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To compare NHL goalies fairly, start with save percentage (SV%) and goals-against average (GAA), then add workload, shot quality and team context. SV% and GAA describe outcomes, but neither accounts for how difficult the shots were. Expected goals against (xGA) estimates that difficulty; goals saved above expected (GSAx) compares actual goals allowed with a model’s expectation. Read those measures alongside playing time, game state, team support and shot-location splits—not as a single definitive ranking.
Why SV% alone does not settle a goalie comparison
Save percentage is saves divided by shots on goal faced. It is useful for summarizing how often a goalie stopped recorded shots, but it treats shots of different difficulty alike. It also excludes pucks that miss the net or hit the post. Goals-against average (GAA) is goals allowed per 60 minutes played; it too is shaped by shot quality and the defending team, not just the goalie. These are conventional outcome measures, not adjustments for the conditions in front of the net. Seattle Kraken/NHL.com explains these limits.
A goalie facing many unobstructed perimeter shots and one dealing with screens, tips or close-range chances may have different workloads even if the shot totals are similar. NHL goalie Philipp Grubauer described the difficulty of classifying a shot after a tip: “How do you separate if somebody shoots from the blue line versus when there’s a guy in front of me who tips it.” That is one reason to treat shot-quality categories as useful evidence rather than a perfect account of what a goalie saw.
What the main goalie metrics tell you
| Metric | What it measures | How to use it |
|---|---|---|
| SV% (save percentage) | Saves divided by shots on goal faced. | Read as a basic outcome rate, not an adjustment for shot difficulty or team defense. |
| GAA (goals-against average) | Goals allowed per 60 minutes played. | Use alongside SV%; it is affected by defensive environment and shot quality. |
| xGA (expected goals against) | The sum of model-assigned scoring probabilities for the shots a goalie faced. | Use to describe the modeled shot-quality workload. Inputs vary among public models. |
| GSAx (goals saved above expected) | In Seattle Kraken’s explanation, expected goals against minus actual goals allowed. | A positive value means fewer goals were allowed than the model expected. Name the provider and check its convention before comparing figures. |
| dFSv% (save percentage above expected) | An Evolving-Hockey measure described by Seattle Kraken as actual SV% relative to the SV% predicted by shot quality. | Use as a model-based comparison; confirm the provider’s scope and definition. |
| GSAA (goals saved above average) | Goals saved relative to an average goalie, using league-average SV% and shots faced. | Keep distinct from GSAx: its baseline is an average goalie, not a shot-quality model’s expectation. |
| High-danger SV% and distance splits | Save outcomes within danger or shot-distance categories. | Use to examine a specific slice of performance; category definitions need not be interchangeable across providers. |
xG models assign a probability to each shot using factors such as location, game state and preceding events, then sum those probabilities. Since providers can use different inputs, xGA and GSAx are not provider-neutral facts. A value is interpretable only with its source, season and scope. The Seattle Kraken overview of goalie analytics also describes “quality starts” and “steals” using expected goals prevented; treat those as source-specific formulations, not universal standards.
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A practical way to compare two NHL goalies
- Match the comparison window. Use the same season and competition—regular season with regular season, or playoffs with playoffs. Label any comparison that mixes them, and align game-state scope where possible.
- Show the workload. Include games, starts, minutes and, where available, shots faced. A rate from a small number of appearances can look more conclusive than its denominator warrants. No universal minimum sample threshold is established by the cited sources, so show the denominator rather than imposing one.
- Read SV% and GAA together. They provide familiar outcome summaries, but neither adjusts for shot difficulty or team defense.
- Add xGA and a model-based result measure. xGA describes the modeled difficulty of the workload; GSAx compares actual goals allowed with expected goals against. If you use GSAx or dFSv%, state the provider, season and scope, and verify how that provider defines the statistic.
- Look at danger or location splits as supporting detail. High-danger SV% or distance categories can show where outcomes differ. They are narrower views, not a complete ranking; check the provider’s category definitions and the number of shots in the split.
- Account for team scoring and rest. A goalie’s wins partly depend on how many goals the team scores. Rest splits can also help describe circumstances around a performance. NHL.com’s 2019 account of its statistics redesign discussed presenting views such as team scoring support and rest alongside goalie statistics.
- Keep the conclusion proportional to the evidence. These numbers can organize a comparison, but they do not replace evaluating technique. Seattle Kraken’s discussion notes that watching technique remains part of goalie evaluation and is less quantifiable.
How to interpret the result without overclaiming
Suppose one goalie has a higher SV% but also faced a less demanding modeled workload, while another has a positive GSAx. That does not automatically prove the second goalie is better: GSAx depends on the model, and neither measure fully captures every factor in a play. Compare the same time window, confirm the model and denominator, then describe the evidence precisely—for example, that one goalie stopped a greater share of shots, while the other allowed fewer goals than a named model expected.
Wins need particular care. NHL.com’s 2019 example contrasted Nashville goalies with similar save percentages but different win totals alongside different team scoring support. It illustrates why wins are not an isolated goalie-skill measure; it is a historical example, not a current comparison or benchmark. The same article reported that league-average SV% fell from .915 in 2015–16 to .910 through 483 games of the 2019–20 season, and that Tristan Jarry recorded a .900-or-better SV% in 10 of 12 starts (83.3%). Those are dated illustrations only, not present-day averages or targets. NHL.com’s article on the redesigned statistics page provides that historical context.
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Where to find NHL goalie splits
NHL EDGE presents official NHL statistics including high-danger and distance-based goalie views. Use it for those categories alongside overall measures, and note the selected season and sample. The cited sources do not establish a current league-average benchmark or universal ranking cutoff, so avoid turning one season’s table into a permanent standard.
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