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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:69.74%69.06%69.67%
NameUseridGCRPercent wonGCR1GCR2
Play Testerplaytester1857290.5/330 = 88.03%18141900
Hexa Sakkbosa601854136.5/151 = 90.40%18261881
Francis Fahystamandua1825247.0/300 = 82.33%18271823
dax00dax001821161.0/167 = 96.41%18281813
Homo Simiaalienum180579.0/99 = 79.80%17821829
Kevin Paceypanther1782615.0/876 = 70.21%17811782
Carlos Cetinasissa1760786.5/1155 = 68.10%17301790
Cameron Milesshatteredglass172015.0/17 = 88.24%17141727
Jochen Muellerleopold_stotch169955.0/92 = 59.78%16831715
H Spetyura169813.0/13 = 100.00%16921705
Fergus Dunihofergus167064.5/101 = 63.86%16721668
Vitya Makovmakov3331664494.0/1015 = 48.67%16441683
Jose Carrilloj_carrillo_vii166288.5/155 = 57.10%16651660
Gary Giffordpenswift165652.5/77 = 68.18%15731740
Tim O'Lenatim_olena163418.5/31 = 59.68%16441624
CSS Dixielandcssdixieland162918.0/25 = 72.00%16171641
David Paulowichdavid_64162812.0/15 = 80.00%16241633
shift2shiftshift2shift161911.0/19 = 57.89%16101627
Stephen Williamsneph161611.0/12 = 91.67%15721660
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%16691541
Pericles Tesone de Souzaperitezz15888.0/8 = 100.00%15881588
ctzctz157812.0/17 = 70.59%15511605
attack hippoattackhippo15785.5/7 = 78.57%15741582
Daniel Zachariasarx1577192.0/361 = 53.19%16001553
TH6notath615767.0/12 = 58.33%15701583
Abdul-Rahman Sibahisibahi157616.0/23 = 69.57%15661585
kokoszkokosz15767.0/8 = 87.50%15571595
Plamen Draganovdraganov15734.0/4 = 100.00%15721573
Alexander Trotterqilin15734.0/4 = 100.00%15741572
Jenard Cabilaomgawalangmagawa157311.0/23 = 47.83%15851561
Stephen Stockmanstevestockman157110.0/16 = 62.50%15741568
Christine Bagley-Joneszcherryz15693.5/5 = 70.00%15681570
je jujejujeju156236.5/61 = 59.84%15551570
Raymond Dlewel156113.0/22 = 59.09%15781544
Isaac Felpsattacker14415585.0/6 = 83.33%15591557
Thor Slavenskyslavensky15555.0/7 = 71.43%15351576
Nicola Caridiniccar15543.0/3 = 100.00%15571550
Nicholas Wolffnwolff15549.0/15 = 60.00%15761531
Roberto Lavierirlavieri200315503.0/3 = 100.00%15451555
pallab basupallab154531.0/60 = 51.67%15261565
michirmichir15432.0/2 = 100.00%15421545
carlos carloscarlos154316.0/27 = 59.26%15231563
S Ssim15436.0/9 = 66.67%15311554
Greg Strongmageofmaple1542106.0/219 = 48.40%15811504
Neil Spargospargo15393.0/4 = 75.00%15321545
Sandra#Paul BRANDLYARDsandravers13067515373.0/4 = 75.00%15361537
Nicholas Wolffmaeko153665.5/141 = 46.45%15601512
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%15461519
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
dicepawndicepawn15211.0/1 = 100.00%15251518
Tom Westtwrecks15211.0/1 = 100.00%15231519
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%14781559
Richard Titlertitle15181.0/1 = 100.00%15191518
Garrett Smithgmsmith15181.0/2 = 50.00%15241512
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
jj15181.0/1 = 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
Georg Spengleravunjahei15189.0/28 = 32.14%15061530
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
pink sockpickett_aaron15152.0/3 = 66.67%15151515
Simon Langley-Evansslangers15151.5/2 = 75.00%15131516
spiptorben15151.0/2 = 50.00%15141516
xxmanxxman15141.0/2 = 50.00%15191509
Nathanlokor15131.0/2 = 50.00%15131514
Max Kovalmaxkoval15121.0/1 = 100.00%15061519
Leon Careyleoncarey15121.0/1 = 100.00%15071518
pheko Motaungcouriermabovini151235.5/70 = 50.71%15651458
Joe Joycejoejoyce151122.5/68 = 33.09%14791542
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%15041504
Kent Weschlerperplexedibex15031.0/3 = 33.33%15041502
Albert Vámosiblackrider_4815031.0/4 = 25.00%15151490
Hans Henrikssonhasurami15022.0/4 = 50.00%14921512
Colin Adamslionhawk15021.0/2 = 50.00%15051500
Gee Beegdimension15021.0/2 = 50.00%15021502
noy noynoy15023.0/7 = 42.86%14871517
Tom Trenchtomdench9515020.5/1 = 50.00%15001503
Colin Weaveruselessgit15011.0/4 = 25.00%15001502
Boyko Ahtarovzdra4150110.0/23 = 43.48%14901511
Eni Lienili149811.5/46 = 25.00%15171480
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 Thompsonjezzat149414.0/57 = 24.56%14691519
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
ugo judeugojude14900.0/1 = 0.00%14961484
Ricardo Florentinoricmf14900.0/1 = 0.00%14931487
wyatt wyattquimssarcasm14900.0/1 = 0.00%14971483
potato imaginatorpotato14900.0/1 = 0.00%14931486
John Badgerjbadger14900.0/1 = 0.00%14961484
jesus babyboypokechamp14900.0/1 = 0.00%14971482
Urvish Desaiurvishdesai14890.0/1 = 0.00%14931486
Milton Haddockmiltonhaddock14890.0/1 = 0.00%14961483
xerisianxxerisianx14890.0/1 = 0.00%14931485
Hsa Saidh14890.0/1 = 0.00%14971481
Esperllynmogik14890.0/1 = 0.00%14961482
makomako14890.0/1 = 0.00%14961482
loveokenloveoken14890.0/1 = 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
Steve Polleychessfan5914890.0/1 = 0.00%14941484
Erlang Shenerlangshen14880.0/1 = 0.00%14951481
Jason Stehlyjasonstehly14880.0/1 = 0.00%14941483
Four PlayerChessfourplayerchess14880.0/1 = 0.00%14931483
Éric Manálangedubble1914880.0/1 = 0.00%14941482
Lamai grouplamai14880.0/1 = 0.00%14941482
gwashinggwashing14880.0/1 = 0.00%14911484
Ben Reinigerbenr14880.0/1 = 0.00%14941481
Florin Lupusorulittlewolf14880.0/1 = 0.00%14941481
zanzibarzanzibar14880.0/1 = 0.00%14921484
thiago regob3aring14871.0/3 = 33.33%14881486
Ivan Velascoswordandsilver14870.0/1 = 0.00%14921483
Rob Brownsteelhead14870.0/1 = 0.00%14911483
DJ Linickdjlinick14870.0/1 = 0.00%14921482
Joseph Yoderjjosseepphh14870.0/1 = 0.00%14851488
László Gadosdani198314871.0/4 = 25.00%14831490
Ronald Brierleybenwb14870.0/1 = 0.00%14861488
Dead Accountqqzlbpdilchr14860.0/1 = 0.00%14921481
dghanddghand14860.0/1 = 0.00%14861487
Lwebato14860.0/1 = 0.00%14861486
anon anonchessvar114860.0/1 = 0.00%14861486
avni avniavni14860.0/1 = 0.00%14871485
François Houdebertfhou14860.0/1 = 0.00%14871484
Bradlee Kingstonbrad1914850.0/1 = 0.00%14891482
john applejohnnyappleseed714850.0/1 = 0.00%14871484
Andy Thomasandy_thomas14850.0/1 = 0.00%14881483
Mike Smolowitzmjs170114850.0/1 = 0.00%14891481
Brock Sampsonthe_iron_kenyan14850.0/1 = 0.00%14881483
William Crewscrewsdude14850.0/1 = 0.00%14881482
maolan leonardruby14850.0/1 = 0.00%14871483
Nasmichael Farrismichaeljay14850.0/1 = 0.00%14881482
Luis Menendezpleyades2114850.0/1 = 0.00%14881482
Gus Dunihoduniho14850.0/1 = 0.00%14891481
Alexandr Kremenakremen14850.0/1 = 0.00%14891481
Bob Greenwadebobgreenwade14850.0/1 = 0.00%14881482
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
Siwakorn Songragskyhistory14840.0/1 = 0.00%14851483
James Sprattwhittlin14840.0/1 = 0.00%14861481
Jacob Eugenioe45w14840.0/1 = 0.00%14851482
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
Doge Masterdogemaster14840.0/1 = 0.00%14861481
yi fang liuliuyifang14830.0/1 = 0.00%14861481
higuyzzz91028 Charles Kimdallastexas14830.0/1 = 0.00%14851481
Solomon Salamasol71014830.0/1 = 0.00%14821484
Antony Vailevichjabberw0cky114830.0/1 = 0.00%14821484
Julianredpanda148317.0/35 = 48.57%14631503
scythian blunderq1234514830.0/2 = 0.00%14861479
Turk Osterburgtalen3141593141514830.0/1 = 0.00%14841481
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
Uri Bruckbruck14820.0/2 = 0.00%14921473
Jun Ocampojunpogi14820.0/2 = 0.00%14871478
Hung Daobyteboy14820.0/1 = 0.00%14831481
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
14810.0/1 = 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
arcasorarcasor14800.0/1 = 0.00%14791481
trtztrtz gfghtrtztrtz14800.0/2 = 0.00%14861475
andres fuentesxabyer14800.0/2 = 0.00%14821479
Bn Emnelk11414800.0/2 = 0.00%14841476
rederikrederik14800.0/1 = 0.00%14781481
Diego M.diego14800.0/3 = 0.00%14841475
Francesco Casalinofrancesco14790.0/2 = 0.00%14841474
voicantvoicant14790.0/1 = 0.00%14761481
N Wolffpoint01iv14791.0/3 = 33.33%14751482
qidb602qidb60214790.0/2 = 0.00%14841473
ologyology14780.0/1 = 0.00%14751481
Ivan Kosintsevbombino14780.0/1 = 0.00%14751481
John Twycrossjt14770.0/2 = 0.00%14771478
Frank Istvánistvan6014760.0/2 = 0.00%14861467
wdtrwdtr14760.0/3 = 0.00%14801473
Steve Hsteve_201014760.0/2 = 0.00%14711481
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
Charles Gilmancharles_gilman14740.0/2 = 0.00%14731474
Travis Comptonblackrood14740.0/2 = 0.00%14721475
Lennon Figueiredogiwseppe14731.0/4 = 25.00%14711476
Pablo Denegrideep_thinker14730.0/2 = 0.00%14761471
Aurelian Floreacatugo1473252.5/745 = 33.89%15571389
danielmacduffdanielmacduff14730.0/3 = 0.00%14711475
cherokee malansailorhertzog14710.0/2 = 0.00%14781464
Kacper Rutkowskikacperrutkowski14710.0/2 = 0.00%14741469
jeremy diniericharles_bukowski14700.0/2 = 0.00%14681472
Pat Quexionezsuperpatzermaste14700.0/4 = 0.00%14711469
Sergey Biryukovsbiryukov14700.0/4 = 0.00%14721469
dfe6631dfe663114700.0/2 = 0.00%14651474
Zoli M Zoltánbaltazarprof14690.0/5 = 0.00%14821457
andrewthepawnandrewthepawn14690.0/2 = 0.00%14661472
iuchi45iuchi4514680.0/2 = 0.00%14671470
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%14651465
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 McGrealagentofchaos14607.0/18 = 38.89%14581463
Andy Lewickiondraszek14590.0/3 = 0.00%14541465
Michael Huntkronsteen3314580.0/3 = 0.00%14481468
Graemegraemecn14580.0/3 = 0.00%14561460
Diceroller is Firecryinto14569.0/18 = 50.00%14121501
Nick Wolffwolff145626.0/72 = 36.11%14281484
Николай Сокольскийalexich14560.0/4 = 0.00%14621450
louisvlouisv14550.0/3 = 0.00%14581453
vitaliy ravitztalsterch14522.0/15 = 13.33%14311474
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%14591444
Aaron Maynardvopi14511.0/6 = 16.67%14471454
Linn Russellfreakat14490.0/3 = 0.00%14491449
Вадря Покштяpokshtya144910.0/27 = 37.04%14401458
Adalbertus Kchewoj14481.0/5 = 20.00%14411454
Sagi Gabaysagig7214460.5/16 = 3.12%14271465
dmitarzvonimirdmitarzvonimir14430.0/5 = 0.00%14401446
heche60heche6014422.0/12 = 16.67%14431442
Jeremy Goodjudgmentality144043.5/127 = 34.25%14391440
Evan Jorgensonsabataegalo14370.0/7 = 0.00%14261448
Evert Jan Karmanevertvb14352.5/11 = 22.73%14181453
Phoenix TKartkr10101014342.0/9 = 22.22%14371430
Matthew La Valleesherman10114316.0/23 = 26.09%14111451
Jon Dannjon_dann14300.0/4 = 0.00%14271433
Alan Galetornadic14293.0/20 = 15.00%14231434
juan rodriguezrodriguez142611.5/38 = 30.26%14421410
Daniil Frolovflowermann14253.0/16 = 18.75%14101439
Jean-Louis Cazauxtimurthelenk14222.0/13 = 15.38%14201424
boukineboukine14214.0/13 = 30.77%13901453
Jeremy Hook10011014212.0/30 = 6.67%14181424
Jack Zavierubersketch14190.0/6 = 0.00%14131426
Arthur Yvrardtorendil14160.0/7 = 0.00%14111421
Paul Rapoportnumerist14140.0/7 = 0.00%14221406
John Davischappy14133.0/17 = 17.65%14021425
Evan Jorgensonejorgens14120.0/7 = 0.00%14031420
yellowturtleyellowturtle14120.0/10 = 0.00%14141410
Samuel de Souzasamsou14110.0/8 = 0.00%14111411
George Dukegwduke140742.5/117 = 36.32%13521462
Митя Стрелецкийsocrat8314070.0/10 = 0.00%13991415
Dmitry Strelyabba8314060.0/10 = 0.00%14201391
darren paullramalam139713.5/99 = 13.64%13751418
Митя Митяbahram13950.0/13 = 0.00%13911400
Bogot Bogotolbog139112.0/44 = 27.27%13741409
mrxx2016mrxx201613760.0/17 = 0.00%13971355
Omnia Nihilosacredchao136313.0/70 = 18.57%13411384
Сергей Маэстроfantomas13561.0/31 = 3.23%13601351
Nakanaka13540.0/11 = 0.00%13231385
Oisín D.sxg135052.0/241 = 21.58%13291371
Diogen Abramelindanko13350.0/35 = 0.00%13181352
Сергей Бугаевскийbugaevsky12933.0/56 = 5.36%12821303
Richard milnersesquipedalian128215.0/262 = 5.73%13001265
Alisher Bolsaniraja8512800.0/46 = 0.00%12561303
wdtr2wdtr2126222.5/185 = 12.16%12691256
per hommerbergper3112522.0/79 = 2.53%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