All entries by this author

Estimating the Lower Bound for the Return on Investment for Hockey Analytics

Last week it was announced that the New Jersey Devils had hired Sunny Mehta to be their Director of Hockey Analytics.  Just over a month ago, Craig Custace wrote a piece($) for ESPN Insider on the New Jersey Devils search for a Director of Hockey Analytics.  In that article, there was an underlying tone that hockey analytics was expensive.  Ryan Lambert of Puck Daddy nailed a part of this a couple days after when he wrote (under #3): I love this idea that more NHL  […]

NHL Referee Analytics from 2012

So last week during the Hockey Analytics panel at the MIT Sloan Sports Analytics Conference, Eric Tulsky referenced a study that Michael Schuckers and his student Lauren Brozowski did on referees in the NHL.  While this work has been available publicly on the conference website, due to some unknown oversight we did not post it here.  So here it is.  The paper is based upon two NHL seasons worth of data.  The data don’t let us know who among the referees made the call just  […]

Out of the Ice Age: Some Hockey Analytics Highlights since #SSAC2012

This week is the 2014 MIT Sloan Sports Analytics Conference.   While the conference has not always had a panel on hockey, there is one this year. To that end, I took a look at some of the innovative pieces of hockey analytics work since the last conference panel in 2012. I’ve made a list below,  in no particular order, along with links and a quick summary of the contribution. Giving the growth of analytics and the growth of hockey analytics, this list is surely incomplete.     Player Usage  […]

Impact of changing teams on THoR and CorsiRel

We’re coming up on the trade deadline for the NHL.  Lots of names are going to be floated and some players will be traded.  Raphael Diaz and Dale Weise were traded today as I was finalizing this post.  According to THoR (all events) Diaz is a player who has been worth 2 or 3 wins per year over a replacement defensemen but will he be worth roughly that for Vancouver?  Certainly the Canucks hope so.  In that vein, I recently got a question about what would  […]

A look at the US Olympic Squad for Sochi from Analytics perspective

So I’ve been in Helsinki this Autumn and as a result haven’t gotten to see much live NHL hockey.  (I’ve seen plenty of pee wee and bantam hockey as well as a couple of SM Liiga matches.)  We’re headed home just after the end of the year and one of the highlights once we get back to the states will be watching the Olympic hockey tournament.  Should be a blast.  To that end, I thought I would take a look at the candidates for the  […]

THoR for ALL

We are excited to present the first results for the Total Hockey Rating model for all events.  Our new model uses much of the same structure found in the original even strength version of THoR but we have added additional terms to account for the additional variability and values.  Here is a link to the original THoR paper as well as to our recent case for why even strength THoR is a very strong measure of player performance.  We have improved on that with this  […]

The Case for THoR

This past February we introduced the Total Hockey Ratings or THoR at the MIT Sloan Sports Analytics Conference where it won 2nd place in the research paper competition.  THoR rates players by taking the probabilities of a subsequent goal for every event for which they are on the ice and accounting for who they played with, who they played against, where their shift started, the score of the game as well as whether or not they were at home.  In doing so we isolate the two-way impact  […]

An Evaluation of the Total Hockey Rating (THoR), Part III

This is the third part of a series that looks in more depth at the Total Hockey Ratings that we developed.  In Part I, I looked at the year to year correlations for THoR,  Gabe Desjardins’ Corsi Rel and  (HART) .  Part II discussed the validity, or the relationship between the response in the THoR model, NP20, and winning.  In this part, we’re going to take a look at some individual players through the lens of THoR.  We’ll look at some players that have come to be associated  […]

An Evaluation of the Total Hockey Rating (THoR), Part II

This is the second part of a series that looks in more depth at the Total Hockey Ratings that we developed.  In Part I, I looked at the year to year correlations for THoR,  Gabe Desjardins’ Corsi Rel and  (HART) .  We found that THoR was a more reliable predictor than the other two metrics.  As I mentioned at the end of that post, this internal consistency is good but for a metric to be useful it has to also be valid.  We’ll see below that THoR  […]

An Evaluation of the Total Hockey Rating (THoR), Part I

Back in March our Total Hockey Ratings finished 2nd as part of the MIT Sloan Sports Analytics Conference Research Paper Competition.  That drew plenty of attention to the outcomes but little to the underlying process and methods.  In this and a couple more posts we’ll take a look and evaluate how well THoR does as a two-way hockey metric.  I’ll start by comparing some of the omnibus (all inclusive) player ratings.  My goal is to   consider how these ratings do at predicting future performance  […]