CIG 2017 race balance
I thought of another CIG 2017 analysis I wanted to do. Here is how bots of each race scored in the tournament.
| race | score |
|---|---|
| terran | 43% |
| protoss | 55% |
| zerg | 48% |
| random | 68% |
The table is not interesting in itself. It reflects the participants more than anything else. Random bots scored well because the 2 random players both finished high; terran scored poorly because 5 of the 7 terrans were in the bottom half. But the numbers make it easier to interpret the next table, which is how each bot scored against opponents of each race.
| bot | race | overall | vT | vP | vZ | vR |
|---|---|---|---|---|---|---|
| ZZZKBot | zerg | 75.43% | 80% | 75% | 74% | 67% |
| tscmoo | random | 73.50% | 81% | 74% | 67% | 54% |
| PurpleWave | protoss | 66.51% | 69% | 74% | 69% | 36% |
| LetaBot | terran | 62.75% | 63% | 55% | 76% | 40% |
| UAlbertaBot | random | 61.67% | 69% | 51% | 64% | 46% |
| MegaBot | protoss | 61.06% | 75% | 45% | 59% | 52% |
| Overkill | zerg | 59.65% | 74% | 45% | 63% | 37% |
| CasiaBot | zerg | 58.32% | 56% | 60% | 65% | 47% |
| Ziabot | zerg | 58.49% | 70% | 51% | 60% | 32% |
| Iron | terran | 58.11% | 65% | 54% | 57% | 50% |
| AIUR | protoss | 56.73% | 71% | 53% | 50% | 35% |
| McRave | protoss | 47.20% | 53% | 42% | 52% | 23% |
| Tyr | protoss | 45.32% | 48% | 36% | 49% | 43% |
| SRbotOne | terran | 45.24% | 54% | 35% | 50% | 29% |
| TerranUAB | terran | 38.58% | 55% | 27% | 42% | 10% |
| Bonjwa | terran | 33.04% | 42% | 30% | 35% | 10% |
| Bigeyes | terran | 30.90% | 31% | 32% | 33% | 24% |
| OpprimoBot | terran | 31.90% | 41% | 24% | 37% | 10% |
| Sling | zerg | 26.07% | 28% | 26% | 28% | 15% |
| Salsa | zerg | 9.52% | 9% | 10% | 10% | 7% |
The “versus random” column means versus Tscmoo and UAlbertaBot, so the other columns tell us more about strategy. We can see that Tscmoo was a little weak versus zerg, but LetaBot was strong. MegaBot and Overkill struggled versus protoss, while CasiaBot was strong against protoss and had some trouble against terran.
I don’t think it’s necessary to normalize the table entries to draw conclusions from them. But if people are interested, I could produce a version of the table with the score of each race (in the first table above) effectively subtracted out so that we can see how each bot did against each race relative to how well it “should” have.
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