In

Part 1 of this series, we gave an introduction to this method of analyzing goalies and the motivation behind it. We discussed why many analysts use even strength save percentage (ESSV%) instead of traditional SV%, and talked about two problems that ESSV% has which we'd like to deal with. This series focuses on Problem 1: ESSV% tends to be very inconsistent especially for goalies who have faced a relatively small number of shots. Our approach is a Bayesian analysis, where we can use "prior information".

Let's begin with a simple example. Let's consider the first 10 shots that Roberto Luongo faced during the 2008-09 season. None of these shots were goals. If this is the only information we have about Luongo, what should our estimate of his true ESSV% be? In other words, what should we expect his ESSV% to be going forward? We knew a lot about Luongo at the beginning of the 2008-09 season, but for the sake of explaining what's going on, let's pretend we didn't.

One approach to answering this question is to use his observed ESSV%. In this case, his ESSV% is 1.000 since he had 10 saves on these 10 shots. This estimate is pretty high... 1.000 is way higher what any typical NHL goalie would ever have. This is a pretty extreme example, since we are using only 10 shots, and it's sort of obvious that observed ESSV% should not be our estimate in this case. But it will help us explain what is going on, and this kind of thing can still happen when using 100, 500, or 1000 shots, though the observed ESSV% won't typically be as extreme.

Instead of using observed ESSV%, we could use a Bayesian estimate. Suppose we know that the league's ESSV% are typically distributed like this: