2018 TCO Marathon 3 - 2018 TCO Marathon Round 3

Marathon MatchTest CaseForecasting

Key Information

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Challenge Overview

Problem Statement

    

Introduction

You are an investor, looking to place your money in various funds. Different funds are managed by different experts, some of whom may be more or less trustworthy than others. Your simple goal is to make the most possible return on your investments, presumably by determining which experts you trust the most and purchasing their investments.

Method Description

You being with 1,000,000 worth of money. Your code will be called numPeriods times in succession. Each time it is called, you will be given five parameters:

  • int[] advice: The expected % rate of return each expert thinks their fund is going to attain over the next period: ranging from -100 to 100, where negative values indicate a fund predicted to lose money, positive values predict gaining money, and 0 predicts no change.
  • int[] recent: The actual gain or loss of what you invested in each expert's fund during the last period. Calculated as a product of the amount you've invested and the actual rate of return each fund experienced, rounded down to the nearest integer.
  • int money: The total amount of money you currently have for investment.
  • int timeLeft: The time left (in ms) for your solution to run.
  • int roundsLeft: The number of investment rounds remaining.
Your code should then return a int[] indicating how much of your money you'd like to invest with each expert for the next period. Each individual investment must be between 0 and 400,000 (inclusive), and the total must be no more than the total amount you currently hold. Note that all previously invested money is always returned to you before the next round, i.e. when you make an investment you do so only for a single round.

Data Generation

For each test case:

  • numExperts is chosen between 10 and 50, inclusive.
  • numPeriods is chosen between 10 and 100, inclusive.
  • For each expert:
    • stDev is chosen randomly in [0.0, 20.0).
    • accuracy is chosen randomly in [0.0, 1.0).

For each period

  • The actual performance of each fund is chosen as a Gaussian with mean 0 and stDev 0.1, capped to be within [-1.0, 1.0].
  • Each expert makes either an accurate or inaccurate prediction according to their accuracy probability:
    • An inaccurate prediction is a Gaussian with mean 0 and stDev 10, capped to be within [-100, 100].
    • An accurate prediction is the real performance summed with a Gaussian having stDev specific to that expert.

Scoring

Your raw score for a test case will be equal to the amount of money you have at the end of all periods in the simulation. Your overall score across all test cases will use relative scoring, with each test case being worth YOUR / BEST, where YOUR is your score on a test case, and BEST is the best score of any competitor on that test case. The relative scores will be summed, divided by the number of test cases, and scaled to 1,000,000 for the leaderboard.

Offline Tester

An offline tester for this contest is available here.

General Info

  • There are 10 example cases, and 100 provisional tests.
  • Time limit is 10s per test case.
  • Memory limit is 1GB.
 

Definition

    
Class:InvestmentAdvice
Method:getInvestments
Parameters:int[], int[], int, int, int
Returns:int[]
Method signature:int[] getInvestments(int[] advice, int[] recent, int money, int timeLeft, int roundsLeft)
(be sure your method is public)
    
 

Examples

0)
    
"1"
Returns: 
"Num Periods = 53
Expert 0: accuracy= 0.4074398012118764, stDev= 2.0094643265249768
Expert 1: accuracy= 0.6588672394716129, stDev= 0.724707643007354
Expert 2: accuracy= 0.15273620134152177, stDev= 14.214792551433202
Expert 3: accuracy= 0.5540723196744161, stDev= 3.1915610849456155
Expert 4: accuracy= 0.4870873748835076, stDev= 18.21900961908166
Expert 5: accuracy= 0.15933955153907886, stDev= 18.279257520293605
Expert 6: accuracy= 0.8644629883165224, stDev= 7.3800511677514375
Expert 7: accuracy= 0.5833538570984111, stDev= 10.785193374912703
Expert 8: accuracy= 0.7616746929019993, stDev= 0.8459666838487623
Expert 9: accuracy= 0.8792629643521901, stDev= 12.47945074431976
Expert 10: accuracy= 0.004852533415334981, stDev= 9.004368127950361
Expert 11: accuracy= 0.023418065070874627, stDev= 17.045071280936945
Expert 12: accuracy= 0.2750299495716141, stDev= 14.100199086076827
Expert 13: accuracy= 0.6105915393993295, stDev= 1.5077559782727956
Expert 14: accuracy= 0.2740690950060459, stDev= 19.098615409408076
Expert 15: accuracy= 0.0012985767526091374, stDev= 3.804044182511299
Expert 16: accuracy= 0.48625958231173216, stDev= 13.670530069185293
Expert 17: accuracy= 0.12657499274364126, stDev= 9.750300508855897
Expert 18: accuracy= 0.20627588621558102, stDev= 2.604174091049003
Expert 19: accuracy= 0.40654083240042427, stDev= 16.913113360882438
Expert 20: accuracy= 0.5207791610048524, stDev= 18.996796975480255
Expert 21: accuracy= 0.27130576360790803, stDev= 7.745009374119904
Expert 22: accuracy= 0.22880198240040295, stDev= 2.256415979615929
Expert 23: accuracy= 0.5862483975935397, stDev= 19.83764220847985
Expert 24: accuracy= 0.26515529117956005, stDev= 19.244296314340087
Expert 25: accuracy= 0.8784288366676899, stDev= 7.2579200456588815
Expert 26: accuracy= 0.05144836759769278, stDev= 5.814643932346241
Expert 27: accuracy= 0.6368701253891716, stDev= 3.6708039042296448
Expert 28: accuracy= 0.3409149871417868, stDev= 5.5587464301538585
Expert 29: accuracy= 0.11667030198550488, stDev= 5.315791216925147
Expert 30: accuracy= 0.5460894065696358, stDev= 2.736609371928247
Expert 31: accuracy= 0.01538437917072677, stDev= 9.892388273005068
Expert 32: accuracy= 0.2424783341430502, stDev= 7.179891816571004
"
1)
    
"2"
Returns: 
"Num Periods = 25
Expert 0: accuracy= 0.004156060469487133, stDev= 5.867531270220168
Expert 1: accuracy= 0.0332853474784498, stDev= 17.09361910399107
Expert 2: accuracy= 0.4528560970449139, stDev= 18.46249281985188
Expert 3: accuracy= 0.8403139374130625, stDev= 10.399023018715924
Expert 4: accuracy= 0.5537556563394254, stDev= 4.946891352410097
Expert 5: accuracy= 0.7852481879545881, stDev= 2.217643467085173
Expert 6: accuracy= 0.27523404968209697, stDev= 7.550382929070931
Expert 7: accuracy= 0.5221413671029602, stDev= 18.98310493003988
Expert 8: accuracy= 0.33436497265077425, stDev= 13.481053877456585
Expert 9: accuracy= 0.17156758281994422, stDev= 15.936723068181122
Expert 10: accuracy= 0.5933309082489288, stDev= 3.285334959996631
Expert 11: accuracy= 0.6632667923558098, stDev= 4.766567097063619
Expert 12: accuracy= 0.686593333134676, stDev= 12.56800032634307
Expert 13: accuracy= 0.7328393347206562, stDev= 0.5309798147962663
Expert 14: accuracy= 0.009830798044731859, stDev= 14.570415270137813
Expert 15: accuracy= 0.010631563122931342, stDev= 10.804180740611894
Expert 16: accuracy= 0.10497686315720944, stDev= 16.515356036297835
Expert 17: accuracy= 0.7473980675164895, stDev= 16.95422408958156
Expert 18: accuracy= 0.035260919142770075, stDev= 16.161891685249667
Expert 19: accuracy= 0.9840225882921698, stDev= 2.561253649850308
Expert 20: accuracy= 0.5599742689419659, stDev= 6.673952380098718
Expert 21: accuracy= 0.39152397912670267, stDev= 7.206638890597281
Expert 22: accuracy= 0.7907551495632886, stDev= 7.691393570039953
Expert 23: accuracy= 0.5827695927812647, stDev= 5.211242965437655
Expert 24: accuracy= 0.5395059419335001, stDev= 18.402491427835006
Expert 25: accuracy= 0.4287634219047317, stDev= 8.373724944167702
Expert 26: accuracy= 0.7526265259733723, stDev= 14.451987193907538
Expert 27: accuracy= 0.8000717922645448, stDev= 9.357437063279955
Expert 28: accuracy= 0.26706790378679923, stDev= 4.678937542810382
Expert 29: accuracy= 0.19789454383609295, stDev= 18.097129025450997
Expert 30: accuracy= 0.6207804172846331, stDev= 5.963791178673286
Expert 31: accuracy= 0.9394852653934785, stDev= 17.444337453948563
Expert 32: accuracy= 0.8034516939606872, stDev= 4.874265802194582
Expert 33: accuracy= 0.9190690636664225, stDev= 16.242982564737726
Expert 34: accuracy= 0.6429989436463263, stDev= 13.334760466047657
Expert 35: accuracy= 0.4781020388759971, stDev= 8.124361554541133
Expert 36: accuracy= 0.9891013801241787, stDev= 12.204185584631507
Expert 37: accuracy= 0.5876616293208933, stDev= 12.53337620188117
Expert 38: accuracy= 0.9771283891496807, stDev= 11.432259568481555
Expert 39: accuracy= 0.3709525452547332, stDev= 0.8454929267111688
"
2)
    
"3"
Returns: 
"Num Periods = 98
Expert 0: accuracy= 0.8052506307828428, stDev= 11.248175522980697
Expert 1: accuracy= 0.5751459632416763, stDev= 11.63731538300424
Expert 2: accuracy= 0.3528161467116111, stDev= 17.04659273037899
Expert 3: accuracy= 0.4115667315001804, stDev= 1.3298690407924885
Expert 4: accuracy= 0.8648662291874527, stDev= 2.704264207308358
Expert 5: accuracy= 0.5766119807831387, stDev= 14.238181683503466
Expert 6: accuracy= 0.1383103724943462, stDev= 0.8269392073132487
Expert 7: accuracy= 0.5425455205006697, stDev= 9.583800780307596
Expert 8: accuracy= 0.8101348794011826, stDev= 9.269358245595054
Expert 9: accuracy= 0.0741327149644132, stDev= 8.117632392901744
Expert 10: accuracy= 0.7305047882714513, stDev= 18.525012483965725
Expert 11: accuracy= 0.11665054992749802, stDev= 2.192735357036877
Expert 12: accuracy= 0.5494055386136554, stDev= 6.412066678928731
Expert 13: accuracy= 0.3587567462143215, stDev= 7.523238436614035
Expert 14: accuracy= 0.09791023036516988, stDev= 2.7464818838868266
Expert 15: accuracy= 0.3408539059665443, stDev= 8.470801788570624
Expert 16: accuracy= 0.23207111280949122, stDev= 8.900413848578172
Expert 17: accuracy= 0.20712369932476593, stDev= 1.219582697324193
Expert 18: accuracy= 0.4255992514344812, stDev= 4.975985721175038
Expert 19: accuracy= 0.4581953363282546, stDev= 7.345206886861018
Expert 20: accuracy= 0.546909232962928, stDev= 10.781567245225895
Expert 21: accuracy= 0.3514512455874915, stDev= 14.052762484445896
Expert 22: accuracy= 0.9367707654760472, stDev= 5.879734174549796
Expert 23: accuracy= 0.9172625177845822, stDev= 16.75337604645172
Expert 24: accuracy= 0.11472237181397182, stDev= 18.683092957328956
Expert 25: accuracy= 0.911985241726879, stDev= 14.668456644664762
Expert 26: accuracy= 0.18556714481519954, stDev= 4.906206007379366
Expert 27: accuracy= 0.41233791324019453, stDev= 0.7952260102631836
Expert 28: accuracy= 0.29168358830435426, stDev= 11.638873838591541
Expert 29: accuracy= 0.17081979655256352, stDev= 13.836683287957863
Expert 30: accuracy= 0.2625500803796339, stDev= 4.888063711076123
Expert 31: accuracy= 0.29811830331922795, stDev= 1.5936877269673966
Expert 32: accuracy= 0.9497939073548082, stDev= 5.642808005668871
Expert 33: accuracy= 0.31599304670199946, stDev= 2.8363688135747034
Expert 34: accuracy= 0.6042412818551887, stDev= 12.348419174358003
Expert 35: accuracy= 0.13291343880203088, stDev= 7.816607323257974
Expert 36: accuracy= 0.05085797223922617, stDev= 6.13341667661031
Expert 37: accuracy= 0.03476503751599336, stDev= 6.352861213921623
"
3)
    
"4"
Returns: 
"Num Periods = 46
Expert 0: accuracy= 0.810723556855427, stDev= 18.151397382829785
Expert 1: accuracy= 0.2844491165636147, stDev= 4.355796778070083
Expert 2: accuracy= 0.8526162758358073, stDev= 9.967092581037743
Expert 3: accuracy= 0.26783070193576963, stDev= 15.984099151175307
Expert 4: accuracy= 0.42041910832875096, stDev= 11.49112818377645
Expert 5: accuracy= 0.5334309300247309, stDev= 14.340871573502037
Expert 6: accuracy= 0.45369194185210904, stDev= 7.209719406431942
Expert 7: accuracy= 0.644566347093862, stDev= 2.587281521762299
Expert 8: accuracy= 0.18898441315322423, stDev= 8.21087978826416
Expert 9: accuracy= 0.586958345884436, stDev= 9.022178420458397
Expert 10: accuracy= 0.4163741585817411, stDev= 14.723401295904093
Expert 11: accuracy= 0.3835693228847783, stDev= 9.323575762833498
Expert 12: accuracy= 0.8634665660085522, stDev= 15.632397845810587
Expert 13: accuracy= 0.48834374407800285, stDev= 2.484531843726101
Expert 14: accuracy= 0.5383073770661987, stDev= 3.6268161447247893
Expert 15: accuracy= 0.9919656052834481, stDev= 16.803908220457153
Expert 16: accuracy= 0.8675423014662549, stDev= 10.82570410207275
Expert 17: accuracy= 0.5057519030696318, stDev= 2.546376630107008
Expert 18: accuracy= 0.37729083838723076, stDev= 9.04645679487156
Expert 19: accuracy= 0.8290590914098399, stDev= 11.264973071914568
Expert 20: accuracy= 0.4815840381665778, stDev= 11.319641868885013
Expert 21: accuracy= 0.1510875629902746, stDev= 8.283380155665753
Expert 22: accuracy= 0.6668488301386784, stDev= 16.821438091168684
Expert 23: accuracy= 0.5897271875046537, stDev= 14.46404174954527
Expert 24: accuracy= 0.9908046553267812, stDev= 0.08610179689162001
Expert 25: accuracy= 0.3280942514306481, stDev= 6.142115147150062
"
4)
    
"5"
Returns: 
"Num Periods = 29
Expert 0: accuracy= 0.6118181271687826, stDev= 3.532041635590315
Expert 1: accuracy= 0.8263097621291636, stDev= 18.899493057083248
Expert 2: accuracy= 0.752576295700182, stDev= 8.551192789588073
Expert 3: accuracy= 0.8390834960228875, stDev= 6.914945173251872
Expert 4: accuracy= 0.7315296811767784, stDev= 9.248501038674712
Expert 5: accuracy= 0.3247947228532815, stDev= 6.361409491897105
Expert 6: accuracy= 0.3167682497631972, stDev= 0.4862750886278122
Expert 7: accuracy= 0.6649705153927326, stDev= 13.187977968076465
Expert 8: accuracy= 0.6647543199036327, stDev= 3.9991841564026265
Expert 9: accuracy= 0.48952346312774386, stDev= 1.203087447155795
Expert 10: accuracy= 0.5535480237031024, stDev= 9.963078819873186
Expert 11: accuracy= 0.8369530804564665, stDev= 6.749743724783532
Expert 12: accuracy= 0.7262787714875316, stDev= 9.476464198396247
Expert 13: accuracy= 0.11426112576934577, stDev= 9.47679046554387
Expert 14: accuracy= 0.6263868093866367, stDev= 11.802883056497027
Expert 15: accuracy= 0.3221879332258999, stDev= 14.470529268415879
Expert 16: accuracy= 0.9946365362173754, stDev= 3.2107622123763124
Expert 17: accuracy= 0.9654775497790695, stDev= 6.81173553587286
Expert 18: accuracy= 0.7676291557777807, stDev= 17.86055083079693
Expert 19: accuracy= 0.3032318245447635, stDev= 16.048926606948502
Expert 20: accuracy= 0.46851898728637875, stDev= 15.427256734012193
Expert 21: accuracy= 0.11101481454990225, stDev= 15.12950374951437
Expert 22: accuracy= 0.8128644460514369, stDev= 15.009778993701751
"
5)
    
"6"
Returns: 
"Num Periods = 76
Expert 0: accuracy= 0.20853438642639333, stDev= 7.390108579285506
Expert 1: accuracy= 0.20072785523483938, stDev= 15.268403922020521
Expert 2: accuracy= 0.052696221205896565, stDev= 12.79889275998353
Expert 3: accuracy= 0.12532509886037269, stDev= 14.122407107022179
Expert 4: accuracy= 0.7981979626326962, stDev= 15.976382473979921
Expert 5: accuracy= 0.9507033443676295, stDev= 10.299795736711898
Expert 6: accuracy= 0.7275393111287717, stDev= 0.6566074459937554
Expert 7: accuracy= 0.6037580104961205, stDev= 1.385889225180419
Expert 8: accuracy= 0.2374445996524076, stDev= 16.63427135001045
Expert 9: accuracy= 0.7818280815954979, stDev= 4.660360069040381
Expert 10: accuracy= 0.14202641343785738, stDev= 4.244045651919455
Expert 11: accuracy= 0.4768018077414017, stDev= 14.47123924288698
Expert 12: accuracy= 0.1378421550505935, stDev= 7.94426543866249
Expert 13: accuracy= 0.23650893599183365, stDev= 8.500014600090564
Expert 14: accuracy= 0.36214851242532264, stDev= 7.274682321180315
Expert 15: accuracy= 0.3315209195962221, stDev= 1.4706655284932513
Expert 16: accuracy= 0.6133538319640138, stDev= 6.055588179488853
Expert 17: accuracy= 0.5863006394530789, stDev= 14.015659116598528
Expert 18: accuracy= 0.5966142036061309, stDev= 11.418268126974372
Expert 19: accuracy= 0.8807135953376702, stDev= 1.6970668959163704
Expert 20: accuracy= 0.5077141101246534, stDev= 3.1044121386306567
Expert 21: accuracy= 0.2312330300686969, stDev= 14.591133265991745
Expert 22: accuracy= 0.3748175983131614, stDev= 0.4447565841257717
Expert 23: accuracy= 0.9207413076956962, stDev= 11.379775289493917
Expert 24: accuracy= 0.8403717508623542, stDev= 19.524898141857264
Expert 25: accuracy= 0.3616506564898372, stDev= 13.552651746155941
Expert 26: accuracy= 0.48438898643952, stDev= 16.268862745818065
Expert 27: accuracy= 0.24913623146366015, stDev= 15.10859255318965
Expert 28: accuracy= 0.365530686560503, stDev= 12.518682427911793
Expert 29: accuracy= 0.08959555470197544, stDev= 1.055345479432015
Expert 30: accuracy= 0.1878590845657978, stDev= 1.6608822023543102
"
6)
    
"7"
Returns: 
"Num Periods = 80
Expert 0: accuracy= 0.00962895673974895, stDev= 12.770753130069256
Expert 1: accuracy= 0.7425885008003883, stDev= 9.812100499056914
Expert 2: accuracy= 0.9526562410702714, stDev= 11.382992670510639
Expert 3: accuracy= 0.6965778929474906, stDev= 5.053253129098742
Expert 4: accuracy= 0.10930853548072361, stDev= 13.733755328878186
Expert 5: accuracy= 0.7420671371961801, stDev= 2.3203336551069675
Expert 6: accuracy= 0.5906156190398598, stDev= 13.933163128189625
Expert 7: accuracy= 0.6241621787949913, stDev= 11.986585373471359
Expert 8: accuracy= 0.7132145064028159, stDev= 12.422575420125694
Expert 9: accuracy= 0.684393213739967, stDev= 16.841269393761
Expert 10: accuracy= 0.2792002785592187, stDev= 19.483723473911773
Expert 11: accuracy= 0.9301855653130899, stDev= 11.897407204837016
Expert 12: accuracy= 6.54360529572906E-4, stDev= 1.78833179124815
Expert 13: accuracy= 0.8624263325843377, stDev= 15.492273221908333
Expert 14: accuracy= 0.45022795964692186, stDev= 15.450749232952552
Expert 15: accuracy= 0.6617432624398352, stDev= 19.137286576451977
Expert 16: accuracy= 0.7404480667151344, stDev= 18.44064599176919
Expert 17: accuracy= 0.04602627126135539, stDev= 18.28101802236438
Expert 18: accuracy= 0.9869525209966808, stDev= 0.23236216289974188
Expert 19: accuracy= 0.35488632847259394, stDev= 6.48102013292708
Expert 20: accuracy= 0.49464907414561554, stDev= 7.212027003757835
Expert 21: accuracy= 0.19116029652948574, stDev= 1.4372568598403612
Expert 22: accuracy= 0.5208332142259199, stDev= 18.633097188635613
Expert 23: accuracy= 0.2552342476001749, stDev= 2.9219086685312057
Expert 24: accuracy= 0.4155881956439871, stDev= 19.805499671351217
Expert 25: accuracy= 0.8448724465096621, stDev= 19.847383446653
Expert 26: accuracy= 0.9173296052813472, stDev= 6.723081559289894
Expert 27: accuracy= 0.8614023375381488, stDev= 6.546381500172882
"
7)
    
"8"
Returns: 
"Num Periods = 44
Expert 0: accuracy= 0.015101867911171962, stDev= 19.673974691895125
Expert 1: accuracy= 0.4518916541223267, stDev= 2.5305818941227565
Expert 2: accuracy= 0.4524563850956287, stDev= 4.303492819192616
Expert 3: accuracy= 0.552841878284241, stDev= 19.707482941458338
Expert 4: accuracy= 0.6648613848196994, stDev= 2.5206193053462767
Expert 5: accuracy= 0.6988860864377723, stDev= 2.423023843128762
Expert 6: accuracy= 0.9059971883976226, stDev= 0.315943327308319
Expert 7: accuracy= 0.7261830053881834, stDev= 4.990066114926062
Expert 8: accuracy= 0.09206402525369639, stDev= 11.364097260818028
Expert 9: accuracy= 0.19721884465998973, stDev= 17.74581512329443
Expert 10: accuracy= 0.9650696637706697, stDev= 15.682111987826916
Expert 11: accuracy= 0.19710433827037022, stDev= 19.028247610633635
Expert 12: accuracy= 0.31471538792446974, stDev= 11.008662958130005
Expert 13: accuracy= 0.9920133304480191, stDev= 10.453566330997177
Expert 14: accuracy= 0.8906250914467895, stDev= 16.33108319576729
Expert 15: accuracy= 0.3128549468555777, stDev= 7.470393008338507
"
8)
    
"9"
Returns: 
"Num Periods = 51
Expert 0: accuracy= 0.8161964531256888, stDev= 5.054619242678875
Expert 1: accuracy= 0.9937522847867144, stDev= 17.074278173135923
Expert 2: accuracy= 0.3524164198611647, stDev= 2.8875924316964974
Expert 3: accuracy= 0.12409468727252004, stDev= 10.638328963534903
Expert 4: accuracy= 0.975971972568888, stDev= 0.2779921602445379
Expert 5: accuracy= 0.490249879266323, stDev= 14.443561761523831
Expert 6: accuracy= 0.7690735112098719, stDev= 13.592499307527412
Expert 7: accuracy= 0.7465871587858929, stDev= 15.590761965193778
Expert 8: accuracy= 0.5678339320041048, stDev= 7.1524013309332695
Expert 9: accuracy= 0.09978396190329764, stDev= 9.926724149991827
Expert 10: accuracy= 0.10224352889203103, stDev= 10.921789809819236
Expert 11: accuracy= 0.6504880958420585, stDev= 16.454415870606894
Expert 12: accuracy= 0.17752759340344915, stDev= 4.852729310715665
"
9)
    
"10"
Returns: 
"Num Periods = 88
Expert 0: accuracy= 0.4129126974821382, stDev= 8.912685888350842
Expert 1: accuracy= 0.36817039279355135, stDev= 13.44318933609642
Expert 2: accuracy= 0.652536330465718, stDev= 7.135292700115179
Expert 3: accuracy= 0.4103362901100053, stDev= 17.84579089730521
Expert 4: accuracy= 0.04264023912364456, stDev= 7.005873893572973
Expert 5: accuracy= 0.11615850078067091, stDev= 18.381947708315398
Expert 6: accuracy= 0.1798445725754464, stDev= 13.76283136687013
Expert 7: accuracy= 0.6853746687904421, stDev= 3.7886738183441815
Expert 8: accuracy= 0.14052422665404096, stDev= 19.787488524541093
Expert 9: accuracy= 0.39208858037105165, stDev= 13.383996771876415
Expert 10: accuracy= 0.6907219037256248, stDev= 5.202756641865505
Expert 11: accuracy= 0.2903368231269936, stDev= 4.175911686733567
Expert 12: accuracy= 0.589090976966511, stDev= 3.3205305509819083
Expert 13: accuracy= 0.740178537263011, stDev= 16.469049385384864
Expert 14: accuracy= 0.7144662417070747, stDev= 19.978949968269262
Expert 15: accuracy= 0.652410276069513, stDev= 12.137150614397829
Expert 16: accuracy= 0.12173077096849583, stDev= 15.595820024656648
Expert 17: accuracy= 0.42520318158734594, stDev= 11.07709414361549
Expert 18: accuracy= 0.15796747316833049, stDev= 6.674644866722299
Expert 19: accuracy= 0.7774045874820095, stDev= 0.8328795459359872
Expert 20: accuracy= 0.4554539364061797, stDev= 19.534871301116148
"

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