FQG: Using Goals Above Replacement to Measure Injury Impact

 

Injuries are an inevitable part of the NHL. An 82 game schedule guarantees that all teams are going to deal with injuries during the season but not all teams deal with them equally. Quantifying the impact of injuries is difficult. The introduction of better individual player impact stats gives us some new tools with which to approach this concept. In particular, DTMAboutHeart‘s Goals Above Replacement stat seems a useful place to start because it allows for estimating how many goals above replacement a team loses while a player is injured.

All injury data in this post comes from NHL Injury Viz. GAR data comes via DTMAboutHeart. Games played data comes from Corsica and standings data is via Hockey-Reference.

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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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Friday Quick Graphs: League Wide Report

 

The All-Star break is now in the past. The trade deadline is less than two weeks away. Teams across the NHL have a pretty good idea of who they are. They know their strengths and weaknesses. The possible outcomes for their seasons are narrowing. Some teams are already locked into playoff spots and only have to worry about positioning. Others will have to slowly accept the reality that this isn’t their year and consider how that impacts their approach at the deadline. This is a perfect time to take a high-level view of the league and look at each team using a series of simple metrics to help get a grasp on where all thirty teams are sitting.

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There’s No Secret to Protecting a Lead

I was born into a family of Islander fans, so I never had a chance to avoid the sadness that comes with that fandom. While Islander fans are sad for a lot of reasons, one constant complaint over the past several years has been their inability to protect a lead.

However, this is not a unique complaint of Islander fans alone. Fans of other teams have similar gripes. For example, the Leafs have been criticized this season on the same grounds. And here’s fellow Hockey Graphs write Asmae when I suggested doing some research on blown leads:

image-0-asmae

So, are some teams particularly bad at holding leads? Asked another way, is keeping a lead a skill distinct from the rest of the team’s performance, or is it just a function of the team’s overall skill and luck?

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Coaching Analysis Part 2: Metropolitan Division

Note: This is Part 2 of the series on coaching analysis. Part 1 is here.

In this post, I’ll do a brief review of each team’s coach history from the current Metropolitan Division. These graphs only show a team’s performance in 5v5 situations from 2005 to 2016. The vertical lines indicate when a season begins. The horizontal line shows the 50% mark, where a team would be if it had as many shots for as shots against. The bold line is a smoothed representation of the team’s shot percentage. The faded bands around the bold line indicate 95% confidence intervals. These intervals show the uncertainty around the smoothed estimation of the data.

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Applying CUSUM to hockey prediction models

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The NHL season is a long and grueling affair and most teams will experience some ups and downs over the course of 82 games. Even a team that had a 67% chance of winning every game it played, would still have a 20% probability of putting up a five-game losing streak. And this is just straight probability theory with fixed probabilities. What happens when you consider all of the factors that go into determining the probability of winning an individual game, let alone predicting performance over an entire season?

Well, I’m not here to answer that question.

What I am here to do is to try to apply an analytical technique that was developed in the 1950s for the purposes of quality control in industrial and manufacturing processes to the game of hockey. Continue reading

25 Games In, What Does the Corsi Say?

Happy Max Corsi Productivity Day! We’ve reached the point in the season where Corsi best predicts future winning percentage. There’s plenty of more advanced ways to better predict how the rest of the season will go, but Corsi offers a simple baseline in a way that helps explain why it is so important.  I’ll first explain what that means and why it matters, then take a look at how we can use it to predict basic shifts in the standings for the rest of the NHL season.

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2016-17 Hockey Graphs Top 50 Players

Welcome to the second annual Hockey Graphs Top 50 Players in the NHL list.

The main reason I put this together last year (you can view that here) was as a basis for comparison against the other, more famous, top 50 players lists. The annual list is a season preview staple for TSN and THN and the rankings are usually slightly controversial. Both lists are created via a poll of various people inside hockey, who are generally very smart people, but who are also prone to old-school thinking with value sometimes being shaped by recency bias, reputation and a winning pedigree.

This list is a bit of the opposite as it comes from mostly outsiders, people who study and analyze the game in the public sphere. That’s not to say these are necessarily smarter people, they just approach the game from a different angle based mostly on underlying trends and numbers over more traditional stats and what is immediately seen on the ice.

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Just How Important is Quality of Competition? Very. Also, not much. It’s All Relative.

*This post is co-authored by DTMAboutHeart and Ryan Stimson*

Recently, the topic of Quality of Competition has been at the forefront of Hockey Twitter. This post hopes to articulate some of the nuance associated with Quality of Competition, as well as Quality of Teammate, metrics and how impactful they are. To do that, we will revisit methods outlined here by Eric Tulsky, namely splitting the competition and teammate quality by position and measuring the impact of each split. Ryan recently wrote about this at the NCAA level, but it has not been looked at with much rigor at the NHL level.

Both Quality of Competition and Quality of Teammates matter. They also don’t matter. It depends on the position and metric you’re looking at. All TOI data is 5v5 and from Corsica. Ryan had the game files of who was on the ice during each 5v5 shot from Micah Blake McCurdy, so that data was used as well. Also, thanks to Muneeb for feedback during this process. Thanks to all!

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