After all, there's a fairly strong correlation between high-importance scenes and overall framecount, which makes sense when you consider that frames are the main currency animation studios work with.
As an example, the final conversation between Okabe and FB in Steins;Gate is really not good animation, but it is crucial to the plot, and the dialogue and voice acting still make it a very impactful scene.
A truly sophisticated approach capable of identifying filler scenes would probably involve machine learning using data that's not (to my knowledge) actually available to the public, like engagement/watchtime statistics.
I'm trying to build something similiar[0] to this by indexing (and plotting) the anime score (given by MAL, something like IMDB for anime) on a daily basis. So in theory you could look at the graph and when you see a spike in score/user/favorites the episode may be good, or bad, or anything.
That's the idea. I was busy getting the indexer stable, so it's not really finished and I haven't spent much time on design. E.g. the airing dates aren't plotted yet etc. The current work is at https://anistats.com.
An example for a overhyped show would be https://www.anistats.com/anime/41353/the-god-of-high-school. And a steady climbing, probably better-than-thought movie would be this: https://www.anistats.com/anime/38088/digimon-adventure-last-...
My big want is a fluff eliminator API. Like a service where people can define timestamps of where the actual good stuff is on a per-episode basis. Then a wrapper service that displays just the slimmed down episode portions chained together. But I don't know if the YouTube API or others even support stuff like that.