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Understanding NHL Advanced Stats: A Beginners Guide Part 4

NHL Analytics

“Advanced stats” has become a kind of buzz word around the hockey world. It’s emerging into the NHL as it did years ago with the MLB. Hockey teams have entire departments for analytics, and it has become a war (pun intended) of sorts between two sides: The Data Nerds vs. Old School Hockey. What I intend to do is break down some of the common stats used in analytics to help people get introduced into the world of NHL analytics.

Be sure to check the rest of the series:

Part One

Part Two

Part Three

Part Four

Understanding NHL Advanced States: Part Four

What is WAR good for? A lot, actually. Today we will be breaking down wins above replacement as a means of evaluating hockey players. In any examples used in this article will come from Evolving Hockey. There are other good websites with very good WAR models such as MoneyPuck.

What is WAR?

WAR is a stat that you may be familiar with from baseball. It is a way to assess players and assign a numerical value to how much a single player contributes to their team. This value can be expressed as either a positive or a negative depending on a players impact. This stat can be used to evaluate players across positions easily by seeing how many “wins” they provide their team. This is a fantastic stat when looking at players who should win things like the Hart Trophy.

Evolving Hockey also can express this as goals above replacement (GAR). This provides similar information and just breaks it down to how many goals a player provides his team above a replacement-level player. It roughly translates from 5.6 GAR to 1 WAR based on their model.

I will do my very best to describe wins above replacement so that you have a knowledge of what it does, what it evaluates, and what are some components featured. I will not attempt to discuss the technical knowledge needed to understand how one is built, as I do not attempt to understand it. If you would like to know in detail about WAR, here is Evolving Wild’s posts on Hockey Graphs that go into incredible detail. Here is part one, part two, and part three.

How to Calculate WAR?

The numbers used to calculate WAR varies from model to model, but looking at Evolving Hockey’s WAR model in a very broad sense, for players it looks at a few things. It includes even-strength offence, even-strength defence, power-play offence, short-handed defence, penalties taken, and penalties drawn. Of course, this is all extremely simplified and it is much more complex when building the stat, however, this gives you a broad idea of what goes into it.

When evaluating a player via this method, a replacement-level player would be zero in every category totalling out to zero GAR and zero WAR. Anything above this threshold is an above replacement player. Any player with a negative value is a below replacement player.

We can see some of the top-level players of the past years to show what elite players obtain in this stat. In 2019-20, Artemi Panarin led the league with 4.4 wins above replacement and 24.9 goals above replacement, mostly due to his excellent even-strength offence. In 2018-19 Mark Stone held that title with 4.6 WAR with the Ottawa Senators and 0.7 WAR with the Vegas Golden Knights.

Bottoming out in the league were Johnny Boychuk with -1.8 in 2019-20 and Ryan Kesler with -2.6 in 2018-19.

Why Use This Stat?

Surely it’s too good to be true. A single, all-encompassing statistic to evenly evaluate forwards, defencemen and goaltenders? People have different views on wins above replacement. There are people who refuse to use it due to the fact that they either don’t like advanced stats or they do not believe there could ever be such a “God” stat. People that hold these opinions, in my experience, tend to overlap. There are people who build these stats and hold high faith in them, as they should if they built them. I fall between these two spaces.

I like to look at wins above replacement as a starting point for evaluation. These stats have evolved a lot quicker than some are willing to admit. At the same time, there is a lot there is not public access to track that could potentially lead to building better models. There is always room for improvement on stats such as this, as with most things in life.

This concludes the Understanding NHL Analytics: A Beginners Guide series. If you have any questions, feel free to reach out on Twitter.

Main photo:
Embed from Getty Images


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