The first problem is the data. The soccer viewing public is largely familiar with event-level data, typically provided by (my previous employer) Opta. They've done a great job normalizing soccer statistics on the cultural level, but the information they've collected at scale isn't that useful for creating good defensive metrics.

Other companies have sensed an opportunity here and have started providing more detailed data around things such as defensive pressure. Suddenly, you can contextualize each offensive event with the level of defensive pressure applied to it. I think this will be a game changer, but we're in early days there.

Other companies provide player-tracking solutions that give you real-time position of all players on the field. This is great because you have a "complete" picture of the game, but it requires a lot of work to build more sophisticated spatial/geometric models.

There's also the "Howard Effect", coined largely as a basketball term, but it's similar to the Maldini example you provided. Some defenders are so good that they don't have to be "active" defenders. That's something which is really difficult to adequately control for.

Statsbomb is a little different. They've started collecting their own data and are offering some free data from various Womens leagues.

https://github.com/statsbomb/open-data

You can do the exact same thing in soccer, if you have the data: You can assign responsibilities to players, just through computer vision. If Messi gets 3 touches in an attacking position when Sergio Ramos is defending him, you can credit that to Ramos, and compare that to Messi's touches vs the average Barcelona opponent.