The NHL Draft acts as the proverbial reset of the NHL calendar. Teams re-evaluate the direction of their organizations, make roster decisions, and welcome a new crop of drafted prospect into the fold. Irrespective of pick position, each team’s goal is to select players most likely to play in the NHL and to sustain success. Most players arrive to the NHL in their early 20s, which leaves teams having to interpolate what a player will be 4-5 years out. This project attempts to address this difficult task of non-linear player projections. The goal is to build a model for NHL success/value using a player’s development — specifically using all current/historical scoring data to estimate the performance of a player in subsequent seasons and the possible leagues the player is expected to be in.
At Hockey-Graphs, we like to provide data-based answers to questions. It’s what we do. But it’s also good to recognize issues in the analytics world that haven’t yet been addressed. Sometimes that’s the case because we don’t have the data we need available, and sometimes it’s because the question has yet to be properly framed. It’s important to know what we don’t know, and to talk about it regardless.
There has been some great draft work done at our site and elsewhere in the last few years, and one of the findings has been the volatility of drafting defensemen relative to forwards. Couple that with claims that forwards have more of an impact on shot rates than defensemen, and one would be tempted to claim that avoiding defensemen altogether would be a solid draft strategy (though I’ll note that most analysts think this is taking the conclusion too far).
The biggest incentive for teams to employ analytics is exploiting market inefficiencies. Whenever you can exploit an inefficiency in a market it gives your team a comparative advantage over the others. In other words, you raise your team’s chances of being a successful club.
I took a look at previous work from Eric Tulsky and Michael Schuckers on draft pick value and used them to show how one may use statistical analysis to take advantage of market inefficiencies.
Let’s take a look.
On this week’s episode, Rhys and Garret talk about Michael Hutchinson’s recent struggles, Jacob Markstrom’s inability to make the NHL transition, the Canucks signing of prospect Ben Hutton, Corey Pronman’s trolling of Rhys, and some Alberta Major Bantam talk to top it all off. Join us on the other side of the break to listen!
Welcome to the second episode of the Hockey Graphs podcast, where Rhys Jessop (of Canucks Army and That’s Offside) and Garret Hohl continue talking about hockey while learning how to podcast. Join us as we discuss the CSS rankings, Vancouver Canucks, Winnipeg Jets, Toronto Maple Leafs, the NHL’s disciplinary practices, and the up coming All-Star game. Continue reading
Welcome to the inaugural episode of the Hockey Graphs podcast, where Rhys Jessop (of Canucks Army and That’s Offside) and Garret Hohl navigate the wonderful world of podcasting for the first time ever. Join us as we discuss Vancouver Canucks and Winnipeg Jets prospects, what the hell is up with the Anaheim Ducks, and, of course, a healthy dose of fancystats. Continue reading
The graph above represents how some may look at and use hockey statistics; the better a player performs in a statistic equates to more skill. This practice can be found in league equivalencies -now more commonly known as NHL equivalencies (or NHLe)- originally contrived here by Gabriel Desjardins.
In truth, almost all of us can be guilty of this at one point or another, like when using evidence like “Player A has a better Corsi%; therefore, he is pushes the play better”. Most reasonably understand that this is not how it works, but it is not discussed often enough. These tools are used to show average expected outcomes. The output is not the only possible outcome. Continue reading
On Monday, Kyle Alexander and CAustin (aka the Puckologist) wrote a post on Raw Charge titled “It’s still okay for an NHL team to draft goaltenders.” This is a topic that isn’t exactly new in the hockey analytics community – on this site alone Garret and myself have written a few posts about how unpredictable goalies are and the general consensus in the hockey analytics community being that goalies are simply not worth drafting in the early rounds of the draft, due to the variability on their results compared to other skaters (particularly forwards).
The Raw Charge guys in their post don’t totally disagree, but do think the talk of avoiding goalies is a bit exaggerated by some, concluding:
However, the gap between goalie drafting and forward drafting isn’t nearly as stark as it’s been made out to be. It’s much more worthwhile to make drafting and development at all positions better than to attempt to specialize in elite forwards to the exclusion of other positions.
Essentially, the Raw Charge guys argue:
1. The Gap between skaters and goalies’ success and failure rates isn’t as big as people think – most evaluative measures used in such studies disfavor goalies by using metrics such as GP by a certain age, where goalies rarely get opportunities to meet such thresholds.
2. The response to whatever gap there actually is should be to try and improve goalie evaluation – similar to how Swedish and Finnish goalie federations’ improved early goalie training to improve their goalie crop – rather than to eschew goalies altogether.
3. The failure of goalies may also have to do with poor development processes rather than bad evaluation.
While all three points do have merit, I think they’re both quite a bit overstated.