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AIIDE 2019 - race balance

The CoG results file is troublesome, so I’m analyzing AIIDE first after all. The purpose of a plan, after all, is not to be executed, but to be changed; contact with the enemy and all that. This year’s AIIDE detailed_results.txt file was easy to read and interpret. I only needed a couple small changes from last year’s script.

Here is my version of the crosstable. It is identical to the official crosstable, so it doesn’t include any new information. If somebody wants it, I can also post my version of the per-map results, but that doesn’t include any new information either.

#botoverallLocuPurpBanaDaQiSteaZZZKMicrIronXiaoMcRaUAlbAITPBunk
1Locutus91.42%45%
45/100
89%
89/100
83%
83/100
97%
97/100
99%
99/100
92%
92/100
99%
99/100
96%
96/100
99%
99/100
99%
99/100
100%
100/100
99%
99/100
2PurpleWave85.54%55%
55/100
44%
44/100
85%
85/100
71%
71/100
100%
100/100
93%
93/100
98%
98/100
97%
97/100
89%
89/100
98%
94/96
100%
100/100
97%
97/100
3BananaBrain68.81%11%
11/100
56%
56/100
51%
51/100
85%
85/100
83%
83/100
71%
71/100
59%
59/100
57%
57/100
69%
69/100
84%
83/99
100%
100/100
100%
100/100
4DaQin63.33%17%
17/100
15%
15/100
49%
49/100
94%
94/100
10%
10/100
83%
83/100
92%
92/100
82%
82/100
41%
41/100
78%
78/100
100%
100/100
99%
99/100
5Steamhammer52.25%3%
3/100
29%
29/100
15%
15/100
6%
6/100
59%
59/100
25%
25/100
67%
67/100
50%
50/100
86%
86/100
91%
89/98
97%
97/100
100%
100/100
6ZZZKBot52.08%1%
1/100
0%
0/100
17%
17/100
90%
90/100
41%
41/100
44%
44/100
55%
55/100
49%
49/100
67%
67/100
90%
90/100
72%
72/100
99%
99/100
7Microwave50.67%8%
8/100
7%
7/100
29%
29/100
17%
17/100
75%
75/100
56%
56/100
13%
13/100
65%
65/100
64%
64/100
82%
82/100
93%
93/100
99%
99/100
8Iron49.25%1%
1/100
2%
2/100
41%
41/100
8%
8/100
33%
33/100
45%
45/100
87%
87/100
26%
26/100
65%
65/100
90%
90/100
100%
100/100
93%
93/100
9XiaoYi48.62%4%
4/100
3%
3/100
43%
43/100
18%
18/100
50%
50/100
51%
51/100
35%
35/100
74%
74/100
36%
36/100
73%
69/95
100%
100/100
98%
98/100
10McRave40.00%1%
1/100
11%
11/100
31%
31/100
59%
59/100
14%
14/100
33%
33/100
36%
36/100
35%
35/100
64%
64/100
43%
43/100
82%
82/100
71%
71/100
11UAlbertaBot28.04%1%
1/100
2%
2/96
16%
16/99
22%
22/100
9%
9/98
10%
10/100
18%
18/100
10%
10/100
27%
26/95
57%
57/100
75%
72/96
89%
89/100
12AITP11.62%0%
0/100
0%
0/100
0%
0/100
0%
0/100
3%
3/100
28%
28/100
7%
7/100
0%
0/100
0%
0/100
18%
18/100
25%
24/96
59%
59/100
13BunkerBoxeR8.08%1%
1/100
3%
3/100
0%
0/100
1%
1/100
0%
0/100
1%
1/100
1%
1/100
7%
7/100
2%
2/100
29%
29/100
11%
11/100
41%
41/100

The race balance is not too interesting this year. In the crosstable, we see protoss at the top, zerg grouped in the middle, and mostly terran on the bottom, so we don’t need numbers to judge the race balance. But here’s the table anyway. The random row and the versus-random column are the least interesting of all, because UAlbertaBot was the only random player.

overallvTvPvZvR
terran29%14%28%50%
protoss70%86%71%80%
zerg52%72%29%88%
random28%50%20%12%

And here’s the breakdown of how each bot performed against each race. The most surprising results are that Steamhammer did poorly against these specific 2 zerg opponents, although ZvZ is in general its strongest matchup, and that weaker participants UAlbertaBot and BunkerBoxeR somehow scored higher against mighty protoss than against middling zerg. In the crosstable above, we can see the matchups which were responsible for the surprises.

#botraceoverallvTvPvZvR
1Locutusprotoss91.42%98%79%96%99%
2PurpleWaveprotoss85.54%98%68%88%98%
3BananaBrainprotoss68.81%79%47%80%84%
4DaQinprotoss63.33%93%30%62%78%
5Steamhammerzerg52.25%78%28%42%91%
6ZZZKBotzerg52.08%69%35%42%90%
7Microwavezerg50.67%68%25%66%82%
8Ironterran49.25%73%23%55%90%
9XiaoYiterran48.62%91%21%45%73%
10McRaveprotoss40.00%63%26%28%43%
11UAlbertaBotrandom28.04%50%20%12%-
12AITPterran11.62%20%4%13%25%
13BunkerBoxeRterran8.08%17%7%1%11%

We also see that XiaoYi annihilated terran opponents, even though it came in a little below Iron overall. Comparing to last year’s results, XiaoYi’s parent bot SAIDA wiped the floor with terrans even harder.

Next: The voluminous per-map crosstables.

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