In this series of articles, we describe a Bayesian approach to analyzing goalies that has these benefits over using Even Strength SV% (ESSV%):
- It is easier to compare goalies who have faced a different number of shots.
- We get error bounds for our estimates.
- Our approach allows us to answer a variety of questions like "What is the probability that Goalie A is better than Goalie B?" and "What is the probability that Goalie A's true ESSV% is above .920?"
Links to a series of articles on A Bayesian approach to analyzing goalies:
Part 1 - Introduction
Part 2 - An example using only 10 shots
Part 3 - Updating estimates with more information
Part 4 - Regression to the mean, Luongo vs Schneider, Thomas vs Rask, Hiller vs Fasth
Part 5 - coming soon
A nice summary of the series and the subject by Eric T here.
Finally, an animation discussed in the series:
This series is based on joint work with Nick Clark.