Evaluating Nordic Drafting – A Potential Market Inefficiency

Over the last decade, teams have taken significant steps to improve their NHL entry draft approach. To do this, a number of teams have bolstered their analytics staff to identify the current “gaps” in prospect scouting. Whether it’s the Detroit Red Wings being the first team to dive head first into drafting Russian players, and then later Swedish players, or the Tampa Bay Lightning prioritizing small, skilled forwards, teams are looking for any available edge. More recently, the Pittsburgh Penguins have put a premium on overage players, as Namita Nandakumar found that overage players make the NHL faster. What’s the next big market inefficiency?

Over the last 30 years, globalization of the NHL has led to kids all over the world taking up hockey. Sweden and Finland, relative to their population, have produced numerous high quality NHL players and have experienced success in international competition. Finland has won the gold medal at three of the last six U-20 World Junior Championships, two of the last four U-18 World Junior Championships, and are the current IIHF World Championships defending champions. Not far behind, Sweden has won three of the last six IIHF World Championships.

Despite their recent success at the international level, Nordic players are picked later and less frequently than their North American counterparts:

Over the last 15 years, more players have been picked from the OHL in the 1st round than players from the top Finnish junior league (Jr. A SM-liiga) in any round. Furthermore, less than 20 percent of skaters taken in rounds 1-3 are from Sweden or Finland. Teams being more willing to draft these players in later rounds. Why is that? Despite their international success, teams haven’t utilized more of their picks on players from the top Nordic leagues. Is there a particular reason for that, or have teams simply missed out on a potential opportunity? First, we have to answer a few questions:

  1. Do these leagues produce similar quality players when compared to players from the CHL and US?
  2. Do players from these leagues make the league at a similar rate to players from the CHL and US?
  3. Are there enough eligible players from these leagues to have a market inefficiency?

To answer the first question, I collected all draft data going back to 2005 (beginning of the modern-day, seven-round draft) from Hockey-Reference. To assess quality of the player, I utilized Hockey-Reference’s Point Shares. Point Shares provide an estimate of the expected number of standings points that are contributed by a particular player. While not perfect, Point Shares are easily interpretable and are one of the few value metrics that allow for comparisons between skaters and goaltenders.

Using all draft picks between 2005-2014, I calculated the median point share for forwards and defensemen drafted from the CHL, USHL, USNTDP as well as the top Nordic professional and junior leagues. I plotted the median (black line) against all individual observations for each league:

Despite the fact that players from Sweden and Finland are taken in later rounds and less frequently, it appears as if they still produce players of similar quality to those drafted from North America. First and foremost, if a first-year, draft-eligible player makes it to the top league in Finland (Liiga) or Sweden (SHL), they should be held in high regard given how their predecessors have performed. Defensemen drafted from the SuperElit league appear to be of similar quality to those drafted from the USHL, slightly better than those from the QMJHL and slightly worse than those from the OHL and WHL. Forwards from the SuperElit league again appear similar to those from the USHL, slightly better than those from the WHL and USNTDP, and slightly worse than those from the OHL. The lack of players drafted from the top Finnish junior league makes it harder to assess overall quality but it certainly does not appear as if there is a significant gap in quality.

The next major consideration for teams drafting from these leagues is understanding what their development timeline might look like. Similar to drafting overagers, if a team with a closing championship window knows that a player might make it to the NHL more quickly, that could influence their decision to draft that player. To evaluate development timelines for each league, I used Evan Oppenheimer’s elite package for R to collect all playing seasons across all leagues for players drafted between 2005 and 2014. I then grouped skaters by position (F vs D) and draft round (Rounds 1-3 vs Rounds 4-7) to identify if players from certain leagues were more likely to make the NHL (defined as the first season where NHL GP exceeded GP in any other league) relative to where they were picked. Finally, I used the survminer package to plot the cumulative probability of a player making the NHL in each of his first seven post-draft seasons.

Forwards and defensemen selected in rounds 1-3 from the top Swedish professional leagues (SHL, Allsvenskan, Division-1) and Finnish league (Liiga) appear to make it to the NHL earlier and more frequently relative to their North American counterparts. This makes sense as these are professional leagues and the players are likely more “ready” to transition to the NHL. However, it’s important to note that the overall number of players selected from these leagues in early rounds is small, with only 11 players taken from Liiga and 32 players taken from the SHL, Allsvenskan, or Division-1.

Shifting gears to the junior leagues, it’s important again to state that our sample size is small. Only eight players were taken from Jr. A SM-liiga (two defensemen) and 38 from SuperElit. Given the relatively small number for the Jr. A SM-liiga, I’ll reserve making a statement of significance regarding their timelines or success rates. With respect to SuperElit, players appear to make it to the NHL later and at a slightly lower rate compared to their North American counterparts.

Rounds 4-7 tell a slightly different story. Once again, players making the top professional leagues appear to make the NHL earlier and at a more frequent rate. Interestingly, the SuperElit places ~20% of defensemen and ~25% of forwards in the NHL within seven years, a rate that is slightly better than that of the North American leagues. Jr. A SM-liiga on the other hand had zero of twelve defensemen drafted make the NHL but had ~20% of forwards make the NHL.

It’s evident that the Swedish and Finnish professional leagues produce higher caliber players that make the NHL faster and more frequently relative to the North American leagues. Notably, it appears as if the SuperElit league is not far behind the CHL and may actually produce players providing more bang for your buck in the later rounds. Given this finding, one could make the argument that more players from the top Nordic professional and junior leagues should be drafted and drafted earlier. This leaves us with our final question – are there enough eligible players from these leagues to actually have a market inefficiency?

Using data from Corsica.hockey, I identified all draft-eligible players in the 2017, 2018, and 2019 drafts. From there, I looked to see the percentage of players drafted from each league in each respective draft:

On average, the CHL has had ~8-10 percent of eligible players drafted. As the USHL and USDP have gotten stronger, they’ve had a higher percentage of eligible players drafted, with nearly 15 percent of eligible players being drafted this past year. Additionally, NHL teams appear clued in to the benefits of drafting from the Nordic professional leagues, although less than 10 percent of eligible Swedish professional players were drafted this past season. However, NHL teams haven’t really dived into the Swedish and Finnish junior leagues with less than 5 percent of eligible players being drafted from these leagues. Still, some teams have made a conscious effort in the past three years to draft from this region.

The Sabres lead the way, having used 50 percent of their draft picks on players training in Sweden or Finland. Other notable teams on this list include Detroit, who for years has mined Sweden for potential gems, and Carolina which is attempting to relocate the country of Finland to Raleigh, North Carolina one draft pick at a time. While there’s certainly an opportunity for improvement right now, this isn’t an inefficiency that’s going to last long. Eventually teams will start scouting and drafting from these leagues more frequently to the point that we shouldn’t expect substantial production differences in a player selected from Sweden/Finland versus North America. As of now, it seems as if we’re still a few years away from that and as such, there’s a market inefficiency to be had.

So, how should we use this information? If I were to explain this to a general manager, I’d offer up the following pieces of advice:

  1. Build up your scouting department in Sweden and Finland, with more emphasis on the SuperElit and Jr. A SM-liiga. With less than five percent of eligible players being drafted, there may be dozens of gems out there that are currently being missed. Additionally, start looking for other leagues that are producing larger numbers of draft-eligible players. Switzerland and Germany stand out to me as the next two potential gold mines.
  2. We seem to be pretty good at scouting North America, in terms of identifying which players make it to the NHL and which ones don’t. If you have a couple of late round picks, it may be worth spending that pick on a player from Sweden or Finland versus a player from North America.
  3. All things being equal, a prospect drafted from Sweden or Finland has a more flexible development timeline given that they don’t have to comply with the rules of the NHL-CHL agreement. If you’re a contender on a tight window, consider drafting a player from Sweden or Finland who may be ready to come over and help earlier.

A sincere thanks to Namita Nandakumar for her assistance in putting together this article

Projecting NHL Skater Contracts for the 2019 Offseason

We recently released the final version of our contract projections for the 2019 NHL free agent class (they can be found here). Our initial projections went up in mid-April, and even though it’s only been a few weeks, we’ve had numerous questions about how the model was designed, how it works, what it means, etc. I thought we might be able to answer all the questions about it on twitter, but alas it was just a dream. A quick recap: this is our third year doing contract projections for the NHL offseason. While the model/projections this year may seem quite complicated, our first version was very simple: a few catch-all stats and a linear regression model to predict salary cap percentage (cap hit / salary cap). We use cap percentage to keep salaries on the same level as the salary cap changes. Over the last few years, we’ve developed a few new methods, and this year we took quite a bit of inspiration from the method Matt Cane used for his 2018 NHL offseason salary projections.

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Statement from Hockey-Graphs about Jason Baik

On Wednesday Night, Hockey-Graphs became aware that one of our contributors, Jason “jsonbaik” Baik, had been convicted of Sexual Assault in Allegheny County, Pennsylvania (Pittsburgh). To be utterly clear, Hockey-Graphs condemns these actions absolutely. Upon becoming aware of this horrible news, we have terminated our relationship with Mr. Baik and all contributions from Mr. Baik have been removed from this site.

We here at Hockey-Graphs wish to express our support for those who have been victims of Sexual Assault, Rape, or related crimes. As such, we encourage our readers to support organizations dedicated to help support victims of such heinous acts. If you can, please consider a donation to National Organizations like the Rape, Abuse & Incest National Network (RAINN) or local organizations such as the Pittsburgh Action Against Rape (PAAR) and the Women’s Center and Shelter of Greater Pittsburgh.

Sincerely,

Hockey-Graphs Editorial.

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Visualizing Goaltender Statistics Through Beeswarm Plots

A picture is worth a thousand words. Yes, it’s a cliché, but when it comes to visualizing data, an individual can tell a story via the choices they make when presenting their data. One of the most common visualizations is a plot showcasing the frequency and distribution of an event. Data like this are often presented in a histogram or box-and-whisker-plot. However, a limitation of both of these types of plots is that neither shows the individual where each data point falls. On the other hand, a beeswarm plot allows the user to see where each individual point falls across a range. A random jitter effect is applied to maintain a minimum distance between each point to minimize overlap.

Inspired by the wonderful graphs from Namita Nandakumar and Emmanuel Perry, I thought I would attempt to visualize how goaltenders have fared in goals saved above average over the course of their careers.

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NL Ice Data: A Swiss Hockey Analytics Website

In the last 10 years, I have been impressed by the development of the hockey analytics community in North America as well as the tools made available to the public in the hope of increasing the general hockey knowledge.

Unfortunately, in Switzerland, the Swiss Ice Hockey Federation (SIHF) does not provide the same level of information as there is in North America and keeps part of its proprietary data for itself. As such, fans and journalists, except on very rare occasions, don’t have access to the same kind of in-depth researches/analyses as there are in the NHL or some other European leagues. Plus/minus is still THE hockey statistic for some journalists or analysts.

The first part of my project with the Hockey-Graphs Mentorship program was to create a platform entirely dedicated to Swiss hockey statistics, called NL Ice Data, the main goal was to exploit as much as possible the available data and to give fans access to additional statistics the SIHF doesn’t necessarily provide:

  • GF/GA: for players, RelGF%, GF/60, …;
  • time on ice deployment and evolution;
  • linemates information;
  • aggregated shot tracker maps per player, goalie and team;
  • and many others.

Current features include the same core of statistics for players, goalkeepers and teams: statistics, fouls, shootouts and shot tracker maps. Easy to use, the website provides interactive tables and charts so that fans can engage more with data. Additional features, charts and metrics will be added along the project.  

By slowly integrating further metrics and concepts after the website’s launch (xG or Game Score for example), the modest goal is to build overall knowledge amongst fans. A secondary goal was to have a platform ready to publish more *advanced* statistics (including at the player level) as soon as the League publishes more of its proprietary data.

How Much Do NHL Players Really Make? Part 2: Taxes

Although published NHL salaries may seem exorbitant at times, players’ annual income is subject to a number of withholdings that limit their take-home pay. As we explained in Part 1 of this series, players lose some of their earnings to escrow – a reconciliation process arising out the Collective Bargaining Agreement between the league and the NHL Players’ Association. Another expense that reduces a player’s earnings is something that all workers in the United States and Canada are subject to: taxes.

screen shot 2019-01-07 at 5.01.40 pm

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Comparing Scoring Talent with Empirical Bayes

Note: In this piece, I will use the phrases “scoring rate” and “5on5 Primary Points per Hour” interchangeably.

Introduction

Nico Hischier and Nikita Kucherov are both exceptional talents but suppose, for whatever reason, we want to identify the superior scorer of the two forwards. A good start would involve examining their 5on5 scoring rates in recent seasons…

compare

Evidently, Hischier has managed the higher scoring rate but that doesn’t convey much information without any notion of uncertainty – a significant issue considering that the chunks of evidence are of unequal sizes. It turns out that Kucherov has a sample that is about three times larger (3359 minutes vs. 1077 minutes) and so it is reasonable to expect that his observed scoring rate is likely more indicative of his true scoring talent. However, the degree to which we should feel more comfortable with the data being in Nikita’s favor and how that factors into our comparison of the two players is unclear.

The relationship between evidence accumulation and uncertainty is important to understand when conducting analysis in any sport. David Robinson (2015) encounters a similar dilemma but instead with regards to batting averages in baseball. He presented an interesting solution which involved a Bayesian approach and more specifically, empirical Bayes estimation. This framework is built upon the prior belief that an MLB batter likely possesses near-league-average batting talent and that the odds of him belonging to one of the extreme ends of the talent spectrum are less likely in comparison. As evidence is collected in the form of at-bats, the batter’s talent estimate can then stray from the prior belief if that initial assumption stands in contrast with the evidence.  The more data available for a specific batter, the less weight placed on the prior belief (the prior in this case being that a batter possesses league-average batting talent). Therefore, when data is plentiful, the evidence dominates. The final updated beliefs are summarized by a posterior distribution which can be used to both estimate a player’s true talent level and provide a sense of the level of uncertainty implicit in such an estimate. In the following sections we will walk through the steps involved in applying the same empirical Bayes method employed by Robinson to devise a solution to our Hischier vs. Kucherov problem.

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