clock menu more-arrow no yes mobile

Filed under:

An Introduction to Analytics Profiles

Part 1 of an 18 Part Series

Tampa Bay Lightning v Boston Bruins - Game Four

As we head into the 2018-19 season, I thought it would be a good idea to create a profile of every current Boston Bruin with at least 1,000 5v5 minutes at the NHL level using only advanced statistics. Since becoming an author here at Stanley Cup of Chowder, I’ve used these statistics in just about all of my posts. However, I recognize not all of our readers are familiar with hockey analytics, or even know how to access them. And perhaps those who are familiar with analytics don’t know how to translate them back to hockey.

If you aren’t open to the use of advanced statistics in hockey, these profiles are not for you. However, these will hopefully be readable, and enjoyable, for those with all levels of familiarity with analytics.

I often hear things like, “Analytics say that [insert player name] is bad.” For one, analytics don’t have a definitive, binomial result. There is no good or bad. And there is a lot of uncertainty when it comes to evaluating a player, data-driven or not. However, people translate statistics differently, which can lead to gems like these.

Although it would take a lot of effort to comment on every player in the National Hockey League, hopefully I can vouch my opinion on some Boston Bruins.

These profiles will cover four elements of analytics. Those are: shot creation, shot suppression, offensive shot quality, and defensive shot quality for 5v5 play. I will also follow each profile off with a few comments about each player. Of course there is more to the game than these four elements, but this is where we have the greatest certainty about players currently. Individual skill and special teams are lagging behind, at least when it comes to evaluating players.

All of the statistics used are publicly available at Corsica, Hockey Viz, or from Evolving Wild. Each component of a players’ evaluation will include the wins above replacement relating to that component, and for shot creation and suppression, will include regularized adjusted plus-minus.

There is a glossary of some commonly used statistics in this series below, but I also highly suggest visiting to read up on these statistics if they peak your interest. For the most part, these statistics won’t be referenced by name, but rather their real-life application. For instance, relCF60 may be written out as, “shot attempts per hour with him on the ice than without him.” This is an effort I made to make it easier for beginners to understand.


  • WAR - Wins Above Replacement is an estimation of the wins a player contributes to a team over a replacement (AHLer or NHLer playing at league minimum).
  • Z-Score - The number of standard deviations above or below average
  • Corsi for/against (CF/CA) - goals + shots on goal + missed shots + shot attempts that are blocked
  • Relative (rel) - A player’s on-ice results minus the teams performance when he is on the bench.
  • Relative to Teammate (relTM) - A player’s on-ice results minus a weighted combination of his teammates’ on-ice results without him.
  • Expected Unblocked Shooting/Save Percentages - Uses a model to determine the chance of each shot becoming a goal that a player is on the ice for.
  • Expected Goals (xG) - Expected unblocked shooting/save percentages multiplied by the total number of unblocked shots.
  • Goals-for percentage (GF%) - Number of goals for divided by the total of goals for and against.