If you’ve ever read a little math, you likely know the dangers of binning continuous data when testing relationships between two variables. It is one of the easiest and most common mistakes that an amateur statistician might make, largely because, intuitively, it seems like it should make sense.
But it doesn’t, and here’s why.
Recently, one of my VanHAC slides was used to justify the Bruins’ decision to take Trent Frederic over Alex DeBrincat (and many others) in the first round of the 2016 NHL Draft. Needless to say, I haven’t slept soundly since then.
Unfortunately, crafting a solid rebuttal isn’t as easy as saying “DeBrincat has a higher ceiling.” To that end, I present a framework for evaluating these types of draft decisions. There are two basic questions to consider:
In my last post, I based some work on The Numbers Game by Chris Anderson and David Sally. It was a fun book about analytics in soccer even though I do not have much of a background in soccer. There was one other section of the book I found particularly applicable to hockey. They created a few charts on the expected number of points a team gets depending on how many goals they score in that game. I went through every regular season game from 2007 – 2016 to produce the below version for the NHL:
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.
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?
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?
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.
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:
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]
Hockey Graphs 