Being honest about “possession” stats as a predictive tool

I often feel that I am the sole defender of goal based hockey analyitics in a world dominated by shot attempt (corsi) based analytics. In recent weeks I have often heard the pro-corsi crowd cite example after example of where corsi-based analytics “got it right” or “predicted something fairly well”. While it is always good to be able to cite examples where you got things right a fair an honest evaluation looks at the complete picture, not just the good outcomes. Otherwise it is analytics by anecdotes which is an oxymoron if there every was one. For example, Kent Wilson

» Read more

Measuring persistence, randomness, and true talent

In Rob Vollman’s Hockey Abstract book he talks about the persistence and its importance when it comes to a particular statistics having value in hockey analytics. For something to qualify as the key to winning, two things are required: (1) a close statistical correlation with winning percentage and (2) statistical persistence from one season to another. More generally, persistence is a prerequisite for being able to call something a talent or a skill and how close it correlates with winning or some other positive outcome (such as scoring goals) tells us how much value that skill has. Let’s look at persistence first. The

» Read more

Who are drags on the Maple Leafs possession game

Even though I am a proponent of shot quality and the idea that the percentages matter (shooting and save percentage) puck control and possession are still an important part of the game and the Maple Leafs are dreadful at it. One of the better easily available metrics for measuring possession is fenwick percentage (FF%) which is a measure of the percentage shot attempts (shots + shots that missed the net) that your team took. So a FF% of 52% would mean your team took 52% of the shots while the opposing team took 48% of the shots. During 5v5 situations

» Read more

How are HockeyAnalysis ratings (HARO, HARD, HART) calculated?

Every now and again someone asks me how I calculate HARO, HARD and HART ratings that you can find on stats.hockeyanalysis.com and it is at that point I realize that I don’t have an up to date description of how they are calculated so today I endeavor to write one. First, let me define HARO, HARD and HART. HARO – Hockey Analysis Rating Offense HARD – Hockey Analysis Rating Defense HART – Hockey Analysis Rating Total So my goal when creating then was to create an offensive defensive and overall total rating for each and every player. Now, here is a step by step guide

» Read more

Tips for using Hockey Fancy Stats

I often get asked questions about hockey analytics, hockey fancy stats, how to use them, what they mean, etc. and there are plenty of good places to find definitions of various hockey stats but sometimes what is more important than a definition is some guidelines on how to use them. So, with that said, here are several tips that I have for people using advanced hockey stats. Don’t over value Quality of Competition I don’t know how often I’ll point out one players poor stats or another players good stats and immediately get the response “Yeah, but he always plays

» Read more

Regular Season Team Stats and Playoff Success

Yesterday HabsEyesOnThePrize.com had a post on the importance of fenwick come playoff time over the past 5 seasons. It is definitely worth a look so go check it out. In the post they look at FF% in 5v5close situations and see how well it translates into post season success. I wanted to take this a step further and take a look at PDO and GF% in 5v5close situations to see of they translate into post season success as well.  Here is what I found: Group N Avg Playoff Avg Cup Winners Lost Cup Finals Lost Third Round Lost Second Round Lost

» Read more

The declining value of fenwick/corsi with increased sample size

The last several days I have been playing around a fair bit with team data and analyzing various metrics for their usefulness in predicting future outcomes and I have come across some interesting observations. Specifically, with more years of data, fenwick becomes significantly less important/valuable while goals and the percentages become more important/valuable. Let me explain. Let’s first look at the year over year correlations in the various stats themselves. Y1 vs Y2 Y12 vs Y34 Y123 vs Y45 FF% 0.3334 0.2447 0.1937 FF60 0.2414 0.1635 0.0976 FA60 0.3714 0.2743 0.3224 GF% 0.1891 0.2494 0.3514 GF60 0.0409 0.1468 0.1854 GA60

» Read more

A Corsi vs Goal based evaluation of Francois Beauchemin

Over the past few years I have had a few discussions with other Leaf fans about the relative merits of Francois Beauchemin. Many Leaf fans argue that he was a good 2-way defenseman who can play tough minutes and is the kind of defenseman the Leafs are still in need of. I on the other hand have never had quite as optimistic view of Beauchemin and I don’t think he would make this team any better. On some level I think a part of the difference in opinion is that many look at his corsi numbers which aren’t too bad

» Read more

Eight Reasons I Don’t Like Fenwick/Corsi in Player Analysis

I have had a lot of battles with the pro-corsi crowd with regards to the merits of using Corsi as a player evaluation tool.  I still get people dismissing my goal based analysis (which seems really strange since goals are what matters in hockey) so I figured I should summarize my position in one easy to understand post.  So, with that, here are 10 significant reasons why I don’t like to use a corsi based player analysis. 1.  Look at the list of players with the top on-ice shooting percentage over the past 5 seasons and compare it to the

» Read more

On-ice shooting percentage is sustainable…

Prior to the season Gabe Desjardins and I had a conversation over at MC79hockey.com where I predicted several players would combine for a 5v5 on-ice shooting percentage above 10.0% while league average is just shy of 8.0%.  I documented this in a post prior to the season.  In short, I predicted the following: Crosby, Gaborik, Ryan, St. Louis, H. Sedin, Toews, Heatley, Tanguay, Datsyuk, and Nathan Horton will have a combined on-ice shooting percentage above 10.0% Only two of those 10 players will have an on-ice shooting percentage below 9.5% So, how did my prediction fair?  The following table tells all. Player

» Read more
1 2