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Game Courier Ratings for %

This file reads data on finished games and calculates Game Courier Ratings (GCR's) for each player. These will be most meaningful for single Chess variants, though they may be calculated across variants. This page is presently in development, and the method used is experimental. I may change the method in due time. How the method works is described below.

There may be a delay while it reads the database and calculates results.

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SELECT * FROM FinishedGames WHERE Rated='on'

You are viewing ratings based on a wildcard that includes all Chess variants played on Game Courier. This is not as meaningful as ratings based on a single variant, which you may find in the Related menu for each preset.

Game Courier Ratings for %
Accuracy:70.06%69.39%70.04%
NameUseridGCRPercent wonGCR1GCR2
Play Testerplaytester1857290.5/330 = 88.03%18131900
Hexa Sakkbosa601854136.5/151 = 90.40%18261881
Francis Fahystamandua1824247.0/300 = 82.33%18271822
dax00dax001821161.0/167 = 96.41%18281813
Homo Simiaalienum180579.0/99 = 79.80%17811829
Kevin Paceypanther1780620.0/885 = 70.06%17861775
Carlos Cetinasissa1759788.5/1158 = 68.09%17301788
Cameron Milesshatteredglass172115.0/17 = 88.24%17151727
Jochen Muellerleopold_stotch169955.0/92 = 59.78%16831715
H Spetyura169913.0/13 = 100.00%16931705
Fergus Dunihofergus167065.5/103 = 63.59%16711669
Vitya Makovmakov3331666503.0/1026 = 49.03%16481683
Jose Carrilloj_carrillo_vii166288.5/155 = 57.10%16651660
Gary Giffordpenswift165652.5/77 = 68.18%15731740
Tim O'Lenatim_olena163418.5/31 = 59.68%16461623
CSS Dixielandcssdixieland163218.0/25 = 72.00%16111652
David Paulowichdavid_64162912.0/15 = 80.00%16241633
shift2shiftshift2shift161911.0/19 = 57.89%16101629
Stephen Williamsneph161611.0/12 = 91.67%15721659
Vitya Makovmakov16127.5/8 = 93.75%16111614
Charles Danielfrozen_methane161135.0/64 = 54.69%15691653
Andreas Kaufmannandreas16077.0/7 = 100.00%16091605
Erik Lerougeerik1605141.5/262 = 54.01%16701540
Pericles Tesone de Souzaperitezz15888.0/8 = 100.00%15881588
ctzctz157812.0/17 = 70.59%15521605
attack hippoattackhippo15785.5/7 = 78.57%15741582
TH6notath615777.0/12 = 58.33%15701583
Abdul-Rahman Sibahisibahi157616.0/23 = 69.57%15661585
kokoszkokosz15767.0/8 = 87.50%15571595
Plamen Draganovdraganov15734.0/4 = 100.00%15731573
Alexander Trotterqilin15734.0/4 = 100.00%15741572
Jenard Cabilaomgawalangmagawa157311.0/23 = 47.83%15851561
Stephen Stockmanstevestockman157210.0/16 = 62.50%15741569
Christine Bagley-Joneszcherryz15703.5/5 = 70.00%15691571
Daniel Zachariasarx1569198.0/374 = 52.94%15811557
Raymond Dlewel156113.0/22 = 59.09%15781544
je jujejujeju156036.5/61 = 59.84%15591561
Isaac Felpsattacker14415585.0/6 = 83.33%15591557
Thor Slavenskyslavensky15555.0/7 = 71.43%15351576
Nicholas Wolffnwolff15549.0/15 = 60.00%15761531
Nicola Caridiniccar15543.0/3 = 100.00%15571550
Roberto Lavierirlavieri200315503.0/3 = 100.00%15451555
carlos carloscarlos154616.0/27 = 59.26%15231569
pallab basupallab154631.0/60 = 51.67%15261566
michirmichir15432.0/2 = 100.00%15421545
S Ssim15436.0/9 = 66.67%15311554
Greg Strongmageofmaple1542106.0/219 = 48.40%15821503
Neil Spargospargo15393.0/4 = 75.00%15331545
Nicholas Wolffmaeko153665.5/141 = 46.45%15601512
Sandra#Paul BRANDLYARDsandravers13067515363.0/4 = 75.00%15351537
Tom e4ktome4k15362.0/2 = 100.00%15351536
Julien Coll Moratfacteurix15342.0/3 = 66.67%15321536
Todd Witterstoddw15342.0/2 = 100.00%15331535
Eric Greenwoodcavalier15344.0/6 = 66.67%15441524
Máté Csarmaszcsarmi15337.0/16 = 43.75%15471518
Jake Palladinocerebralassassin15312.0/2 = 100.00%15271535
Matthew Montchalinmatthew_montchal15313.0/4 = 75.00%15291533
Fred Koktangram15282.0/3 = 66.67%15291527
joe rosenbloombootzilla15282.0/3 = 66.67%15271529
Chuck Leegyw6t152717.5/39 = 44.87%15131542
Uwe Kreuzercaissus15272.0/2 = 100.00%15241530
Joseph DiMurotrojh15261.0/1 = 100.00%15341519
je jujejujejujeju15252.0/2 = 100.00%15131537
Yeinzon Rodríguez Garcíayeinzon15241.0/1 = 100.00%15281520
Adrian Alvarez de la Campaadrian15243.5/6 = 58.33%15231524
Simon Runspeakablegamer15223.0/6 = 50.00%15211524
dicepawndicepawn15211.0/1 = 100.00%15251518
Tom Westtwrecks15201.0/1 = 100.00%15221519
von raidervonraider15201.0/1 = 100.00%15191520
Larry Wheelerbrainburner15191.0/1 = 100.00%15211518
Dougbughouse15191.0/1 = 100.00%15201518
John Gallantbigjohn151916.0/34 = 47.06%14781560
Richard Titlertitle15181.0/1 = 100.00%15191518
Garrett Smithgmsmith15181.0/2 = 50.00%15241512
Georg Spengleravunjahei15189.0/28 = 32.14%15061530
strings 808017424strings80801742415181.0/1 = 100.00%15181518
Trevor Savagesavage15181.0/1 = 100.00%15181518
yas kumkumagai15181.0/1 = 100.00%15181518
David Levinsmidrael15181.0/1 = 100.00%15181518
whitenerdy53whitenerdy5315181.0/1 = 100.00%15181518
15181.0/1 = 100.00%15181518
Antonio Bruzzitotonno_janggi15181.0/1 = 100.00%15181518
eunchong leeeunchong15181.0/1 = 100.00%15181518
Angel47 Usmanangel4715181.0/1 = 100.00%15181518
calebblazecalebblaze15181.0/1 = 100.00%15181518
Jan Żmudajanzmuda15171.0/1 = 100.00%15181517
Titus Ledbettertbl215171.0/1 = 100.00%15181517
bosa6bosa615171.0/1 = 100.00%15161519
M Wintherkalroten15171.0/1 = 100.00%15181516
Hesham Husseinegy_sniper15171.0/1 = 100.00%15171517
Samuel Hoskinscouriergame15171.0/2 = 50.00%15291505
Nobody Importantcomradm15171.0/1 = 100.00%15161518
Aaron Smithzirtoc15162.5/5 = 50.00%15131520
Georges-Clounet Jesuispartoutgeorgesclounet15161.0/1 = 100.00%15131519
Antonio Barratotonno15161.0/1 = 100.00%15141518
spiptorben15151.0/2 = 50.00%15141516
pink sockpickett_aaron15152.0/3 = 66.67%15151515
Simon Langley-Evansslangers15151.5/2 = 75.00%15131516
xxmanxxman15141.0/2 = 50.00%15191509
Nathanlokor15141.0/2 = 50.00%15131514
Max Kovalmaxkoval15121.0/1 = 100.00%15061519
Leon Careyleoncarey15121.0/1 = 100.00%15071518
pheko Motaungcouriermabovini151235.5/70 = 50.71%15651459
Joe Joycejoejoyce151122.5/68 = 33.09%14791543
xeongreyxeongrey15108.0/17 = 47.06%15161504
mystery playercentipede15102.0/5 = 40.00%15131506
Antoine Fourrièreantoinefourriere15091.5/2 = 75.00%15081511
Anthony Viensstarkiller15082.0/4 = 50.00%14991517
As Bardhiasbardhi15081.0/2 = 50.00%15131502
Zachary Wadeazost1215073.0/5 = 60.00%15011513
Graeme Neathamgrayhawke15051.0/2 = 50.00%15031508
Natalia Dolindowhitetiger15041.0/2 = 50.00%15031504
Boyko Ahtarovzdra4150410.0/23 = 43.48%14901517
Kent Weschlerperplexedibex15031.0/3 = 33.33%15041502
Albert Vámosiblackrider_4815031.0/4 = 25.00%15151490
Gee Beegdimension15021.0/2 = 50.00%15021502
Hans Henrikssonhasurami15022.0/4 = 50.00%14921512
noy noynoy15023.0/7 = 42.86%14871518
Colin Adamslionhawk15021.0/2 = 50.00%15051500
Tom Trenchtomdench9515020.5/1 = 50.00%15001503
Colin Weaveruselessgit15011.0/4 = 25.00%15001502
Eni Lienili149811.5/46 = 25.00%15161480
Thom Dimentunwiseowl14982.0/5 = 40.00%14991497
Juan Pablo Schweitzer Kirsingerdefender14971.0/2 = 50.00%14951499
John Smithultimatecoolster14973.0/9 = 33.33%14971497
Armin Liebhartlunaris149725.0/58 = 43.10%14491544
Max Fengwowimbob111214941.0/3 = 33.33%14971492
Jeremy Thompsonjezzat149314.0/57 = 24.56%14671518
DFA Productions70nyd014920.0/1 = 0.00%14961489
don anezdonanez14920.0/1 = 0.00%14961488
Michael Christensenjustsojazz14920.0/1 = 0.00%14961487
hubergerdhubergerd14920.0/1 = 0.00%14961487
vikvik14910.0/1 = 0.00%14961486
kunkunkunkun14910.0/1 = 0.00%14971486
Hugo Mendes-Nuneshugo199514910.0/1 = 0.00%14971485
Fabner Cruz Gracilianofabner14910.0/1 = 0.00%14971484
Bob Brownbobhihih14900.0/1 = 0.00%14971484
Ricardo Florentinoricmf14900.0/1 = 0.00%14931487
ugo judeugojude14900.0/1 = 0.00%14961484
wyatt wyattquimssarcasm14900.0/1 = 0.00%14971483
potato imaginatorpotato14900.0/1 = 0.00%14931486
John Badgerjbadger14900.0/1 = 0.00%14961484
Urvish Desaiurvishdesai14900.0/1 = 0.00%14931486
jesus babyboypokechamp14900.0/1 = 0.00%14971482
Milton Haddockmiltonhaddock14890.0/1 = 0.00%14961483
xerisianxxerisianx14890.0/1 = 0.00%14941485
Hsa Saidh14890.0/1 = 0.00%14971481
loveokenloveoken14890.0/1 = 0.00%14941484
Esperllynmogik14890.0/1 = 0.00%14961482
makomako14890.0/1 = 0.00%14961482
Steve Polleychessfan5914890.0/1 = 0.00%14941484
Matias I.tsatziq14890.0/1 = 0.00%14961481
Anders Gustafsonancog14890.0/1 = 0.00%14961481
Hafsteinn Kjartanssonhnr0114890.0/1 = 0.00%14961481
Jason Stehlyjasonstehly14880.0/1 = 0.00%14941483
Erlang Shenerlangshen14880.0/1 = 0.00%14951481
Éric Manálangedubble1914880.0/1 = 0.00%14941482
Four PlayerChessfourplayerchess14880.0/1 = 0.00%14941482
gwashinggwashing14880.0/1 = 0.00%14911484
Ben Reinigerbenr14880.0/1 = 0.00%14941481
Lamai grouplamai14880.0/1 = 0.00%14941481
zanzibarzanzibar14880.0/1 = 0.00%14921484
Ivan Velascoswordandsilver14870.0/1 = 0.00%14921483
Rob Brownsteelhead14870.0/1 = 0.00%14911483
László Gadosdani198314871.0/4 = 25.00%14841490
DJ Linickdjlinick14870.0/1 = 0.00%14921482
Joseph Yoderjjosseepphh14870.0/1 = 0.00%14861488
Ronald Brierleybenwb14870.0/1 = 0.00%14861488
thiago regob3aring14871.0/3 = 33.33%14871486
dghanddghand14870.0/1 = 0.00%14861487
Dead Accountqqzlbpdilchr14860.0/1 = 0.00%14921481
Lwebato14860.0/1 = 0.00%14861486
anon anonchessvar114860.0/1 = 0.00%14871486
avni avniavni14860.0/1 = 0.00%14871485
François Houdebertfhou14860.0/1 = 0.00%14871484
john applejohnnyappleseed714860.0/1 = 0.00%14871484
Bradlee Kingstonbrad1914850.0/1 = 0.00%14891482
Andy Thomasandy_thomas14850.0/1 = 0.00%14881483
maolan leonardruby14850.0/1 = 0.00%14881483
Mike Smolowitzmjs170114850.0/1 = 0.00%14891481
William Crewscrewsdude14850.0/1 = 0.00%14881482
Brock Sampsonthe_iron_kenyan14850.0/1 = 0.00%14871483
Nasmichael Farrismichaeljay14850.0/1 = 0.00%14881482
Luis Menendezpleyades2114850.0/1 = 0.00%14881482
Bob Greenwadebobgreenwade14850.0/1 = 0.00%14881482
Gus Dunihoduniho14850.0/1 = 0.00%14891481
Alexandr Kremenakremen14850.0/1 = 0.00%14891481
Travis Comptonironlance14850.0/1 = 0.00%14881481
Paolo Porsiapillau14850.0/1 = 0.00%14881481
Kyle Hagemanfoofoo9914840.0/1 = 0.00%14881480
Derek Mooseelevatorfarter14841.0/3 = 33.33%14841484
James Sprattwhittlin14840.0/1 = 0.00%14861481
Giuseppe Acciarocoopwie14842.0/5 = 40.00%14791488
sixtysixty14840.0/3 = 0.00%14871480
Jeremy Goodyamorezu14840.0/1 = 0.00%14851482
andy lewickiherlocksholmes14840.0/1 = 0.00%14861481
yi fang liuliuyifang14830.0/1 = 0.00%14861481
scythian blunderq1234514830.0/2 = 0.00%14871480
Siwakorn Songragskyhistory14830.0/1 = 0.00%14841483
Jacob Eugenioe45w14830.0/1 = 0.00%14841482
Solomon Salamasol71014830.0/1 = 0.00%14821484
Doge Masterdogemaster14830.0/1 = 0.00%14851481
Julianredpanda148317.0/35 = 48.57%14631503
Antony Vailevichjabberw0cky114830.0/1 = 0.00%14821484
higuyzzz91028 Charles Kimdallastexas14830.0/1 = 0.00%14851481
Turk Osterburgtalen3141593141514830.0/1 = 0.00%14851481
Jun Ocampojunpogi14830.0/2 = 0.00%14871479
manolo manolomanolo14830.0/1 = 0.00%14831483
Nicholas Archerchess_hunter14830.0/2 = 0.00%14881477
Dan Kellydankelly14830.0/1 = 0.00%14841481
Paul2memorysorowthorn14830.0/1 = 0.00%14831482
MichaÅ‚ Jarskihookz14830.0/1 = 0.00%14821483
Jose Canceljoche14830.0/1 = 0.00%14831482
Tony Quintanillatony_quintanilla14830.0/1 = 0.00%14821483
Andreas Bunkahlebunkahle14830.0/1 = 0.00%14831482
Roberto Cassanotamerlano14820.0/1 = 0.00%14841481
btstwbtstw14820.0/1 = 0.00%14841481
wabbawabba14820.0/1 = 0.00%14841481
cdpowercdpower14820.0/1 = 0.00%14841480
legendlegend14820.0/2 = 0.00%14921473
Hung Daobyteboy14820.0/1 = 0.00%14831481
Uri Bruckbruck14820.0/2 = 0.00%14921473
Minh Dangminhdang14820.0/1 = 0.00%14811482
anna colladoapatura_iris14820.0/1 = 0.00%14811482
Joseph Grangercdafan14820.0/1 = 0.00%14811482
luigi mattagigino4214820.0/1 = 0.00%14801483
Thomas Meehanorangeaurochs14820.0/1 = 0.00%14821481
Robin Sneijderrobinwooter214820.0/1 = 0.00%14811482
Виктор Байгужаковbajvik14820.0/1 = 0.00%14821481
Wottonwotton14810.0/1 = 0.00%14811481
Harry Gaoharrygao14810.0/1 = 0.00%14811481
y kumyasuhiro14810.0/1 = 0.00%14811481
Ryan Schwartzshunoshi14810.0/1 = 0.00%14811481
ben chewben558214810.0/1 = 0.00%14811481
jj14810.0/1 = 0.00%14811481
Babo Jeffbabojeff14810.0/1 = 0.00%14811481
wonsang leewonsang14810.0/1 = 0.00%14811481
Vitali Maslanskivitali_1014810.0/1 = 0.00%14811481
paulblazepaulblaze14810.0/1 = 0.00%14811481
blundermanblunderman14810.0/1 = 0.00%14811481
Abe Anonapostateabe14810.0/1 = 0.00%14801482
Mark Thompsonmarkthompson14810.0/2 = 0.00%14921469
championchampion14810.0/2 = 0.00%14851476
trtztrtz gfghtrtztrtz14810.0/2 = 0.00%14861475
arcasorarcasor14800.0/1 = 0.00%14791481
andres fuentesxabyer14800.0/2 = 0.00%14821479
Bn Emnelk11414800.0/2 = 0.00%14841476
Diego M.diego14800.0/3 = 0.00%14851475
rederikrederik14800.0/1 = 0.00%14791481
Florin Lupusorulittlewolf14790.0/2 = 0.00%14871472
Francesco Casalinofrancesco14790.0/2 = 0.00%14841474
voicantvoicant14790.0/1 = 0.00%14761481
qidb602qidb60214790.0/2 = 0.00%14841473
N Wolffpoint01iv14781.0/3 = 33.33%14741482
ologyology14780.0/1 = 0.00%14751481
Ivan Kosintsevbombino14780.0/1 = 0.00%14751481
John Twycrossjt14770.0/2 = 0.00%14771478
Steve Hsteve_201014770.0/2 = 0.00%14721481
Frank Istvánistvan6014760.0/2 = 0.00%14861467
wdtrwdtr14760.0/3 = 0.00%14801473
Alexander Krutikovlonewolf14761.0/4 = 25.00%14721479
Ivan Ivankillbill22514760.0/1 = 0.00%14701481
Francisco Magalhãeslowcarbknight14760.0/1 = 0.00%14701481
Szling Ozecszling_ozec14750.0/3 = 0.00%14781472
tedy efwttei27fmrw7de14750.0/1 = 0.00%14691481
Nathan Holdenlinsolv14750.0/1 = 0.00%14681482
Todor Tchervenkovtchervenkov14741.0/4 = 25.00%14731474
Travis Comptonblackrood14740.0/2 = 0.00%14721475
Charles Gilmancharles_gilman14740.0/2 = 0.00%14731474
Lennon Figueiredogiwseppe14731.0/4 = 25.00%14711476
Pablo Denegrideep_thinker14730.0/2 = 0.00%14761471
danielmacduffdanielmacduff14730.0/3 = 0.00%14711475
Aurelian Floreacatugo1472252.5/746 = 33.85%15571387
Kacper Rutkowskikacperrutkowski14710.0/2 = 0.00%14741469
cherokee malansailorhertzog14710.0/2 = 0.00%14781464
Pat Quexionezsuperpatzermaste14710.0/4 = 0.00%14721470
Sergey Biryukovsbiryukov14710.0/4 = 0.00%14721470
jeremy diniericharles_bukowski14710.0/2 = 0.00%14691472
dfe6631dfe663114690.0/2 = 0.00%14641474
Zoli M Zoltánbaltazarprof14690.0/5 = 0.00%14821457
andrewthepawnandrewthepawn14690.0/2 = 0.00%14661472
iuchi45iuchi4514680.0/2 = 0.00%14661470
A tomiatomi14684.5/16 = 28.12%14591476
Memedes Lulagiwseppe314670.0/2 = 0.00%14691466
A. M. DeWittchessshogi14670.0/3 = 0.00%14731462
Zac Sparxkrinid14660.0/2 = 0.00%14671465
Donut Donutdonutdonut14650.0/2 = 0.00%14661465
playshogiplayshogi14650.0/2 = 0.00%14661463
Scott Crawfordmathemagician14640.0/7 = 0.00%14741455
Michael Nelsonmikenels14640.0/2 = 0.00%14621466
michael collinsverderben14641.0/5 = 20.00%14701458
Namik Zadenamik14630.0/2 = 0.00%14611465
andy lewickietaoni14620.0/2 = 0.00%14611464
Scott McGrealagentofchaos14617.0/18 = 38.89%14581463
Andy Lewickiondraszek14600.0/3 = 0.00%14541465
Michael Huntkronsteen3314580.0/3 = 0.00%14481468
Graemegraemecn14580.0/3 = 0.00%14561460
Nick Wolffwolff145626.0/72 = 36.11%14281485
Николай Сокольскийalexich14560.0/4 = 0.00%14621450
louisvlouisv14550.0/3 = 0.00%14581453
vitaliy ravitztalsterch14532.0/15 = 13.33%14311475
Joshua Tsamraku14525.0/12 = 41.67%14311474
John Langleyjonners14520.5/4 = 12.50%14521451
Dayrom Gilallahukbar14510.0/3 = 0.00%14511452
Michael Schmahlmschmahl14515.0/15 = 33.33%14621441
Вадря Покштяpokshtya145111.0/28 = 39.29%14451457
Aaron Maynardvopi14511.0/6 = 16.67%14481454
Linn Russellfreakat14490.0/3 = 0.00%14491449
Adalbertus Kchewoj14471.0/5 = 20.00%14401454
Sagi Gabaysagig7214460.5/16 = 3.12%14271465
Diceroller is Firecryinto144310.0/21 = 47.62%13951491
dmitarzvonimirdmitarzvonimir14430.0/5 = 0.00%14401446
heche60heche6014422.0/12 = 16.67%14431442
Jeremy Goodjudgmentality144043.5/127 = 34.25%14391441
Evan Jorgensonsabataegalo14370.0/7 = 0.00%14261449
Evert Jan Karmanevertvb14352.5/11 = 22.73%14181453
Phoenix TKartkr10101014342.0/9 = 22.22%14371430
Matthew La Valleesherman10114316.0/23 = 26.09%14111452
Jon Dannjon_dann14300.0/4 = 0.00%14261433
Alan Galetornadic14293.0/20 = 15.00%14241433
juan rodriguezrodriguez142611.5/38 = 30.26%14361416
Daniil Frolovflowermann14253.0/16 = 18.75%14111439
boukineboukine14214.0/13 = 30.77%13901453
Jeremy Hook10011014212.0/30 = 6.67%14171425
Jack Zavierubersketch14190.0/6 = 0.00%14131426
Arthur Yvrardtorendil14160.0/7 = 0.00%14111421
Jean-Louis Cazauxtimurthelenk14142.0/14 = 14.29%14121416
John Davischappy14143.0/17 = 17.65%14021425
Paul Rapoportnumerist14120.0/7 = 0.00%14221402
Evan Jorgensonejorgens14120.0/7 = 0.00%14041420
yellowturtleyellowturtle14120.0/10 = 0.00%14141410
Samuel de Souzasamsou14110.0/8 = 0.00%14111411
Митя Стрелецкийsocrat8314080.0/10 = 0.00%13991417
George Dukegwduke140742.5/117 = 36.32%13521463
Dmitry Strelyabba8314050.0/10 = 0.00%14211389
darren paullramalam139713.5/99 = 13.64%13751418
Митя Митяbahram13950.0/13 = 0.00%13931397
Bogot Bogotolbog139212.0/44 = 27.27%13741409
mrxx2016mrxx201613760.0/17 = 0.00%13981354
Omnia Nihilosacredchao136313.0/70 = 18.57%13411384
Сергей Маэстроfantomas13561.0/31 = 3.23%13611351
Nakanaka13530.0/11 = 0.00%13221384
Oisín D.sxg135052.0/241 = 21.58%13291371
Diogen Abramelindanko13360.0/35 = 0.00%13181353
Сергей Бугаевскийbugaevsky12933.0/56 = 5.36%12821304
Alisher Bolsaniraja8512800.0/46 = 0.00%12561303
Richard milnersesquipedalian127915.0/264 = 5.68%12971261
wdtr2wdtr2126122.5/187 = 12.03%12681254
per hommerbergper3112522.0/80 = 2.50%12131291

Meaning

The ratings are estimates of relative playing strength. Given the ratings of two players, the difference between their ratings is used to estimate the percentage of games each may win against the other. A difference of zero estimates that each player should win half the games. A difference of 400 or more estimates that the higher rated player should win every game. Between these, the higher rated player is expected to win a percentage of games calculated by the formula (difference/8)+50. A rating means nothing on its own. It is meaningful only in comparison to another player whose rating is derived from the same set of data through the same set of calculations. So your rating here cannot be compared to someone's Elo rating.

Accuracy

Ratings are calculated through a self-correcting trial-and-error process that compares actual outcomes with expected outcomes, gradually changing the ratings to better reflect actual outcomes. With enough data, this process can approach accuracy to a high degree, but error remains an essential element of any trial-and-error process, and without enough data, its results will remain error-ridden. Unfortunately, Chess variants are not played enough to give it a large data set to work with. The data sets here are usually small, and that means the ratings will not be fully accurate.

One measure taken to eke out the most data from the small data sets that are available is to calculate ratings in a holistic manner that incorporates all results into the evaluation of each result. The first step of this is to go through pairs of players in a manner that doesn't concentrate all the games of one player in one stage of the process. This involves ordering the players in a zig-zagging manner that evenly distributes each player throughout the process of evaluating ratings. The second step is to reverse the order that pairs of players are evaluated in, recalculate all the ratings, and average the two sets of ratings. This allows the outcome of every game to affect the rating calculations for every pair of players. One consequence of this is that your rating is not a static figure. Games played by other people may influence your rating even if you have stopped playing. The upside to this is that ratings of inactive players should get more accurate as more games are played by other people.

Fairness

High ratings have to be earned by playing many games. They are not available through shortcuts. In a previous version of the rating system, I focused on accuracy more than fairness, which resulted in some players getting high ratings after playing only a few games. This new rating system curbs rating growth more, so that you have to win many games to get a high rating. One way it curbs rating growth is to base the amount it changes a rating on the number of games played between two players. The more games they play together, the more it approaches the maximum amount a rating may be changed after comparing two players. This maximum amount is equal to the percentage of difference between expectations and actual results times 400. So the amount ratings may change in one go is limited to a range of 0 to 400. The amount of change is further limited by the number of games each player has already played. The more past games a player has played, the more his rating is considered stable, making it less subject to change.

Algorithm

  1. Each finished public game matching the wildcard or list of games is read, with wins and draws being recorded into a table of pairwise wins. A win counts as 1 for the winner, and a draw counts as .5 for each player.
  2. All players get an initial rating of 1500.
  3. All players are sorted in order of decreasing number of games. Ties are broken first by number of games won, then by number of opponents. This determines the order in which pairs of players will have their ratings recalculated.
  4. Initialize the count of all player's past games to zero.
  5. Based on the ordering of players, go through all pairs of players in a zig-zagging order that spreads out the pairing of each player with each of his opponents. For each pair that have played games together, recalculate their ratings as described below:
    1. Add up the number of games played. If none, skip to the next pair of players.
    2. Identify the players as p1 and p2, and subtract p2's rating from p1's.
    3. Based on this score, calculate the percent of games p1 is expected to win.
    4. Subtract this percentage from the percentage of games p1 actually won. // This is the difference between actual outcome and predicted outcome. It may range from -100 to +100.
    5. Multiply this difference by 400 to get the maximum amount of change allowed.
    6. Where n is the number of games played together, multiply the maximum amount of change by (n)/(n+10).
    7. For each player, where p is the number of his past games, multiply this product by (1-(p/(p+800))).
    8. Add this amount to the rating for p1, and subtract it from the rating for p2. // If it is negative, p1 will lose points, and p2 will gain points.
    9. Update the count of each player's past games by adding the games they played together.
  6. Reinitialize all player's past games to zero.
  7. Repeat the same procedure in the reverse zig-zagging order, creating a new set of ratings.
  8. Average both sets of ratings into one set.


Written by Fergus Duniho
WWW Page Created: 6 January 2006