Advanced Stat Series – Value of Differing Metrics





The modern advent of public data and websites providing new sports metrics to evaluate players has become somewhat of a double-edged sword. On one hand, the democratization of sports statistic analysis has resulted in a whole new area of the business teams are building out, and on the other, it has polarized fans in such a gritty, historical, and noisy sport such as hockey.


The point of this column thus far has been to set the table and give background on why all metrics are tracked and how they are valued. This evolution is a trend that has occurred throughout the development of all sports, and hockey is having somewhat of a Renaissance.


This month in the column, we’ll be taking a very high-level look at the definitions and use cases of different metrics and attempt to clarify just why we track these events. But first, a quick outline of an important concept to understand beforehand.


In any statistical test, one of the most significant factors that help judge the efficacy of a study is sample size.


If sample sizes are small, variations in data will be exaggerated far heavier in analysis. To use a hockey example, if you were to try to protect the significance of the effect of shot distance and scoring goals for a certain player, and you only selected a sample size of one month of games, your potential for a weak experiment is much more likely than if you selected a sample of two months, or a season, or even a career.


Here's a look at a good supplementary article on the actual math behind variance and standard deviation here.


When it comes to hockey, the fundamental principle that analysts live by is, “if you have the puck, the other team doesn’t, therefore only you can score.” In short, having the puck = good.


Analysts quickly found that teams that take shots more, correlate heavily to teams that spend more time in the offensive zone, and the more time a team spends in an offensive zone, the more likely that team is to score more than their opponent. Of course, individual play may directly lead to goals for or against that is harder to predict, but this is where sample size comes in.


In Rob Vollman’s book, Stat Shot, he notes early on that the biggest benefit of shot attempts (or Corsi attempts, as outlined last month) is that the sample size is enormous and publically available. Over time, some analysts preferred to filter blocked shots out of the equation to isolate situations that are more likely scoring chances, commonly referred to as Fenwick attempts.


Unblocked shot attempts of