## Goaltending Statistics - Glossary of Terms

I use some non-standard terminology on this site (and some standard terminology). This page attempts to catalogue the various metrics presented here.

### AGE

For each season, I define the age of the player as their age as of January 1 of that season. This is roughly the midpoint of most regular seasons, and has a certain calculation advantage.

### GP

This is the number of games played by the goaltender during the season. I only count games where a goaltender appears in net (and not games on the bench as the dressed backup). Many European leagues differentiate between games dressed and games played in; on this site, GP reflects the latter.

### MIN

This is the number of minutes played by the goaltender during the season. Where available, I keep this information at the minutes/seconds level in my database, and report only the (rounded) minutes here.

### W, L, T, OTL

These are the number of wins, losses, ties, and overtime/shootout losses recorded by the goaltender during the season.

For every league that I can think of, the current definition of these are the goaltenders in net at the time of the game-winning goal (the N+1th goal, when the losing team scores N goals).

At some levels of leagues, the published statistics do not allow easily to see whether a goaltender won or lost in overtime - recent KHL statistics have this feature. Here, I've included both into the "T" column.

Some leagues separate OTL and SOL - I combine these here. If I have separate shootout statistics, I keep them separately (in another column).

### WPCT

This is the winning percentage of the goaltender during the season, although it's better expressed as points percentage:

(2*W + T + OTL) / [2*(W + L + T + OTL)]

For instance, Henrik Lundqvist had a record of 33-24-5 in 2013-14. This is 71 points earned in 62 decisions, or a winning percentage of (2*33+5)/[2*(33+24+5)] = 57.3%.

Note that for seasons with "loser points", this will result in the "average" goaltender having a points percentage of greater than 50%.

### GA

The number of goals allowed by the goaltender during the season.

### GAA

The goals-against average for the goaltender during the season:

GAA = (GA)*(60)/(MIN)

The intent of the goals-against average is to put seasons of different length on an even footing, reporting the number of goals the goaltender gives up (on average) in a sixty-minute game.

### SA

The number of shots faced by the goaltender during the season. Only "shots on goal" are counted here - shots that would have gone into the net had the goaltender not stopped them (posts do not count as shots, for instance).

Pre-1982 NHL shot (and save percentage) information comes from a second-hand Excel workbook done by Roger Brewer, using data from the Hockey Summary Project (a tremendous endeavor by both). It is not considered official National Hockey League dogma.

### SVPCT

The fraction of shots faced by the goaltender that were prevented from becoming goals, expressed as a decimal.

SVPCT = (SA - GA)/(SA)

### S/60

The number of shots the goaltender faced in an average sixty-minute game during the season.

S/60 = (SA)*(60)/(Min)

### ShO

The number of shutouts accumulated by the goaltender during the season. To record a shutout, a goaltender has to be the only goaltender on the ice for a team allowing zero goals (there are also "team shutouts" where two goaltenders share a shutout).

### G, A, PiM

The number of goals, assists, and penalty minutes accumulated by the goaltender during the season. These are traditionally "skater statistics", but can be interesting or revealing for goaltenders.

### UNI#

The sweater (uniform) number(s) worn by the goaltender during the season. I'm working to get more of these into the database.

### SOUT

The goaltender's performance in tiebreaking shootouts during the season. Four numbers are presented here: shootouts won, shootouts lost, shootout saves made, and shootout shots faced.

Note that I vary the definition of a "save" here to include any attempt that does not result in a goal (including shots that go wide). If you prefer, think of this as shootout goals prevented.

### S+/30

This is the first of several save percentage translations on the site, and this one is pretty straightforward.

During an average 30-shot game, how many additional saves will this goaltender make above and beyond what a league average goaltender would make? This is just the goaltender's save percentage minus the league average save percentage, multiplied by 30. I chose 30 as a representation of a "typical" game across eras, instead of using each goaltender's actual shots faced per game. Otherwise, goaltenders on teams that allow a large number of shots would have their totals magnified (for good or for bad).

(In the calculation of league-average save percentage, I remove the goaltender in question from the totals.)

### Z-SCORE

Many times, differences in save percentage between goaltenders can be the result of random fluctuation. This statistic asks the question "if this goaltender were truly a league average goaltender, facing the number of shots that they faced, how remarkable would their actual performance have been?".

For instance, suppose that a league-average goaltender had a save percentage of 90%, and faced 100 shots on goal. The truly league average goaltender would allow 10 goals on these 100 shots. Suppose that our goaltender instead allowed 8 goals. Assuming a binomial distribution (I note that this may not be fair), we can calculate how many standard deviations above (or below) average this goaltender's performance was:

ZSCORE = ((Saves) - (Shots * League Average SV%)) / SQRT (Shots * League Average SV% * (1 - League Average SV%))

Or, in this case:

ZSCORE = (92 - 90) / SQRT (100 * 0.9 * 0.1) = 0.67, indicating that the goaltender was above average but not in a statistically significant fashion.

Truly remarkable performances (good and bad) start at about 2 standard deviations away from average, and the larger the number, the more significant.

(In the calculation of league-average save percentage, I remove the goaltender in question from the totals.)

### GD

This statistic, Goal Differential, measures how many goals the goaltender prevented above a league-average goaltender. I would have preferred "goals above average" here, but the abbrevation presents its own problems.

A league-average goaltender would allow (1 - League Average SV%) * (Shots Faced) goals, and so:

GD = ((1 - League Average SV%) * (Shots Faced)) - (Goals Against)

### GAR

This statistic measures how many goals the goaltender prevented above a replacement-level goaltender. One problem with the statistic above (Goal Differential), is that it suggests the notion that an "average" goaltender has no value. To the contrary, playing at the league average generally earns NHL goaltenders millions of dollars each year.

This statistics, Goals Above Replacement, attempts to quantify that value by comparing how many goals a goaltender prevented above a replacement-level goaltender. On this site, "replacement level" represents the best goaltender that a team could find on short notice with small resource expenditure (either the top goaltender on their minor league team, or the top free agent available). I need to analyze this more rigorously at some point, but here, replacement level is defined as 1.5% below league average (so if the league average goaltender is at 90%, then replacement level is defined as 88.5%). And thus:

GAR = ((1 - (League Average SV% - 0.015)) * (Shots Faced)) - (Goals Against)

### SNW%

This statistic, Support-Neutral Winning Percentage, asks the following question - if a goaltender played for a team that allowed as many shots against as shots for, and was playing against a league average goaltender, what would their winning percentage be? On this site, the calculation is done using the "basic" Pythagorean formula to predict winning percentage

SNW% = (Goals Scored^2) / ((Goals Scored^2) + (Goals Allowed^2))

Or in this case:

SNW% = ((1 - League Average SV%) * Shots Allowed)^2 / (Goals Against^2 + ((1 - League Average SV%) * Shots Allowed)^2)

Note that, unlike the goaltender's winning percentage, this metric is guaranteed to be such where a league average goaltender scores out with a 50% winning percentage.

### SNW, SNL

Support-Neutral Wins (and Support-Neutral Losses) take a goaltender's support-neutral winning percentage (calculated above) and partitions their actual number of decisions by that percentage.

For instance, suppose that a goaltender had an (actual) record of 7-3-0, with a support-neutral winning percentage of 60%. They had ten decisions, and so their support-neutral wins would be 6 (and support-neutral losses would be 4).

On some level, this suggests that things not measurable by save percentage (either team offense, or team defense, or biases in save percentage) gave the goaltender an "extra" win.

### VAR

Variation is a measure of a goaltender's game-to-game consistency within a season.

For each opponent in the league, a "benchmark save percentage" is developed, based upon their non-empty net shooting percentage. If we make the (admittedly simplifying) assumption that shots faced in a single game represent a binomial distribution, then we can estimate (for each game) how many standard deviations above (or below) average a goaltender's actual game performance represented.

The variation metric is then the standard deviation of the above metric. For instance, if a goaltender plays five games in a season, and has in each game, he is 0.5 standard deviations above average, then his variation score would be zero (since his performance does not vary).

A low variation represents a more-consistent goaltender, while a high variation represents a less-consistent goaltender. Note that in this case, a goaltender can be terrible but still be consistent (so long as his performances are consistently terrible.

Over the course of a full season, a goaltender of average consistency will have a variation of about one (1.0). For seasons with very few games played, variation will be artificially low (to take an extreme example, a goaltender with one game played in a season will have a variation score of 0, since all of their games are identical).

### BAVG, AVG, AAVG

Below-Average, Average, and Above Average performances represent the number of times in a season that a goaltender performed at a league-wide level, above that level, or below that level.

For each opponent in the league, a "benchmark save percentage" is developed, based upon their non-empty net shooting percentage. If we make the (admittedly simplifying) assumption that shots faced in a single game represent a binomial distribution, then we can estimate (for each game) how many standard deviations above (or below) average a goaltender's actual game performance represented.

Performances within 0.5 standard deviations of average are grouped as "average", with performances below that (and above that) grouped as "below average" (and "above average"). Season totals are weighted by shots faced in each game.

### SoS

Strength of Schedule (SoS) is a measure of the strength of the average opponent faced by the goaltender. Do some goaltenders play a disproportionate share of their games against better opponents (either through coaching decisions or random fluctuation)?

I developed an estimate of each team's strength - using their entire (regular season plus postseason data), starting with each team's goal differential (GF minus GA), then adjusting for schedule (each team's average opponent's goal differential). This is an iterative process, but does converge to a metric that estimates how many goals better (or worse) a team is compared to average during the season. The top teams in the league are typically about +1, and the bottom teams in the league are typically about -1 (although some of the early-1990 expansion teams hovered around -2). I also develop separate strength scores at home and on the road, just in case (for instance) a team only plays their goaltender on the road. For those of you who remember the "Norris Power Index" that I published in the mid-to-late 1990s, this is that algorithm.

Once these strength ratings have been developed, I calculate SoS as the minutes-weighted average strength of opponent.

SoS balances to zero in the regular season, but should be positive in the postseason - since a team is facing above-average opponents (by definition).

Using time played to weight the strength metrics appears to inflate the metrics of backup goaltenders. Why? Because they're more likely to enter the game in relief against a strong opponent (and conversely, the starter is more likely to get a short night). p>

### OpS%

Opponent-weighted Save Percentage (OpS%) is the save percentage that a league-average goaltender would expect to have if they played the same mix of opponents as the goaltender in question. Do some goaltenders play a disproportionate share of their games against stronger shooters and offenses than other goaltenders?

To answer the question, I took each team's (regular season plus postseason) non-empty net shoting percentage. For an individual goaltender, I take the (shots-weighted) average opponent shooting percentage, and then subtract the result from one. This puts the result on the same scale as save percentage, so that one can compare it to the goaltender's actual performance.

(Note that this is not the average save percentage of a goaltender's opponents.)

OpS% balances to the league-wide save percentage over the course of the season.

Using shots faced to weight the strength metrics appears to inflate the metrics of backup goaltenders. Why? Because they're more likely to enter the game in relief against a strong opponent (and conversely, the starter is more likely to get a short night). I still prefer it this way, since it's a measure of what actually happened (if Glenn Healy faces 500 shots in a year, but 100 of those are against juggernauts in relief, then I want to know that when I'm evaluating his performance).

Lastly, I should note here that, while save percentages are considered a decent statistic for evaluation of a goaltender's individual performance (certainly better than goals-against average or wins and losses), it is by no means a perfect statistic. I will write more to this at a later date, but for now, please keep that in mind.