Theoretical Problems vs Real Problems of the Toronto Maple Leafs

I am taking a bit of a hiatus away from my hiatus on blogging because I read something a few days ago that caught my attention both as a Leaf fan and as a hockey analytics enthusiast and felt compelled to respond. The Leafs have been a pretty interesting team this year with a significant infusion of youthful talent in Matthews, Marner and Nylander. They may even be good enough to capture a playoff position or at least seriously contend for one. That said, they are still a flawed team. Offensively they are a dramatically improved team and are near the

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Going On Hiatus…

I just finished an 8-part series on roles and statistics with a specific intent of showing that certain types of players or playing styles have impacts on save percentage. It was received well by some, not so well by others. Clearly there are detractors however this is a case of been there, done that for me. I went through the same debates 5-6 years ago with on-ice shooting percentage. I am confident down the road the hockey analytics community will begin to accept that players can influence save percentage just like most have now accepted players can influence on-ice shooting percentage. It’ll just

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Roles and Stats Part VIII: Review and Looking Forward

This is my 8th and final post in my series on the relationship between roles and statistics and this will be a review of what we have learned so far and maybe more important what it all means and doesn’t mean. Series Posts Part I: Background and Methodology Part II: Roles and Offensive Stats – Forwards Part III: Roles and Defensive Stats – Forwards Part IV: Roles and Offensive Stats – Defensemen Part V: Roles and Defensive Stats – Defensemen Part VI: Who’s Who? Part VII: Can roles predict future shooting and save percentage? Review of Findings In this series of posts I used three different player

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Roles and Stats Part VII: Can roles predict future shooting and save percentage?

In the first six parts of this series I have looked at the relationship between roles and player statistics and, particularly for forwards, there is a clear relationship between how a player is used and their resulting statistics. Now this isn’t an absolute relationship as we saw with Brandon Sutter in part six of this series but it does exist. Thus far I have only presented an “in-sample” relationship (relationship between current season role and current season stats) but can roles predict future statistics as well? This is what I intend to show in this post. Leading and Trailing TOI% I am

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Roles and Stats Part VI: Who’s Who?

This is part six of my series on the relationship between roles and statistics. I recommend you read some or all of the previous posts before reading this one starting with my first post where I provide a background and the overall methodology of this research project. In this sixth post I want to look at which players are in some of the more extreme roles that have the most significant impacts on save percentage. Since we found there isn’t much of a relationship between roles and save percentage for defensemen I will focus on forwards. As a refresher, here

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Roles and Stats Part V: Roles and Defensive Stats – Defensemen

This is part five of my series on roles and stats. In part four of the series I looked at how roles relate to the offensive statistics of defensemen and found that there was a relationship however it was not nearly as strong as for forwards (as one would expect). Today I will look at roles of defensemen and defensive statistics which one would think ought to show a stronger relationship. Let’s have a look. (If you have not already, it is recommended to at least read part I of the series where I discuss methodology before proceeding). Leading and Trailing

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Roles and Stats Part IV: Roles and Offensive Stats – Defensemen

This is part four of my series on roles and stats. The first was an introduction and a description of methodology. The second and third parts of the series looked at offensive and defensive statistics for forwards respectively. This post will look at how roles relate to offensive statistics for defensemen. Leading and Trailing TOI% The above chart looks at the impact of roles, as identified by leading and trailing TOI%, on offensive production. There is definitely a pattern here similar to what we saw for forwards however it is to a much smaller degree. This makes sense as defensemen are less

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Roles and Stats Part III: Roles and Defensive Stats – Forwards

This is part three of my series on how roles impact statistics. In Part I I looked at a brief history of the debate and also described the methods used in this research. The history section is optional but I recommend reading the methods portion as it describes the statistics and methods used which will not be discussed in great detail here. The second post in the series looked at the relationship between roles and offensive statistics (GF60 Rel, CF60 Rel, and Sh% Rel) for forwards. In this third post I will look at the defensive statistics (GA60 Rel, CA60 Rel and Sv% Rel)

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Roles and Stats Part II: Roles and Offensive Stats – Forwards

This is part two of my series on how roles impact statistics. In Part I I looked at a brief history of the debate and also described the methods used in this research. The history section is optional but I recommend reading the methods portion as it describes the statistics and methods used which will not be repeated in great detail here. This second post in the series will look at the relationship between roles and offensive statistics (GF60 Rel, CF60 Rel, and Sh% Rel) for forwards. Leading and Trailing TOI% The chart above looks at the relationship between leading and trailing ice

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Roles and Stats Part I: Background and Methodology

This is the first of what will be a series of posts on the impact of roles on stats with a particular interest in the impact of roles on shooting and save percentages. Background My involvement in hockey analytics has been somewhat outside of the main stream. I am a firm believer in shot quality and the ability of players to influence on-ice shooting and save percentages while hockey analytics has generally trended (almost exclusively) towards puck possession metrics like Corsi. As a result, over the years I have fought many battles with many people on shot quality starting first with

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