Can you explain in more detail?
If you have a multi-dimensional set of factors of things and you design a metric to collapse them down into a single dimension, what you're really measuring is a combination of the values of the factors and your weighting of them. Since the "correct" weighting is a matter of opinion and everybody's use-case is different, a single-dimension metric isn't very useful.
This goes for any situation where you're picking the "best" among a set of choices, not just for compression algorithms, by the way.
Like, if you're trying to compress a given file, and one algorithm compressed the file by 0.00001% in 14 seconds, another compressed the file 15% in 20 seconds, and the third compressed it 15.1% in 29 hours, then the middle algorithm is probably going to be the most useful one.
User A is trying to stream stuff that has to have latency less than 15 seconds, so for him the first algorithm is the best. User B is trying to shove the entire contents of Wikipedia into a disc to send on a space probe, so for him, the third algorithm is the best.
You gave a really extreme[ly contrived] example, so in that case you might be able to say that "reasonable" use cases would prefer the middle algorithm. But differences between actual algorithms would not be nearly so extreme.