Love the "simplify" vs. "clarify" distinction! I work in sports analytics and that's something I feel pretty strongly about.
The clearest example of this that I've noticed of this in my work is that there's a tendency in some sports analytics circles to move towards "all-in-one" metrics. These are numbers that aim to boil down the entirety of a player's value into a single number.
This is convenient and easy in a lot of ways (i.e. now you can directly model a player's value against their salary), but I think in practice it usually abstracts away too much information, like the specific things a player might do well and the role they're being used in. A lot of my work is around presenting that data in ways that are easier to digest but not in a way that hides the underlying information.
I appreciate any discussions around that type of nuance in data viz!
Love the "simplify" vs. "clarify" distinction! I work in sports analytics and that's something I feel pretty strongly about.
The clearest example of this that I've noticed of this in my work is that there's a tendency in some sports analytics circles to move towards "all-in-one" metrics. These are numbers that aim to boil down the entirety of a player's value into a single number.
This is convenient and easy in a lot of ways (i.e. now you can directly model a player's value against their salary), but I think in practice it usually abstracts away too much information, like the specific things a player might do well and the role they're being used in. A lot of my work is around presenting that data in ways that are easier to digest but not in a way that hides the underlying information.
I appreciate any discussions around that type of nuance in data viz!
Thanks, Shri, that's really interesting. And as a sports fan, I completely agree! It's hard to boil down all of the metrics into a single number.
Thanks again!
Jon