Playoff Success Math - Part 3

So in my last two posts I have been writing about my amateur analysis of what regular season performance statistics actually correlate to playoff success.  I got some fairly decent results, I think.

The only extra review I did since my last post was to run one more statistic; 5v5 "Close" Corsi percentage.  "Close" is defined as when the game is within 1 goal in the 1st or 2nd period, or tied in the 3rd period.  I thought that could be a good one, because teams play differently depending on the score: they'll sag back on defense with the lead, and attack more when trailing (particularly in the 3rd period).  I thought controlling for those factors would produce a better result.

Turns out, it did.  Here's one last visual chart of correlation factors for each regular season statistic.

 

Using 5v5 Corsi "Close" gives a slight boost to the correlation factor over plain old 5v5 Corsi or Fenwick.

So what to do next?

At this point, I identified 4 robust statistics; total goal difference, Corsi close, PK, and save percentage as leading indicators.  Then, I used Excel to a run a 4-variable regression using the last 5 years of the NHL data that produced the chart above.  Giving that I have not done this stuff since Econometrics my senior year of college, I'd probably bungle an in-depth explanation of everything that means.  For these purposes, it suffices to say that this process spits out a formula for a linear trend line into which you can plug raw data, then get a predicted number of playoff wins.  

Armed with this equation, I then went back over the last 5 years and did just that.

In many ways, these results turned out better than I expected.  With the sole exception of the 2010 Flyers, 9 out of 10 Stanley Cup Finalists were top-5 ranked team according to my formula.  In 3 out of 5 years, the team ranked 1st or 2nd in these rankings actually won the cup.  While top-5 teams occasionally do bomb out in the first round, in most cases the loss is to another top ranked team or there is a mitigating factor; 2014 St. Louis entered the postseason in horrible slump, 2012 Pittsburgh had a mental collapse against the hated rival Flyers, and 2011 Pittsburgh was playing without injured Malkin and Crosby...   These are pretty solid results!

On the flip side, being in the bottom-3 spells nearly certain doom.  13 out of 15 bottom-3 teams lost in the first round.  The 2010 Montreal squad was the only team that did anything of note from such a low position.

Okay, so modeling the past isn't the hard the part.  What does the formula say for the future?  I ran it again using NHL statistics from this season, as of today.  Here are the results:

So there it is.  There's Chicago and then a cluster of comparable teams.  Are the two eventual Stanley Cup finalists found in that top-5, as has been the case the last 4 consecutive seasons?  At the other end, some may be surprised to see the Islanders at second-to-bottom, given all the fans they've won over with their play this year.  I can tell you they're down there because their penalty kill and team save percentage are clearly the worst of playoff teams.  I will update these numbers at the end of the season to get final ratings, at which time I'll have to ask myself how much confidence I want to put into my magic formula!

Nevertheless, I'm pretty pleased with the end result of my little investigation.

Playoff Success Correlation - Redux

So following up on my previous blog post regarding what regular season statistics are most likely to translate into post season wins, I decided to re-run the test with some adjustments.

To measure success, I used raw postseason wins again.  For the moment, I do not have a better method.

To measure regular season performance, instead of looking at where each team finished in each category (1st to 30th), I used the direct statistic.  In addition, because average PP, PK and save percentage varies league wide from season to season, for those three statistics, I calculated the teams statistic against the league average from that particular season.

Here are the results:

Conclusions
 -In general, making these adjustments did not change the qualitative results.  Instead, in nearly every category in resulted in a small increase in the magnitude of correlation.

-Corsi and Fenwick correlation showed the most significant increase in correlation, to the point that it implies a different qualitative conclusion.  Whereas Corsi and Fenwick were no better in predicting playoff success than simple 5v5 goal differential when looking at it via league standing, when you use the more specific Corsi or Fenwick percentage (as I did in the second analysis), it does become a significantly better predictor.  Interesting.  This throws out much of my rationalization in my last post.


-When I was re-digging into the data, the Penguins of 2008 and 2009 were an anomaly.  Those teams reached the finals in both of those seasons (and winning the cup in 2009), despite very poor performance in Corsi, PK, and save percentage.  This drags down the 7 year correlation in those statistics, and in a possibly misleading fashion.

-Defaulting to the 5 year statistical window (the green bars), PK quality remains the strongest predictor by a wide margin.  After that, total goal differential, Corsi, Fenwick, and total shot differential are all about equal in correlation strength.  Save percentage lags behind, with a decent correlation.  5v5 goal difference is weaker still, power play is worthless, and I didn't even bother looking at shooting percentage in my second analysis.