We recently released the final version of our contract projections for the 2019 NHL free agent class (they can be found here). Our initial projections went up in mid-April, and even though it’s only been a few weeks, we’ve had numerous questions about how the model was designed, how it works, what it means, etc. I thought we might be able to answer all the questions about it on twitter, but alas it was just a dream. A quick recap: this is our third year doing contract projections for the NHL offseason. While the model/projections this year may seem quite complicated, our first version was very simple: a few catch-all stats and a linear regression model to predict salary cap percentage (cap hit / salary cap). We use cap percentage to keep salaries on the same level as the salary cap changes. Over the last few years, we’ve developed a few new methods, and this year we took quite a bit of inspiration from the method Matt Cane used for his 2018 NHL offseason salary projections.
Look, nobody knows what analytics actually is anyway, so why are we still talking about it? At its most basic, analytics is simply a tool. Much like a hammer is a tool.
Maybe too much like a hammer. As the old saying goes, when all you have is a hammer, everything looks like a nail. The same may be true for analytics. At least in some contexts. Yes, analytics is simply a way to draw meaning out of data, but just because you finally figured out how to apply gradient boosting to your ridge regression model doesn’t mean you should.
Once you think of analytics as a tool, a means to an end, then it’s much easier to see that it’s not just a tool, but an entire toolbox. And when you reach into that toolbox, the tool you take out should depend on what you want to accomplish.Continue reading
On Wednesday Night, Hockey-Graphs became aware that one of our contributors, Jason “jsonbaik” Baik, had been convicted of Sexual Assault in Allegheny County, Pennsylvania (Pittsburgh). To be utterly clear, Hockey-Graphs condemns these actions absolutely. Upon becoming aware of this horrible news, we have terminated our relationship with Mr. Baik and all contributions from Mr. Baik have been removed from this site.
We here at Hockey-Graphs wish to express our support for those who have been victims of Sexual Assault, Rape, or related crimes. As such, we encourage our readers to support organizations dedicated to help support victims of such heinous acts. If you can, please consider a donation to National Organizations like the Rape, Abuse & Incest National Network (RAINN) or local organizations such as the Pittsburgh Action Against Rape (PAAR) and the Women’s Center and Shelter of Greater Pittsburgh.
It’s that time of year! The ’18-19 NHL regular season ended on Saturday, and that means the time to argue about the NHL player awards has begun. Now of course, the actual awards are voted on by PHWA members, General Managers, and the NHL Broadcasters’ Association for each respective award. However, we (Josh and Luke) decided it would be interesting to see which players the HG writers (and fellow hockey statistics minds) would choose to win the various end-of-season awards. The group of voters is made-up of as many Hockey Graphs writers as we could pester into completing the annoyingly buggy google survey, along with various other writers and hockey people who are in some way associated with the hockey statistics community. Continue reading
A picture is worth a thousand words. Yes, it’s a cliché, but when it comes to visualizing data, an individual can tell a story via the choices they make when presenting their data. One of the most common visualizations is a plot showcasing the frequency and distribution of an event. Data like this are often presented in a histogram or box-and-whisker-plot. However, a limitation of both of these types of plots is that neither shows the individual where each data point falls. On the other hand, a beeswarm plot allows the user to see where each individual point falls across a range. A random jitter effect is applied to maintain a minimum distance between each point to minimize overlap.
Inspired by the wonderful graphs from Namita Nandakumar and Emmanuel Perry, I thought I would attempt to visualize how goaltenders have fared in goals saved above average over the course of their careers.
With the release of the Kindle version of my book, I wanted to provide an excerpt to promote it. You can purchase the Kindle at the above link or the paperback here. If you already purchased a paperback, you should be able to obtain the Kindle version for free on your Amazon account. Let me know if you run into any trouble there. Enjoy!
In part 1 of this series we covered the history of WAR, discussed our philosophy, and laid out the goals of our WAR model. In part 2 we explained our entire modeling process. In part 3, we’re going to cover the theory of replacement level and the win conversion calculation and discuss decisions we made while constructing the model. Finally, we’ll explore some of the results and cover potential additions/improvements.Continue reading
In the last 10 years, I have been impressed by the development of the hockey analytics community in North America as well as the tools made available to the public in the hope of increasing the general hockey knowledge.
Unfortunately, in Switzerland, the Swiss Ice Hockey Federation (SIHF) does not provide the same level of information as there is in North America and keeps part of its proprietary data for itself. As such, fans and journalists, except on very rare occasions, don’t have access to the same kind of in-depth researches/analyses as there are in the NHL or some other European leagues. Plus/minus is still THE hockey statistic for some journalists or analysts.
The first part of my project with the Hockey-Graphs Mentorship program was to create a platform entirely dedicated to Swiss hockey statistics, called NL Ice Data, the main goal was to exploit as much as possible the available data and to give fans access to additional statistics the SIHF doesn’t necessarily provide:
- GF/GA: for players, RelGF%, GF/60, …;
- time on ice deployment and evolution;
- linemates information;
- aggregated shot tracker maps per player, goalie and team;
- and many others.
Current features include the same core of statistics for players, goalkeepers and teams: statistics, fouls, shootouts and shot tracker maps. Easy to use, the website provides interactive tables and charts so that fans can engage more with data. Additional features, charts and metrics will be added along the project.
By slowly integrating further metrics and concepts after the website’s launch (xG or Game Score for example), the modest goal is to build overall knowledge amongst fans. A secondary goal was to have a platform ready to publish more *advanced* statistics (including at the player level) as soon as the League publishes more of its proprietary data.
In part 1, we covered WAR in hockey and baseball, discussed each field’s prior philosophies, and cemented the goals for our own WAR model. This part will be devoted to the process – how we assign value to players over multiple components to sum to a total value for any given player. We’ll cover the two main modeling aspects and how we adjust for overall team performance. Given our affinity for baseball’s philosophy and the overall influence it’s had on us, let’s first go back to baseball and look at how they do it, briefly.Continue reading
Wins Above Replacement (WAR) is a metric created and developed by the sabermetric community in baseball over the last 30 years – there’s even room to date it back as far as 1982 where a system that resembled the method first appeared in Bill James’ Abstract from that year (per Baseball Prospectus and Tom Tango). The four major public models/systems in baseball define WAR as such:
- “Wins Above Replacement (WAR) is an attempt by the sabermetric baseball community to summarize a player’s total contributions to their team in one statistic.” FanGraphs
- “Wins Above Replacement Player [WARP] is Prospectus’ attempt at capturing a players’ total value.” Baseball Prospectus
- ”The idea behind the WAR framework is that we want to know how much better a player is than a player that would typically be available to replace that player.” Baseball-Reference
- “Wins Above Replacement (WAR) … aggregates the contributions of a player in each facet of the game: hitting, pitching, baserunning, and fielding.” openWAR