It's a terrible metric that fails basic dimensional analysis. Anytime you see a ratio of logs, you should already be suspicious. Say algorithm A is twice as fast as algorithm B. Then we'll have alpha r/R log(T)/log(t). The alpha r/R is constant, so the only interesting part is log(T)/log(t) = log(2t)/log(t) = log2/log(t) + 1. We can make this apparently unitless number take any value we want just by changing the unit used to measure time (or if a unit were fixed, by changing the amount of data used to test).

It's sort of vaguely superficially sensible, but the idea of being able to reduce comparison of compression algorithms (which fundamentally trade off speed for compression ratio) to a 1-dimensional value is laughable. Charles Bloom's Pareto frontier charts (http://cbloomrants.blogspot.com/2015/03/03-02-15-oodle-lz-pa...) are one of the more reasonable options.

The metric divides compression ratio by log compression time. (Ignore for now the normalization to a standard compressor.) This is:

    r / log(T)

That doesn't seem like a good metric, because it overvalues changes in T. For example, say we currently can manage 10% compression (ratio = 100/90 = 1.11) and it takes us 16ms. That's

    r / logT = 1.11 / log(16) = 0.2775

Now we have two proposals. One brings us to from 10% compression to 55% compression (ratio = 100/45 = 2.22) while the other one drops compression time to 4ms:

    r / logT = 2.22 / log(16) = 0.555
    r / logT = 1.11 / log( 4) = 0.555

But improving compression by 5x matters more than improving speed by 4x. Take this to the extreme: a compressor that exits immediately leaving its input unchanged has the best possible score here, despite being useless.

Sure, but "lol what's the weissman score HASHTAG SILICONVALLEYTHESHOW \m/" doesn't lead me to think that the poster was legitimately asking for the score so they could compare.