While we could certainly go in this direction, we're not planning to, because in our experience optimizations for different workloads are largely distinct, and this use case is already handled well by PyPy.
Isn't this use case the scientific computing use case? That's a fairly large part of the ecosystem to give up on!
I think it's still a relatively low effort way (just need to write a scraper) to create a benchmark on a diverse set of algorithmic tasks that have clearcut criteria on AC/TLE/WA. PyPy is often 10x faster than cpython on these problems (and just 2x slower than equivalent C++ solution) so it will be a much nicer headline too if you can achieve similar performances!
Though I can also see how it can be completely irrelevant for server workloads. Pypy's unicode is so slow, some people on codeforces still use pypy2 over pypy3 just to avoid it. And c extensions is so bad on pypy, you can often get better performance on cpython if you need to use numpy.
This is just a comment on my personal use of Python for competitive programming: I've never used numpy for competitive programming or thought that it would be a good tool for that. PyPy seems like a great solution for the highly-numerical algorithms that these contests tend to lead to.
So I would not call this "scientific computing". Personally I consider competitive programming to be it's own use case.
And as much as we want to improve scientific computing in Python, it's very hard since the work is done in C. Our current hope is to help mixed workloads, such as doing a decent amount of data-preprocessing in Python before handing off to C code.
While we could certainly go in this direction, we're not planning to, because in our experience optimizations for different workloads are largely distinct, and this use case is already handled well by PyPy.