The Tool Desk
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Why wins, save percentage and goals-against average are not enough
A win depends partly on how many goals a goalie’s team scores. NHL.com illustrated the difference in a 2019-20 example: Pekka Rinne had 10 wins and Juuse Saros had four, even though their save percentages were similar—.893 and .894. The article reported goal support of 4.09 goals per game for Rinne and 2.35 for Saros across 18 and 16 games, respectively. Those figures describe that historical example, not current performance. NHL.com’s 2019 statistics article shows why a win total needs context.
Traditional goalie statistics answer narrower questions:
- Save percentage (SV%) is saves divided by shots on goal faced. In its basic form, it treats shots alike, regardless of how dangerous each one was.
- Goals-against average (GAA) is goals allowed per 60 minutes of play. It summarizes the rate of goals allowed but does not adjust for shot quality.
Both figures are useful descriptions of results. Neither, on its own, tells you how difficult the goalie’s workload was. The Seattle Kraken’s explainer on goaltending statistics discusses these measures and their limitations.
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How shot-quality measures add context
Expected-goal models estimate the likelihood that shots will become goals. A goalie’s results can then be compared with what the model expected from the shots faced. These measures are only as comparable as their definitions and inputs: providers may use different shot data, populations or calculations, so name the source when reporting a figure.
- Goals Saved Above Expectation (GSAx) compares expected goals against with goals actually allowed. Positive GSAx means the goalie allowed fewer goals than the model expected; negative GSAx means more. The precise result depends on the model and its inputs.
- Goals Saved Above Average (GSAA) estimates the difference between a goalie’s saves and the saves expected at league-average save percentage on the shots faced. It supplies a league-average comparison, but does not itself model detailed shot danger.
- Expected save percentage and differential save percentage compare actual performance with a model’s expected save percentage. The Kraken explainer describes expected save percentage against unblocked shots and a differential between actual and expected save percentage. Check the provider’s exact formula and which shots it includes before comparing values.
These statistics help frame a question—did the goalie outperform a stated baseline given the shots faced?—rather than produce a universal ranking. The Kraken’s introduction to hockey analytics explains why data definitions and limitations matter.
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Why teams and analysts look at situations and workload
A season-wide rate can hide the circumstances in which a goalie faced shots. Where the data is available, analysts can separate five-on-five play from special teams, examine high-danger chances or close situations, and consider shot volume and minutes played. Starts, relief appearances and rest also describe the demands placed on a goalie; they are context, not standalone grades.
NHL.com’s 2026 season coverage offers a dated example of combining measures rather than relying on one number. It reported Ilya Sorokin at .878 on high-danger shots, compared with a .814 NHL average, with 412 high-danger saves on 469 high-danger shots faced. The same article reported a .922 five-on-five save percentage and +35.2 Saves Above Projected. These are figures from that article’s 2026 season snapshot, not evergreen rankings. The NHL EDGE article provides its season-specific context.
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That article defines NHL EDGE Saves Above Projected as total shots on goal faced minus the summed projected goal rates for eligible inferred shot attempts. Attempts over 60 feet, beyond the goal line or against an empty net are excluded. A positive value means more saves than an average goalie would be projected to make given the included shot quality. This NHL EDGE measure should not be assumed interchangeable with a third-party GSAx statistic.
What tracking data can—and cannot—show
NHL EDGE publishes statistics based on puck and player tracking and machine-learning models. The League describes a system using sensors embedded in pucks and player jerseys alongside overhead cameras, deployed across all 32 arenas during the 2021-22 season. Its public-facing tracking data can add detail to shot and player analysis, but it does not establish every NHL club’s internal evaluation process or capture every part of goaltending. See the NHL’s description of NHL EDGE.
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Positioning, technique, vision and what a goalie can see are difficult to reduce to a public statistic. The Kraken’s analysis notes limits in reliable public information about shot angle, goalie setup and the goalie’s view. Public numbers can sharpen an evaluation, but they cannot reveal all of those details or how every team weighs them alongside its own assessments.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare two goalies fairly
Make the comparison on aligned terms rather than treating one number as a complete verdict:
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- Choose a named shot-quality measure. Compare GSAx or another expected-goals measure, and identify the provider. Different models may use different shot data and definitions.
- Match the situation. Compare the same game state where possible, such as five-on-five, and inspect high-danger or close-situation performance if the data is available.
- Check the workload and sample. Note games and minutes played, shots faced, starts versus relief appearances and rest. Rates from a brief appearance may be extreme without representing typical performance; a perfect save rate over a short stint is not proof a goalie will stop every shot.
- Account for team environment. Goal support affects wins, while team defense affects the chances a goalie faces. A comparable stopping result can coexist with a different win total.
- State what the model leaves out. Say whether a value comes from NHL EDGE or another provider, what shots are included and which relevant factors are not measured.
Definitions of labels such as “quality start” and “steal” also vary. In the Kraken explainer, a quality start means facing above-average shot quality and beating expectations in goals prevented; a steal is a case where goals prevented above expectation exceed the final score. These are illustrative labels, not universal standards.
How much does a recent hot streak tell you?
A run of strong games is real as a description of recent results, but those results alone do not prove a stable short-term change in skill. A 2021 study examined 48,431 shots faced by 93 goalies in NHL playoffs from 2008 through 2016. Under the models and shot-based windows it tested, the authors found no evidence for a hot-hand effect. That finding does not mean a goalie cannot have an excellent run; it cautions against treating recent results alone as proof of a lasting shift. The study and its methods define the scope of that conclusion.
What public statistics cannot tell you about a team’s full evaluation
Public sources describe available statistics and tracking tools; they do not document how every NHL organization combines analytics with scouting, video review, goalie-coach assessments, health considerations or proprietary models. It would overstate the evidence to claim that all teams use one weighting system or one definitive goalie score. The practical public-facing approach is to read results alongside shot quality, situation, workload and team context, while recognizing that some technical and perceptual parts of performance remain outside the public data.
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