From High to Low: What Happens when Goalies Get Traded?

To continue the discussion on shots against per game and their effects on goalie play, I went through Hockey Reference's data banks to look at goalies who had been traded mid-season to see what the effects were on their statistics with their new team. I took every goalie in the save percentage era (since 1983-84) who was traded during the season and had played at least 500 minutes with both teams.

Here is the breakdown of how they did, broken down into three roughly equal groups by shot differential between the two teams:

	Low	Shot	Team	High	Shot	Team
Diff	Win%	GAA	Sv%	Win%	GAA	Sv%
0-2	.471	2.78	.902	.456	3.02	.896
2-5	.542	2.78	.900	.459	3.27	.893
5+	.510	2.93	.887	.375	3.63	.890

What does this tell us? The GAA differential increased as the shot differential did, which is to be expected since more shots against means more chances to allow goals. There was also a distinct relationship in terms of winning percentage for goalies going from low shot to high shot teams. Clearly it is much easier to win games on teams that are good at preventing shots against.

There was not, however, a direct relationship between save percentage and shots. The goalies with the largest shot differentials actually had similar save percentages on both teams, whereas goalies with a smaller differential did slightly better on the lower shot team. This is evidence that shot quality is not directly related to shots against, but varies on a team-by-team basis.

It is obvious that goalie results are very team-dependent. Save percentage is not perfect, but it is by far the best measuring stick for goalies, since the variance in save percentage was much lower than the variance in GAA or especially winning percentage. If we take the sample of goalies who faced a difference of 7 shots against or more after being traded, we can see this quite clearly:

Low Shot Team: .886, 2.94, .531 win%
High Shot Team: .886, 3.98, .320 win%

The exact same goalies stopped pucks at exactly the same average rate both before and after the trade, but they allowed over one goal more per game with one of the teams and had a difference in winning percentage of .211, which would be the equivalent of 35 points in the standings over an 82 game schedule. The moral of the story is that using straight GAA or winning percentage statistics to rate goalies is not a good idea. If you have to use unadjusted stats then use save percentage, but all goalie stats are dependent on the rest of the team.

There was one result that was a bit of a surprise, and that was the lack of variance in shutout results. For the group as a whole, the shutout rate on the lower shot teams was .048 per 60 minutes, and for the higher shot group it was .041. That is a difference of about half a shutout per season for a starter with a typical 60 game workload. That was not too surprising, but the underlying distribution was a little unusual. For goalies with either a low shot differential (0-2 shots difference) or a high shot differential (5+) there was very little difference in shutout rate. The goalies with the large differentials were actually very slightly more likely to record a shutout on the higher shot team. Only in the 2-5 shots per game range was there a large discrepancy, and it was in the expected direction (.048 on lower shot teams, .032 on higher). Sample size is an issue here, however, because shutouts are so infrequent. One or two goalies who managed to get lucky for a half-season in terms of shutouts could easily have skewed the overall totals.

My main beef with shutouts is that they are an arbitrary stat. Why do shutouts get all the hype, but one-goal games get no credit even though both of them nearly always result in a win? However, I am starting to think that there may be some useful information in shutouts. Certainly shutouts are still team-dependent to a large degree, but they are a dominance indicator. Great goalies on bad teams (e.g. Luongo in Florida) will still usually post a relatively high number of shutouts, especially compared to the other goalies around them. My expected shutouts ranking, which was designed to remove some of the team factors, seemed to pass the common sense test for the most part. I'd still prefer another more inclusive metric to shutouts (for example, the number of times a goalie allowed 2 goals or fewer, given that team winning percentages are very high across the board when teams gives up 2 or less, and also since 1 and 2 goal games are more frequent than shutouts they would likely be less subject to variance and more consistent from year to year), but that would require a lot of additional work to compile.