A goalie’s wins, save percentage and goals-against average describe outcomes, but they do not show how difficult the shots were or how much the defense shaped those outcomes. A stronger evaluation pairs workload and basic results with shot-quality-adjusted metrics, situation-specific splits and team context—while treating every model-based number as an estimate, not a verdict on talent.
What the basic goalie stats tell you—and what they leave out
Save percentage is the share of shots on goal a goalie stops. Goals-against average (GAA) is goals allowed relative to the goalie’s playing time. Both are useful summaries, but neither accounts for whether the shots were routine or especially dangerous. A goalie facing a steady diet of close-range chances is not being tested in the same way as one facing mostly low-danger attempts.
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That is why adjusted measures compare goals or saves with an estimate of what an average goalie might have been expected to produce against the shots faced. They add context; they do not erase the influence of teammates or model assumptions.
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Expected goals against and expected save percentage
MoneyPuck defines expected goals against as the sum of expected-goal values for the unblocked shot attempts a goalie faces. Its expected save percentage estimates the save percentage an average NHL goalie would have produced against that shot quality. These definitions belong to MoneyPuck’s model; another provider may use different inputs or calculations. MoneyPuck’s glossary explains its goalie metrics.
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Goals Saved Above Expected (GSAx)
In MoneyPuck’s definition, GSAx = expected goals against − actual goals conceded. A positive result means the goalie allowed fewer goals than the model expected; a negative result means more. For example, if the model assigns 4.0 expected goals against and the goalie concedes 3, the hypothetical GSAx is +1.0. This example is illustrative, not a real player result.
Save Percentage Above Expected
Save Percentage Above Expected compares actual save percentage with expected save percentage. It answers a related question in percentage-point terms, but it is not interchangeable with GSAx: the metrics use different expressions of performance, and their interpretation depends on the provider’s model and definitions.
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NHL EDGE Saves Above Projected
NHL EDGE uses a separate measure called Saves Above Projected. NHL.com describes it as comparing shots on goal with the summed projected goal rate of inferred attempts, while excluding specified categories such as shots from beyond 60 feet, beyond the goal line or against an empty net. That makes it related in purpose to GSAx, but not necessarily the same calculation. Name the provider when reporting either value and avoid treating figures from different systems as directly comparable. NHL EDGE’s explainer describes the tracking context and measure.
A repeatable sequence for evaluating a goalie
- Set the comparison’s scope. Identify the league, season, regular season or playoffs, strength state, goalie appearances and data provider. Keep the time period and model consistent when comparing players; if definitions or model versions differ, flag that limitation.
- Record workload and baseline outcomes. Note games or minutes played, shots faced, goals against, save percentage and GAA. These establish the scale of the sample and the outcome, but do not assign all credit or blame to the goalie.
- Add a shot-quality adjustment. Review expected goals against, expected save percentage, GSAx or NHL EDGE Saves Above Projected, using the provider’s own definition. Read a positive adjusted result as performance above that model’s expectation—not as a model-free measurement.
- Break results into relevant situations. Look at 5-on-5 and high-danger results rather than relying only on an all-situations season total. NHL.com’s 2026 goalie analysis uses high-danger save measures and 5-on-5 save percentage as examples of useful evaluation lenses. The feature’s discussion illustrates those splits; its player-specific figures are tied to that season, not evergreen benchmarks.
- Check secondary outcomes and team context. If available, examine observed rebounds and freezes against expected rates. MoneyPuck cautions that an elevated rebound count can reflect goalie rebound control or the team’s ability to clear the front of the net, so do not assign every second chance solely to the goalie. MoneyPuck’s methodology notes explain this rebound caveat. Tracking and zone context can help describe what the team allowed, but team-level zone time is not itself a goalie skill statistic. NHL.com’s NHL EDGE explainer covers the evolving public presentation of tracking and danger-zone information.
- State the uncertainty. Treat adjusted results as model-relative, especially in a small sample. Hockey performance is difficult to summarize and assign among players because the result of a shot depends on more than the goalie; a peer-reviewed review of hockey analytics discusses these broader measurement challenges. Annual Reviews’ overview provides that context.
How to compare two goalies fairly
Use the same provider, time period and situation definitions for both goalies. A compact comparison should include:
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- Workload: games or minutes and shots faced, so a large sample is not mistaken for better performance.
- Adjusted results: the same provider’s GSAx, expected save percentage or equivalent measure for each goalie.
- Situation splits: high-danger and 5-on-5 results, with the sample stated where available.
- Secondary outcomes: rebound and freeze information, interpreted alongside the defense’s ability to clear the crease.
- Uncertainty: sample size, model construction and team environment, rather than a rank based on one narrow split.
If one goalie has a higher GSAx from one model and another has a stronger value from a different provider, that is not a clean head-to-head result. Resolve the provider and definition mismatch before drawing a comparison.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Separate past performance from prediction
A positive past GSAx says a goalie conceded fewer goals than one model expected over the measured sample. It does not by itself show that the goalie will sustain the same level or that a recent run signals a lasting change in ability.
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A 2021 study by Likang Ding, Ivor Cribben, Armann Ingolfsson and Monica Tran tested 48,431 shots for 93 goaltenders in the 2008–2016 NHL playoffs. It reported no evidence for a hot-hand effect across the recent-shot windows tested. That finding applies to that NHL playoff sample and those tested definitions; it is not proof about every league, situation or possible measure of momentum. The study’s abstract and publication details describe its scope.
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