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SSCAIT scores - raw data for yesterday’s graph

Here is a csv file of rating differences and score ratios. It contains all the data that went into yesterday’s graph, plus the names of the opponents so we can see which bots are behind each dot. It is over 4MB for 80,798 games, hardly Big Data territory but not something you want to look through by eye either.

Krasi0 asked about the outliers in the upper left, where the weaker bot upset the stronger with a huge score ratio. I pulled out the 44 games where the weaker bot was at least 50 Elo behind and won with a score ratio of 100 or more. There’s quite a variety of bots and it looks like most of the losers are genuine strong opponents, but we can’t tell from this why they lost so badly.

winnerloserrating_diffscore_ratio
Odin2014Soeren Klett-121.241105546074102
Maja NemsilajovaGaoyuan Chen-87.5441567081255133.75
EradicatumXVRMartin Rooijackers-176.159988871814134.75
Florian RichouxAndrew Smith-75.4167275722925139.75
EradicatumXVRMartin Rooijackers-160.787872111647123.6
Jakub Tranciktscmooz-234.397880730261107.85
EradicatumXVRTomas Vajda-310.393156864715238.75
Odin2014tscmoo-341.595996071025111.2
Jakub TrancikKrasimir Krystev-206.879443535575118.5
Martin RooijackersTomas Vajda-185.169798041552178
Martin RooijackersTomas Vajda-196.771893565481196.5
Jakub TrancikFlorian Richoux-201.533152887389146
Martin RooijackersTomas Vajda-214.947508799781194.5
EradicatumXVRSoeren Klett-59.609552771464121.625
Martin RooijackersTomas Vajda-197.317290128813173.75
EradicatumXVRMartin Rooijackers-153.322771550638134
NUS BotTomas Cere-217.342932814072102.25
EradicatumXVRMartin Rooijackers-299.534513717939140
Martin RooijackersTomas Vajda-177.764917764824171.5
Jakub Tranciktscmoo-114.300240687596113.25
Martin RooijackersTomas Vajda-261.726998366137185.5
Tomas VajdaICELab-61.7649472971907110.3
Maja NemsilajovaGaoyuan Chen-444.702632047678148.5
Jakub TrancikAurelien Lermant-96.3561952340121144
Adrian SternmullerSerega-165.832601805773127.6
Ian Nicholas DaCostaTomas Cere-180.057678684451109.25
OpprimoBotRadim Bobek-107.746261964435187.75
Martin RooijackersTomas Vajda-276.603234707109108.2
Florian RichouxMartin Rooijackers-117.736046527607126
Igor LacikGaoyuan Chen-94.2338975266132121.75
Florian RichouxAndrew Smith-186.917595581173123.25
Radim BobekUPStarcraftAI-62.9693177806437136.5
Tomas VajdaAndrew Smith-79.8133863519054152.771739130435
Jakub TrancikFlorian Richoux-84.9235344515139128
Martin RooijackersTomas Vajda-78.9815583248117207
Jakub TrancikPeregrineBot-69.7933587952098121.25
Dave ChurchillAndrew Smith-94.5789654176522180.75
Tomas VajdaWuliBot-52.5760915924302182.5
Soeren KlettDave Churchill-155.311160590808361.25
Odin2014PeregrineBot-94.3129452431417124.55
DAIDOESMegaBot-178.609296376345115.875
OpprimoBotMartin Rooijackers-786.274344230815307.75
Soeren KlettDave Churchill-68.2015666358432109

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krasi0 on :

Hmmm, the situations are very diverse. I honestly have no clue what could have happened. In such cases, it may be the safest option to assume a late game crash (of the stronger bot) of some sort or a tournament manager issue.

tscmoo on :

I think most of these are software bugs in the stronger opponent :D

Jay Scott on :

They could be cases of overstepping the time limit. If my interpretation is right, there’s no way to recognize time infractions in the data.

Nathan Gamble on :

When is this dataset from? It can't be the one you'd been working on in the previous few posts (17th Aug 2016 to 17th Sep 2016) because about half of these bots were deactivated about a year ago.

Jay Scott on :

It is the full SSCAIT data set that Krasi0 sent me, recording games from 25 Dec 2013 to 27 Sept 2016. For this analysis it didn’t make sense to drop old data.

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