Annual Rate Charts (Introduction)

I debuted the Annual Rate Charts (ARCs) on twitter over a month ago and since I’m unleashing the Tableau to the public, I wanted to ensure proper documentation was available.

You can find the link to the interactive Tableau Charts HERE!

What are Annual Rates Charts?

Annual Rate Charts (ARCs) are a way to measure the production and expected production of a player relative to their time on the ice. Production is quantified using the Goals Above Replacement (GAR) and Expected Goals Above Replacement (xGAR) models from Evolving-Hockey. If you want an in-depth analysis of how GAR is calculated, documentation is available here. In short, GAR is a single number that captures the contribution of that player in different game situations. GAR is subdivided into several categories, including even strength offense & defense, power play, penalty kill, takeaways and faceoffs. The number for each player represents the number of goals more (positive) or less (negative) that a player contributions relative to a replacement-level player (by position).

Expected GAR (xGAR) is also a single number assigned to each player, which is calculated based on the on-ice performance of a player (including rates, quality, shooting and goaltending). This number is the expected number of goals more or less above replacement-level that the player should contribute, based on their on-ice actions. In other words, xGAR represents the performance of a player, while GAR represents the results of that performance. When I tested the relationship between these values, I find that xGAR captures approximately 86% of the variation in GAR, which is quite substantial for a model including human subjects.

Annual Rate Charts (ARCs) can be differentiated from many other publicly available visualizations using GAR and xGAR data as it is relative to the amount of time a player is on the ice. These rates can be differentiated from many other popular visualizations that  display GAR data in aggregate form. There are strengths and weaknesses to both rate and aggregate data, but both are useful in their own way.

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New Podcast Coming Soon!

I was blessed with an opportunity to join The Hockey Podcast Network to do an original content podcast on NHL Analytics. Episodes of the Ice Analytics Podcast will be released every Friday beginning on December 27. This podcast will posit one NHL-related question each week and explore the answer using the available data. I will also be joined by a guest from the hockey or statistics community to get an insider prospective on these topics. Show notes, including data sources and visualizations, will be available on

I hope you find these topics to be as informative as I do!

Follow me on twitter @IceAnalytics.

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GAR and Cap (2018-2019): Defensemen (cont.)

Following up from the previous post here, which examined the best (and worst) GAR performers in each game situation, this article will examine defensemen value relative to cap hit. For more information on how GAR is calculated, please check out the introduction. Before delving into the nuances of player value, I present an illustration of the cap hit and GAR of all defensemen:

GAR per Dollar (D)

Continue reading “GAR and Cap (2018-2019): Defensemen (cont.)”

GAR and Cap (2018-2019): Defensemen

The first positional group of interest is defensemen, who will be presented in the three different game situations (Even Strength, Power Play and Short Handed). Before diving into the GAR values for individual defensemen, be sure to check out the introduction, which outlines the process for data collection and team-aggregate values. The following charts illustrate the GAR of defensemen in different game situations and time-on-ice:

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Objective Thoughts Concerning Nylander

I apologize in advance that I am even covering this whole spectacle. Anyone living in/near the GTA is probably exhausted from all the speculation surrounding William Nylander. All that being said, I wanted to present the situation in an objective fashion with certain conditions that can be logically understood. It should be noted that I am not arguing that the Leafs trade Nylander, but merely presenting a thought experiment based on two factors: (1) age and (2) AAV.

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Moneypuck: GAR and Salary

One of the recent developments within the NHL Analytics community has been the discovery and application of the GAR (Goals Above Replacement) statistic.  This is based on WAR (Wins Above Replacement) which has yielded fruitful results for Major League Baseball.  This is the first attempt at capturing the value of a player in a single measurement, which can then be used to compare the relative performance of players on different teams.

What is GAR?

GAR is a measurement of an individuals’ goal contribution to the team, relative to a replacement-level player. The logic is that you need a positive goal differential in order to win hockey games, therefore you need players that can generate offense and/or be responsible defensively.  Replacement-level players refers to your average replacement option – low salary free agents or AHL call-ups.  Players with a positive GAR are better goal contributors than a replacement player.  Players with a negative GAR are worse goal contributors than a replacement player.

Continue reading “Moneypuck: GAR and Salary”