One of the temporary obstacles to the adoption of hockey analytics right now is that front offices don't always know how and where to find good analysts, and really good statisticians and programmers don't know how to get their work noticed. In time, this will change, but for right now it is a real problem.
I've been doing my part by putting teams in touch with good people that I know, and promoting everybody's work in my books, and everywhere else that I can.
That helped for a while, but I ran out of friends pretty quickly. Those who weren't snapped up in the 2014 summer of analytics were certainly gone by the end of 2015. Plus, I've been getting more and more calls from various organizations as interest in hockey analytics continues to grow.
That's why I opened things up in February, and invited everybody who might be looking for a future in hockey analytics to contact me, and let me know what they're looking for. Then, I can pass along the right opportunities to the right people, and give front offices even more options from which to choose.
As you can expect, the response was a little overwhelming, and hundreds of people reached out to me. Some people were just looking for a little bit of advice and maybe some part-time hobby work on the side, while others were deeply passionate about full-time careers in the sport.
It took me nine months, but after taking up to a dozen calls per week, I managed to follow up with everybody. It was very time-consuming, but it was also a highly rewarding experience.
There were so few of us when I got my start over 15 years ago, and it's been wonderful to have so many engaging conversations with such passionate people this year. I managed to help a lot of them get a start, including several dozen who got audiences with NHL front offices, and quite a few who got full-time NHL jobs for this season.
It has been an educational experience for me as well. I got the opportunity to learn a lot about how teams are building their analytics departments, what kinds of opportunities are available for those in our field, what skills teams are looking for, and what people have done to get ahead.
I plan to write a complete chapter in my next book that includes everything I've learned, but the main key is to set yourself apart from the pack. Here are some tips on how to do that.
1. Create something.
Whether your write a paper or online articles, hold a conference, invent a statistic, build a website, participate in a manual tracking project, or start a podcast, it helps to create a name and an identity for yourself.
2. Network, and build up your contacts.
Most jobs are not posted publicly, and those that are generally receive between 500 and 1000 responses. Either way, those who have contacts are those who hear about the jobs, and who get on the short lists.
3. Get some work experience.
Contact the junior, college, and minor-league teams in your area, and offer your assistance. Think outside the box, and contact player agencies, and third-party consulting companies too. Even if most of them ignore you, or even if it's part-time or unpaid, you only need one response to get started.
4. Take yourself to the next level.
Whether you're into manual tracking, programming, or statistical analysis, take the time to master your craft. That could mean studying textbooks, using software and other technology, and getting lots of practise in order to complete a wider variety of tasks, completing them faster, and with greater accuracy (just like an NHL player would).
5. Build a portfolio.
When you get that meeting and/or interview, it helps to be prepared with examples of your work, even if it's just in one small area of the game. Make it something practical and memorable, and tailor it to the organization in question, if possible.
6. Get your financial house in order.
Quite frankly, it's shocking how little most of these jobs pay, compared to similar work in other fields. This is a by-product of how badly so many people want to work in hockey, and how many organizations aren't yet properly budgeted for analytics. It would be highly unfortunate to pass on a great opportunity because of debts, a high cost of living, or the inability to re-locate.
7. Don't be an ass on social media.
I have personally seen some golden opportunities get flushed down the toilet because someone was acting highly unprofessionally and disrespectfully on social media, usually by trashing players, front offices, journalists, or fellow statisticians. Most of the time, they didn't even realize that they blew a great opportunity. And for goodness sake, do not write about sex, religion, money or politics -- the last of which appears to be the hardest to resist!
8. Show, don't tell.
Whenever you see a statistician fail to make a point about Corsi or PDO, it's probably because they're telling others how it works, rather than showing them. Part of the appeal of player usage charts is that we're not telling the reader about zone start percentages, of quality of competition, or usage, or Corsi -- we're showing them. Likewise, don't go to a meeting or interview to tell someone what you can do for their organization, come prepared to show them. That might mean video, or a demonstration, or a chart, or some tables, or specific recommendations, who knows? But find specific ways to show them how you can help.
I'll add more tips as I think of them, so check back on this post every once in a while.
Whether you're looking for standard information or something fancier, about teams or individual players, overall or in a specific aspect of the game, about the NHL or another league, in table format or in charts, the following list of links is intended to be a complete account of all known sources of hockey analytics data.
Only data resources are included below, not analysis, and only those whose data is kept active. The list only includes data that can be viewed online, not those that require a download. There are also limited listings for statistical data that strays a little bit more into fantasy hockey and/or gambling categories.
If you're aware of a data resource that isn't included in this list, please contact me (vollman at hockey abstract dot com).
Starting in 2007-08 with Behind the Net and Hockey Analysis, there are a number of sites that have zone percentages and team shot-based metrics like Corsi, Fenwick, SAT, and many of them are broken down and/or adjusted for factors such as score and manpower situation. Several of these sites allow you to look at these numbers in both regular season and the playoffs, over multiple seasons, between specific date ranges, and even specific types of games, like those that were decided in overtime.
Note that slight differences can exist in the numbers if a site is a game or two behind another, if they include empty-net situations, and if they're examining all even-strength situations, or just strictly 5-on-5.
Individual Game Stats
Looking for non-traditional data about a specific game, even while it's in progress? Drawn from information in the NHL game files, most of the following sites graphically portray where all the shots (and possibly other events) came from, and some contain a graphical time-lapse portrayal of each team's share of shot attempts and/or their chances of winning the game. A few of these sites even have shift charts for every player, and more specific information about player matchups, usage, and deployment.
Individual Player Stats
The aforementioned websites for team statistics also have most of the same shot-based metrics for individual players, along with additional details like zone starts, quality of competition, on-ice shooting and save percentages, penalty drawing, ice time, and real-time scoring stats (RTSS).
Just like with team stats, most sites have playoff data for all skaters and goalies, can be examined over multiple seasons and/or specific date ranges, can be broken down and/or adjusted for factors such as score and manpower situation, and isolated by such factors as position, rookie status, or nationality. In many cases the raw counts are supplemented by rate statistics (per game or per minute) and/or by percentage, and some stats are calculated relative to a player's teammates.
Individual Player Odds and Ends
There are a few sites that contain some additional sources of interesting individual player information that goes beyond what's listed above. Some of this information isn't hockey analytics in the strictest sense, but rather good sources of related data.
Tools and Visualizations
Ever since player usage charts rolled out in 2011, displaying these kinds of non-traditional statistical data in graphical form has exploded in popularity. Beyond those that are already covered in other sections, here is where they can be found.
Salary Cap Data
The salary cap can be very complicated, and the following sites include more than just cap hits and term, but also a combination of pretty sophisticated views to study possible buyouts and qualifying offers, who is on professional tryout (PTO), needs to clear waivers, is eligibile for offer sheets, an entry-level slide, arbitration, or UFA status.
The following websites use a variety of different statistical approaches to calculates the odds of each team making the playoffs, as well as their potential seeding, and the odds of winning the Stanley Cup.
The NHL Entry Draft is unquestionably one of the more interesting applications for statistical hockey analysis. Expect this collection of useful data and tools to grow!
Obviously, all of the above links apply to the NHL, but there are several sources of data for non-traditional stats in other professional hockey leagues. To keep it concise, only those with at least something more than plus/minus and faceoff percentages will be included here.
To state that I'm passionate about the hockey analytics community is an understatement. From the moment I first conversed with Iain Fyffe in 2000, I was hooked on the idea of connecting with people who shared my interest.
After a variety of email discussions with an ever-growing number of hobbyists, Iain organized the Yahoo Hockey Analysis Group in 2004, which introduced us to an even larger body of enthusiastic people. I can't even begin to recount what I learned from them, and how much I enjoyed those interactions.
1. The Edmonton Hockey Analytics Conference, May 24, 2014
Host: Rob Vollman
As the field was beginning to explode, it made sense to organize some events where we could all meet in person, share our work, and compare notes. That desire was further fueled when a small conference (that was really more of a glorified organizational meeting) held in Edmonton began with the somber news that the greatly admired Tore Purdy, who went by the pseudonym JLikens on his popular Objective NHL blog, had recently passed away. We may have lost the opportunity to meet him, but we were determined not to lose that opportunity with others.
2. The Calgary Hockey Analytics Conference, September 13, 2014
Host: Rob Vollman
Recap by Bruce McCurdy, Recap by Scott Cullen, Recap by Sean McIndoe, Social Media Summary by Puck Donkey
Unofficially recognized as the first, the next Alberta Hockey Analytics Conference was a solid success. Chris Snow of the Calgary Flames was the opening speaker, other speakers and guests flew across the country on their own dime, and everybody was very generous in helping to recoup the relatively considerable costs of hosting the event at the Saddledome. It was a great event in its own right, and also demonstrated the appetite for these types of events, and the basic structure of those to come.
3. The Pittsburgh Hockey Analytics Workshop, November 8, 2014
Hosts: Andrew Thomas and Sam Ventura
Recap by Stephen Burtch, Recap by Arik Parnass, Recap by Eric Bowser, Recap by DC Sports Dork, Twitter Stream from Sportsnet
The next great stride forward took place in Pittsburgh, courtesy of Andrew Thomas and Sam Ventura of War on Ice, the former of whom was one of the speakers in Calgary. This was the first conference held in the United States and the first one that was recorded (here's Part 1 of the Pittsburgh Hockey Analytics Workshop), and it established the new standard attendance level, at 200.
4. The First Ottawa Hockey Analytics Conference, February 7, 2015
Hosts: Mike Schuckers and Dr. Shirley Mills
Photos and presentations, Interview with Mike Schuckers, Recap by Shannon Proudfoot, Recap by Goose Monster, Recap by Ron Guillet, Recap by Paul Berthelot, Recap by Sean Tierney
The next conference was hosted at Carleton University by Mike Schuckers, who was the keynote speaker at the Calgary conference, and Dr. Shirley Mills. This continues to stand out as the most memorable of all these conferences, because of how well organized it was, the star-studded lineup of speakers and panelists, the social events before and after, and the mainstream attention it garnered.
5. The D.C. Hockey Analytics Conference, April 11, 2015
Hosts: Arik Parnass and Robb Tufts
From one nation's capital to another, the fifth conference was held at the Georgetown School of Continuing Studies, right near the Verizon Center where the Washington Capitals were hosting the New York Rangers in a game with profound playoff seeding implications. In fact, it was so close that I was able to step away briefly to join John Walton in the press box and promote the conference on air. Building on the best traditions of previous conferences, the D.C. Hockey Analytics conference was available by live stream, had a number of great speakers giving more numerous but shorter presentations followed by Q&A panels, introduced breakout sessions in separate rooms and follow-up surveys, and wrapped it all up with a great social event.
6. The First Rochester Hockey Analytics Conference, October 10, 2015
Hosts: Ryan Stimson, Matt Hoffman, Paul Wenger
Recap by Scott Cullen, Recap by John Matisz, Recap by David Johnson, Recap by Luke P, Recap by Sean Tierney
In what has since become an annual conference, the first Rochester Hockey Analytics Conference was built in the same tradition as the DC conference, with short presentations followed by panel discussions. Leveraging the results of surveys, this conference featured the widest variety of speakers in terms of their different backgrounds, perspectives, approaches, and levels of sophistication. Perhaps most notably, the list of speakers included more than one woman for the first time. The event was broadcast live, had a strong focus on grassroots manual tracking projects, and just had that sense that it was on the cutting edge, and taking that next big stride from the foundation established by the first five conferences.
7. The Second Ottawa Hockey Analytics Conference, January 16, 2016
Hosts: Mike Schuckers and Dr. Shirley Mills
Almost overshadowed by the announcement that War on Ice would be shutting down, Ottawa was the first to make a hockey analytics conference an annual event. Just like the last year's, this event was known for being very well-organized and well-attended, with an incredible slate of speakers and panelists, and fun social events in the evening. They were also the first to secure some notable sponsorship for the event, as well as some additional mainstream coverage from CBC News and Radio Canada, and I was even pleased to play a role in arranging for the TSN Hockey Analytics program to broadcast its show live from Carleton University.
8. The Panthers Analytics Workshop (PAWS), February 13, 2016
Hosts: Brian Macdonald and the Florida Panthers
Even though this event was hosted by the Florida Panthers, it still deserves a place on this list for having the same grassroots feel as the others. The usual culprits were in attendance, although the speakers were organized almost exclusively into panels, and it had essentially the same structure, content, and style as the previous conferences. Hopefully, this event will be remembered as the first of several similar conferences hosted by NHL teams.
9. The Vancouver Hockey Analytics Conference, April 9, 2016
Hosts: Tim Swartz, Oliver Schulte, Cam Lawrence, Josh Weissbock
Though hosted at Simon Fraser University by some if its professors and students, the Vancouver Hockey Analytics Conference was a well-balanced partnership of academia, fan-driven groups like Hockey Graphs and Canucks Army, the local media, and even some front offices. As has become the tradition, the presentations were numerous but short, the presentations were recorded, and blended in with several panel-driven discussions. The entire event was wrapped up with a social event.
10. The Second Rochester Hockey Analytics Conference, September 10, 2016
Hosts: Ryan Stimson and Matthew Hoffman
Coming up next is the second annual hockey analytics conference hosted in Rochester. As is the custom in this area, there will be many brief presentations, followed by Q&A panels. It's also noteworthy that one of the few remaining big names in hockey analytics will be in attendance, Eric Tulsky. Finally, one of the newest traditions that will hopefully catch on is the rec hockey game that is being organized to wrap things up.
11. Babson College Hockey Analytics Conference, October 1, 2016
Hosts: Luke Donoho, Rick Cleary, George Recck
Taking on the promotional title "the Long Change", Babson College, located near the location of the annual MIT Sloan Conference on Hockey Analytics, hosted the most recent hockey analytics conference. Speakers included Michael Schuckers, Michael Lopez, and Rob Vollman, among many others. There was a media panel, and breakout sessions for students and guests to get a hands-on demonstration of various new advances.
12. The Second Vancouver Hockey Analytics Conference, March 11, 2017
From the website: "The Vancouver Hockey Analytics Conference (#VanHAC) is the largest hockey analytics gathering on the West Coast. The conference gives the hockey analytics community a means to show new ways to think about the sport we all love. VanHAC is organized by the community for the community. Our main goal is to strengthen the hockey analytics community by providing more opportunities to share knowledge and ideas, encouraging support and education for speaking at conferences, and increasing the visibility of developers, organizations, and companies within the community."
13. The Third Ottawa Hockey Analytics Conference, May 6, 2017
Hosts: Mike Schuckers and Dr. Shirley Mills
Though specific details are yet to be released, it has been announced that the third annual Ottawa Hockey Analytics Conference will take place at Carleton University on May 6, 2017. Judging from past events, it should be a great event.
I'm jealous of baseball fans.
Baseball fans have a wide variety of statisticians, stats, books, and websites from which to choose, while we hockey fans have always had to hunt and scrounge for every little stat. And, once we find a great resource, they usually get hired by an NHL team, and it disappears.
That's why I want to promote your work.
That's why there are hundreds of references in the pages of Hockey Abstract.
That's why I have referenced the work of about a hundred different analysts over the years, whether they just a beginner, or one of the grizzled veterans.
That's why I organize and promote hockey analytics conferences.
That's why I hunt for the new names, rather than just reinforce the established ones.
That's why I want as many people involved as possible.
Ultimately, I want to be able to find interesting statistical perspectives of our sport, both quickly and easily. Some day, I want baseball fans to be jealous of us!
With that in mind, please don't be shy or subtle about letting me know what you're working on, or if you're aware of someone else whose work could use a louder voice.
It doesn't matter if you're a beginner, or a seasoned pro, because I'm happy to spread the word however I can. I just have a few rules:
I also don't mind if you're making a few bucks on it. You deserve whatever assistance you can get, and I'm more than happy to promote your fundraising efforts, and to make a contribution of my own. After all, my philosophy has always been to re-invest any profits I make on my own books back into the community in one fashion or another.
We have some truly brilliant people in our field who are doing some amazing work, and there are plenty more getting their start. Please join me in spreading the word as best we can, and continue building our community into an ever-richer network of great ideas!
Did Florida go overboard in signing Keith Yandle to a 7-year contract worth $6.35 million per season? I think so.
Yandle is practically the prototype of a puck-moving, scoring-focused defenseman who isn't trusted defensively. He's used in the offensive zone, against secondary competition, and more often when the team is chasing a lead than when it is protecting one. He's a power play specialist who is rarely used to kill penalties.
To challenge my perspective and/or to illustrate my point, I built the Keith Yandle Index to find other defensemen like him.
Here are the 10 categories in the Keith Yandle Index:
1. Between ages 28 and 30.
2. Scores at an even-strength rate of 1.0 points per 60 minutes, or greater.
3. Averages at least 3:00 minutes of power play ice time per game, or more
4. Has a scoring rate of 4.0 points per 60 minutes on the power play, or greater
5. Averages 0:30 seconds of shorthanded ice time per game, or less
6. Throws 2.0 hits per 60 minutes, or less
7. Has positive shot-based numbers, relative to his teammates
8. Ranks no higher than sixth in defensive zone start percentage, among his team's defensemen
9. Ranks no higher than fifth in quality of competition, among his team's defensemen
10. Didn't miss a single game.
Going back to the 2008-09 season, the closest match was Brian Campbell in 2008-09 with the Chicago Blackhawks, who scored a 9/10.
That's probably a bad example because Campbell has just signed an 8-year contract worth over $57 million. Or, maybe it's a good example, because ultimately this wasn't seen as a good contract, and the Blackhawks had to jettison it to the Panthers a couple of years later.
On the plus side, Campbell became a much more well-rounded defenseman in Florida. He started killing penalties and taking on top opponents (for a few years), and responded to the more challenging role very well. Perhaps Yandle can, too.
There were five defensemen who scored 8/10, who might serve as better cautionary tales:
Marc-Andre Bergeron with the Minnesota Wild in 2008-09
Tobias Enstrom with the Atlanta Thrashers in 2009-10
Kurtis Foster with the Tampa Bay Lightning in 2009-10
Tomas Kaberle with the Toronto Maple Leafs in 2009-10
Lubomir Visnovsky with the Edmonton Oilers and Anaheim Ducks in 2009-10
Enstrom is a bad example because that was literally the one season where he didn't kill penalties or take on top opponents, while Yandle's been this way for his entire career. The other four are pretty solid examples, however. Should any of them signed a long-term contract as the 13th highest-paid defenseman?
Reaching down one more level, Dan Boyle, Christian Ehrhoff, Shayne Gostisbehere, John Klingberg, Brian Rafalski, Kevin Shattenkirk, and Mark Streit were all 7/10 at one point or another. I think those also serve as great examples, except that they were at different points in their careers. Several of the older defensemen were previously more complete, two-way defensemen, while the younger players may yet develop that side of their game. So, these might serve as better examples of which players are headed in Yandle's direction, and where Yandle might go from here.
The following seven, high-level suggestions for developing new stats is from the Hockey Abstract 2015 Update.
1. Before you begin, see what else is out there, and don't waste time re-inventing the wheel. Consider building on what exists before developing something from scratch.
2. At the same time, don't call someone else's work “flawed” when you've got a different perspective, or a small improvement to make. That's a loaded term that's rarely anything more than a massive exaggeration.
3. Don't be afraid! Get your stuff out there, and ask for people to review it. You will absolutely be criticized, often unfairly, but don't let that stop you. The critics are only right about you and your work if you give up on it.
4. Once complete, open up your formula and equations to others, and make your data available on your website. Yes, there will be some people who rip you off, or use your work without credit, but it's also the best way to improve your work, and to see it adopted by a larger audience.
5. To further help promote your work, think carefully about how to name it. Quite frankly, Corsi, Fenwick, and PDO aren't great examples of how to name statistics. To be fair, players usage charts were originally poorly named as OZ-QoC charts, so we have all made this mistake.
6. Be generous about promoting the work of others. It will mean a lot to them, since it comes from one of the few people who truly understands and appreciates the hard work and talent that this work requires.
7. Finally, do it for the pure love of it all. It's exciting to win the rare opportunity to work with NHL front offices and/or the mainstream media, but for the most part it's a lot of sacrifice for nothing more than a bag of peanuts.
Developing a new statistic, and seeing its adoption spread to fellow fans, the media, and the front offices themselves, is highly rewarding. But, it's also a lot of work, will expose the author to a great deal of criticism and theft, and will frequently be used without credit (or passed off as someone else's). Ultimately, this is a labour of love, but one that will never go unappreciated by those who have been there before.
When it comes to sports predictions, that's a phrase that people are really scared to hear. But why? Is it really so bad to be wrong?
Nobody is perfect. Nobody is going to be right all the time, and nobody can (reasonably) expect someone else to be. Plus, it's not like predictions are ever meant as a personal attack on the player or team involved, or that the pundit is somehow personally responsible for any aspect of a player's successes or failures.
More importantly, there's a lot to learn from being wrong. Assuming you were open and transparent about the reasoning behind your sports predictions (with yourself, at a minimum), being wrong gives you new information to learn something new, and to arrive at new insights.
Don't be afraid of being wrong, because you will be wrong.
And don't be afraid of jerks, because there will be jerks, but that's nothing to be afraid of. They go after you regardless of whether you're right or wrong, and usually just because you have the audacity to even exist.
If there's anything to be afraid of, then it's what you'll fail to learn because you were too scared to share your ideas.
Just look at me. I've been wrong many times, and have received some pretty harsh criticism over the years, but nothing happened to me. I didn't vanish in a puff of smoke.
As an example, consider my most high-profile error ever, which was in my very first self-published book, Hockey Abstract. In it, I predicted that the Ottawa Senators would win the President's Trophy in 2013-14 (Note: They didn't).
I didn't just get some small detail wrong here. I wrote a book, slapped my name on the cover like I was the next Bill James or something, and then made a bold prediction about how a team that squeaked into the playoffs was going to be the best team in the league. I even repeated the prediction on countless radio spots throughout that pre-season. It was a pretty big error -- much bigger than the little mistakes that most people are afraid of making.
And what happened to me? Nothing. I wrote another book the next year, and it sold even better than ever. In fact, people loved the short section in Hockey Abstract 2014 where I went back to that original prediction, figured out where it went wrong, and how to improve on it in the future.
There are reasons everyone went easy on me. I was very open and transparent about how I reached that prediction, I was candid and modest about the limitations of my approach, and it was also a mistake that many other people made.
But do you want to know the real reason people went easy on me? Because I was actually and clearly and completely wrong.
That's the big secret! When you're actually wrong beyond any doubt, people go easy on you. But, when there's an element of doubt, that's when people really explode.
The classic example is Bryan Reynolds, who had a full-on meltdown about the prediction we made for the Minnesota Wild in Hockey Prospectus 2013-14. In it, we concluded that "Minnesota is a bubble team, and a healthy Josh Harding might just be what ultimately makes the difference." In my view, that's a prediction that was as accurate as it was bold.
At the time, Harding was a backup with a .915 career save percentage, and coming off a season with an .863 save percentage in five games. To describe the Wild as nothing more than a bubble team, and one whose fate hinged on a secondary player like Harding, was bold. And what happened? Harding led the NHL with a .933 save percentage and a 1.65 goals-against average, the Wild grabbed a wild card spot, and struggled to get by the awful Avalanche in the first round. You would think that a fan would be scrambling to find a book with that kind of rare insight, but nope. Meltdown.
In fairness, reasonable people could quibble on just how accurate that entire prediction was, or the entire book, but it was certainly not one whose wrongness would reach any kind of consensus. And yet, that's the prediction that caused the greatest stir at Hockey Prospectus over its eight seasons.
As another example, last season I predicted the Chicago over Tampa Bay Stanley Cup on a weekly radio segment I have in Calgary right before the playoffs began, but one listener can't take me seriously because I mispronounced a player's name. (Incidentally, this was in a thread where they took exception to Karri Ramo and Lance Bouma being included in last summer's list of the worst contracts).
I do make mistakes, all the time, but I never get any heat for them! Even just this season, I made some really beauties, including:
And yet, the most heat I received was for an article in which I argued that the Flyers will be a surprisingly tough and even match for the Capitals in round one. That series went six games, the last three of which were decided by one goal (not counting empty-netters), and Washington's even-strength goal differential was +1. The accuracy of a prediction is always up for debate, but there's a reason why this one got more heat than the others: it wasn't actually wrong.
People are loudest about your mistakes when it's tenuously arguable that you even made one. Critics will invest a lot of effort arguing about the accuracy of reasonably solid predictions, and save their energy when it's clear and obvious.
So, let that be the final reason not to be afraid of being wrong: the harshest criticism always is usually reserved for those who largely got it right.
Stage 1: Strictly Volunteer
Everything you do is on your own dime. You may be invited to speak at conferences, to appear on TV or radio, or to consult with NHL teams, agents, or media organizations, but none of it is paid - not even your expenses. Even your writing is unpaid, or has bare minimum compensation (i.e. 2 cents per word, or less). This stage will cost you money.
Stage 2: Breaking Even
Eventually, many people find ways to pay for their hobby. Whether it's ad revenue for their own websites, donations and patrons, increased pay for written work, or the sales of a book, product, or a service, this stage is reached when there's enough money coming in to cover expenses, but not enough to quit your day job.
Stage 3: Getting Paid
Ever since the 2014 "summer of analytics," many hobbyists have advanced into stage three, which involves actually getting paid for their work, either in a front office, with mainstream media, a well-funded website, or with a third party consulting company (possibly of their own creation). It is just barely enough to live on, but far less than what they would make in another field.
Stage 4: Career
This stage is reached when some job security has been achieved, either through full-time employment with benefits, or from a regular and guaranteed consulting assignment, at the very least. Furthermore, it's at this stage that the compensation begins to rival what someone with the same skills, experience, and qualifications would earn in another field.
Stage 5: The Boss
Finally, there's the stage where the former hobbyist is in charge, and making the decisions. Now, they're the ones with a budget to work with, and they're the ones selecting hobbyists to move up the lower stages. In essence, it's all part of a cycle.
Why are the Hockey Abstract player usage charts out of date, and when will they be updated?
As explained right below the title on those charts, the data comes from the Behind the Net website. Robb Tufts routinely gets an updated copy of the data from Behind the Net, and updates and formats the Tableau visualization in question, which is a product of his own design and programming (though I like to think that I've had some influence). However, there have been no updates in almost two months.
So why isn't the data on Behind the Net being updated?
All of the data for Behind the Net, and virtually every other hockey stats website out there, comes directly from parsing the NHL's game files. You may remember that the NHL re-designed its website February 1, right? Well, that affected the location and format of those data files, which "broke" some of the automated feeds.
Since then, some developers have had the opportunity to update the programming behind their own hockey stats website to reflect these changes, but that's not always feasible. It takes a lot of time to design these websites, and everyone is doing it as a spare-time hobby, on a volunteer basis.
You can usually count on people to find the spare time at some point in the summer, but mid-season format changes are a great way to submarine most of the websites that are being managed by people with families and full-time jobs.
Can I get the data somewhere else in the mean time?
Not at the moment, no, not all of it.
Player usage charts use three primary pieces of information; Relative Corsi (aka shot attempts, SAT), zone start percentages, and quality of competition. The first two are available in a number of different places in one form or another, but not quality of competition.
What is quality of competition?
Essentially, we know roughly how many minutes and seconds that every player has spent on the ice against every single opponent, based on information contained in the NHL game files. You can estimate the average level of competition a player faces by using that information to calculate a weighted average of his opponents for a particular statistic. For example, you could use scoring rate, plus/minus, ice time, or whatever you want.
For these player usage charts, a player's quality of competition is based on the weighted average of his opponents Relative Corsi (which is simply the team's shot attempt differential per 60 minutes when that player is on the ice, minus the same thing when he's not).
This particular variation of quality of competition is only available at Behind the Net, nowhere else. In fact, this statistic was innovated by Gabriel Desjardins himself, who runs Behind the Net. In double fact, unless Hockey Analysis or some other now-defunct website beat him by a narrow margin, I believe that Behind the Net was also the first website of any kind to provide information like Corsi, zone start percentages, and quality of competition.
Can you parse the data and calculate it yourself?
Wow, no. I know how to do it, but that's a lot of work, and I have my hands full as it is.
Ok, how about someone else?
Robb Tufts has chatted with a few other developers to see if anyone else has plans to add this statistic to their website, and has contemplated doing it himself, but everybody is stretched on time.
The fact that this statistic has existed for years, and that so many websites have come and gone in that time without including quality of competition, means that I wouldn't count on it happening any time soon.
How about using one of the other variations of quality of competition?
There is one possibility. War on Ice has a variation of quality of competition (and teammates) that's based on the weighted average ice time of a player's opponents, instead of on Relative Corsi. It was an idea first advanced a few years ago by Eric Tulsky, now of the Carolina Hurricanes.
Both versions produce very similar results. The only real difference is that sometimes the best players don't get the most ice time. In such cases, the time-based variation will slightly favour those players who are taking on the big-name, big-minute players, while the original Corsi-based version will slightly favour those taking on the most effective players, whether they're on the top lines or not.
However, the differences between them are almost negligible, and the substitute is perfectly satisfactory.
Even if we went this route, there are a few problems. First, it would take time to program the player usage charts to get the data from somewhere else. Furthermore, such a change would be redundant, since War on Ice already has a player usage chart tool of its own, which does indeed use the time-based version of quality of competition.
Finally, the designers of War on Ice have been hired by NHL teams, Andrew Thomas and Alexandra Mandrycky by the Minnesota Wild, and Sam Ventura by the Pittsburgh Penguins. As such, the website is no longer being maintained, and may some day disappear altogether, just like Extra Skater disappeared when Darryl Metcalf was hired by the Toronto Maple Leafs in August, 2014.
So what should we do?
Be patient. The player usage charts aren't likely to be updated in a timely manner, but they will be updated. Eventually, Gabriel Desjardins will have time to update Behind the Net, or someone else will add his metric to their website, or some other solution will present itself.
In the mean time, use the War on Ice player usage charts, stay tuned for updates, and remember the importance of supporting those who create these innovations and/or who make them available to others. In Desjardins' case, his favourite charity is Education in Need for El Salvador, and in my case, I appreciate all book sales.
Monday, February 29 is the NHL trade deadline day, and everybody's eyes will be glued to their TV sets and mobile devices. While mainstream media like TSN and Sportsnet can handle all the breaking news, and provide all the traditional analysis, what about those who want to know more about the underlying numbers? That's where we come in!
Independent hockey analytics pundits like me will be posting their thoughts on all the breaking trades on Twitter using the #NHLTrade16 hashtag. They will post interesting stats, charts, links, and graphs, answer questions, and provide their own assessment of each trade.
See you then!
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