#fancystats: Where do we go from here?

Stats.

I want to preface this article with the fact that I am not a mathematician or statistician. I’m a lawyer. In fact, they lied to us in law school and told us we wouldn’t have to do math once we were out practicing. So even if you love my ideas, I have no real skill set to design or implement them. This is purely for conceptual discussion purposes.

Ok, with that out of the way, I wanted to talk about #fancystats for a minute. It’s becoming clear that organizations around the league are starting to recognize the usefulness and momentum that these types of statistics have, evidenced by more and more front offices disclosing their emphasis on integrating them into their management processes.

However, I think we can all agree that the concepts and statistical methodologies are rudimentary at best at this point. It’s also completely understandable. Baseball has led the way in the revolution of statistical analyses, but it has a massive advantage on all other sports: each play happens in a vacuum, and at most there are 2-4 players involved in any given play. This level of isolation makes it incredibly convenient to look at individual performance within that play and assign value to it. The causal relationship between each player on the field is limited, and unlike hockey, plays happening minutes prior have very little bearing on what you are measuring.

Facing this difficulty, logical and relevant concepts have been the focal point of hockey #fancystats, such as possession. Focusing on possession makes perfect sense on it’s face; if you have the puck and are shooting at the opposing goal, the other team cannot by definition be doing the same. If they aren’t possessing and shooting, they certainly are not scoring, which is a good thing.

An entire paradigm of statistical analyses have cropped up around this concept, Corsi, Fenwick, QualComp, Zone Starts, etc. When you put them together, it begins to paint a picture of what is going on when an individual player is on the ice. They are complicated calculations/relationships built around fairly simplistic concepts that make logical sense.

So, in contemplating where #fancystats might go from here, I started thinking about inverse relationships and context (welcome to my brain). Certain stats will find themselves fall out of relevance (PDO is already under attack), some will gain context and refinement, and others will emerge as useful tools. Conceptually speaking, it begs certain questions of weighted value and the value of efficiency.

What I mean by that is, sure, when you possess the puck, it can only be a good thing. It’s a good skill to have. Now, is it time to look deeper into the context of that possession? How do you measure and how much value would you place on counter attacking skills? Neutral zone turnovers leading to offensive chances? For defensemen, efficiency of the first pass in exiting the zone? Rate in which a forechecker either creates a turnover or forces an extended shift for the defensive unit, even if they aren’t possessing the puck? For that matter, true possession not related to shot count?

In addition to the difficulty of quantifying these concepts, how do you assign value to them? Can the central possession calculation itself provide more context than just shots for and against? I believe that the answers to these questions are the cocoon that hockey statistical analysis must climb out of in order to really be able to quantify this game in a truly meaningful way. There are plenty of guys out there more mathematically adept than I who I am confident can answer the bell.

Now, don’t even get me started on goaltender analysis. I actually read an article the other day advocating for the demise of GAA. I was down with this premise, so I read on. Then I realized he was advocating for it because it doesn’t tell us anything that save percentage doesn’t. He described GAA as save percentage, plus noise. Not to rag the author here, he is at least attempting a move in the right direction, but all save percentage is, is the most basic of mathematical calculations, plus noise.

We have talked around here about quality shot save percentage and other various ways forward for goalie analysis, but I’m coming to the belief that until we are able to measure positional efficacy and use it as the foundation of a quality-shot type analysis, we will never really have a meaningful statistical look at the position.

So, that’s what I think. How about you? You hear that, sports mathematicians? Go make this happen, and you will have a much more educated and well-analyzed game. Plus it will piss idiot beat writers off who don’t want to have to use their brains. Sounds like a plan to me.