Behind the Numbers: Results matter in the end

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Every once-in-a-while I will rant on the concepts and ideas behind what numbers suggest in a series called Behind the Numbers, as a tip of the hat to the website that brought me into hockey analytics: Behind the Net.

Player tracking, what is it good for? Absolutely nothing.

Okay, that’s a lie. A catchy lie though, especially if you are into late-60s / early-70s soul. It would also be pretty disingenuous of myself, since helping run the technical side of a tracking company is my day job.

However, whether you call it individual player tracking, microstatistics, or whatever, there seems to be some misunderstanding to some on what the numbers being measured represent and what they should do with this information.

Not to put Elliotte Friedman on the spot, but I thought he summarized the movement of some opinions very well when in one of his 30 Thoughts pieces he said:

“I’ve mentioned this season I’m down on analytics, moving toward a preference for player tracking.”

I’m not going to beat up on Friedman for the small misnomer of the word analytics, a term that simply means the computational analysis of statistics or data. What he meant was that he was moving his faith from output statistics, such as Corsi, and moving towards player tracking results to help formulate his opinion in player evaluation.

In the article, Friedman is suggesting Kris Russell’s history of having his team out chanced and outshot be dismissed, at least in good part, as Russell had exceptional zone exit numbers (at least, according to Sportlogiq’s proprietary database of an unknown sample of games).

However, there is an error in this assumption.

I understand the wanting for evolution, moving past what we currently have in understanding of what makes a player and how they help or hurt their team garner wins. This is the same drive that has moved the hockey analytics community to better and better measurements of a player’s performance as we have moved from raw Corsi, to adjusted numbers, to expected goal models.

It may be best to ask why we care about certain things the way we do.

Why do we care about goals? Well, the entire desired result is to outscore the opposition. Hockey is literally a game of statistics, where the team with the most goals wins.

Why do we care about shot metrics like Corsi, Fenwick, and expected goals? Well, this is because often in analysis we care more about who will outscore in the future more so than who did outscore in the past.

Why do we care about player tracking? Well, we wish to know the different parts of a player’s decision making and their ability to successfully execute those choices, which become the outcomes we observe.

In short, player tracking matters in the context of garnering good results. Being a good zone exit player tells you in part why a player has the results in shots and goals that they do.

This reminds me of an old concept I laid out before, which eventually became the image at the top of this article. My good friend Mike Fail took this one step further with the graphic you can check out at the bottom.

When we look at a player in terms of the eye-test, we cannot dismiss the results they provide. The eye-test in traditional scouting merely looks at the different factors to explain how a player drives their results.

Strength, skating, grit, etc. are all methods of driving shot volume and quality to ultimately garner more goals than the opposition. If they do not do this, why does it matter?

Breakouts, entering the zone, loose puck recoveries, passes, etc. are again just methods of driving shot volume and quality to ultimately garner more goals than the opposition. If they do not do this, why does it matter?

These factors are still important, and with understanding these inputs better, we can better understand what drives results. This can help in optimizing player roster deployment, line creation, and line matching. It can help in video coaching and player development. NHL teams can use this information on their AHL players in choosing call-ups that best fit role.

It can even lead us into constructing improved metrics in evaluating players.

However, these things are not end-game measures of themselves. Corsi, expected goals, or any other shot metric is not a WAR statistic, but it at least represents a large portion in trying to control shot volume and quality.

Player tracking just looks at a player’s effectiveness in one very specific area of the game.

Statistics are powerful tools, but one must keep in mind what they represent. Both the microstatistics derived from player tracking or the more macrostatistics like Corsi are no different.

They are tools useful in coaching and player evaluation, provided one uses each tool to their strength and understand their shortfalls.


Image full resolution can be found here.

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