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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Grit vs. Skill: Tanner Glass vs. Pavel Buchnevich

After losing 4-1 to the Montreal Canadiens on March 4, the Rangers recalled Tanner Glass from the their AHL affiliate, the Hartford Wolfpack. Rather than attribute the loss to the Rangers playing poorly––since the Canadiens outshot the Rangers 35-27, won 63% of faceoffs, and had Carey Price in net––much of the blame for the loss was placed on the Rangers lack of “grit” and “toughness.” According to the Rangers, the difference makers in that game were Dwight King, Andrew Shaw, and Steve Ott.

Since recalling Tanner Glass, he has played in six games, and has recorded a goal and an assist. Many view having a tough player like Glass in the lineup as a deterrent. In his first game back with the Rangers against the Tampa Bay Lightning, Glass put his toughness on display early by fighting Luke Witkowski. Later that period, Gabriel Dumont of the Lightning boarded Rangers’ defenseman Steven Kampfer––something that Glass’s presence should have deterred, right?

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Friday Quick Graphs: Navigating the Trade Deadline’s Hype

This year’s trade deadline was uneventful. March 1st was filled with a bunch of small trades that we probably made a bigger deal out of than we should have. However, just a little over two weeks have gone by and people are already looking for a winner. As a follower of analytics, it would be unfair of me to decide less than ten games in who won the deadline. Mainstream media gets a ton of clicks for those posts though, so let’s evaluate them.

A post from Sportsnet found that the last trade of the deadline held the most value. The Bruins traded a 6th round pick to the Jets for Drew Stafford. Stafford has had the worst season of his career. His -3.38 rel CF% is by far the worst of his career, his all situations 1.74 points per 60 is below career average, and he has suffered from the second lowest shooting percentage of his career. The question is: where is the value in Drew Stafford?

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Garret’s look back at VanHAC

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Hello all,

Josh and I want to off the top thank everyone for making VanHAC17 such a wonderful success. The Vancouver Canucks for hosting, catering, and supplying so much support and resources. Our financial sponsors Canucks Army and HockeyData. Our helpful registration desk volunteers. Our panelists Dan Murphy and Dimitri Filipovic. Our presenters (more on them below). And a huge applause and thank you to our wonderful keynote speaker: Meghan Chayka.

Let me break down how this conference and the weekend surrounding it went from my perspective.

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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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Second Units and Zone Entries: Why teams should go all-in on the 4 forward power play

Using 4 forwards on the power play is generally a good strategy. Four forward units take more shots, score more often on those shots, and post a better goal differential than 3 forward groups do.

It’s also a strategy that has become more popular over the last few years. 4 forward units have accounted for roughly 56% of the 5-on-4 ice-time this season, up 4% from last year and more than 15% from 5 years ago.[1]

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Mikael Granlund, Playing Behind the Net, & Predicting Goals

Recently, I showed how passing data is a better predictor of future player scoring than existing public metrics. In this piece, I’m going to show that by accounting for shot quality via passing metrics we can more accurately predict a team and player’s on-ice goal-scoring rates. I’m going to do this by quantifying the pre-shot movement that occurs when a player is on the ice. Finally, I’ll spend some time discussing certain forwards/teams that caught my eye. All data is from 5v5 situations and special thanks to Dr. McCurdy for pulling the on-ice player data for me. All non-passing project data is from Corsica.

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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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Measuring the Importance of Structure on the Power Play

tl;dr

  • We can measure a team’s power play structure using shot location data, creating a Power Play Structure Index that quantifies their ability to establish and shoot from a structured formation.
  • A Team’s Power Play Structure Index is a stronger predictor of future goal scoring than past goals, but weaker than shot attempt generation.
  • When examined together with shot attempt generation, power play structure is a significant predictor of future goals, although slightly less important than shot attempt generation.
  • A team’s structure index can provide valuable additional insight into why certain power plays succeed or fail.

Edit 2017-02-15: An earlier version of this piece had a small error in the regression coefficient for PP Structure Index. While the article previously indicated the coefficient was -0.19, it should in fact be -0.30. The text both above and below has now been corrected.

Introduction

The importance of structure in a team’s power play is something that’s really easy to see. We’ve all watched a power play executing at the top of its game: the puck flies from player to player, leaving defenders pivoting in place to try to keep up. Each shot looks exactly like it was diagramed by the coach, with attackers working to set up a specific shot from a specific player in a specific location.

A solid structure doesn’t just look good; it actually produces better results. Arik Parnass has written extensively on the importance of structure to power play success, showing that teams who get set up in a dangerous formation score more goals than those who don’t.

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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:

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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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