson distributions, and then enumerates the entire best-of-7 outcome space. There is no Monte Carlo simulation: the math is exact, so the numbers are reproducible every time the page builds.
The series entering Game 6: Carolina 80%
Where the model stood: Carolina had won Game 5 4-2 at home to take a 3-2 series lead, its most complete game of the Final. Jordan Staal opened the scoring (his sixth of the series), Andrei Svechnikov added two power-play goals, and Sebastian Aho chipped in as the Hurricanes punished an undisciplined Vegas; rookie Brandon Bussi made 21 of 23 for his second straight win. With Carolina needing just one win from the last two, the recomputed numbers were Carolina 79.8%, Vegas 20.2%. The math was simple from there: Carolina could close it out by winning Game 6 in Las Vegas, or by losing it and taking Game 7 at home. It took the first path, winning Game 6 3-0 to lift the Cup.
The most likely series outcomes from here, in order:
CAR in 6 50.8%
CAR in 7 29.0%
VGK in 7 20.2%
CAR in 4 0.0%
The series-length distribution (how many games it takes, regardless of winner):
What the 3-2 lead meant entering Game 6
One win from two chances is most of the edge
The series probability jumps because Carolina now needs only one win, and it has two shots at it. The model rates the Hurricanes about 50.8% in Game 6 at T-Mobile Arena and 59.0% in a Game 7 back in Raleigh. Even if Vegas holds serve in Game 6, Carolina would still be a clear favorite in a winner-take-all game on home ice. Stacking those two chances is why the projection sits at Carolina 80%: Vegas has to win both remaining games, the model puts that parlay at only about 20%.
The underlying team quality has been Carolina's all along
Five close games have not changed a full season of process. Carolina posted a 56% expected-goals share this season (Vegas 54%), and the Hurricanes have out-chanced Vegas at 5-on-5 across the series. Game 5 was the cleanest expression of that edge yet: a disciplined, special-teams-driven win in which Carolina controlled most of the night. The model still expects close games, but the Hurricanes have been the better team and now have the scoreboard to match.
Vegas's path: win two, starting at home
Game 6 was at T-Mobile Arena, where the matchup was closest to even (50.8% Carolina). Vegas needed to win it to force a Game 7, and then win that one in Raleigh, where the model had the Golden Knights as clear underdogs. It was not impossible (this series had already produced wild swings, and Vegas had won twice on Carolina ice), but it was a two-game must-win against the run of play. In the end the two-game ask proved too much: Carolina won Game 6 3-0, so Game 7 was never needed, and the 80% favorite closed it out exactly as projected.
Expected goals: Game 6 in Las Vegas
Carolina's first chance to close it out was Game 6 on the road, where the model expected a near-even game: 3.14 goals from Carolina and 3.09 from Vegas, about 50.8% for the Hurricanes. That was the cost of playing at T-Mobile Arena, where Vegas was at its most dangerous. The model gave Carolina a much bigger edge in a potential Game 7 back in Raleigh (3.40 to 2.86, 59.0% to win), but the Hurricanes never needed it: they took the near-even road game outright, winning 3-0 behind a Bussi shutout to seal the Cup. The actual result landed under the projected near-even total, a low-event night rather than the shootout the series had often produced.
The most likely goalscorers
For each skater, the model blends actual and expected goal rate, regresses small samples toward a position-appropriate baseline (so a hot fourth-liner is not overstated), scales by expected ice time, and adjusts for the opposing defense and goalie. The result is each player's probability of scoring in a representative game.
Carolina
Seth Jarvis C 32.7%
Andrei Svechnikov R 28.6%
Sebastian Aho C 26.7%
Jackson Blake R 24.5%
Logan Stankoven C 22.1%
Nikolaj Ehlers L 22.1%
Jordan Staal C 21.2%
Taylor Hall L 18.6%
Vegas
Mark Stone R 32.0%
Pavel Dorofeyev R 31.3%
Tomas Hertl C 28.2%
Jack Eichel C 27.8%
Ivan Barbashev L 21.1%
Mitch Marner R 21.0%
Braeden Bowman R 16.5%
Reilly Smith R 16.3%
A note on goaltending (the honest part)
Goaltending is the highest-variance input in playoff hockey, and it is where this analysis is most uncertain, so we are explicit about it. Vegas has gone with Carter Hart, not Adin Hill, throughout. The bigger live story is in the Carolina net, covered below. The starters' regular-season goals-saved-above-expected:
- Brandon Bussi (CAR): the rookie has taken over the Carolina net, winning Games 4 and 5 (18 of 21, then 21 of 23) after Andersen was scratched. He has almost no NHL regular-season sample, so the model cannot rate him directly.
- Carter Hart (VGK): raw season GSAx/60 -0.247 (below average over 18 games); he has been beatable in the series, including four goals on a .833 night in the Game 5 loss.
The Bussi wrinkle: with almost no NHL season sample to rate, the model keeps a regressed-Andersen baseline for Carolina rather than trying to price a rookie, and it applies just 35% of each starter's observed figure because one season of goalie GSAx has low year-to-year reliability (about 0.35). That deliberately mutes the goaltending signal: the projection rests on team quality and the series scoreline rather than a bet on which goalie gets hot. The practical implication is that the model is, if anything, conservative on Carolina right now, since Bussi has outplayed the Andersen baseline it is using.
What this analysis is, and is not
It was a transparent, real-data projection, re-run after each result through the Final. It is not a guarantee, and it could not be "right" about a single series: a best-of-7 is one sample, and a 80% favorite still loses 20% of the time. Carolina winning is one outcome in the favorite's column, not validation of the exact number. The value is in calibration across many projections, not certainty on one, and a single hot goaltender (on either side, in a series that produced a rookie shutout to clinch) can break any model in this sport.
Frequently asked questions
- What data does this Stanley Cup Final analysis use?
- Real 2025-26 regular-season data: official team records from the NHL API, and expected goals (xG), goalie goals-saved-above-expected (GSAx), and per-skater goal rates and ice time from MoneyPuck. Every input traces to a public source, stamped 2026-06-02.
- How is the series projection calculated?
- Each team gets an attack and defense rating from its season xG relative to the league average of 3.13 expected goals per game. For each of the seven games, the model sets expected goals for both sides (adjusted for the opposing starter and home ice), computes an exact single-game win probability from two Poisson distributions, then enumerates the entire best-of-7 outcome space analytically. There is no simulation, so the numbers are exact and reproducible.
- Did the projection change during the series?
- Constantly, which was the point of re-running it. Pre-series: Carolina 64% on home ice. Vegas won Game 1 to flip it; Carolina won Game 2 in OT to flip it back; Vegas won Game 3 in double OT to lead 2-1; Carolina won Game 4 on the road to even it 2-2; and Carolina won Game 5 at home 4-2 to take a 3-2 series lead. Entering Game 6 the projection was Carolina 80% and Vegas 20%, computed over the remaining best-of-two from the 3-2 scoreline. Carolina closed it out at the first opportunity, winning Game 6 3-0 in Las Vegas to take the Cup 4-2.
- How does the analysis handle goaltending?
- Carefully, because it is the highest-variance input in playoff hockey. The model uses regressed season GSAx for the starters (Frederik Andersen for Carolina, Carter Hart for Vegas), applying just 35% of the observed figure because one season of goalie GSAx has low year-to-year reliability (about 0.35). The live wrinkle resolved in Carolina's favor: rookie Brandon Bussi started and won Games 4 and 5 and then closed out the Final with a Game 6 shutout. The model carried a regressed-Andersen baseline because Bussi had almost no NHL sample to rate, which means its Carolina projection was, if anything, conservative on the goaltending.
- How reliable is a single-series projection?
- A best-of-7 is a single sample; a 80% favorite still loses 20% of the time, and this series produced multigoal comebacks in several games. Carolina winning here is one data point in the favorite's column, not proof the number was exact. The value of a model like this is calibration across many projections, not certainty on one. The goalscorer figures are the most testable: across a full slate, players given a 30% chance to score should score about 30% of the time.
Data sources: NHL API (api-web.nhle.com/v1/standings) for official records; MoneyPuck.com season summaries (teams, goalies, skaters) for xG, GSAx, goal rates, TOI. Season: 2025-26 regular season. Stamped 2026-06-02.