Oilers and Analytics 

A couple thoughts on analytics, and how best to integrate it into hockey operations.

With the Oilers cutting ties with analytics consultant Tyler Dellow, the club has an opportunity to re-set its current approach to analytics going forward. Now it was my hope that Tyler’s role would’ve expanded from a consultant position, perhaps into a director type, who would then lead a group of analysts to delve into various topics. It was my hope that the Oilers would’ve applied a more complete analytics strategy that could support and influence all aspects of hockey operations including on-ice tactics, player personnel decisions, drafting and salary cap matters.

And let’s not kid ourselves: analytics is going to have a role in professional sports, but its exact definition and scope is going to vary depending on the sport, league, team and goals. Analytics is becoming more and more ingrained in all industries whether it is healthcare, oil and gas, government, and the thought of the value of analytics fading in hockey is completely bogus.

Analytics should never be treated as some magic bullet or switch that an operations manager can just flip and make things happen. Analytics is a continuous process, one in which a business need or goal leads to questions. From there, an organization looks to its data to see what can be answered, and what other data can be collected and refined to potentially answer that question. From there, analysis of the data is done, which leads to discussion with the operations side and, quite often, leads to more data collection/analysis/discussion.

Before all of this is even thought about, teams like the Oilers have to treat analytics as a new concept that has to be assessed carefully before it’s legitimately integrated. And like most organizations, the Oilers need to look at three areas: people, process, technology (or tools).


  • Do you have the right skills in your organization that can support a team of people whose focus it will be to analyze data?
  • Do you have the right type of managers that will keep analytics top of mind when reviewing the operations they oversee?
  • Are people willing to learn the skills needed to either work with or within an analytics team?


  • How will the organization facilitate the work of the analytics team?
  • Regular presentations to management? Crossover meeting between the analytics team and, say, the coaching staff?
  • Do these collaborations need to be formalized or do you let the analytics team set up ad hoc meetings and working groups?


  • Do you have the necessary tools for your analytics team, or is there a chance you’ll have to invest in some additional applications?
  • It would be safe to assume that your analysts would dictate what tools are used, and the organization has to be prepared to support them.

One thing that I hope the Oilers consider doing is finding a way to tap into the knowledge of the fan community, especially those that spend hours analyzing data and publishing their work online. And this really isn’t a long shot for the team. Keep in mind, the Oilers have put together a volunteer advisory group in the past to support hockey operations and they’ve also hosted a Hackathon competition where they posted a question, released a pile of data and rewarded the best solution (nice work Parkatti!). What I think the Oilers can do here is enhance these two concepts and turn them into actual, formalized programs that can be sustained and provide value to the club.


One thing I have trouble wrapping my head around is when NHL teams hire a consultant or two to support their analytics process. Above is a chart from Gartner, which does a really nice job breaking out analytics into four types: Descriptive (what happened?), Diagnostic (why did it happen?), Predictive (what will happen?) and Prescriptive (how can we make it happen?). What we see without even caring about value and difficulty of each type are complex tasks that each require more than just a data analyst or consultant.

Looking at this through a hockey lens, the first type would be simple reporting, as in how many goals happened for and against. With Diagnostic, you would start looking into shot shares/location/player deployment/line match-ups, etc, basically looking at the things that you think do a good job at predicting goals. These first two types of analytics have become pretty standard things in the hockey world, and are published daily by fans online. But if you’re running a team, your hockey operations department could be looking at more than just goals for instance and the things that lead to goals. Maybe you want AHL player data, or a better way to track the passes that lead to shots. And if that’s the case, you need a way to gather and refine that data, which could require manual tracking and someone with programming experience. If your club wants the findings shared in a certain way, you may need someone who specializes in reporting or even dashboard reporting. And it’s also here that you may need someone who can break down video and compile their findings quickly for the coaches or management to use.

And when you start getting into the prescriptive analytics, you’ll absolutely need someone on the analytics team that has coaching experience or someone that can marry the data to the actual on-ice plays to make sense of it and provide recommendations. Reviewing these types of analytics and the potential value it can bring to a team, it would be imperative that a team like the Oilers put together a complete analytics team. This should include a director type, along with analysts well versed in reporting (dashboards), programming and on-ice coaching. Teams like Toronto, Florida and New Jersey have this structure, which should become the norm among NHL clubs soon.

It’ll be interesting to see what the Oilers do in regards to their approach to analytics. In my experience, analytics is one of the many tools that business leaders rely on to make informed decisions and is part of a holistic approach to finding success. The purpose of analytics, especially in a business setting, is to provide evidence, drive discussion and support the corporate goals. And it can only be leveraged to its full capacity when there’s complete support, at the strategic level and the operations level.

Curious to hear the thoughts of others on this one. Every industry is different, so I’d be interested in hearing how others have implemented/experienced analytics.

Also joined Lowetide on TSN 1260 to talk about this further. Audio is here.

Where I Stand

Couple events have occurred over the past few weeks that had me thinking about how my work is interpreted and used in various hockey-related discussions. I figure using this blog as a platform would be the best way to summarize my thoughts on this matter, and clarify any issues that arise in the future.

“Analytics Guy”

This always makes me cringe. For the simple fact that “analytics” is used so interchangeably, especially in hockey, that I often have to re-familiarize myself with the concept. To me, analytics is the process of collecting raw data, refining it, applying different models, finding correlations and ultimately, looking for some sort of pattern to make a decision on. I do some of this, but leave the hard work to people who know what they’re doing.

My approach is to start with a question and then find the data that’s already been scraped from NHL.com and aggregated in an easy to use format. Thanks to websites like War on Ice, Hockey Analysis, Behind the Net and Natural Stat Trick, all I have to do is find the metric that’s been derived from the analytics (i.e., Corsi, Fenwick, etc) and apply it to whatever question or topic I have. I do look for patterns. I do look for correlations. But the bulk of the work is done by real analytics-type people with backgrounds in computational science and statistics. Once I have the data, and run my analysis, I try to explain in 750 words why my topic matters, what I found and what I think the next steps are.

“Corsi Guy”

Now this one is relatively new.

Last week when Mark Fayne was put on waivers to be sent to the AHL, I openly questioned how Eric Gryba was any better than Fayne. Without a doubt Fayne has struggled mightily this season, even getting benched at times and healthy scratched. But I still consider him ahead of guys like Gryba and others for the simple fact that he’s a proven player and has more experience playing against top competition. Gryba has not looked good to me at all, and does not appear to have the ability to move up and down the lineup like Fayne would. For what it’s worth, my own analysis found that Fayne wasn’t shooting at a frequency that McLellan expects from his defencemen, and this might be why he’s been waived.

Now I do look to shots and shot attempt data mainly because it’s a good indicator of possession and has been reviewed and analyzed by some very bright people (Arctic Ice Hockey, Pension Plan Puppets, SB Nation to name a few). It’s not perfect and can’t answer every question, but I have my reasons for using it.

First off, shots and shot attempts tend to be the best metric for the question I have or the topic I’m exploring. My thoughts aren’t that overly complicated, so I can typically track down the exact data set I need rather quickly, without having to using any modelling to test correlations. If I can’t find the dataset, I ask around. That’s how I found things like Ryan Stimson’s passing project or Corey Sznajder’s Zone Entry project.

Quick note: What I stress to anyone who’s looking into any sort of analytics, whether it be hockey or business, is to approach the data with specific questions. And be ready for continuous analysis and discussion. Analytics does not provide any sort of final answer. In my opinion, the best analytics articles are the ones that leave you with more questions.

I also like the shot data because it’s readily available to anyone and everyone. Using a data source that’s used by many other people gives my work some credibility and also makes my work verifiable.

Having said all of that, I’ve always remained open to new metrics that have some thought and explanation to it. Hockey analytics is only in its infancy, so I expect people to collect and aggregate data which can only push the discussion along. Examples include dCorsi, Dangerous Fenwick,  xGoals and the results from manual data collection projects.

So should using metrics such as Corsi or sharing the work of others who use Corsi make me a “Corsi Guy”? Hardly.

“Analytics Community”

This one sounds all warm and fuzzy, but it’s been used as a way to put down a whole group of people when really the target might be one or two.

Another problem with this phrase is the generalization of the intended participants. There are some in  this community that are the actual statisiticians who parse through and test the data. There are some that do the aggregation (i.e., War on Ice). There’s the visualization people. And then there are those that have an understanding of the data and just like reading articles about it. So when someone says “Analytics Community”, I really have no idea who this is referring to and tend to ignore the rest of their issue.

And finally, there is a lot (a LOT) of disagreement among fans when it comes to the application of analytics to hockey. Player A might look great to one person using this metric, while Player B might look better using another metric. But when someone says “Analytics Community”, it sounds like everyone is on the same page have come to the same results.

“Edmonton Media”

There are a few local media types, ones that work full time for one of the major outlets, that tend to stir the pot to draw extra attention to their work. We know this is part of the game when it comes to covering sports in Edmonton. A lot of it is what I refer to as scripted ignorance. For instance, taking a shot at the “analytics community” is a good way to get under the skin of a lot of people and draw attention to themselves. It’s usually the same three or four local reporters that tend to do this. This doesn’t bother me because statistical analysis has been done for a long, long time. It’s a way for fans to get into the game and it helps to add to the discussion. Plus, the beauty of modern communication technology is that individual fans create their own little ecosystem and control what information they receive, create and share.

What does bother me is how the rest of the folks who cover the game get lumped with the few ignorant ones. Outside of our Oilers bubble, “Edmonton Media” does not have a good reputation, which isn’t fair to the individuals who actually do make a conscience effort of expanding their scope to include analytics. And the reputation of Edmonton being a tough place to play is warranted, but has been driven by a lot of the garbage content produced by the few.


As always, feedback is appreciated.

Analytics Roundtable on TSN 1260

If you’re interested in analytics, Jason Gregor hosted a roundtable on TSN 1260 featuring Lowetide, Darcy “Woodguy” McLeod and Michael Parkatti. The group discussed some of the key metrics, how they’re applied, and what some of the limitations might be. Below is a link to the audio.


  • I’ve always been interested in the metrics that have been derived from analytics such as shot shares, Corsi and PDO. So it’s always great to hear that more people are interested in it and are raising new questions to consider. Digging into the numbers is a big part of being a modern-day fan. It reflects the curious nature of human beings, pushing the knowledge and information that surrounds the game of hockey.
  • For me, numbers are  especially useful when confronted with bogus hockey information and speculation. Years ago, we would hear about trade rumors for instance and often take things for face value. Now, we can quickly dispel some of the bad information by knowing things like salary information and on-ice data like time on ice. It’s important to consider that this additional layer of analysis can be misleading if taken out of context. This is when individuals have to take an active role in critically assessing whatever information is presented.
  • And just to be clear, this type of analysis isn’t for everybody. If analyzing the data and metrics doesn’t enhance your enjoyment of the game, don’t worry about it. The numbers really are a way to get into the game and supplement some of the discussions we have on a day to day basis as fans. The best way to make use of the data is to come in with questions and be willing to expand your scope of knowledge and understanding. Analytics really is a continuous process and should never result in a final product.


Productivity of Players Under Eakins and Nelson

Source: Winnipeg Free Press

Source: Winnipeg Free Press

In my last post, I focused on the longest losing streaks each coach has had behind the bench this year. The purpose was to find out why Nelson’s losing streak was somewhat dismissed, while Eakins losing streak received a far greater backlash. Eakins’ 11-game skid had some decent underlying numbers at 5-on-5, but had some sketchy goaltending and a weak powerplay and failed to have any positive results. Nelson just finished off a 7-game streak, where they were absolutely lights-out on the powerplay, but had some troubling underlying stats at 5-on-5. My guess is that the success of the powerplay, and the point production of the young guns like Eberle, RNH and Yakupov gave the perception that Nelson was doing a better job.

I received a comment that suggested that individual players have benefited from the coaching change and their production has been better under Nelson. At first glance, it appears to be true. Eberle and RNH in particular have been outstanding over the past few weeks, with a large chunk of their points coming on the powerplay. Make no mistake, Nelson has done very well with the man advantage, something he was known for at the AHL level, and deserves full credit for its success. My take, however, is that 5-on-5 play is much more important, so I decided to take a look at the productivity of players at even strength under the two different coaches.

Please note, I exclude the five games that MacTavish was behind the bench in all of my comparisons involving Nelson. I’ve included in the list below the players who played under both coaches. (Source: War on Ice)

Eakins Nelson



P60 CF% TOI/Gm Games P60 CF% TOI/Gm
Ryan.Nugent-Hopkins C 29 1.97 51.83 15.79 37 1.73 50.57 15.00
Nail.Yakupov LR 31 0.93 48.90 12.47 36 1.51 43.48 13.26
Taylor.Hall L 25 1.65 53.15 16.04 14 2.31 50.38 14.86
Jordan.Eberle R 30 1.82 53.97 15.41 37 2.24 50.29 15.23
Benoit.Pouliot L 20 2.19 51.47 10.95 29 1.44 51.23 12.96
Boyd.Gordon C 27 1.11 46.68 10.02 37 0.63 42.02 10.33
Luke.Gazdic L 10 0.00 45.37 7.03 19 1.33 46.30 7.13
Matt.Hendricks LR 27 0.68 47.54 9.80 35 0.87 43.63 11.80
Leon.Draisaitl CL 31 0.88 52.76 11.05 2 5.73 56.76 10.47
Tyler.Pitlick RC 7 0.68 51.54 12.57 2 0.00 30.43 10.70
Iiro.Pakarinen RL 5 1.65 50.00 7.25 12 0.00 47.17 10.79
David.Perron RL 31 1.73 52.01 13.43 2 2.49 57.14 12.07
Teddy.Purcell RL 31 1.23 54.70 12.56 37 0.85 46.23 13.37
Justin.Schultz D 30 0.92 50.99 17.32 37 0.44 50.58 18.59
Jeff.Petry D 30 0.33 53.00 17.97 24 0.74 43.09 16.93
Keith.Aulie D 12 0.00 51.60 12.59 10 0.45 36.86 13.28
Mark.Fayne D 31 0.53 49.49 14.52 37 0.22 44.19 15.05
Andrew.Ference D 28 0.24 48.45 18.04 37 0.94 41.98 15.46
Oscar.Klefbom D 10 0.33 53.52 18.34 37 0.97 50.91 18.40
Martin.Marincin D 12 0.34 51.29 14.56 20 0.00 49.00 16.07
Nikita.Nikitin D 22 0.53 50.57 15.46 15 0.27 45.83 14.83

Looking at the point production (points per 60), the two players that saw an increase of their 5-on-5 production under Nelson are Eberle and Yakupov. Hall’s numbers increase, but that may have been because he was banged up early in the season. What’s surprising is the decrease in productivity for players like RNH, Pouliot, Gordon, Purcell and even Schultz. What’s troubling is the decrease in the possession numbers (Corsi For %) across the board. We are seeing that the team does struggle with possession in all score situations (whether they’re trailing, leading or the game is tied) under Nelson, while Eakins had something figured out when it comes to 5-on-5 play.

And here are the players who were coached by one and not the other. Included are guys like Lander, Roy and Klinkhammer who have all done relatively well with Nelson behind the bench, but still struggle possession wise.

Eakins Nelson


Gm P60 CF% TOI/Gm Gm P60 CF%


Anton.Lander C 28 1.19 48.29 10.82
Derek.Roy C 37 1.67 45.93 13.63
Ryan.Hamilton L 16 0.32 40.99 11.54
Rob.Klinkhammer L 32 0.48 46.22 11.67
Matt.Fraser LR 28 1.21 41.71 10.62
Drew.Miller RC 3 0.00 53.25 11.19
Jordan.Oesterle D 6 0.75 49.64 13.42
Will.Acton C 3 0.00 44.68 9.22
Bogdan.Yakimov C 1 0.00 61.54 10.05
Mark.Arcobello CR 31 0.89 49.86 13.10
Steven.Pinizzotto R 13 1.20 43.51 7.68
Jesse.Joensuu RL 20 0.63 45.93 9.57
Brandon.Davidson D 3 0.00 42.86 10.52
Darnell.Nurse D 2 0.00 56.36 15.21
Brad.Hunt D 6 0.00 50.00 15.82

What’s become apparent is that individuals are producing more points, but it’s due in large part to the successful powerplay. Stripping the powerplay away, however, gives us a better assessment on how the team is doing for the majority of the game. In this case, the production has increased for some and decreased for others. When it comes to possession, which is a key indicator of team success, the entire team is struggling mightily.

Both Eakins and Nelson are qualified NHL coaches, having found success at the AHL level, and will likely be employed in some capacity next season in the NHL. Nelson should definitely be considered for the OIlers head coaching position next season along with other experienced coaches available this summer. The problem is that the Oilers are struggling to assemble an NHL caliber roster, and until they do, it really won’t matter who the coach is next season.

Talking analytics and advanced stats on Inside Sports

Had the opportunity to be a guest on Inside Sports last night. Host Reid Wilkins invited me on to discuss analytics and the enhanced stats that have been released by the NHL.

You can hear the full interview here (starts at 2:30):

Couple notes I want to add:

  • You can access the enhanced stats on NHL.com. Keep in mind, this is the first phase of the NHL’s stats initiative. We can expect some data visualization tools and player tracking in the near future. [NHL.com]
  • There are tons of good articles that provide an introduction to analytics and advanced stats. This one from Sports Illustrated gives an excellent primer on Corsi, Fenwick, PDO and QualComp.
  • The best website for advanced stats is War on Ice. Has everything you need, excellent functionality (i.e., filters), easy to use, and has an excellent glossary. HockeyStats.ca is also pretty solid, as well as Nice Time on Ice. New data visualization websites are popping up every week, so it’s worth keeping an eye on.
  • A few of the original blogs/websites that pushed the growth of analytics were Behind the Net, Irreverent Oiler Fans, Objective NHL and mc79hockey.com. A couple of those sites have been taken down as they’ve been hired by NHL clubs.
  • Reid and I briefly touched on Taylor Hall and how his possession numbers align with his boxcar stats. Ryan Batty of the Copper and Blue had an excellent piece from last season that covered this: Taylor Hall – Points vs Corsi.
  • We also discussed shot quality, which I would argue is good to know, but doesn’t predict future outcomes as well as Corsi/Fenwick. This piece by Eric Tulsky at NHL Numbers explains the correlation differences.  Nick Mercadante of Blue Shirt Banter also has a solid post on this.
  • If you’re wondering why Boyd Gordon is the Oilers MVP, please read this excellent piece: Boyd Gordon – Superhero.
  • Shawn Horcoff was accustomed to doing a lot of the heavy lifting as an Oiler. Prime example of a solid two-way centerman who went up against the best players in the NHL and started often in the defensive zone.
  • My research at the University of Alberta focused on  hockey fans and their online collaboration to develop new information and knowledge pertaining to the game. You can read more about it here, or access the full research paper. Also recommend reading my post Finding the SuperFan.
  • Michael Parkatti and I put together a public lecture at the U of A last year on hockey analytics. You can watch the full session on Livestream.
  • I touched on a few of the reasons why analytics was significant and how fans are really the drivers of new information and knowledge. More of my thoughts can be found here: Importance of Hockey Analytics II.

Thanks to Reid for having me on his show. Definitely a unique experience!

Looking Back at the Oilers’ 2005-2006 Season

img003Being a terrible hockey team for almost a decade impacts a lot of things. The low morale of fans, the constant trade rumors and bogus narratives, and the negative perception of management and owners are all tied to loser franchises. These are things that can easily be reversed if the team starts winning, but unfortunately, that isn’t happening any time soon.

And as the playoff drought continues for the Oilers, the history of a franchise also starts to get diminished. There’s this notion that the Oilers have been bad far past the 2006 cup run. The narrative makes sense: they were the 8th seeded team that barely made the playoffs in 2006 and beat out Detroit (1st), San Jose (5th) and Anaheim (6th) to get to game seven of the finals. Following the loss to the Hurricanes, the team went on to missing the playoffs nine straight years. Add it all up, and you could safely assume that the 2006 run was a complete fluke.

What gets overshadowed by the Oilers remarkable playoff run in 2006 is their regular season performance. While it’s true that the team finished 8th and clinched a playoff berth in the last week of the season, there are some underlying numbers worth highlighting. Here’s their overall record

82 41 28 13 95 0.574 38-25-9 10-6-4 15-15-2 13-4-3

That Northwest division was quite the killer that season. All five teams had points percentages above 0.500. No other division was as this tight. The Oilers needed 95 points to qualify for the playoffs and  were only four points back of 5th place San Jose. The club ranked 14th on the powerplay (18.1%) and 8th on the penalty kill (84.1).

Here’s a high level snap shot of the Oilers’ advanced stats from the 2005-2006 regular season. I took into account all situations and found a comparative team from the 2013-2014 season based on rank. Regular season shot attempt data is worth reviewing, as it’s been a pretty good predictor of championships.

All Situations

Corsi For % of total Fenwick % of total Shot Differentials Total Percentage of shots On ice shooting percentage On ice save percentage PDO Off Zone Starts


52.2 53.7 347 53.8 10.2 88.4 98.6 53.1


6th 3rd 3rd 3rd 17th 30th 26th


2013-2014 Comparison St. Louis Chicago LA LA Montreal NYI Calgary


 Source: War on Ice

The Oilers put together some fantastic numbers over a full season, and compare well to some of the more recent top teams. The Oilers were a strong possession team over 82 games and had the sixth highest offensive zone starts. The team allowed the fewest shots in the league, but had the worst on-ice save percentage. That of course impacted the PDO, which was one of the lowest in the league. Worth noting that the Avalanche, who finished 7th overall, had the second highest on-ice shooting percentage (11.4%), while the 6th seeded Ducks combined their excellent possession stats with the sixth highest on-ice save percentage (90.9%).

Chris Pronger was instrumental in the team’s possession numbers, but the Oilers had a very well constructed roster that started with talent down the middle. They had the second best team-faceoff percentage that season (53.4%), with Horcoff and Stoll both finishing with 65+ points. The wingers took close to half of the total shots, with Smyth and Hemsky finishing with 66 and 77 points respectively. And of the top six defencemen based on total ice time, only one was under 30. Not bad roster management. Source: Hockey Reference

The weakness of the Oilers that year was definitely in goal as the club struggled all season to get consistent performances. The goaltending was pitiful with no clear cut starter all season. Only two shutouts registered in 2005-2006, while the Flames got 10 from their keepers. It was finally at the deadline that Lowe acquired Roloson from Minnesota for a first round pick and sent away Morrison (waivers) and Conklin (AHL).



Minutes Wins Losses T/O SV% GAA


Jussi Markkanen


2016 15 12 6 0.880 3.12


Mike Morrison


892 10 4 2 0.884 2.83


Dwayne Roloson


1163 8 7 4 0.905 2.42


Ty Conklin


922 8 5 1 0.880 2.8


 Source: Hockey Reference

Piecing together these stats is really just a way to get a fair assessment of what the team actually was ten years ago. The narratives grow and evolve over time depending on the context, so it’s critical to ground our understanding of the game in some degree of quantitative evidence. A blend of the advanced stats derived from analytics with the standard boxcar numbers of the players give a much better assessment of the team’s regular season success.

If there are additional stats or stories from that season worth mentioning, let me know.

NHL to Provide Advanced Stats

The NHL recently announced that they’ll be adding some of the “advanced” stats to their website for fans to access. These stats have really been developed by online hockey fans since about 2005 thanks in large part to people like Gabe Desjardins and Vic Ferrari, whose websites developed the core principles of hockey analytics. Over the past few years, the field of hockey analytics has grown to the point where many of those who pushed the discussion on analytics are being hired by NHL teams. The field is still in its infancy as the data collection tools and application of the analysis to game situations is still developing.

The NHL involvement with collecting data and publishing it on their website has been long overdue. While they continued expanding their traditional stats, fans developed their own websites and blogs that collected game-data (using NHL.com) and aggregated advanced stats. Fans worked outside the traditional model of information consumption and became sources and distributors of information themselves. Blogs especially played a critical role as fans discussed the stats, collaborated and developed strong information networks.

What the growth of hockey analytics has confirmed:

  1. Compared to any other type of fan (i.e., comic book, movies, celebrities, etc), sports fans spend the most time and energy on their fandom. They are connected before, during and after games by reading articles, playing fantasy league, consuming content (TV, radio, web). They’re a big reason why mobile technology is the beast that it is today.
  2. The web is a magical place that allows human beings to develop social networks to break down the barriers to information. If people want information, they will get it. The web is just designed that way.
  3. When an online community is connected to one another by something that they are truly passionate about, they’re extremely generous with their time and energy. Hockey bloggers are intrinsically motivated to not only produce content, but also share their support to others.

Knowing what we know about online hockey fans, it would be a huge mistake for the NHL to charge fans any sort of fee to access hockey data. The league has already taken steps to restrict fans from scraping the data from NHL.com and using it for their own websites. Becoming the sole source of data is likely their ideal vision, but they have to find a balance to ensure they play a role in the field of hockey analytics. Analytics is an excellent tool for fan engagement, so it would be in their best interest to keep the data open, easy to access and easy to use.

Professional sports leagues should really want their fans to be informed and to develop knowledge to whatever level they want. The league is much better served if they have a fanbase that’s free to interact with data, push any sort of hockey discussion and share their thoughts across their networks. That’s what fan engagement is at its very core.

Keep the Data Open

To put up any sort of barrier, whether it be a fee for data or technological restrictions, would be detrimental to the overall interaction between the game and its fans.

Knowledge and information will always be free. The barriers and the traditional models to keep data and information from the general public have been dropping in every aspect of our social world as the creativity of human beings will always get what it wants. Think of the illegal downloads of music and films; the open data projects of Governments and the death of encyclopedia books.

If the NHL does try to restrict access to data, you can be sure that fans will work together to collect the data themselves. The tools are available, the network is already established and the motivation for people to participate will be high. There are also companies collecting data of other sports that can quite easily adapt for hockey and begin collecting data at a  much larger scale.

Really, the NHL doesn’t stand a chance if they put a barrier to their data.

If you’re interested in learning more about collaborative online communities mixed with information/knowledge management topics, I highly recommend the following:

  • Benkler, Y. (2011). The Penguin and the Leviathan: The Triumph of Co-operation over Self-Interest. New York: Crown Business
  • Lessig, L. (2008). Remix: Making Art and Commerce Thrive in the Hybrid Economy. New York: Penguin Press.
  • Shirky, C. (2010). Cognitive Surplus. New York: Penguin Press.