SSCAIT scores - summary by bot
I’m looking into the scores recorded in the SSCAIT game data, which I have up to 27 September. So far I haven’t found anything too interesting, but it’s not entirely useless either.
According to the SSCAIT rules, a player’s score is the sum of units killed plus buildings razed: BWAPI::Player::getKillScore() + BWAPI::Player::getRazingScore()
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Here’s basic score information for the dates between 17 August 2016 and 27 September 2016. It’s the same date range I used in the SSCAIT crosstables, chosen so as not to smear too many different bot versions into one table. The difference and ratio columns are all arithmetic means. They give the difference or the ratio between the winner’s and loser’s scores. (Well, the loss score ratio is the ratio between the loser’s and winner’s scores, to make it easier to compare by eye.) Games in which either side had a score of 0 (no kills) or -100 (crash) are left out.
bot | games | win % | mean score | mean win score | mean loss score | win score diff | loss score diff | win score ratio | loss score ratio |
---|---|---|---|---|---|---|---|---|---|
krasi0 | 298 | 86.91% | 54850 | 55676 | 49368 | 45776 | 9306 | 15.58 | 3.06 |
Iron bot | 242 | 80.17% | 23967 | 24835 | 20459 | 18644 | -7699 | 16.77 | 4.06 |
Marian Devecka | 211 | 92.42% | 21120 | 22213 | 7798 | 9442 | -29578 | 8.97 | 8.94 |
Martin Rooijackers | 260 | 79.23% | 25077 | 28722 | 11170 | 22046 | -27686 | 9.14 | 13.59 |
tscmooz | 247 | 73.68% | 20322 | 25134 | 6847 | 12118 | -15203 | 9.84 | 10.71 |
tscmoo | 272 | 71.69% | 35035 | 41591 | 18435 | 23035 | -24015 | 5.49 | 6.53 |
LetaBot CIG 2016 | 256 | 73.05% | 26635 | 30229 | 16896 | 22995 | -21810 | 9.03 | 4.24 |
WuliBot | 234 | 65.81% | 8441 | 8627 | 8084 | 6814 | -18057 | 17.22 | 12.69 |
Simon Prins | 224 | 65.62% | 24256 | 28058 | 16998 | 21971 | -12391 | 17.68 | 12.18 |
ICELab | 242 | 66.53% | 36286 | 43099 | 22745 | 28818 | -26350 | 6.47 | 10.22 |
Sijia Xu | 236 | 63.98% | 12119 | 14598 | 7715 | 9913 | -23460 | 15.79 | 8.28 |
LetaBot SSCAI 2015 Final | 236 | 64.41% | 19400 | 24725 | 9764 | 18854 | -17427 | 11.04 | 5.24 |
Dave Churchill | 239 | 56.49% | 6656 | 8412 | 4377 | 6394 | -16370 | 17.41 | 11.89 |
Chris Coxe | 175 | 57.71% | 2989 | 3886 | 1763 | 3468 | -3798 | 23.59 | 6.49 |
Tomas Vajda | 230 | 64.78% | 34027 | 39627 | 23727 | 34048 | -19234 | 36.52 | 5.11 |
Flash | 251 | 64.14% | 11435 | 13872 | 7075 | 8335 | -25305 | 7.78 | 9.89 |
Zia bot | 236 | 51.69% | 13082 | 17640 | 8205 | 8544 | -15518 | 12.12 | 7.28 |
PeregrineBot | 131 | 51.91% | 3560 | 5415 | 1558 | 4651 | -6587 | 22.77 | 12.65 |
tscmoop | 258 | 52.33% | 15653 | 23128 | 7448 | 8055 | -30718 | 9.95 | 14.85 |
Andrew Smith | 257 | 56.03% | 15896 | 18391 | 12717 | 12180 | -26508 | 8.84 | 7.42 |
Florian Richoux | 223 | 53.36% | 14775 | 22804 | 5588 | 8518 | -22805 | 6.30 | 11.30 |
Carsten Nielsen | 275 | 50.91% | 10147 | 12121 | 8100 | 8140 | -20057 | 11.68 | 6.89 |
Soeren Klett | 228 | 45.18% | 39869 | 57319 | 25490 | 43732 | -12973 | 9.32 | 11.69 |
Jakub Trancik | 204 | 45.10% | 15552 | 15965 | 15212 | 11133 | 2909 | 14.55 | 6.41 |
Tomas Cere | 281 | 44.13% | 18317 | 32027 | 7488 | 18054 | -28958 | 7.13 | 16.25 |
MegaBot | 185 | 55.68% | 16661 | 22464 | 9372 | 16042 | -17473 | 9.21 | 16.45 |
Aurelien Lermant | 288 | 37.15% | 18580 | 36669 | 7886 | -12822 | -22641 | 1.25 | 12.13 |
Odin2014 | 131 | 46.56% | 12552 | 16757 | 8887 | 8998 | -16757 | 16.23 | 11.67 |
Gaoyuan Chen | 258 | 39.53% | 11417 | 16855 | 7861 | 8691 | -26698 | 7.13 | 13.62 |
DAIDOES | 101 | 27.72% | 10794 | 23254 | 6015 | 17054 | -10093 | 12.43 | 13.22 |
Igor Lacik | 105 | 35.24% | 13186 | 28378 | 4920 | 14545 | -14483 | 7.66 | 7.40 |
Matej Istenik | 260 | 29.62% | 17509 | 32652 | 11137 | 19086 | -11509 | 7.54 | 7.64 |
NUS Bot | 96 | 32.29% | 7069 | 12885 | 4295 | 8953 | -14754 | 8.73 | 13.61 |
Roman Danielis | 244 | 22.95% | 19086 | 43268 | 11883 | 20858 | -27151 | 3.83 | 10.30 |
ZerGreenBot | 6 | 66.67% | 9762 | 13554 | 2180 | 2291 | -8045 | 3.44 | 10.87 |
Ian Nicholas DaCosta | 116 | 16.38% | 4048 | 10358 | 2812 | 6223 | -8795 | 7.56 | 12.45 |
AwesomeBot | 115 | 26.96% | 8100 | 18712 | 4184 | 9280 | -16898 | 3.11 | 20.40 |
Johan Kayser | 249 | 17.67% | 12965 | 35732 | 8078 | 23303 | -10207 | 10.87 | 11.14 |
Martin Vlcak | 102 | 31.37% | 11386 | 20558 | 7194 | 10658 | -15459 | 12.34 | 14.06 |
Rob Bogie | 59 | 50.85% | 12777 | 16248 | 9186 | 9555 | -22662 | 13.03 | 4.92 |
Christoffer Artmann | 197 | 17.26% | 7712 | 24928 | 4121 | 15025 | -15194 | 4.94 | 16.67 |
Marek Gajdos | 72 | 11.11% | 5187 | 16426 | 3782 | 12820 | -9671 | 5.80 | 16.60 |
Travis Shelton | 106 | 16.98% | 7942 | 17816 | 5922 | 9374 | -9455 | 3.51 | 17.10 |
Bjorn P Mattsson | 190 | 18.95% | 4995 | 14730 | 2719 | 7300 | -15237 | 3.99 | 28.59 |
Vladimir Jurenka | 142 | 28.17% | 9561 | 15333 | 7297 | 8970 | -11691 | 5.60 | 7.47 |
neverdieTRX | 137 | 14.60% | 8493 | 21777 | 6222 | 13883 | -10783 | 4.55 | 11.22 |
OpprimoBot | 218 | 12.39% | 15672 | 25258 | 14317 | 17671 | -5654 | 20.18 | 13.84 |
Sungguk Cha | 122 | 25.41% | 15263 | 34072 | 8855 | 21740 | -11468 | 6.50 | 8.50 |
Jacob Knudsen | 92 | 19.57% | 7285 | 19250 | 4375 | 11994 | -12193 | 5.08 | 18.43 |
HoangPhuc | 12 | 91.67% | 15148 | 16316 | 2300 | 9550 | -22345 | 3.14 | 10.72 |
ButcherBoy | 15 | 6.67% | 1777 | 5655 | 1500 | 3105 | -6543 | 2.22 | 18.09 |
The most striking point is that Krasi0 was ahead in points, on average, in the games that it lost. So was Jakub Trancik’s cannon bot. The data that I have does not record the cause of losses. It’s perfectly possible to lose while ahead on points, when you fight efficiently and destroy masses of enemy stuff before dying. But you may also be ahead on points when you crash or overstep the time limit.
Score increases as the game goes on. I think that the score diff columns mostly tell us how long the games were. So, for example, Marian Devecka’s Killerbot often won short games and hung on through a long fight in lost games. The score ratio columns seem more informative about how far ahead the winner was at the end of the game. Killerbot tended to win with about the same point ratio that it lost with. Krasi0 won with a huge point ratio and lost with a small ratio, which might reflect its defensive style. Iron, which is super-aggressive, also won with a huge ratio and lost with a small ratio, which in its case might mean that it won after a long series of pinpricks or lost in a sudden collapse.
The numbers are not easy to interpret! But they must mean something.
Tomorrow: I’ll try to dig out something about the rate of failing to start up.
Comments
krasi0 on :
OTOH, the A Lermant case appears to be rather interesting: WSR 1.25, LSR 12.13. Does that mean that it's always on the verge of losing even when it wins?
Jay Scott on :
Jay Scott on :
Jay Scott on :
Jay Scott on :
krasi0 on :
Jay Scott on :
krasi0 on :