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A goalie can stop shots well and still lose if the team scores little; another can earn a win without facing the same workload or shot danger. NHL teams and analysts therefore look beyond the standings column, combining shot-quality measures, game situations, workload and team context. Public statistics clarify parts of the picture, but no single number—and no public account—captures every club’s evaluation process.
Why wins and losses do not grade a goalie by themselves
A win records the result of a team game. It depends on the goalie’s work, but also on how many goals the team scores and what chances its defense allows. In a 2019 NHL.com example, Pekka Rinne and Juuse Saros had similar save percentages, .893 and .894, over 18 and 16 games, yet their win totals were 10 and 4. Their teams provided 4.09 and 2.35 goals per game, respectively. Those are figures from that 2019-20 example, not current performance data. NHL.com’s comparison illustrates why wins are useful team context, not a stand-alone goalie grade.
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What traditional goalie statistics show—and miss
Save percentage
Save percentage (SV%) is saves divided by shots on goal faced. It answers what share of recorded shots the goalie stopped, but its basic calculation treats shots alike: a long, unobstructed shot and a close-range chance each count as one shot. It does not adjust for difficulty.
Goals-against average
Goals-against average (GAA) is goals allowed per 60 minutes of play. It summarizes the rate of goals allowed, but it also does not adjust for the quality of the chances. Both measures remain useful descriptions of results; neither explains, on its own, how demanding the shots were. The Seattle Kraken’s explainer on goaltending statistics describes these measures and their limits.
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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 value means the goalie allowed fewer goals than the model expected; a negative value means more. It is a model-based comparison, not a direct observation of every aspect of a save. Its result depends on the provider’s expected-goals model and the data inputs available.
Goals Saved Above Average
Goals Saved Above Average (GSAA) estimates the difference between the goalie’s saves and the saves expected using league-average save percentage over the shots faced. It provides a league-average baseline, but does not itself model detailed shot danger in the way a shot-quality measure does.
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Expected save percentage and provider differences
Expected save percentage (xFSV%) estimates save percentage against unblocked shots in the Seattle explainer’s description; differential save percentage (dFSv%) compares actual and expected save percentage. Formulas and shot populations can differ by provider, so a number should be labeled with its source and methodology before it is compared with another number. The Kraken’s Beyond the Box Score introduction discusses why definitions and available inputs matter.
Which situations and workload teams examine
Overall season rates can conceal meaningful differences in the conditions a goalie faced. Analysts may separate five-on-five play from special teams, inspect high-danger chances or close situations, and consider shots faced, starts, relief appearances and rest. These cuts help describe the assignment; they do not automatically identify a cause or make small samples reliable.
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NHL.com’s 2026 season coverage of Ilya Sorokin offers a dated example of combining measures: it reported a .878 high-danger save percentage, compared with a .814 NHL average, on 412 high-danger saves from 469 high-danger shots faced; it also reported a .922 five-on-five save percentage and +35.2 Saves Above Projected. These numbers belong to that article’s 2026 season snapshot, not an evergreen ranking. The NHL EDGE account defines its Saves Above Projected as shots on goal faced minus summed projected goal rates for eligible inferred shot attempts. Attempts over 60 feet, beyond the goal line, or against an empty net are excluded. That NHL EDGE measure should not be assumed interchangeable with a third-party GSAx figure.
What tracking data can—and cannot—tell you
NHL EDGE publishes statistics based on puck and player tracking and machine-learning models. The League describes sensors embedded in pucks and player jerseys, supported by overhead cameras, and deployment across all 32 arenas during the 2021-22 season. The NHL’s explanation of EDGE outlines that tracking system and its public-facing stats.
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More tracking creates more measurable context, not a complete account of goaltending. Public data may not establish every goalie’s positioning, vision, setup, or what the goalie could see on a particular play. The Kraken’s analysis notes limits in reliable public information about shot angle, goalie setup and sightlines. Public sources also do not establish how every NHL club combines statistics with video, scouting, coaching assessments, health information or proprietary models. A claim about one universal NHL team workflow would go beyond what these public descriptions show.
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- Choose a named shot-quality measure. Compare GSAx or another expected-goals measure, and state which provider produced it; models can use different definitions and data.
- Match the game situation. Compare like with like, such as five-on-five performance, and inspect high-danger or close-situation data where available.
- Check the workload and sample. Look at minutes, games, shots faced, starts versus relief appearances, rest and season length. A brief appearance with a perfect save rate is not enough to conclude that a goalie will keep stopping every shot.
- Account for the team environment. Goal support affects wins, while defensive play shapes the chances the goalie faces.
- State what the model leaves out. Identify the data source and any relevant limits rather than treating one metric as a complete goalie ranking.
A practical short comparison pairs a result measure, such as wins or goals allowed, with a named shot-quality measure and a situational split; it then accounts for sample size and team context. Do not collapse these separate questions into a universal ranking unless the ranking method and its limits are made clear.
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How much to infer from a hot streak
A run of strong games can be real without proving that a goalie’s underlying skill has permanently shifted. 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 is limited to that study’s data and methods; it does not mean goalies cannot have excellent stretches. It means recent results alone are not proof of a stable short-term skill change. The study’s abstract and paper describe its analysis.
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