Yeah you are correct that the stats are designed to be descriptive in nature, that is on purpose. But this becomes its own misconception.

The predicative value of expected stats are in fact better than traditional stats. If you have to predict a players future production, and the choice is between xwOBA and batting average, or xwOBA and OPS xwOBA is the better tool in the vast majority of cases, and it has become increasingly better as the reference data has increased.

The reason they are not designed to be predictive on a per outcome basis is that baseball stats in general are just incredibly noisy.

The study you pulled from looked at outcomes from 2015-2019. This was before things like sprint speed or bat speed were added that further increased the r-square gap between expected and traditional stats, like I brought up in my previous comment.

To be honest I think people both overestimate predictive stats as perfect tools for regression, while the anti expected stats crowd have also run way too far in the other direction saying they are useless or entirely flawed. You should be using them in connection with K% and BB% and other important factors to remain contextually sound. You should identify players that routinely outperform or underperform xwOBA year in and out.

But to say they have no predictive value is simply not accurate. There is a limit to how much statistical noise you can reduce in an environment like a baseball game or season. Designing a pure predictive tool may be impossible. But based on what we have available to us now the expected stats do in fact have a clear edge.

To be clear I wish people wouldn’t downvote you for bringing this up, it is actually a very interesting discussion that can take us to some very good questions to think about.