A goalie can stop shots well and still lose if the team scores too little. Wins and losses describe the team’s result, not a goalie’s performance in isolation. To assess a goalie more fairly, teams and analysts look at shot difficulty, goals allowed relative to expectation, game situations, workload, and the defense in front of the goalie.
Why wins and losses do not tell the whole story
A win depends on both goaltending and goal support. In a 2019 NHL.com example covering the 2019–20 season, Pekka Rinne had 10 wins and Juuse Saros had four, even though their save percentages were similar: .893 and .894. Their teams scored 4.09 goals per game with Rinne and 2.35 with Saros, respectively, in the cited samples of 18 and 16 games. That historical example illustrates why a goalie’s record needs team context; it is not a current comparison. NHL.com’s 2019 statistics-section overview also reported that league-average save percentage was .915 in 2015–16 and .910 through 483 games of the season then in progress.
Wins remain useful for describing results, but goal support and team defense affect those results. A goalie facing fewer or easier chances may have a different statistical profile from one repeatedly tested by dangerous shots.
What traditional goalie statistics measure—and miss
Save percentage
Save percentage (SV%) is saves divided by shots on goal faced. It answers how often a goalie stopped the shots recorded against them. In its basic form, it treats those shots alike: it does not distinguish a long-range attempt from a close-range chance with traffic.
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Goals-against average
Goals-against average (GAA) is goals allowed per 60 minutes of play. It summarizes the rate at which goals were conceded, but, like basic save percentage, does not adjust for the quality of the shots. The Seattle Kraken’s explainer on goaltending statistics discusses these measures and their limitations.
How shot-quality measures add context
Goals Saved Above Expectation
Goals Saved Above Expectation (GSAx) compares expected goals from the shots faced with the goals actually allowed. A positive result means the goalie allowed fewer goals than the model expected; a negative result means more. The estimate depends on the expected-goals model and the data it uses, so a GSAx figure is meaningful only alongside its provider or methodology.
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Goals Saved Above Average
Goals Saved Above Average (GSAA) estimates the difference between a goalie’s saves and the saves expected using league-average save percentage across the shots faced. It provides a comparison with an average goalie, but does not itself model detailed shot danger.
Expected and differential save percentage
Expected save percentage estimates the save rate based on shot information; differential save percentage (dFSv%) compares actual and expected save percentage. Providers may use different shot populations and formulas. For example, the Kraken’s account describes expected save percentage against unblocked shots. Check the definition before comparing figures from different sources. The Kraken’s introduction to hockey analytics provides additional context on public measures and their limits.
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Why teams examine situations and workload
A goalie’s overall rate can hide how performance varies with the demands of the game. Analysts can break out five-on-five play, high-danger chances, close situations, shot volume, starts, relief appearances, and rest. Those details help distinguish, for example, a strong result against difficult chances from a high overall save rate built on a lighter workload.
NHL.com’s 2026 season account of Ilya Sorokin provides a dated example of combining several measures: it reported a .878 high-danger save percentage against an NHL average of .814, with 412 high-danger saves on 469 high-danger shots faced, a .922 five-on-five save percentage, and +35.2 Saves Above Projected. These are figures from that article’s 2026 season snapshot, not current or evergreen rankings. Read the NHL EDGE account and its stated measure.
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What NHL EDGE tracking can—and cannot—show
NHL EDGE publishes statistics built from NHL puck and player tracking and machine-learning models. The League describes a system using sensors in pucks and player jerseys alongside overhead cameras, deployed in all 32 arenas during the 2021–22 season. The NHL’s overview of NHL EDGE explains the tracking system.
In the 2026 Sorokin article, NHL EDGE’s Saves Above Projected is calculated as total shots on goal faced minus the summed projected goal rates for eligible inferred shot attempts. Attempts from beyond 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 for the included shot quality. This is the NHL EDGE definition used in that article; it should not be treated as interchangeable with a third-party GSAx figure.
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Tracking expands the information available, but public metrics do not capture every aspect of goaltending. They cannot, on their own, fully establish positioning, vision, technique, or what the goalie could see on a particular play. Public sources also do not establish how every NHL club combines statistics with scouting, video, goalie-coach assessments, health, contract considerations, or proprietary models. Avoid attributing one universal evaluation process to all teams.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare two goalies more fairly
- Choose a shot-quality measure and name its source. Compare GSAx or another expected-goals measure, checking which shots and inputs the provider includes.
- Separate game situations. Where available, look at five-on-five, special teams, high-danger chances, or close situations rather than assuming one overall percentage describes every demand.
- Check the workload and sample. Review minutes, games, shots faced, starts versus relief appearances, and rest. A small sample can make a rate look unusually high or low without representing typical performance.
- Account for the team environment. Consider scoring support and the defense’s effect on the chances faced, alongside the goalie’s stopping results.
- Keep the measures distinct. Pair an outcome measure, such as wins or goals allowed, with a shot-quality measure and a relevant situational split. Explain the sample and model rather than collapsing them into a supposedly definitive ranking.
Labels such as “quality start” and “steal” are not universal standards. The Kraken explainer uses them for particular ways of describing whether a goalie faced above-average shot quality and exceeded expectations; check the definition before relying on such labels.
Are hot streaks proof that a goalie has improved?
A run of excellent games is worth describing, but recent results alone do not prove a stable change in skill. A 2021 study analyzed 48,431 shots faced by 93 goalies in NHL playoffs from 2008 through 2016 and found no evidence for a hot-hand effect under the models and shot-based windows it tested. That finding does not mean goalies cannot have excellent runs; it cautions against treating a short streak as proof of a lasting performance shift. The study’s abstract and paper describe its sample and analysis.
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