Humanness results
Humanness % is calculated by dividing the number of times a player (bot or human) was judged to be human
by the total number of times it was judged. For this calculation, only judgements made by humans are counted.
Even though we call this "humanness", it really only measures how humanlike a player seems to the judges.
This explains why it is possible for bots to get a rating higher than the ratings of the real humans - the bots
are good at tricking the judges.
Each player was judged around 25 times. This makes it unlikely that the high humanness ratings are a fluke.
For example, if a bot was really only 35% human, then there is only about a 5% chance of getting more than a 50% rating.
If a bot was really only 30% human, then this drops to about 2%.
As well as humans and competition bots, a few epic bots, the bots built into the game, briefly took
part. These bots don't play by the same rules as the competition bots, but their ratings are reported
here for interest.
Most human bots
| bot name |
humanness % |
| MirrorBot |
52.2 % |
| UT^2 |
51.9 % |
| ICE-CIG2012 |
36.0 % |
| NeuroBot |
26.1 % |
| GladiatorBot |
21.7 % |
| AmisBot |
16.0 % |
| average |
34.2 % |
|
Most human humans
| player name |
humanness % |
| Samaneh Rastegari |
53.3 % |
| Craig Speelman |
52.2 % |
| John Weise |
30.8 % |
| Chris Holme |
26.3 % |
| average |
41.4 % |
|
Most human epic bots
|
humanness % |
| average |
37.8 % |
|
Judging results
Judging accuracy is calculated as the number of correct judgements made by a player, divided by the
total number of judgements made by that player. Note that the bots also judged (but their judgements
were not used in the calculation of humanness ratings).
Best bot judges
| bot name |
accuracy % |
| MirrorBot |
60.0 % |
| NeuroBot |
56.4 % |
| UT^2 |
54.6 % |
| GladiatorBot |
50.5 % |
| ICE-CIG2012 |
45.8 % |
| AmisBot |
44.4 % |
|
Best human judges
| human name |
accuracy % |
| Chris Holme |
60.9 % |
| John Weise |
60.8 % |
| Craig Speelman |
50.0 % |
| Samaneh Rastegari |
47.8 % |
|
