A Defense of WAR from a WAR-Skeptic

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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Testing and Final Remarks

(Photo by Andre Ringuette/NHLI via Getty Images)

(Photo by Andre Ringuette/NHLI via Getty Images)

This is Part 5 of a 5 part series detailing my WAR model, Part 1 of the series can be found here, Part 2 of the series can be found here, Part 3 of the series can be found here and Part 4 of the series can be found here.

Introduction

In the beginning of this exercise I set out to try and encapsulate the best estimate of a NHL player’s true value. An adjusted plus-minus system (XPM) was introduced to help contextualize shot attempt numbers. An box plus-minus system (BPM) was introduced to help contextualize metrics such as goals and assists. Ability to win faceoffs as well as to draw and not take penalties were also included

“WAR is not meant to be a perfectly precise indicator of a player’s contribution, but rather an estimate of their value to date. Given the imperfections of some of the available data and the assumptions made to calculate other components, WAR works best as an approximation. WAR is trying to answer the time-honored question: How valuable is each player to his team? Comparing two players offensively is useful, but it discounts the potential contribution a player can make by saving runs on defense or special teams. WAR is a simple attempt to combine a player’s total contribution into a single value.

The goal of WAR is to provide a holistic metric of player value that allows for comparisons across teams and years and a framework for player evaluation. While there will likely be improvements to the process by which we calculate the inputs of WAR, the basic idea is something fans and analysts have desired for decades. WAR estimates a player’s total value and allows us to make comparisons among players with vastly different skill sets.  (FanGraphs). 

The final study will examine the repeatability and predictiveness of the WAR components.

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Extras, Blending and Seasonal Adjustment

(Photo by Rich Graessle/Icon Sportswire)

(Photo by Rich Graessle/Icon Sportswire)

This is Part 4 of a 5 part series detailing the my WAR model, Part 1 of the series can be found here, Part 2 of the series can be found here and Part 3 of the series can be found here.

Introduction

Now that we have covered the overall player models here and here, we will explore how to blend these two together to achieve maximum out-of-sample predictive power. We will touch on what I have coined the “extras” section made up of penalties and faceoffs. Faceoffs are a fairly standard and well accepted player skill, even though it is overvalued by many hockey “traditionalists.” Penalties are an aspect of player analysis that typically goes unaccounted for in most current analysis. Finally, we will implement a yearly adjustment most commonly used in baseball WAR.

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Introducing Box Plus-Minus

EDMONTON, AB - OCTOBER 23: Connor McDavid #97 of the Edmonton Oilers skates during a game against the Washington Capitals on October 23, 2015 at Rexall Place in Edmonton, Alberta, Canada. (Photo by Andy Devlin/NHLI via Getty Images)

(Photo by Andy Devlin/NHLI via Getty Images)

This is Part 3 of a 5 part series detailing the my WAR model, Part 1 of the series can be found here and Part 2 of the series can be found here.

Introduction

Box Plus-Minus (BPM) is a box score-based metric for evaluating a hockey player’s quality and contribution to the team. It is very different than an Expected Plus-Minus type model, which is a play-by-play regression metric. BPM relies on a player’s box score information to estimate a player’s performance relative to replacement level. Box Plus-Minus type metrics have long populated basketball circles, there is a great summation of some of the original creations here, with many newer versions popping up including Dredge, DRE and Player Tracking Plus Minus. A version has even already been brought to hockey in the form of Game Score. Here I will attempt to create my own version of Box Plus-Minus for the NHL.

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Introducing Expected Plus-Minus

sidney-crosby-nhl-chicago-blackhawks-pittsburgh-penguins

This is Part 2 of a 5 part series detailing the my WAR model, Part 1 of the series can be found here.

Introduction

“Basically, anything that WOWY can do, I think can be done better with regression-type methods”Andrew C. Thomas, Lead Hockey Researcher Minnesota Wild

Adjusted Plus-Minus metrics were first introduced into NBA circles around 2004 by Dan Rosenbaum. The basketball community has since seen many iterations including; Steve Ilardi/Aaron Barzilai, Joseph Sill and Jeremias Engelmann. Soon after these metrics made their debut into the public sphere they were adopted for hockey and have themselves seen many different iterations; Schuckers/D.Lock/Wells/Knickerbocker/R.Lock, Brian Macdonald, Gramacy/Jensen/Taddy, Thomas/Ventura/Jensen/Ma and Emmanuel Perry. I even made my own attempt in the summer of 2015 which I coined Corsi Plus-Minus. These metrics have struggled to take hold amongst the hockey community for whatever reason, unlike in basketball circles.

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A Primer on @DTMAboutHeart’s WAR Model

paveldatsyukmoves

Introduction

Hockey stats have existed for about as long as the game itself. Simple boxscore stats such as goals and assists can be traced back almost a full century now. These stats have helped inform fans, coaches, and managers of the value held by players. Around the 1950’s the Montreal Canadiens began to track a player’s plus-minus with the idea that simple boxscore stats failed to capture many important elements of a game. Plus-minus was a good start towards tracking impact that is not realized in traditional boxscore stats, but has been recently shown to be quite incomplete and lacking by modern evaluation standards.

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