Raccoon Jesus
We were right there
There is a "0-axis" which essentially shows the average. You can eyeball it by seeing essentially where all the bars consistently split, or you can look at the bottom of the chart and find the 0. Bars to the left of it say the contributions of that particular area (offense, defense, production, and miscellaneous) were below average. Bars to the right of it are above average. The scores then get "added".
Fiala's production of 3 assists definitely boost his "production" area, as well as Kopitar's 2 goals. So, you can see the lighter blue being much longer than what anyone else. I don't know exactly what Dom's model utilizes to measure each component, but it's to give examples.
The higher up on the chart, the more positive your contributions on the ice were overall.
So, this chart is saying Fiala's contribution of offense, defense, production, and miscellaneous was better than anyone else on the Kings. And that Jordan Spence was the worst.
It's based on data that is returned from some API call to the NHL statistics gathered. So, Spence keeping the puck on the powerplay multiple times because they were clearing attempts has zero impact on this model, because that sort of data isn't tracked. A bad pass to a teammate who can't control it may show that the teammate has a giveaway, instead of the original passer who set up the teammate poorly.
Then, of course, the model puts different weight on different things - for example, if Kevin Fiala gives the puck away on the powerplay that leads to a shorthanded goal against, it will have just as much weight as a player making an errant pass in the offensive zone that gets picked off. A "giveaway" without any additional context may have smaller impacts.
This is probably more than what you asked for. But the way this game went exposes some serious flaws in Dom's model of calculating a "raw game score" based on how statistics are logged in the game. It's part of why using raw analytics is a very unwise approach. BUT, it can be a good tool when used properly to evaluate and measure what you have seen, because implicit biases from the eye test can also be flawed.
A little bit of an aside maybe but kind of a furthering, this is also why Martinez-muzzin looked like one of the best pairings in the world by stats but worst in actual results
Some of these stats are good at capturing play volume but not catastrophic moments
Muzzin Martinez could possess the puck, play d, drive play forward. But then they’d pass the puck directly to the other team for a tap in as well. Erik karlsson is another great example.
Spence above is an example of the counter
But they’re useful as a mode of inquiry as long as you can void confirmation bias