MISCELLANEOUS STATS
NEUTRAL WINNING PERCENTAGE
This essay describes a method for evaluating goaltenders which is, in theory, free from bias created by the team the goalie plays for and the teams he faces.
Each goaltender’s season is broken down by the number of times which he allows zero goals in a game, one goal in a game, two goals, and so on. The records for all teams in the league are then broken down based on the number of goals they allowed in each game. We then use these league numbers to compute an expected record for the goaltender, based on the number of times he allowed each number of goals against.
Adding the results gives us a won-lost-tied record. We then convert the record into a winning percentage, so we can compare goaltenders directly.
This winning percentage is offence-neutral. The number of goals the goaltender’s team scores has no effect upon the percentage. The bias resulting from playing for a high- or low-scoring team is eliminated.
We have already eliminated the distortion caused by team offence. Two types of distortion remain: distortion from team defence, and distortion from opponent’s offence. Fortunately, both of these can be compensated for
GA- & GAA- (ADJUSTING GAA FOR SHOTS AGAINST)
if we could adjust GAA to incorporate the number of shots allowed GAA would be a more useful metric.
as the number of shots faced increases so does the number of goals, but the trend is more logarithmic than linear. If save percentage increases with shots against, we wouldn’t expect goals allowed to double just because shots against doubled.
We can look at how many goals we would expect an average goalie to give up, given the amount of shots against. Then, if we look at how many goals a given goalie gave up in each game and compare it to the expected goals against set by our model, we can come up with a shot adjusted goals against metric that provides an estimate of how many additional goals a goalie prevented (or allowed) over the course of a season.
DIGR
STANDARDIZED GOALS AGAINST
(May 31, 2017)
I propose a new framework for evaluating goaltending performance, taking into account the difficulty of shots faced as well as the quality of skaters playing for both teams.
Adjusting For Skater Quality:
We can compute the “eventual goal probability” starting from any state, that is, the chance that, starting from a given state, the puck will wind up in the net before the defenders manage to clear it.
This produces a transition matrix which I imagine as representing what would transpire if the goalie in question were provided with league-average skater context instead of the teammates and opponents they actually faced. Then, by computing the long-run probability of a shot being converted into a goal, we can compare two goaltenders more fairly.
Adjusting For Shot Quality:
not all shots are equally easy to handle.
some accounting should be made to handle difficulty of shots faced.
Standardized Goals Against or sGA, that is, the number of goals that a given goalie would allow per hundred shots if they faced a typical distribution of shots, calculated from how they performed on the shot distribution they did face
The two adjustments described here (replacing skater terms with league averages and shot standardization) can be combined to obtain a stat that I call “sGA*”.
Most surprisingly, the least repeatable measure of goaltending talent is 5v5 save percentage,
Forming the difference between expected goals allowed and actual goals allowed and then dividing by the number of shots puts this notion on the same arithmetic footing as the other ones, allowing for comparisons. It is the most sophisticated existing model for goaltending evaluation to date
much more surprising is the equally strong repeatability from all-situation save percentage, which indiscriminately buckets together shots from all different contexts.
GPAx (CLUTCH PERFORMANCE)
(Dec 23, 2017)
The goal support a goalie receives is (largely[1]) independent of their ability and outside of rebounds, so are most chances they face
Building on this we can evaluate a goaltender’s ability to win games as a function of the quality of chances they face and the goal support they receive.
The best way for a goaltender to help his team win is to make more saves than expected with the contested idea that some are more likely to make those saves in high leverage situations than others, albeit in an indirect way.
An additional goal prevented is worth about 0.37 points in the standings. Or, more precisely, 2.73 goals per point.
xGSv%
(Mar 17, 2021)
I have noticed a recent shifting in the conversation away from traditional Goaltending metrics such as save percentage and goals against average, towards more focused dissection of High Danger save percentages.
If we know the majority of information is contained in a goalie’s ability to stop the most dangerous shots, why do we let Low Danger shots determine how we rate goalies to such a high degree?
Virtually all current advanced Goaltending metrics function on a system that penalizes goalies for the goals they allow rather than crediting them for the saves they make.
The disparity between the xG value and the actual goal surrendered is far higher for a Low Danger shot than a High Danger shot.
We penalize goalies far more for what amounts to random noise than we reward them for actual skill.
Maybe we could account for this by crediting Goalies for the xG of the shots they stop, rather than penalizing them for the goals they allow.
I propose the idea of xGSv%.All we need to do is divide a goaltender’s total xG saved by the total xG of all shots they faced.
Low danger shots won’t inflate the numerator or denominator considerably. Saving easy to stop shots won’t make a goalie look amazing. Allowing the occasional random point shot to go in won’t make them look horrible.
The main skill in Goaltending is saving the hard to stop, high xG shots. The only way to post a good xGSv% would be to stop more of those shots.
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