Add loss minimization rating algorithm with data and results
This commit is contained in:
posij118
parent
4e459c4888
commit
11ec939510
4 changed files with 25985 additions and 1 deletions
25134
data/games.json
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25134
data/games.json
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468
results/min_loss_rating_list.json
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468
results/min_loss_rating_list.json
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@ -0,0 +1,468 @@
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{
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"players": [
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{
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"id": 1,
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"rating": 1685.2675415133688,
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"username": "asaelr",
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"\u03c3": 66.31240370576553
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},
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{
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"id": 6,
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"rating": 1431.613182299928,
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"username": "ElenaDhynho",
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"\u03c3": 29.98866321273064
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},
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{
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"id": 7,
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"rating": 1567.0774916128976,
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"username": "Eliclax",
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"\u03c3": 126.30005538960775
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},
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{
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"id": 8,
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"rating": 1712.039271618434,
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"username": "Fafrd",
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"\u03c3": 59.80163231910586
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},
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{
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"id": 9,
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"rating": 1361.5113611693616,
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"username": "Feich59",
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"\u03c3": 144.39407407480272
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},
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{
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"id": 10,
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"rating": 1579.6672353819206,
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"username": "gw12346",
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"\u03c3": 270.3079683864935
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},
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{
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"id": 11,
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"rating": 1423.2932864211616,
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"username": "hakha",
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"\u03c3": 85.28363174484276
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},
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{
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"id": 12,
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"rating": 1872.6154944614254,
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"username": "Helana",
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"\u03c3": 143.85959224548074
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},
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{
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"id": 13,
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"rating": 1527.081117882728,
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"username": "hnter",
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"\u03c3": 86.70665567737284
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},
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{
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"id": 14,
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"rating": 1659.6782951052335,
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"username": "inseres",
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"\u03c3": 54.91823796429652
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},
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{
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"id": 15,
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"rating": 1742.9546724334089,
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"username": "Jay",
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"\u03c3": 193.84935304947757
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},
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{
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"id": 16,
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"rating": 1667.9465689249616,
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"username": "kimbifille",
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"\u03c3": 15.161265503279141
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},
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{
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"id": 17,
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"rating": 1649.333225086678,
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"username": "Kowalski1337",
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"\u03c3": 235.3362393234084
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},
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{
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"id": 18,
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"rating": 1677.9132076067115,
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"username": "macanek",
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"\u03c3": 208.0527366434835
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},
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{
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"id": 19,
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"rating": 1836.1454620500044,
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"username": "MarkusKahlsen",
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"\u03c3": 297.71026337163414
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},
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{
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"id": 20,
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"rating": 1605.4748903814996,
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"username": "Neema",
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"\u03c3": 119.12840085642274
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},
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{
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"id": 21,
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"rating": 1552.9114478185877,
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"username": "newduke",
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"\u03c3": 109.77086600282932
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},
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{
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"id": 22,
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"rating": 1224.413480638347,
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"username": "NishaNoire",
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"\u03c3": 261.66467716062857
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},
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{
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"id": 25,
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"rating": 1484.5905774236187,
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"username": "omegaxis",
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"\u03c3": 216.00665850825928
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},
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{
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"id": 27,
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"rating": 1802.0629338345077,
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"username": "pianoblook",
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"\u03c3": 123.37987103512832
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},
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{
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"id": 28,
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"rating": 1664.2567844629314,
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"username": "posij118",
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"\u03c3": 80.37917530567991
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},
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{
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"id": 29,
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"rating": 1702.5018997700438,
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"username": "purplejoe",
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"\u03c3": 100.77153063473354
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},
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{
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"id": 31,
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"rating": 1892.418588622356,
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"username": "rahsosprout",
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"\u03c3": 104.5777908934287
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},
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{
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"id": 32,
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"rating": 1835.7346031461793,
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"username": "Ramanujan",
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"\u03c3": 95.89853187628516
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},
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{
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"id": 33,
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"rating": 1443.2077282559123,
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"username": "ricardodd",
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"\u03c3": 39.34358984367649
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},
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{
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"id": 34,
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"rating": 1040.187091444465,
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"username": "RIMBarisax",
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"\u03c3": 366.4360349731977
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},
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{
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"id": 35,
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"rating": 1837.7815078205804,
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"username": "rz",
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"\u03c3": 199.78453636449262
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},
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{
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"id": 36,
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"rating": 1696.3214849037167,
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"username": "Sagnik Saha",
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"\u03c3": 136.01387381633026
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},
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{
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"id": 37,
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"rating": 1605.7373915962864,
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"username": "sodiumdebt",
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"\u03c3": 181.85538044442802
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},
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{
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"id": 38,
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"rating": 2117.1508966520855,
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"username": "spring",
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"\u03c3": 57.24559998584215
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},
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{
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"id": 39,
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"rating": 1512.0428621984959,
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"username": "StKildaFan",
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"\u03c3": 123.53879720946766
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},
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{
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"id": 40,
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"rating": 1688.979579509527,
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"username": "str8tsknacker",
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"\u03c3": 139.30078280466165
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},
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{
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"id": 41,
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"rating": 1773.724951734035,
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"username": "Sturm",
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"\u03c3": 116.05248815088189
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},
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{
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"id": 42,
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"rating": 1510.323393769054,
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"username": "TimeHoodie",
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"\u03c3": 105.6566442712854
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},
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{
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"id": 43,
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"rating": 1719.6120753928153,
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"username": "vEnhance",
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"\u03c3": 256.98388483269576
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},
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{
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"id": 44,
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"rating": 1605.1854946464819,
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"username": "vermling",
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"\u03c3": 39.05915568336428
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},
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{
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"id": 45,
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"rating": 1842.1832125075996,
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"username": "wateroffire",
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"\u03c3": 78.80111543154953
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},
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{
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"id": 46,
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"rating": 1603.6026225502553,
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"username": "WillFlame",
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"\u03c3": 367.7052871728946
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},
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{
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"id": 47,
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"rating": 1747.3129095523243,
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"username": "Yagami Black",
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"\u03c3": 124.1498189921402
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},
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{
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"id": 48,
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"rating": 1249.7131073005326,
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"username": "youisme",
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"\u03c3": 119.28343113428912
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},
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{
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"id": 51,
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"rating": 1252.822074342758,
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"username": "Libster",
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"\u03c3": 262.48847926040565
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},
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{
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"id": 52,
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"rating": 1710.980099683423,
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"username": "maxeymo",
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"\u03c3": 61.64513100559617
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},
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{
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"id": 53,
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"rating": 1784.677252501476,
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"username": "joano580",
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"\u03c3": 290.4564102793344
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},
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{
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"id": 54,
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"rating": 1483.9387182979765,
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"username": "ReaverSe",
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"\u03c3": 67.78852340589508
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},
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{
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"id": 55,
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"rating": 1739.6678580930318,
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"username": "benzloeb",
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"\u03c3": 153.68915774257988
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},
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{
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"id": 56,
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"rating": 1554.8486203468085,
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"username": "percolate",
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"\u03c3": 168.9058732288808
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},
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{
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"id": 58,
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"rating": 1454.7815820698524,
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"username": "Random Guy JCI",
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"\u03c3": 14.92058859287638
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},
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{
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"id": 59,
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"rating": 1635.1817290999422,
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"username": "aara",
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"\u03c3": 113.14163640238154
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},
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{
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"id": 60,
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"rating": 1541.843324991505,
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"username": "amattias",
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"\u03c3": 173.9589588268638
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},
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{
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"id": 64,
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"rating": 1559.44040061339,
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"username": "wtfitsnotbutter",
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"\u03c3": 240.16937231126366
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},
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{
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"id": 65,
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"rating": 1441.4652215451595,
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"username": "Alix_Eisenhardt",
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"\u03c3": 125.35709305406003
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},
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{
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"id": 67,
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"rating": 1640.3718839938117,
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"username": "Vivarus",
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"\u03c3": 121.95895258848924
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},
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{
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"id": 69,
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"rating": 1582.1212409759808,
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"username": "FrozenStella",
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"\u03c3": 145.14226927184038
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},
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{
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"id": 70,
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"rating": 1557.096282467627,
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"username": "GameConqueror",
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"\u03c3": 99.69687144224555
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},
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{
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"id": 71,
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"rating": 1662.4377474617993,
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"username": "seungapark",
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"\u03c3": 145.15724952841202
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},
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{
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"id": 72,
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"rating": 1508.3443601141673,
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"username": "sinuni_hung",
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"\u03c3": 61.81213974983676
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},
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{
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"id": 73,
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"rating": 1803.5327953459553,
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"username": "TheDaniMan",
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"\u03c3": 134.82661666333226
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},
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{
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"id": 79,
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"rating": 1482.0644773366396,
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"username": "Kaznad",
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"\u03c3": 405.28035311445757
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},
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{
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"id": 80,
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"rating": 1086.1355090178276,
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"username": "Kernel",
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"\u03c3": 211.39242310002254
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},
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{
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"id": 81,
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"rating": 1290.2977493468493,
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"username": "Le Codex",
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"\u03c3": 128.6528429208025
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},
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{
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"id": 82,
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"rating": 1554.5865377995306,
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"username": "Nipalup",
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"\u03c3": 186.7194364889647
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}
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],
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"variants": [
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{
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"id": 0,
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"rating": 1344.571218042761,
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"num_players": 3,
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"name": "No Variant",
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"num_suits": 5,
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"\u03c3": 31.335534554470627
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},
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{
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"id": 1,
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"rating": 1380.3108266495492,
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"num_players": 3,
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"name": "6 Suits",
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"num_suits": 6,
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"\u03c3": 33.55653633693724
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},
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{
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"id": 51,
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"rating": 1751.8509769680584,
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"num_players": 3,
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"name": "Clue Starved (6 Suits)",
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"num_suits": 6,
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"\u03c3": 11.424872579830076
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},
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{
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"id": 52,
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"rating": 1677.1056853935763,
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"num_players": 3,
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"name": "Clue Starved (5 Suits)",
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"num_suits": 5,
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"\u03c3": 40.42763640571963
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},
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{
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"id": 0,
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"rating": 1436.6029781659563,
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"num_players": 4,
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"name": "No Variant",
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"num_suits": 5,
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"\u03c3": 45.77891871651097
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},
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{
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"id": 1,
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"rating": 1408.254084838167,
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"num_players": 4,
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"name": "6 Suits",
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"num_suits": 6,
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"\u03c3": 14.953524984939428
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},
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{
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"id": 51,
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"rating": 1820.7964465875705,
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"num_players": 4,
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"name": "Clue Starved (6 Suits)",
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"num_suits": 6,
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"\u03c3": 81.2114821701358
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},
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{
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"id": 52,
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"rating": 1719.526642788024,
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"num_players": 4,
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"name": "Clue Starved (5 Suits)",
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"num_suits": 5,
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"\u03c3": 38.5663698641606
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},
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{
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"id": 0,
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"rating": 1373.7429515570884,
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"num_players": 5,
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"name": "No Variant",
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"num_suits": 5,
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"\u03c3": 72.00085092075848
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},
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{
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"id": 1,
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"rating": 1522.642598263524,
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"num_players": 5,
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"name": "6 Suits",
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"num_suits": 6,
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"\u03c3": 36.55838248550461
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},
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{
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"id": 51,
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"rating": 1895.801292104732,
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"num_players": 5,
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"name": null,
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"num_suits": 6,
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"\u03c3": 14.915817466505452
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},
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{
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"id": 52,
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"rating": 1968.4174574449692,
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"num_players": 5,
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"name": "Clue Starved (5 Suits)",
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"num_suits": 5,
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"\u03c3": 50.09621953978423
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}
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]
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}
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@ -28,7 +28,7 @@ DB_TABLE_NAMES = [
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DATABASE_SCHEMA_PATH = 'install/database_schema.sql'
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DEFAULT_DB_CONFIG_PATH = 'install/default_db_config.yaml'
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DEFAULT_CONFIG_PATH = 'install/default_config.yaml'
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DEFAULT_CONFIG_PATH = '../install/default_config.yaml'
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VARIANTS_JSON_URL = 'https://raw.githubusercontent.com/Hanabi-Live/hanabi-live/main/packages/game/src/json/variants.json'
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382
src/minimize_loss.py
Normal file
382
src/minimize_loss.py
Normal file
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from typing import Iterable, List
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from config import config_manager
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from constants import UNWINNABLE_SEED_FRACTION
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from dataclasses import dataclass
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import json
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import datetime
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import numpy as np
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from numpy.typing import NDArray
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from scipy.optimize import minimize, Bounds
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MAX_RATING_DIFF = 1200
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RATING_PRIOR = 1600
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# This allows us to do integer optimization on more fine-grained grid.
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LCM_FACTOR = 60
|
||||
NUM_RANDOM_INITS = 50
|
||||
NUM_CV_ITERS = 50
|
||||
|
||||
VARIANT_DEFAULT_RATINGS = np.array([1400, 1400, 1800, 1800]).reshape((1, 4))
|
||||
VARIANT_NUM_PLAYERS_MODIFIERS = np.array([0, 0, 100]).reshape((3, 1))
|
||||
|
||||
# As of season 1, this is [1400 1400 1800 1800 1400 1400 1800 1800 1500 1500 1900 1900].
|
||||
VARIANT_RATING_PRIORS = np.ravel(
|
||||
VARIANT_DEFAULT_RATINGS + VARIANT_NUM_PLAYERS_MODIFIERS
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class GlobalInfo:
|
||||
rated_ids: Iterable[int]
|
||||
variant_ids: Iterable[int]
|
||||
player_counts: Iterable[int]
|
||||
user_names: Iterable[str]
|
||||
game_counts: Iterable[int]
|
||||
|
||||
@property
|
||||
def rating_list_length(self):
|
||||
return len(self.rated_ids) + len(self.variant_ids) * len(self.player_counts)
|
||||
|
||||
@property
|
||||
def rated_id_indices_in_rating_list(self):
|
||||
return {id: index for index, id in dict(enumerate(self.rated_ids)).items()}
|
||||
|
||||
|
||||
@dataclass
|
||||
class GameRow:
|
||||
game_id: int
|
||||
num_players: int
|
||||
users: List[str]
|
||||
user_ids: List[int]
|
||||
user_rating_changes: List[float]
|
||||
user_ratings_after: List[float]
|
||||
variant_id: int
|
||||
variant_name: str
|
||||
num_suits: id
|
||||
rating_type: int
|
||||
seed: str
|
||||
score: int
|
||||
num_turns: int
|
||||
datetime_finished: datetime.datetime
|
||||
league_id: int
|
||||
num_bdrs: int
|
||||
num_crits_lost: int
|
||||
game_outcomes: List[str]
|
||||
variant_rating_change: float
|
||||
variant_rating_after: float
|
||||
|
||||
|
||||
def calculate_loss(
|
||||
rating_list: Iterable[int],
|
||||
game_list: Iterable[GameRow],
|
||||
global_info: GlobalInfo,
|
||||
p_win_lookup_table: NDArray[np.float32],
|
||||
player_rating_priors: Iterable[int],
|
||||
player_prior_weight: float,
|
||||
variant_rating_priors: Iterable[int],
|
||||
variant_prior_weight: float,
|
||||
):
|
||||
loss = np.float32(0)
|
||||
|
||||
for game in game_list:
|
||||
team_ratings = [
|
||||
rating_list[global_info.rated_id_indices_in_rating_list[user_id]]
|
||||
for user_id in game["user_ids"]
|
||||
]
|
||||
team_rating = sum(team_ratings) / len(team_ratings)
|
||||
|
||||
variant_rating = rating_list[
|
||||
calculate_variant_index_in_rating_list(
|
||||
len(game["users"]), game["variant_id"], global_info
|
||||
)
|
||||
]
|
||||
|
||||
rating_diff = int(np.round(LCM_FACTOR * (team_rating - variant_rating)))
|
||||
p_win = calculate_p_win(rating_diff, p_win_lookup_table)
|
||||
|
||||
game_won = game["game_outcomes"].count("Win") > 0
|
||||
loss += calculate_loss_for_one_game(p_win, game_won)
|
||||
|
||||
# Add two dummy single-player games for each player with prior_weight * square-root of number of games weight - one won and lost
|
||||
for player_rating_prior, rated_id, game_count in zip(
|
||||
player_rating_priors, global_info.rated_ids, global_info.game_counts
|
||||
):
|
||||
team_rating = rating_list[global_info.rated_id_indices_in_rating_list[rated_id]]
|
||||
variant_rating = player_rating_prior
|
||||
|
||||
rating_diff = int(np.round(LCM_FACTOR * (team_rating - variant_rating)))
|
||||
p_win = calculate_p_win(rating_diff, p_win_lookup_table)
|
||||
|
||||
loss += (
|
||||
np.sqrt(game_count)
|
||||
* player_prior_weight
|
||||
* calculate_loss_for_one_game(p_win, True)
|
||||
)
|
||||
loss += (
|
||||
np.sqrt(game_count)
|
||||
* player_prior_weight
|
||||
* calculate_loss_for_one_game(p_win, False)
|
||||
)
|
||||
|
||||
# Add two dummy single-player games for each player with prior_weight * square-root of number of games weight - one won and lost
|
||||
for variant_rating, variant_rating_prior in zip(
|
||||
rating_list[len(global_info.rated_ids) :],
|
||||
variant_rating_priors,
|
||||
):
|
||||
team_rating = variant_rating_prior
|
||||
|
||||
rating_diff = int(np.round(LCM_FACTOR * (team_rating - variant_rating)))
|
||||
p_win = calculate_p_win(rating_diff, p_win_lookup_table)
|
||||
|
||||
loss += (
|
||||
np.sqrt(game_count)
|
||||
* variant_prior_weight
|
||||
* calculate_loss_for_one_game(p_win, True)
|
||||
)
|
||||
loss += (
|
||||
np.sqrt(game_count)
|
||||
* variant_prior_weight
|
||||
* calculate_loss_for_one_game(p_win, False)
|
||||
)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
def calculate_loss_gradient(rating_list, *args):
|
||||
y = calculate_loss(rating_list, *args)
|
||||
eye = np.eye(len(rating_list), dtype=int)
|
||||
|
||||
gradient = [
|
||||
calculate_loss(rating_list + eye[i], *args) - y for i in range(len(rating_list))
|
||||
]
|
||||
|
||||
return gradient
|
||||
|
||||
|
||||
def calculate_minloss_ratings(
|
||||
game_list: List[GameRow],
|
||||
global_info: GlobalInfo,
|
||||
p_win_lookup_table: NDArray[np.float32],
|
||||
player_rating_priors: int | Iterable[int] = RATING_PRIOR,
|
||||
player_random_init_stdev: int = 0,
|
||||
player_prior_weight=0.5,
|
||||
variant_rating_priors: int | Iterable[int] = RATING_PRIOR,
|
||||
variant_prior_weight=0.5,
|
||||
variant_random_init_stdev: int = 0,
|
||||
):
|
||||
player_rating_priors = np.broadcast_to(player_rating_priors, len(rated_ids))
|
||||
variant_rating_priors = np.broadcast_to(
|
||||
variant_rating_priors, len(variant_ids) * len(player_counts)
|
||||
)
|
||||
|
||||
prior_rating_list = np.concatenate(
|
||||
(
|
||||
player_rating_priors
|
||||
+ np.random.normal(0, player_random_init_stdev, len(player_rating_priors)),
|
||||
variant_rating_priors
|
||||
+ np.random.normal(
|
||||
0, variant_random_init_stdev, len(variant_rating_priors)
|
||||
),
|
||||
),
|
||||
dtype=np.float32,
|
||||
)
|
||||
|
||||
rating_list = minimize(
|
||||
calculate_loss,
|
||||
prior_rating_list,
|
||||
(
|
||||
game_list,
|
||||
global_info,
|
||||
p_win_lookup_table,
|
||||
player_rating_priors,
|
||||
player_prior_weight,
|
||||
variant_rating_priors,
|
||||
variant_prior_weight,
|
||||
),
|
||||
bounds=Bounds(RATING_PRIOR - MAX_RATING_DIFF, RATING_PRIOR + MAX_RATING_DIFF),
|
||||
jac=calculate_loss_gradient,
|
||||
tol=0.0001,
|
||||
)
|
||||
|
||||
return rating_list
|
||||
|
||||
|
||||
def calculate_p_win_lookup_table(rating_diff_indices: Iterable[int]):
|
||||
lookup_table = np.full(2 * MAX_RATING_DIFF * LCM_FACTOR + 1, 0.5, dtype=np.float32)
|
||||
|
||||
for rating_diff_index in rating_diff_indices:
|
||||
p_win = calculate_p_win(rating_diff_index, None)
|
||||
lookup_table[rating_diff_index] = p_win
|
||||
|
||||
return lookup_table
|
||||
|
||||
|
||||
def calculate_p_win(
|
||||
rating_diff_index: int, lookup_table: NDArray[np.float32] | None = None
|
||||
):
|
||||
if lookup_table is None:
|
||||
return (1 - UNWINNABLE_SEED_FRACTION) * (
|
||||
1 / (1 + 10 ** (-(rating_diff_index / LCM_FACTOR) / 400))
|
||||
)
|
||||
else:
|
||||
return lookup_table[rating_diff_index]
|
||||
|
||||
|
||||
# Cross-entropy loss
|
||||
def calculate_loss_for_one_game(p_win: np.float32, game_won: bool):
|
||||
if game_won:
|
||||
loss = -np.log(p_win)
|
||||
else:
|
||||
loss = -np.log(1 - p_win)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
def calculate_variant_index_in_rating_list(
|
||||
player_count: int, variant_id: int, global_info: GlobalInfo
|
||||
):
|
||||
return (
|
||||
len(global_info.rated_ids)
|
||||
+ global_info.player_counts.index(player_count) * len(global_info.variant_ids)
|
||||
+ global_info.variant_ids.index(variant_id)
|
||||
)
|
||||
|
||||
|
||||
def calculate_player_count_and_variant_id_from_variant_index(
|
||||
index: int, global_info: GlobalInfo
|
||||
):
|
||||
num_players = global_info.player_counts[index // len(global_info.variant_ids)]
|
||||
variant_id = global_info.variant_ids[index % len(global_info.variant_ids)]
|
||||
|
||||
return num_players, variant_id
|
||||
|
||||
|
||||
def write_data(random_init_rating_lists, cv_rating_lists):
|
||||
with open("../results/min_loss_rating_list.json", "w") as f:
|
||||
rating_list = np.average(random_init_rating_lists, axis=0)
|
||||
rating_stdevs = np.std(cv_rating_lists, axis=0, ddof=1)
|
||||
|
||||
players = []
|
||||
for rated_id, user_name, rating, rating_stdev in zip(
|
||||
global_info.rated_ids, global_info.user_names, rating_list, rating_stdevs
|
||||
):
|
||||
players.append(
|
||||
{
|
||||
"id": rated_id,
|
||||
"rating": rating,
|
||||
"username": user_name,
|
||||
"stdev": rating_stdev,
|
||||
}
|
||||
)
|
||||
|
||||
variants = []
|
||||
|
||||
for i, (rating, rating_stdev) in enumerate(
|
||||
zip(
|
||||
rating_list[len(global_info.rated_ids) :],
|
||||
rating_stdevs[len(global_info.rated_ids) :],
|
||||
)
|
||||
):
|
||||
(
|
||||
num_players,
|
||||
variant_id,
|
||||
) = calculate_player_count_and_variant_id_from_variant_index(i, global_info)
|
||||
variant_name = None
|
||||
|
||||
for game in game_list:
|
||||
if (
|
||||
len(game["users"]) == num_players
|
||||
and game["variant_id"] == variant_id
|
||||
):
|
||||
variant_name = game["variant_name"]
|
||||
num_suits = game["num_suits"]
|
||||
|
||||
variants.append(
|
||||
{
|
||||
"id": variant_id,
|
||||
"rating": rating,
|
||||
"num_players": num_players,
|
||||
"name": variant_name,
|
||||
"num_suits": num_suits,
|
||||
"stdev": rating_stdev,
|
||||
}
|
||||
)
|
||||
|
||||
s = json.dumps({"players": players, "variants": variants})
|
||||
f.write(s)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
with open("../data/games.json") as game_data:
|
||||
game_list = json.loads(game_data.read())
|
||||
|
||||
p_win_lookup_table = calculate_p_win_lookup_table(
|
||||
np.arange(-MAX_RATING_DIFF * LCM_FACTOR, MAX_RATING_DIFF * LCM_FACTOR + 1)
|
||||
)
|
||||
|
||||
config = config_manager.get_config()
|
||||
|
||||
rated_ids_dupes = [ID for game in game_list for ID in game["user_ids"]]
|
||||
rated_ids = list(set(rated_ids_dupes))
|
||||
|
||||
variant_ids_dupes = [game["variant_id"] for game in game_list]
|
||||
variant_ids = list(set(variant_ids_dupes))
|
||||
|
||||
nums_players = [game["num_players"] for game in game_list]
|
||||
|
||||
player_counts = list(range(config.min_player_count, config.max_player_count + 1))
|
||||
game_counts = np.concatenate(
|
||||
[rated_ids_dupes.count(ID) for ID in rated_ids],
|
||||
np.zeros(len(variant_ids) * len(player_counts)),
|
||||
)
|
||||
|
||||
user_names = list(np.full((len(rated_ids),), ""))
|
||||
for game in game_list:
|
||||
for user_id, user_name in zip(game["user_ids"], game["users"]):
|
||||
user_names[rated_ids.index(user_id)] = user_name
|
||||
|
||||
global_info = GlobalInfo(
|
||||
rated_ids, variant_ids, player_counts, user_names, game_counts
|
||||
)
|
||||
|
||||
for variant_id, num_players in zip(variant_ids_dupes, nums_players):
|
||||
game_counts[
|
||||
len(rated_ids)
|
||||
+ calculate_variant_index_in_rating_list(
|
||||
num_players, variant_id, global_info
|
||||
)
|
||||
] += 1
|
||||
global_info.game_counts = game_counts
|
||||
|
||||
random_init_rating_lists = []
|
||||
cv_rating_lists = []
|
||||
|
||||
for _ in range(NUM_RANDOM_INITS):
|
||||
random_init_rating_lists.append(
|
||||
calculate_minloss_ratings(
|
||||
game_list,
|
||||
global_info,
|
||||
p_win_lookup_table,
|
||||
variant_rating_priors=VARIANT_RATING_PRIORS,
|
||||
player_random_init_stdev=200,
|
||||
variant_random_init_stdev=200,
|
||||
).x
|
||||
)
|
||||
|
||||
for _ in range(NUM_CV_ITERS):
|
||||
cv_game_list = np.random.choice(game_list, len(game_list), replace=True)
|
||||
cv_rating_lists.append(
|
||||
calculate_minloss_ratings(
|
||||
cv_game_list,
|
||||
global_info,
|
||||
p_win_lookup_table,
|
||||
variant_rating_priors=VARIANT_RATING_PRIORS,
|
||||
player_random_init_stdev=200,
|
||||
variant_random_init_stdev=200,
|
||||
).x
|
||||
)
|
||||
|
||||
write_data(random_init_rating_lists, cv_rating_lists)
|
||||
Loading…
Reference in a new issue