A Defense of WAR from a WAR-Skeptic

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Note: This was originally intended to be a tweet-thread which grew far too long and unmanageable, so you’re getting a poorly-written post instead. Apologies in advance.

Recently, David Johnson, owner of the awesome puckalytics.com has been on a bit of a warpath (pun intended) against the use of WAR/GAR. Most of David’s arguments can be found here and here, but there are some other comments in this thread.

I consider myself a bit of a WAR skeptic. I think Dawson’s work is great, but I think there are limitations/issues with it. A good summary of some of my concerns can be found in another ill-advised and long tweet thread.

With that being said though, I still think it’s extremely useful as a first pass to start discussion. WAR can be broken down into 5 useful components to see where a players impact derives from.

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A New Look at Aging Curves for NHL Skaters (part 1)

How do NHL players age? When do they peak? How quickly do they decline? Questions about player aging in the NHL have been debated for years, and an incredible amount of research has already been done trying to answer these questions. Within the past 3 years, however, it seems a general consensus has been reached. Rob Vollman summarizes this quite well in his book Stat Shot: The Ultimate Guide to Hockey Analytics: “Most players hit their peak age by age 24 or 25 then decline gradually until age 30, at which point their performance can begin to tumble more noticeably with the risk of absolute collapse by age 34 or 35.”

The vast majority of this work has been done looking at points, goals, shot attempts, special teams, etc., but the release of Dawson Sprigings’ WAR (Wins Above Replacement) model gives us a new statistic from which we can derive value and, possibly, a new way to look at how NHL skaters age. It seems only natural that we’d revisit the NHL player aging question using this new model. If you’re unfamiliar with his WAR model, you can read all about it here.

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Strong and Weak Links: Talent Distribution within Teams

In the salary cap world, hockey is a game of resource allocation. Each team is given a set amount of money to acquire players. Consequently, hockey inevitably becomes about tradeoffs. When building a team, every dollar spent on one player is a dollar that can’t be used for another. There are certainly times when you can get a bargain, but you will always have to make decisions about spending priorities.

One frequent prioritization question is high-end quality vs. depth. How much should a team focus on the very top of its lineup vs. ensuring it has adequate depth? Should a team maximize its strengths or minimize its weaknesses?

This question is relevant to many front office decisions. The Bruins traded Tyler Seguin for several assets, and some argued that the Penguins should do the same with Evgeni Malkin to improve their depth. As Steven Stamkos approached free agency, many teams were deciding just how much they would be willing to pay him while knowing that signing him would inevitably come at a cost lower down the roster.

We can think through these tradeoffs by studying talent distribution within a team. If you hold total talent constant, is it better to have a team where everyone is equally talented, or one where a few elite players are trying to shelter a few terrible ones? We know from current Florida Panthers consultant Moneypuck that contending teams have at least one elite player, but to my knowledge, very little work has been done on the broader question of total team structure. This article mirrors my presentation at the Vancouver Hockey Analytics Conference 2017, at which I dug into talent inequality within teams to demonstrate:

  • Hockey is a strong link game, i.e., the team with the best player usually wins
  • Therefore, teams should prioritize acquiring the very best elite talent, even at the cost of having weaker depth than opponents
  • This is important for roster construction now and has the potential to become even more important as teams get better at assessing talent and market inefficiencies become less common

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