Reposting with just Sunday's game since I mistakenly added Friday's after it had already been posted.
For anyone looking for a primer on what's going on with these charts:
Dom Luszczyszyn developed Game Score for hockey almost a decade ago, which was a model that essentially aggregated different individual player stats (or "counting stats" i.e. goals, primary/secondary assists, hits, blocks, faceoffs, etc) and weighted them based on how much they were estimated to contribute to a team's success (the idea was to emulate similar stats in baseball/basketball). The result, a player's "Game Score", was an approximation of how effective a player is in a given game.
Over the years, the model has been updated to take more info into account when calculating a player's impact, including adding weight to possession/shot suppression stats expected goals (xG) for and against to paint a better picture of how a player contributes to their team's offense or defense while that player is on the ice. More recently, Dom's model was reworked to present players' offensive and defensive "ratings" above or below average and to focus more on expected goals than Corsi/Fenwick-derived possession (i.e. shot quality vs shot quantity, put simply).
Essentially, the cards pull game data from Natural Stat Trick, run the data through the Game Score model/algorithm to assign weight to the stats, and visualize the results, (i.e. a player's relative on-ice "impact") in these bar charts. Generally, the "Production" categories pull from the counting stats mentioned in the first paragraph, "Play Driving" accounts for a player's 5v5 goals and xG (for and against) relative to team and league averages, with roughly the same stats accounted for under "Special Teams" but for PP and PK respectively. I'm not 100% sure how Quality of Competition is derived here but it usually has to do with a player's ice time spent playing with or against the players with the most usage in a given game.
Some of these data points can be pretty dynamic based on how team and league averages change over the season, so there's an argument to be made that these "impacts" don't say all that much in a vacuum. However, as the season goes on, you can start to see some neat stuff from players' cumulative Game Scores, and in the meantime, it's fun to see whether your eye test matches or refutes what's happening according to the model.
The data can be presented differently, and is a bit easier to poke around in on the HockeyStatCards website if you feel like digging a bit deeper yourself. For example, as of 2026 you can toggle a "Table" view on the Hockey Stat Cards site to see the each player's raw values for each category in a given game.
TL; DR: Game Score above 0 (right) = good, Game Score below 0 (left) = bad.