EXPERIMENT 14-c:

After Experiments 14-a and 14-b, I felt that I had proven my point and that I
could stop doing experiments. But then I also noticed that there was almost
a "glass ceiling" at 47.50% that none of my mutant strategies had surpassed.

I became curious to see if I could tweak my fartoffski formulae to achive any
higher win rates and ran 10,000 games with very conservative double points
[0.60, 0.65, 0.70, 0.75, 0.80] and beaver points [0.42, 0.44, 0.46, 0.48, 0.50]. I
also increased the too good point to GWC > 0.85

Against the bot's 18,370 (51.81%) the mutant won 17,084 (48.19%) points and
against the bots 5,006 (50.06%) the mutant won 4,994 (49.94%) of the games.

With these slightly better results, I ran 10,000 more games with double points
set back to [0.55, 0.60, 0.65, 0.70, 0.75] and more spread out beaver points to
[0.53, 0.56, 0.59, 0.62, 0.65]. I set the too good point even higher at GWC > 0.9

Against the bot's 19,194 (52.51%) the mutant won 17,361 (47.49%) points and
against the bots 5,019 (50.19%) the mutant won 4,981 (49.81%) of the games.

With slightly worse results in the second batch, the average for all 20,000 games
came out to bot's 37,564 (52.17%) vs the mutant's 34,445 (47.83%) of points and
to bots 10,025 (50.13%) vs the mutant's 9,975 (49.88%) of the games.

This is like "moon-walking". My tweaks didn't result in any considerable changes
neither for the better nor for the worse. Interesting.

One thing to keep in mind is that all of my mutants have won consistently more
than what would be predicted from their ER/PR. The detailed stats include cube
error rates for individual games and total error rates for batches of 1,000 games.
You can download them and do your own calculations any which ways you want.