Exceeding Pythagorean Expectations: Part 4

“Zdeno Chara 2012” by Sarah Connors. Licenced under Public Domain via Commons.

Zdeno Chara 2012” by Sarah Connors. Licensed under Public Domain via Commons.

This is the fourth part of a five part series. Check out Part 1, Part 2, Part 3, Part 5 here. You can view the series both at Hockey-Graphs.com and APHockey.net.

So now, four parts into this five part series, is probably a good time to discuss my original hypothesis and why I started this study. As I mentioned in my previous post, baseball has already gone through its Microscope Phase of analytics, where every broadly accepted early claim was put to the test to see whether it held up to strict scrutiny, and whether there were ways of adding nuance and complexity to each theory for more practical purpose. One of the first discoveries of this period was that outperforming one’s Pythagorean expectation for teams could be a sustainable talent — to an extent. Some would still argue that the impact is minimal, but it’s difficult to argue that it’s not there. What is this sustainable talent? Bullpens. Teams that have the best relievers, particularly closers, are more likely to win close games than those that don’t. One guess that I’ve heard put the impact somewhere around 1 win per season above expectations for teams with elite closers. That’s still not a lot, but it’s significant. My question would be, does such a thing exist in hockey?

Of course, there are no “closers,” strictly speaking. But there are players who close out games more often than others, and there are players whom coaches trust in late game lead-protecting situations more than others. Does a team with players who thrive in such situations have a higher chance of exceeding their expectation?

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Exceeding Pythagorean Expectations: Part 3

“Pythagorus Algebraic Separated” by John Blackburne. Licenced under Public Domain via Commons. The 2006 Red Wings may have been the best hockey team since the lost season.

Pythagorus Algebraic Separated” by John Blackburne. Licensed under Public Domain via Commons.

This is the third part of a five part series. Check out Part 1, Part 2, Part 4, Part 5 here. You can view the series both at Hockey-Graphs.com and APHockey.net.  

Since the last post was getting a little long, I decided to hold off on releasing the full Pythagorean results. Linked you will find a table of every team since the lost season, sorted by the difference between its adjusted point total and its Pythagorean expectation. Essentially, the teams that have the highest numbers in the right-most column are likely to have been the most fortunate, and those at the bottom were possibly unlucky. If you look at the 2014-2015 results below, you will see which teams should be a little bit worried about their chances, and those which may be ready for a rebound. Tomorrow, I will address what the point of this whole study was, and we’ll look at some more data.

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