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.
It is far from shocking that the National Hockey League has no peer among major American sports leagues in terms of racial homogeneity. Most estimates place the proportion of White players in the league in the range of 92-95%, far from comparable leagues like the National Football League, National Basketball Association and even Major League Baseball.
In the past year, the league celebrated an obscure but rather dubious milestone. If you combined all the faceoffs* taken by every Black player** in the NHL between 2008 and 2019, you would end up with 14,375 total faceoffs, or about 20 fewer than Golden Knights center Paul Stastny in that time frame (according to Hockey-reference.com). It was only in this past season that the total of the Black players overtook Stastny.
This work is co-authored with Madeline Gall.
While scouting for some sports is straightforward (college football → NFL), scouting for the NHL can be a more arduous process. With players from over 45+ international ice hockey leagues, each with its own regulations and difficulties, how can one adequately assess the quality of a player’s performance? Comparisons between leagues are not easily made; 18 points for an eighteen year old playing against other eighteen year olds in a minor league should not be attributed the same value as 18 points for an eighteen year old playing against veterans in the NHL.
There have been other attempts to account for this, including player translation variables, like that of Rob Vollman’s hockey translation factors, and Gabriel Desjardin’s NHL Equivalency Ratings (NHLe). Desjardin’s NHLe previously tackled the issue of comparing and predicting player performance for League-to-NHL transitions (moving from another league into the NHL). It was great for a quick, general comparison and certainly has its advantages (easy and quick to calculate), but there are some drawbacks to its method. For starters, it didn’t necessarily control for team quality, position, and age. Translation factors are calculated using statistics from players who have played at least 20 games in the given league before playing at least 20 in the NHL. That means there’s a lot of valuable data about these in-between transitions that aren’t being used.
In this project, we introduce a new method for comparing and projecting player performance across leagues using an adjusted z-score metric that would account for these drawbacks. This metric controls for factors such as age, league, season, and position that affect a player’s P/PG metric, and could be applied to any league of interest. This new metric is necessary as there are many characteristics that vary from league to league. Due to the different playing styles and opponent difficulty, there is not one consistent metric to make comparable evaluations of player performance for hockey leagues around the world. Other factors such as goalie strength, penalty rates, and rink dimensions are also inconsistent across international leagues. Scenarios could occur in which players of similar strength could appear to have seemingly different performances.Continue reading
While the primary focus of the hockey analytics community has been around roster optimization, there has been a small subset of the community that has worked a great deal on prospect analytics. This includes the work of Gabriel Desjardins’ on NHL Equivalent scoring, Josh Weissbock and Cam Lawrence’s work on Player Cohort Success (since purchased by the Florida Panthers), and Rhys Jessop’s work on adjusted scoring metrics.
As a big fan of prospect scouting and analytics, I wanted to add to the community by expanding upon the work done by Jessop.
I’ve been playing with some NCAA prospect numbers lately and I had a hypothesis.
To set the stage, under the current CBA NHL teams have up to 30 days after a prospect leaves school to sign their drafted prospects to an NHL Entry Level Contract (ELC), or by August 15th after they’ve graduated.
What this means is that teams have an incentive to encourage players they think will become NHLers to sign as soon as possible. The trade-off with signing an NCAA player is the player loses their amateur eligibility and automatically has to move to another league. NCAA prospects typically move on to the AHL or NHL but it is not unheard of to see prospects take a side-step to the CHL in the odd circumstance.