Quantify the proportion of data not explained by explanatory variables in linear regression Say I'm in a classic linear regression situation:
$Y = \beta_0X_0 + \beta_1X_1 + \dots + \beta_NX_N + \epsilon$
And that I know that my explanatory variables $X$ only explain some part of my regressand $Y$, what sort of metrics could I use to estimate the proportion of this unexplained part? I know about the fraction of variance unexplained (FVU) that can be calculated with the coefficient of determination, but after a quick search on the internet I get the feeling that it's not used that often?
I may be wrong but I'm looking for potential alternative ways of quantifying this unexplained proportion in my system.

$R^2$ is the proportion of variance explained, at least under particular conditions, which you have.
Consequently, $1-R^2$ would be the proportion of variance left unexplained. While this metric is not as common as $R^2$ (I’d never thought of it before I saw this question), it should not cause controversy.
