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Start with the two familiar results
Save percentage (SV%)
Save percentage is saves divided by shots on goal faced. It is an easy-to-read measure of how often a goalie stops shots, but it counts a long-range shot and a dangerous close-range chance alike. It also excludes missed shots and posts. The team’s defense and the quality of chances allowed therefore shape the opportunities behind a goalie’s SV%. Seattle Kraken analytics explainer.
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Goals-against average (GAA)
GAA is goals allowed per 60 minutes of goalie playing time. It conveys how many goals a goalie conceded relative to time on the ice, but does not adjust for shot volume, shot danger or the defense in front of the goalie. Read it as an outcome, not a stand-alone measure of individual skill. Seattle Kraken analytics explainer.
Add a shot-quality adjustment
Expected goals against and GSAx
Expected goals against (xGA) estimates how many goals the shots faced would be expected to produce under a particular shot-quality model. Inputs differ by model; the Kraken describes factors that can include location, game state, preceding shots and shooter identity. GSAx—Goals Saved Above Expected—is expected goals against minus actual goals allowed. A positive result means the goalie allowed fewer goals than the model expected. Seattle Kraken analytics explainer; Seattle Kraken hockey analytics explainer.
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For example, the Kraken’s explainer illustrates a goalie allowing two goals against 5.2 expected goals: that is more than three goals saved above expectation in the example. It is an illustration of the calculation, not a recorded season result. The source’s writer, Alison Lukan, also reports Philipp Grubauer asking: “How do you separate if somebody shoots from the blue line versus when there’s a guy in front of me who tips it,” Grubauer said. “That’s a scoring chance because the player tips it – it may or may not be seen as high danger, but there’s a difference there too.” That distinction helps explain why shot-quality models can be informative without fully representing what a goalie sees.
Expected save percentage differential
Expected-save-percentage differential, variously written dFSv% or dFSV%, compares actual save percentage with an expected save percentage based on shot quality. It expresses performance as a rate rather than a goals-saved total. Provider names and calculations can differ, so check a source’s definition before treating two values as equivalent. Seattle Kraken analytics explainer; Seattle Kraken hockey analytics explainer.
Rank #2
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GSAA is not GSAx
Goals Saved Above Average (GSAA) compares a goalie with a league-average save rate on the shots faced. It answers a different question from GSAx, which adjusts for shot quality. Malter Analytics defines GSAA relative to a league-average goalie and describes its own “saved above expected” calculation using league goal rates by shot area and strength. These are provider-specific definitions, not universal NHL standards. Malter Analytics goalie leaderboard and definitions.
Use workload, consistency and team context
A rate stat and a cumulative total tell different stories. A goalie with more starts or shots faced has more opportunity to accumulate GSAx; GSAx per 60 adds a time-based rate for comparison. Check starts and shots against (or shots against per 60) alongside efficiency, then look at game logs or quality-start rate to understand consistency. Game situation and team defensive environment matter as well.
Rank #3
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Providers also define eligibility and supporting measures differently. Malter Analytics states its quality-start definition and minimum-game rules on its leaderboard. PuckRank’s 2025–26 report combines starts, shots against per 60, SV%, GAA, GSAx, GSAx per 60, quality-start percentage and team goals against per game; its method requires at least 20 regular-season starts and complete workload coverage. Those rules belong to that report, not to an NHL-wide standard. Malter Analytics; PuckRank 2025–26 goalie burden and support index.
The report offers a dated illustration of why one column cannot serve as a universal ranking: among its eligible cohort, Scott Wedgewood led GSAx at 23.3 and SV% at 92.1%, while Arvid Soderblom faced the highest shot rate at 31.6 shots per 60. These are PuckRank’s source- and cohort-specific 2025–26 figures, not general benchmarks for goalies or other seasons. PuckRank, published July 19, 2026; dataset generated August 31, 2026.
Rank #4
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Compare goalies on like terms
For a fair comparison, keep the season and competition window, provider and model consistent. Then read outcome, adjustment and context together:
- Raw results: SV% and GAA show what happened.
- Shot-quality adjustment: compare GSAx or an expected-save-percentage differential, using the same provider and definition.
- Scale and opportunity: include starts, shots against and whether the adjusted figure is cumulative or per 60.
- Consistency: consult quality-start percentage or game logs, noting the source’s definition.
- Environment: consider team defense and game situation rather than attributing every difference to the goalie.
The NHL goalie statistics page and NHL EDGE provide official baseline data. For adjusted measures, select a consistent provider and season; do not compare numbers from different models as if they were calculated the same way.
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What public numbers can—and cannot—establish
Public shot data do not reliably capture everything a goalie sees or does, including setup, sightlines and technique. GSAx and expected save percentage are useful starting points, not complete measurements of goaltending. Seattle Kraken hockey analytics explainer.
A 2012 study by Brian Macdonald, Craig Lennon and Rodney Sturdivant found some evidence that adjusted save percentage was more consistent and predictive than raw save percentage, but described the evidence as far from overwhelming and the predictive improvement as small. The authors’ shot-probability model had a 0.764 area under the receiver operating characteristic curve; that figure evaluates the model’s ability to distinguish shot outcomes, not a goalie’s performance rating. The study supports using weighted-shot measures alongside traditional stats, not replacing them. Macdonald, Lennon and Sturdivant, “Evaluating NHL Goalies, Skaters, and Teams Using Weighted Shots” (2012).
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