With the release of the Kindle version of my book, I wanted to provide an excerpt to promote it. You can purchase the Kindle at the above link or the paperback here. If you already purchased a paperback, you should be able to obtain the Kindle version for free on your Amazon account. Let me know if you run into any trouble there. Enjoy!
In 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.
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.
And now I’m doing a post about it.
Over the weekend, the fourth annual Sports Analytics Conference held at the Rochester Institute of Technology took place. It was an absolutely amazing time and a very busy couple of days. Below are the slides and streams from the Friday Night Tableau Workshop as well as the talks from Saturday. Enjoy!
Note: In this piece, I will use the phrases “scoring rate” and “5on5 Primary Points per Hour” interchangeably.
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…
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.
By Ingrid Rolland and Michael Lopez
At the 10-minute mark of the first period during Game 2 of Tampa Bay’s 2nd-round series with Boston, Torey Krug was sent to the box for two minutes after committing this slashing violation against Brayden Point.
The Lightning cashed in on the ensuing power play, with Point scoring the game’s opening goal.
Fast forward to later in this same game, with Tampa Bay clinging to a 3-2 advantage and less than four minutes remaining in regulation. Brad Marchand skated past Anton Stralman for a scoring chance, and the Lightning defender reached around to commit what looked to be a similar violation to the one deemed a penalty on Krug above.
No penalty on Stralman was called, however, and Tampa Bay held on for a 4-2 win. It was the first of the team’s four consecutive triumphs over the Bruins that earned the Lightning a spot in the Eastern Conference Final.
“We hate to harp on the ref’s, but tonight they deserved to get harped on,” opined NBC’s Jeremy Roenick after the game. “How can you call [Krug’s] penalty early in the game, in such a big playoff game?”
Earlier this year, Rushil Ram, Mike Gallimore, and Prashanth Iyer launched Tape to Tape, an online tracking system that can be used to record locations of shot assists, zone exits, and zone entries. Rushil and I will be running the Tape to Tape Project in order to compile a database of these statistics with the application Rushil created. We have already had close to 30 trackers sign up from an announcement on Twitter last week.
Each individual will track zone exits, zone entries, and shot assists for games they sign up for. Once the games are complete, the data will be exported to a public Dropbox folder. The goal with this project is to enhance our understanding of these microstats as they pertain to coaching decisions, player performance, and wins. What follows next is a description of what we will be tracking, a brief summary of the research that describes why these specific microstats are important, and how we will be tracking these events.
In July of 2016, Dom Luszczyszyn released a metric called Game Score. Based on the baseball stat created by Bill James (and ported to basketball by John Hollinger) the objective of game score is to measure single game player productivity.
While it’s often easy to compare players across larger sample sizes, comparing two different players’ performance on a given night can be difficult. If player A has a goal, two shots, and took a penalty, did that player outperform player B who had two assists and one shot? Game score attempts to answer that question by weighting each of the actions of each player to give us a single number representing their overall performance in that game.
Unlike Dom, whose main goal was to create a better way to evaluate single game performance, mine was to create a better statistic to evaluate the total contributions of players. There are no advanced metrics, like Corsi For percentage, or even Goals For percentage, available at this time in the NWHL. Because of this, points are the best way to evaluate players, even though other box score stats are available.
Chris Watkins joined Adam Stringham to discuss all of this year’s biggest deadline deals. Did the Rangers get enough of a return for Ryan McDonagh? Why were the Red Wings unable to move Mike Green? Any comments are appreciated, the goal is to produce a podcast that people want to hear. Please subscribe to the podcast on iTunes!