Player Card Blog
This is a little blog I’ll use to describe and explain the player cards I’ve recently made, and the purpose of these cards. These cards are subject to change, so I’ll often make edits or modifications to this article from time to time.
Why I made these player cards
There is a large number of excellent visualizations out there, that display an NHL player’s statistical results. Recently, I decided to take a bit of a different approach and make player cards with my own methodology.
Many player cards do a superb job of displaying a player’s “overall” results; e.g. “Player A’s EV Offence is in the 84th percentile and his WAR places in the 78th percentile.” Stuff like that.
However, a common flaw with publically available stats is determining the process behind these results. They do a phenomenal job at showing a player’s results and metrics, but aren’t as useful at displaying how they achieved their results.
E.g. a Player Card may state that Player A’s offence ranks in the 90th percentile, meaning his offence is superior to 90% of the league. But why are his results so good? What specific offensive areas do they excel in?
Saying things like “Foward A must improve offensively,” or “Defenceman B must improve defensively” are some pretty broad statements in my mind; what specifically do they need to improve?
I desired to develop player cards that:
A) Displayed a player’s overall on-ice results, similar to every other card
B) Displayed a player’s deployment, similar to most cards
C) Gave an idea of a player’s play-style, and specific micro-level strengths and weaknesses
D) Didn’t have any sort of “Overall” measure. Many player cards that possess some sort of “WAR%” rating can often cause some readers to base a player’s value solely off of that, and consequently ignore valuable context and some of the other metrics on the card. I wanted to make a more subjective card, where the reader has to make their own judgements and conclusions
Here’s a glossary and explanation of all the categories, and my thought process behind some of them.
*Note: This card uses data only from the 2020-21 and 2021-22 seasons
Overall Results / Deployment
*Note, every stat is presented in percentiles, which are scores between 0% and 100%. In simple terms, the percentile value of a category indicates what percent of the league that player is superior to, in regards to that specific category. E.g. Player A’s EV Offence score is at 96%. This means that Player A’s EV Offence is superior to roughly 96% of the league.
EV Offence and EV Defence uses RAPM xGF (Expected Goals For) and RAPM xGA (Expected Goals Against) respectively.
These stats are used to determine how good a player is at generating and driving scoring chances for, and preventing scoring chances against.
RAPM is a tool developed by EvolvingHockey, that attempts at isolating a player’s on-ice results for teammates, competition, zone starts, and other factors that could influence a player’s results.
However, this process isn’t perfect. The primary problem is its predictive capabilities, and numerous players with RAPM results on one team, may not repeat similar results on a different team. I also feel it can severely underrate the impact of deployment, especially players that play difficult roles on poor teams.
They’re still exceedingly valuable tools, but I feel context is required.
PP Offence evaluates how good a player is at generating GF (goals for) on the power play (RAPM PP GF/60).
PK Defence measures how well a player prevents xGA (expected goals against) on the penalty kill (RAPM PK xGA/60).
Some may wonder why many models use actual and expected goals for offence, but only expected goals against for defence. The primary reason is that goals against can be heavily impacted by goaltending. xGA has a considerably higher year-to-year sustainability than actual GA, although in some rare cases, players that allow exceedingly high-quality chances against may always have a low GA, regardless of their xGA.
Finishing uses a player’s impact on finishing and scoring on the chances they create (Goals Above Expected).
Penalties is an overall ratio of a player’s taken penalties, vs drawn penalties.
TOI is self-explanatory. It’s a measure of how much time-on-ice a player plays every game.
Competition, per PuckIQ, measures how much time a player spends against elite competition (i.e. top lines and top defence pairings).
Play-Style Categories (all stats are at EV)
Corey Sznajder (@ShutdownLine on Twitter, I highly recommend subscribing to his Patreon), does a brilliant job at manually tracking microstats (i.e. individual player analytics such as zone entries, zone exits, etc) for the public analytics community.
Thanks to his work, I was able to create radar charts that display a player’s play-style.
I took a lot of inspiration from Bryce Chevallier, who originally made similar radar charts at Architecte Hockey. I’d highly recommend taking a look at his work as well.
To clarify, I utilize this chart solely to determine a player’s play-style, and certain strengths and weaknesses. It isn’t the same thing as “overall” results, so the stats under “Overall Results” do a superior job at this. They shouldn’t be solely used for player evaluation purposes.
*Note: I understand that some of the “labels” can be a bit hard to read. I use Google Spreadsheets to make these cards, and unfortunately, I’m physically unable to make the font larger or more clear. You should be able to zoom in when looking at the cards in my tweets and OilersNation articles.
This section is used for both forwards and defencemen, and it’s pretty self-explanatory. It primarily uses Shots and Shot Attempts. The purpose of this category is to see which players shoot at a high rate.
Some players that excel in this metric: Auston Matthews, Alex Ovechkin, Nathan MacKinnon, Darnell Nurse
Offensive Zone Play-Making
This section is used for forwards and defencemen. The primary variables used are Weighted Assists, Offensive Zone Shot Assists, High-Danger Passes, and Chance Assists. The purpose of this category is to identify and evaluate passing and play-making abilities within the offensive zone.
Some players that excel in this metric: Connor McDavid, Leon Draisaitl, Mitchell Marner, Jonathan Huberdeau
This section is used for forwards and defencemen. The primary variables used are Defensive Zone Shot Assists, Passed Zone Exits, Zone Exit Assists and Neutral Zone Shot Assists. The purpose of this category is to identify the best players in the league at breaking the puck out of their own zone.
Some players that excel in this metric: MacKenzie Weegar, Cale Makar, Artemi Panarin, Mark Stone
This section is used for forwards and defencemen. The primary variables used are Controlled Zone Entries with Chances, Rush Shots and Rush Assists. The purpose of this category is to see which forwards are superb at carrying the puck into the zone and generating chances off the rush.
Some players that excel in this metric: Connor McDavid, Mathew Barzal, Leon Draisaitl, Nathan MacKinnon
This section is used for forwards and defencemen. The primary variables used are Zone Entries, Controlled Zone Entries, Controlled Entry%, Carried Zone Exits, Possession Zone Exits, and Possession Exit%. The purpose of this category is to identify how well players can individually move and carry the puck through all three zones.
Some players that excel in this metric: Connor McDavid, Mathew Barzal, Roman Josi, Cale Makar
This section is only used for forwards. The primary variables used are Forecheck Pressures, Offensive Zone Takeaways and Recovered Dump-Ins. The purpose of this category is to identify the best forwards at forechecking, disrupting zone exits, and pressuring opposing defenders in the offensive zone.
Some players that excel in this metric: Ondrej Kase, Nino Niederreiter, Oliver Bjorkstrand, Evgeni Malkin
Dump and Chase
This section is only used for forwards. The primary variables used are Dump-Ins, Dump In% and Dump-Ins with Chances. The purpose of this category is to see which forwards frequently dump the puck into the offensive zone, and how well they perform at creating chances off their dump-ins.
Some players that excel in this metric: Brett Ritchie, Matt Martin, Nick Foligno, Darren Helm
This section is only used for forwards. The primary variables used are Rebounds, Deflections, Tip-Ins, and Inner Slot Chances. The purpose of this category is to see which forwards excel at being a net-front presence, and generating chances directly in front of the net.
Some players that excel in this metric: Seth Jarvis, Brady Tkachuk, Anders Lee, Sidney Crosby
This section is only used for defencemen. The primary variables used are Entry Denials, Entry Denial Success Rate, and Controlled Entry Against per Entry Target. The purpose of this category is to identify the best defencemen at stepping up at the blue-line and efficiently denying entry attempts.
Some players that excel in this metric: Charlie McAvoy, MacKenzie Weegar, Jared Spurgeon, Aaron Ekblad
This section is only used for defencemen. The primary variables used are Entries with Chances Allowed, and Chances Allowed per Entry Target. This category is significantly correlated to Entry Shutdown, but it solely evaluates a defenceman’s ability at preventing chances off the rush.
Some players that excel in this metric: Charlie McAvoy, MacKenzie Weegar, Jared Spurgeon, Aaron Ekblad
This section is only used for defencemen. The primary variables used are Average Shot Quality per Shot Attempt Allowed, Slot Chances Against, and Shot Blocks per Shot Attempt Allowed. The purpose of this category is to identify a defenceman’s ability at defending chances off the cycle and the forecheck, in their own zone.
Some players that excel in this metric; Christopher Tanev, Adam Pelech, Jonas Brodin, Ilya Lyubushkin
This section is only for forwards and defencemen. This is a self-explanatory category and uses Hits as the sole, primary variable. The purpose is to see which players are the most physical, and if coaches are deploying certain players solely due to their physicality, rather than on-ice impact.
Some players that excel in this metric; Milan Lucic, Radko Gudas, Matt Martin, Brendan Lemieux
As stated before, RAPM isn’t a fully perfect tool.
Some teams suffer from scorekeeper bias, such as Carolina and Minnesota. To make this as simple as possible, several Carolina players’ “EV Offence” category is higher than it should be, and several Minnesota players’ “EV Defence” category is also higher than it should be. Remember to keep this in mind.
I’m currently doing research on having a higher understanding of forward defence from a statistical level, and separating it into multiple aspects. This is why the “Defensive” category on the style chart is the exact same as “EV Defence,” for the time being.
I’m also doing research on the impact of coaching systems from a statistical perspective, so none of these metrics account for coaching.
Of course, Corey Sznajder does a remarkable job at tracking these stats, but it’s essentially impossible for a single person to track every single game, for every single team. Consequently, the play-style chart isn’t 100% accurate, and as stated before, I use it solely for obtaining a general idea of a player’s play-style. This is also why I set a high TOI minimum; at least 400 TOI in the past two seasons.
The play-style chart doesn’t account for competition or role difficulty. Again, this is something that’s important to keep in mind.
These cards won’t be available to anyone else (yet). Due to EvolvingHockey’s rules of use, I can definitely post these cards in articles and tweets, but I can’t grant access to the entire database to someone else, especially since I use stats from behind their paywall.
I certainly have some ideas on using alternative websites and metrics to make these cards public, but at the current moment, I will be the sole person to have access to them. However, if there are any specific players you desire to see, please feel free to ask in my DMs! I can always answer 1-2 requests from time to time. :)
If you have any other questions or any suggestions/ideas for these cards, I’d love to answer and/or hear them!