# Correlation between shot-share and goal-share

Here’s a graph showing every team’s regular season performance at even-strength (5v5) from the 2007/08 season to the 2015/16 season (Source: Corsica Hockey). Getting a higher proportion of the total goals scored for and against is the key to winning games, so that’s displayed in the vertical axis. On the horizontal axis is the team’s share of all shot attempts (i.e., Corsi). Each dot represents every single team by season since 2007/08, with the lockout-shortened 2012/13 season excluded since each team only played 40 games. Comparing a 40-game season to an 82-game season might give us unreliable results (team’s can get hot or cold over a shortened length) so I left that season out and compared apples to apples.

The black bars that cut through the graph horizontally and vertically mark the average across all the teams over the eight seasons. Not surprisingly, it’s right at 50.01% for both goal-share and shot-share. I’ve also added a trend line, which has a r-squared value of 0.32. It’s obviously not a perfect correlation between shot-share and goal-share, but you increase your chances of scoring if you’re outshooting your opponents. Teams that posted a higher than average shot-share but didn’t see their goal-share get higher than 50% (bottom right quadrant) often had external factors at play. It could’ve been injuries to key players or a lower than normal shooting or save percentage. On the flip side, team’s might have a lower than average shot share, but still outscore oppoenents (top left quadrant). This would be due to a higher than normal shooting percentage (shooters get hot) or team save percentage (goalie gets hot). Hockey is weird like that.

Highlighted in the graph are eight orange dots with a black ring around it. These are the Oilers eight seasons, and not surprisingly they’re all in the bottom left quadrant where you don’t want to be. They’ve been terrible at outshooting opponents, and as expected, they haven’t been able to get a higher share of the total goals except for that one time in 2008/09. They had one of the worst shot-shares in the league that season (46.35%), but thanks in large part to a slightly higher than normal shooting percentage, and some great goaltending from Roloson (a 92.45 save percentage), the Oilers managed to get 50.9% of the total goals.

This season the Oilers would be in the top right quadrant, as they’re getting 52.00% of the total shot attempts, which has them 9th in the league, and are also getting 52.88% of the total goals (i.e., goal-share). Generating shots is a repeatable skill and is dependent on the coaching staff’s on-ice tactics and the skill of the players. A team’s ability to generate shots very often remains consistent through a season, and season-to-season, as long as the coaching staff remains and players stay healthy. The Oilers have a good thing going with their ability to out-shoot opponents, but this in no way guaranetees goals. A lot has to happen for a shot to become a goal, and goalies can always go on hot and cold stretches. But as long as they keep their share of shot attempts up, they’re putting themselves in a position to succeed.

# Thoughts on the Oilers: Shot differentials under Nelson; Keeping Fasth; Pre-Pronger Era

With Nelson behind the bench, the overall perception of the Oilers has improved as they’ve started winning games. Even though Nelson has only won three in regulation since Eakins was fired, there’s a general sense of optimism that the team just might start turning it around. Unfortunately, they’ve been getting outshot quite regularly, which never ends well for a club.

 2014-15 Oilers Shot Differentials (5on5) Coach Games % of shots shot differentials Eakins 31 49.4% -18 MacT/Nelson 5 45.1% -23 Nelson 16 45.0% -71

The team is getting the wins, but it shouldn’t hide the fact that the club is still weak in a number of areas. I’ve liked Derek Roy’s play and hope the Oilers can either sign him or at least be inspired to seek out a legit NHL centerman. The defence is still in need of an NHL player or three. And the goaltending, which hasn’t been awful as of late, will likely get revamped for the 2015 season.

Glad to see Fasth getting some starts.  He’s looked pretty good, especially the other night against Pittsburgh when the rest of the team played terribly for the majority of the game. His 5-on-5 save percentage (adjusted) has been respectable  in 2015. Whether that’s because of the head coaching change or the new goaltender coach is anyone’s guess. Thought I’d break it out by coach and goalie coach.

 Adjusted Save% (5on5) Head Coach Scrivens Fasth Eakins 90.28 88.97 MacT/Nelson 89.55 66.96 Nelson 90.68 93.25 Adjusted Save% (5on5) Goalie Coach Scrivens Fasth Chabot 90.03 86.97 Schwartz 90.59 91.00

Source: War on Ice

The team is leaking shots at an alarming rate, so Fasth will have to continue standing on his head to bail out his team. He should be the guy the team goes forward with, but will likely be shipped for a draft pick at the trade deadline. It’s also encouraging to hear the Oilers are pursuing goaltender Matt O’Connor, who has played extremely well at the college level. Just wish we had, you know, developed our own goalie.

Definitely hoping the Oilers can beat the Leafs in Toronto on HNIC. Not because I dislike the Leafs, who are actually fun to watch with guys like Kessel and Kadri on their roster. But because of the positive attention the Oilers will get in the biggest media market. Oilers win and they’ll receive lots of praise for winning under the new coach and the prospects coming down the pipe (with lots of links to Nurse’s performance at the World Juniors). The Leafs on the other hand will receive negative coverage for not rebuilding soon enough like the Oilers (hah!) and point to the tough decisions Shanhan and company will have to make at the deadline. Fluff pieces are nice once in a while.

Edmonton Oilers (1990-2014)

I’ve been looking back at some of the success the Oilers had before this nine year drought. One thing that stands out is how well the teams were built even before the 2005-2006 season, and how good they were at generating shots. People can quip that the 2006 run was a fluke or that it was all Pronger. Their regular season was outstanding from start to finish. Had the goaltending been stronger before Roloson showed up at the deadline, they would’ve finished much higher than 8th. But the few seasons before the lockout, the team was generating shots and holding their own against some of the best teams in the league. They missed the playoffs, sure, but the team was getting some very nice production from all areas of the roster. No idea if it was the ownership change, or the management structure, but something altered the way this team evaluates professional and amateur players. The Oilers made some excellent trades in the early aughties, but haven’t been able to replicate that success under Katz. Here’s hoping the promotion of Bob Green can make an impact.

If you haven’t checked it out yet, be sure to listen or read Jason Gregor’s interview with Ryan Smyth. Smyth’s personality came through really well on the radio making the whole interview exceptional. He’s not the most polished guy when he talks about the game, but you can tell from his interview why he was revered in Edmonton. The guy had a tremendous career and really connected well with the community. Think it’s only a matter of time before someone hires him for a player personnel manager role.

It’s Been Nine Years – Copper and Blue

Trade Talk: Save the damn puck edition – The Oilers Rig

# 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.

# Looking back at the 1996-1997 Oilers and the “Golden Era”

It’s funny how the human memory works. Between the mid-nineties and the 2006 Cup run, the Oilers were a decent team that would barely squeak into the playoffs and typically get eliminated in the first round. It was frustrating as a fan since the Oilers couldn’t compete with other clubs financially and were typically eliminated early on in the playoffs. The Oilers weren’t  good enough to win the Cup and not bad enough to pick higher in the draft.

Now with the Oilers completely lost in the abyss, and on their way to a ninth straight season out of the playoffs,  this previous era doesn’t seem so bad. Over a span of nine seasons (1996-2006), the Oilers made the playoffs seven times. They had complete rosters, with centers, goalies and yes, even defencemen! Sure they often made the playoffs because another team sputtered down the stretch, but they would make it nonetheless.

The 1996-1997 team stands out for me for two reasons: it was the first season where I actually followed the Oilers; and it was the club that broke the four year playoff drought and upset the Stars in the first round. The team was made up of pretty decent players: Doug Weight, Jason Arnott, Ryan Smyth, Mike Grier, Todd Marchant, Boris Mironov and of course Curtis Joseph. A lot of young players that the team had acquired through trades during the playoff drought. Based on the player’s production and the team’s success, the Oilers management team was clearly doing something right with their player evaluations and making the most of a limited payroll.

It’s no surprise that when the Oilers were getting a fair share of the shots and got decent goaltending, the Oilers had a decent chance of making the playoffs. What these shot differential numbers show is that there was a distinct “Golden Era” when the Oilers were competitive. Worth noting that the two seasons that the Oilers missed the playoffs (2001-02 and 2003-04), the club was outshooting opponents at a decent clip and getting good goaltending. Them the breaks, I guess.

Edmonton Oilers (1990-2014)

The Oilers have clearly shifted their philosophy on how they evaluate players. Looking at defencemen alone, between 1996 and 2006, the Oilers acquired the following players by trade: Roman Hamrlik, Janne Niinimaa, Jason Smith, Igor Ulanov, Eric Brewer, Steve Staios, Jaroslav Spacek and Chris Pronger. These players had been developed elsewhere and the Oilers made some very savvy trades to get them around their primes. The Oilers have not even come close to replicating that kind of success when building a blue line, instead deploying rookies and aging veterans since 2006.

Edmonton Oilers (1990-2014)

Worth noting that Curtis Joseph was a big reason why the Oilers made the playoffs even when the team was getting outshot in 1996. Joseph left after only two seasons, but the Oilers replaced him with Tommy Salo (via trade in 1998) and got good production from the Swede until 2003. Since 2006, the Oilers have had pretty reasonable team save percentages but have unfortunately been getting outshot at a ridiculous pace.

For a team that’s been in a nine year drought, the Oilers have very little to show for it. The draft record speaks for itself, but the Oilers have also made very few trades for developed/developing prospects who could help in the next year or two. Since the 2006 run, the Oilers appear to have focused more on spending money than on actual evaluations. They signed Sheldon Souray and Shawn Horcoff to big contracts and pursued players like Marian Hossa, Dany Heatley and recently, David Clarkson.

Any thoughts, let me know. You can also access the table I put together here: Edmonton Oilers – Historical – December 2014.

Sources:

Sporting Charts

NHL.com

Hockey Reference

# Google Glass in Professional Hockey

With the recent announcement that Google will soon release its wearable, augmented reality glasses, there has been a lot of discussion about how the technology can be used.

What’s most intriguing to me is how these wearable devices can be used in professional hockey. I’ve come up with three uses.

1. Professional hockey teams could use the device to send real-time information to players throughout the game. This could include who is on the ice with them, what play to run or where to place the puck.
2. Coaches could use the data collected by these devices and apply them to their strategies for each game.
3. Those outside the game, such as fans, hockey analysts, sports journalists or league officials could use the data collected to do their own analysis.

These are all just random ideas, so until the limitations of the device are shared, we can dare to dream. I’d be interested to hear what others think of using Google Glass in professional sports.

# Hockey Reporting and Hockey Analysis

“We shape our tools,and then our tools shape us.” – Marshall McLuhan

Influence of Technology on Sports Journalism

The impact of web technology has had a profound impact on how professional sports are consumed by fans. For example, fans have numerous options when they want to watch or listen to a game, including, among others, using mobile technology or data tracking software. Fans can interact and share content with others by publishing their thoughts and ideas about hockey on blogs and other social media platforms. To keep up with their loyal fan base, the NHL has readily adopted technology and software that helps their fans get closer to the game. Examples include providing detailed statistics available online or social media promotions to connect players to fans.

One area of professional sports that technology continues to strongly influence is sports journalism. Specifically, individuals who are employed by television broadcasters that hold NHL distribution rights, such as TSN or Sportsnet, newspapers and the NHL. These individuals typically have direct access to players and managers and are responsible for providing news and updates regarding NHL-related activity.

In the past, individuals who covered sports for the local news channel or newspaper were considered “reporters”. They would attend games and produce a story using the results of the event. Within the story would be quotes from players and coaches, a summary of key events within the game and maybe a preview of the next game. Since speculation is an important facet for professional sports, gossip regarding players and team could also be included, depending on the reporter.

Today, individuals who cover sports for mainstream media outlets are labeled all sorts of things. “Insiders”. “Analysts”. “Correspondents”. At first glance, they all appear to have the same role, which is to cover the game and provide some sort of content for fan consumption. But it’s the technology they use that differentiates them, as not all sports journalists produce the same kind of content. Understanding the tools they and what type of content they produce, can allow us to classify them and understand their roles and objectives.

Defining Reporting and Analysis

The technology sports journalists use differentiates those who report on the game, and those who analyze the game. Both “Reporting” and “Analyzing” are interchanged regularly, not only in sports journalism, but also other industries such as information management. While both actions produce content, they each entail different objectives.

I did a quick search online and came across this differentiation of Reporting and Analysis on a blog from Adobe, a major software company:

Report­ing: The process of orga­niz­ing data into infor­ma­tional sum­maries in order to mon­i­tor how dif­fer­ent areas of a busi­ness are per­form­ing.

Analy­sis: The process of explor­ing data and reports in order to extract mean­ing­ful insights, which can be used to bet­ter under­stand and improve busi­ness performance.

So applying these definitions to sports journalism, I’ve come up with this:

Hockey Reporting: The process of coordinating data and information into summaries that describe hockey-related events. This is someone that summarizes current events, including games, player or team performance and current rumors.

Hockey Analysis: The process of exploring data and reports in order to extract mean­ing­ful insights, which can be used to bet­ter under­stand the game and support further analysis and continue extending the knowledge surrounding the game. This would be someone that could summarize current events, but spends more time looking deeper into the data from hockey games to provide further insight.

These definitions need some work, so I’m hoping to get feedback from anyone interested.

Why the need to classify sports journalists?

It’s critical for fans to understand the roles and objectives of the contents’ producer. The present environment for hockey fans contains a lot of information, and it’s really up to them to filter through the noise to find value in the content available online, in print and on television. Fans do more than just consume the content as they have demonstrated their ability to extend the content by providing their own feedback and raising new, applicable ideas.

By understanding the producers role, fans can put the content into perspective before extending the content and building new ideas. This is not to say that what sports reporters produce cannot be built upon by fans. But a more appropriate response can be made after understanding what the producers objectives are. And it’s much more beneficial to the game if ideas are built on solid claims and information, rather than bogus hockey rumors, for example.

As always, feel free to leave feedback below or contact me directly!

# Collaboration and Hockey Analytics

Source: Wikimedia Commons

Data analytics is a collaborative exercise with the network (both operational and social) being a critical component of any analysis. The right environment has to be in place for people to contribute, develop and share data. To transform the data into information,  context is drawn from the network as individuals apply their backgrounds, experiences and ideas to push the development of a concept. Once the data transforms into information (and later knowledge), the network will distribute the information to those who can use it and develop it further.The importance of collaboration was highlighted at the Analytics, Big Data and the Cloud conference, which presented various topics related to data analytics such as health, productivity and community. One session of personal, and academic interest, was related to professional sports. A description of the session: Continue reading