Was thinking a bit about the different kinds of maps, specifically regarding production distribution. Some maps seem to place more emphasis on controlling the right regions than others.

I then had an idea: to use the Gini coefficient to measure the distribution of production within maps. Pretend that each site is a person and the map is the nation; each site's production is effectively its income. Luckily, this is pretty easy to calculate too!

Here's a fairly golfed 3-line calculator (relies on numpy because I was lazy, sorry, but should be easy to rewrite it to not use numpy ):`import sys, json, numpy`

prods = numpy.cumsum(sorted(numpy.array(json.loads(open(sys.argv[1], 'r').read())['productions']).flatten()))

print(str((len(prods)*prods[-1]-2*numpy.trapz(prods)+prods[0])/len(prods)/prods[-1]))

Pass it the filename of the replay as the command line argument.

I tried it on a sample replay (specifically `ar1482947270-2437412300.hlt`

) and got a coefficient of 0.306, which is appreciably less than the US and on par with a country such as Hungary.