The Chess Variant Pages
Custom Search



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.

Game Filter: Log Filter: Group Filter:
Tournament Filter: Age Filter: Status Filter:
SELECT * FROM FinishedGames WHERE Rated='on'

Warning: 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:69.56%69.13%68.52%
NameUseridGCRPercent wonGCR1GCR2
Hexa Sakkbosa601868136.5/151 = 90.40%18251910
Francis Fahystamandua1849247.0/298 = 82.89%18271870
dax00dax001814148.0/154 = 96.10%18091819
Kevin Paceypanther1792457.0/557 = 82.05%18021783
Carlos Cetinasissa1736619.5/965 = 64.20%17221750
Cameron Milesshatteredglass171215.0/17 = 88.24%17051718
Jochen Muellerleopold_stotch170055.0/92 = 59.78%16871713
H Spetyura168713.0/13 = 100.00%16801693
Gary Giffordpenswift168160.5/85 = 71.18%15811782
Play Testerplaytester167418.5/25 = 74.00%16761673
Fergus Dunihofergus167363.5/101 = 62.87%16701677
Jose Carrilloj_carrillo_vii166987.5/155 = 56.45%16681670
David Paulowichdavid_64162311.0/13 = 84.62%16291616
shift2shiftshift2shift162011.0/19 = 57.89%16261614
Charles Danielfrozen_methane162035.0/64 = 54.69%15941646
Tim O'Lenatim_olena16176.5/8 = 81.25%16181615
Vitya Makovmakov3331616331.0/722 = 45.84%15641668
Homo Simiaalienum16167.0/8 = 87.50%16061626
Vitya Makovmakov16127.5/8 = 93.75%16111613
Andreas Kaufmannandreas16087.0/7 = 100.00%16101605
Daniel Zachariasarx159168.0/128 = 53.12%15911590
Pericles Tesone de Souzaperitezz15888.0/8 = 100.00%15881588
ctzctz157912.0/17 = 70.59%15531605
kokoszkokosz15777.0/8 = 87.50%15601595
erikerik1577140.5/260 = 54.04%16201533
Abdul-Rahman Sibahisibahi157616.0/23 = 69.57%15651587
attack hippoattackhippo15765.5/7 = 78.57%15721579
je jujejujeju157036.5/60 = 60.83%15671573
Alexander Trotterqilin15704.0/4 = 100.00%15711570
Stephen Stockmanstevestockman156910.0/16 = 62.50%15741565
TH6notath615667.0/12 = 58.33%15621571
Jenard Cabilaomgawalangmagawa156611.0/23 = 47.83%15781554
John Gallantbigjohn156116.0/28 = 57.14%15481573
Raymond Dlewel156013.0/22 = 59.09%15761543
Thor Slavenskyslavensky15585.0/7 = 71.43%15391577
Isaac Felpsattacker14415585.0/6 = 83.33%15591557
Nicholas Wolffnwolff15549.0/15 = 60.00%15781530
Nicola Caridiniccar15543.0/3 = 100.00%15571550
Greg Strongmageofmaple155094.0/200 = 47.00%15981501
Roberto Lavierirlavieri200315493.0/3 = 100.00%15441555
pallab basupallab154731.0/60 = 51.67%15331561
carlos carloscarlos154716.0/27 = 59.26%15261567
S Ssim15436.0/9 = 66.67%15311554
michirmichir15402.0/2 = 100.00%15421537
Nicholas Wolffmaeko153765.5/142 = 46.13%15591515
Tom e4ktome4k15362.0/2 = 100.00%15351536
Neil Spargospargo15343.0/4 = 75.00%15271542
Eric Greenwoodcavalier15344.0/6 = 66.67%15431525
Todd Witterstoddw15342.0/2 = 100.00%15331535
Matthew Montchalinmatthew_montchal15313.0/4 = 75.00%15291533
Jake Palladinocerebralassassin15312.0/2 = 100.00%15281534
Julien Coll Moratfacteurix15312.0/3 = 66.67%15281533
Fred Koktangram15282.0/3 = 66.67%15301527
joe rosenbloombootzilla15282.0/3 = 66.67%15281529
Joseph DiMurotrojh15281.0/1 = 100.00%15331523
Uwe Kreuzercaissus15272.0/2 = 100.00%15241529
Yeinzon Rodríguez Garcíayeinzon15241.0/1 = 100.00%15281520
Adrian Alvarez de la Campaadrian15243.5/6 = 58.33%15241524
Chuck Leegyw6t152317.5/39 = 44.87%15101537
Tom Westtwrecks15201.0/1 = 100.00%15231518
von raidervonraider15201.0/1 = 100.00%15191521
Larry Wheelerbrainburner15201.0/1 = 100.00%15211519
dicepawndicepawn15191.0/1 = 100.00%15211518
Todor Tchervenkovtchervenkov15181.0/1 = 100.00%15171519
Richard Titlertitle15181.0/1 = 100.00%15191518
Garrett Smithgmsmith15181.0/2 = 50.00%15241512
whitenerdy53whitenerdy5315181.0/1 = 100.00%15181518
jj15181.0/1 = 100.00%15181518
Trevor Savagesavage15181.0/1 = 100.00%15181518
Antonio Bruzzitotonno_janggi15181.0/1 = 100.00%15181518
eunchong leeeunchong15181.0/1 = 100.00%15181518
calebblazecalebblaze15181.0/1 = 100.00%15181518
yas kumkumagai15181.0/1 = 100.00%15181518
Angel47 Usmanangel4715181.0/1 = 100.00%15181518
David Levinsmidrael15181.0/1 = 100.00%15181518
Jan Żmudajanzmuda15171.0/1 = 100.00%15181517
Titus Ledbettertbl215171.0/1 = 100.00%15171518
bosa6bosa615171.0/1 = 100.00%15161518
Hesham Husseinegy_sniper15171.0/1 = 100.00%15161518
M Wintherkalroten15171.0/1 = 100.00%15171517
Aaron Smithzirtoc15172.5/5 = 50.00%15141520
Joe Joycejoejoyce151720.5/58 = 35.34%14701563
Georges-Clounet Jesuispartoutgeorgesclounet15161.0/1 = 100.00%15141518
Antonio Barratotonno15161.0/1 = 100.00%15151517
pink sockpickett_aaron15152.0/3 = 66.67%15151515
Simon Langley-Evansslangers15151.5/2 = 75.00%15131516
xxmanxxman15131.0/2 = 50.00%15191507
Leon Careyleoncarey15131.0/1 = 100.00%15081518
pheko Motaungcouriermabovini151235.5/70 = 50.71%15651459
spiptorben15111.0/2 = 50.00%15141509
Antoine Fourrièreantoinefourriere15101.5/2 = 75.00%15051515
Nathanlokor15091.0/2 = 50.00%15111507
mystery playercentipede15092.0/5 = 40.00%15111507
Georg Spengleravunjahei15099.0/28 = 32.14%15001518
Anthony Viensstarkiller15072.0/4 = 50.00%15011513
xeongreyxeongrey15078.0/17 = 47.06%15121502
Zachary Wadeazost1215063.0/5 = 60.00%14991513
As Bardhiasbardhi15051.0/2 = 50.00%15091501
Christine Bagley-Joneszcherryz15030.5/1 = 50.00%15051500
Gee Beegdimension15031.0/2 = 50.00%15041501
Albert Vámosiblackrider_4815021.0/4 = 25.00%15151490
Graeme Neathamgrayhawke15021.0/2 = 50.00%15011504
Colin Adamslionhawk15021.0/2 = 50.00%15051500
Hans Henrikssonhasurami15022.0/4 = 50.00%14921512
Tom Trenchtomdench9515020.5/1 = 50.00%15021501
Kent Weschlerperplexedibex15011.0/3 = 33.33%14981503
noy noynoy15003.0/7 = 42.86%14871513
Colin Weaveruselessgit14991.0/4 = 25.00%14991499
Thom Dimentunwiseowl14982.0/5 = 40.00%15011495
Juan Pablo Schweitzer Kirsingerdefender14971.0/2 = 50.00%14941500
Eni Lienili149611.5/46 = 25.00%15101482
John Smithultimatecoolster14953.0/9 = 33.33%14951494
Max Fengwowimbob111214941.0/3 = 33.33%14981491
Hugo Mendes-Nuneshugo199514920.0/1 = 0.00%14961488
Fabner Cruz Gracilianofabner14920.0/1 = 0.00%14961488
Anders Gustafsonancog14920.0/1 = 0.00%14961487
Bob Brownbobhihih14910.0/1 = 0.00%14961487
wyatt wyattquimssarcasm14910.0/1 = 0.00%14961486
jesus babyboypokechamp14910.0/1 = 0.00%14961485
kunkunkunkun14910.0/1 = 0.00%14951486
Steve Polleychessfan5914900.0/1 = 0.00%14941487
Hsa Saidh14900.0/1 = 0.00%14951485
loveokenloveoken14900.0/1 = 0.00%14941486
hubergerdhubergerd14900.0/1 = 0.00%14961484
xerisianxxerisianx14900.0/1 = 0.00%14941486
Michael Christensenjustsojazz14900.0/1 = 0.00%14961484
Ben Reinigerbenr14900.0/1 = 0.00%14941485
Matias I.tsatziq14890.0/1 = 0.00%14941484
Jason Stehlyjasonstehly14890.0/1 = 0.00%14941484
don anezdonanez14890.0/1 = 0.00%14961483
ugo judeugojude14890.0/1 = 0.00%14951484
Éric Manálangedubble1914890.0/1 = 0.00%14951484
DFA Productions70nyd014890.0/1 = 0.00%14961482
Hafsteinn Kjartanssonhnr0114890.0/1 = 0.00%14961482
John Badgerjbadger14890.0/1 = 0.00%14951483
Ricardo Florentinoricmf14890.0/1 = 0.00%14951483
vikvik14890.0/1 = 0.00%14961481
makomako14890.0/1 = 0.00%14961481
Samuel Hoskinscouriergame14890.0/1 = 0.00%14951482
potato imaginatorpotato14880.0/1 = 0.00%14951482
Esperllynmogik14880.0/1 = 0.00%14951481
Urvish Desaiurvishdesai14880.0/1 = 0.00%14951481
DJ Linickdjlinick14870.0/1 = 0.00%14911483
Rob Brownsteelhead14870.0/1 = 0.00%14911483
DDetective47qqzlbpdilchr14870.0/1 = 0.00%14911482
Boyko Ahtarovzdra4148710.0/23 = 43.48%14861488
Ivan Velascoswordandsilver14860.0/1 = 0.00%14911481
Mike Smolowitzmjs170114860.0/1 = 0.00%14891482
Bradlee Kingstonbrad1914850.0/1 = 0.00%14891481
Gus Dunihoduniho14850.0/1 = 0.00%14881483
Luis Menendezpleyades2114850.0/1 = 0.00%14881483
Andy Thomasandy_thomas14850.0/1 = 0.00%14881482
Travis Comptonironlance14850.0/1 = 0.00%14881482
Erlang Shenerlangshen14850.0/1 = 0.00%14891481
Nasmichael Farrismichaeljay14850.0/1 = 0.00%14891481
Brock Sampsonthe_iron_kenyan14850.0/1 = 0.00%14891481
Aurelian Floreacatugo1484239.5/678 = 35.32%15691399
Derek Mooseelevatorfarter14841.0/3 = 33.33%14841484
Jun Ocampojunpogi14840.0/2 = 0.00%14881480
James Sprattwhittlin14840.0/1 = 0.00%14871481
Julianredpanda148417.0/35 = 48.57%14631505
Alexandr Kremenakremen14840.0/1 = 0.00%14861481
yi fang liuliuyifang14840.0/1 = 0.00%14851482
andy lewickiherlocksholmes14840.0/1 = 0.00%14861481
Jeremy Goodyamorezu14840.0/1 = 0.00%14861481
Jean-Louis Cazauxtimurthelenk14830.0/1 = 0.00%14851481
Turk Osterburgtalen3141593141514830.0/1 = 0.00%14851481
sixtysixty14830.0/3 = 0.00%14851481
higuyzzz91028 Charles Kimdallastexas14830.0/1 = 0.00%14861480
Solomon Salamasol71014830.0/1 = 0.00%14831483
scythian blunderq1234514830.0/2 = 0.00%14891478
Antony Vailevichjabberw0cky114830.0/1 = 0.00%14841482
Paolo Porsiapillau14830.0/1 = 0.00%14841482
manolo manolomanolo14830.0/1 = 0.00%14841481
kittredge Drakedghand14830.0/1 = 0.00%14841481
Dan Kellydankelly14830.0/1 = 0.00%14841481
Jose Canceljoche14830.0/1 = 0.00%14821483
MichaÅ‚ Jarskihookz14830.0/1 = 0.00%14811484
btstwbtstw14830.0/1 = 0.00%14831482
Hung Daobyteboy14830.0/1 = 0.00%14841481
Roberto Cassanotamerlano14830.0/1 = 0.00%14831482
Tony Quintanillatony_quintanilla14820.0/1 = 0.00%14841481
Andreas Bunkahlebunkahle14820.0/1 = 0.00%14841481
Ronald Brierleybenwb14820.0/1 = 0.00%14841480
cdpowercdpower14820.0/1 = 0.00%14831481
Nicholas Archerchess_hunter14820.0/2 = 0.00%14881476
Joseph Grangercdafan14820.0/1 = 0.00%14811482
Thomas Meehanorangeaurochs14820.0/1 = 0.00%14811483
anna colladoapatura_iris14820.0/1 = 0.00%14811482
luigi mattagigino4214820.0/1 = 0.00%14821481
Uri Bruckbruck14820.0/2 = 0.00%14921472
Minh Dangminhdang14820.0/1 = 0.00%14811482
Robin Sneijderrobinwooter214820.0/1 = 0.00%14821481
Виктор Байгужаковbajvik14820.0/1 = 0.00%14811482
Giuseppe Acciarocoopwie14812.0/5 = 40.00%14791484
legendlegend14810.0/2 = 0.00%14901473
wonsang leewonsang14810.0/1 = 0.00%14811481
ben chewben558214810.0/1 = 0.00%14811481
y kumyasuhiro14810.0/1 = 0.00%14811481
Ryan Schwartzshunoshi14810.0/1 = 0.00%14811481
paulblazepaulblaze14810.0/1 = 0.00%14811481
Babo Jeffbabojeff14810.0/1 = 0.00%14811481
14810.0/1 = 0.00%14811481
Vitali Maslanskivitali_1014810.0/1 = 0.00%14811481
Harry Gaoharrygao14810.0/1 = 0.00%14811481
Abe Anonapostateabe14810.0/1 = 0.00%14811481
blundermanblunderman14810.0/1 = 0.00%14801482
László Gadosdani198314811.0/4 = 25.00%14781484
Mark Thompsonmarkthompson14810.0/2 = 0.00%14921469
arcasorarcasor14800.0/1 = 0.00%14791481
Diego M.diego14800.0/3 = 0.00%14841475
rederikrederik14800.0/1 = 0.00%14791480
championchampion14800.0/2 = 0.00%14841475
qidb602qidb60214790.0/2 = 0.00%14831475
voicantvoicant14790.0/1 = 0.00%14771480
Bn Emnelk11414780.0/2 = 0.00%14831473
ologyology14780.0/1 = 0.00%14741481
Ivan Kosintsevbombino14780.0/1 = 0.00%14741481
Francesco Casalinofrancesco14770.0/2 = 0.00%14841470
Frank Istvánistvan6014770.0/2 = 0.00%14851468
andres fuentesxabyer14770.0/2 = 0.00%14791474
trtztrtz gfghtrtztrtz14760.0/2 = 0.00%14781475
Alexander Krutikovlonewolf14761.0/4 = 25.00%14731479
Armin Liebhartlunaris147619.0/45 = 42.22%14691483
wdtrwdtr14760.0/3 = 0.00%14781473
Ivan Ivankillbill22514760.0/1 = 0.00%14701481
tedy efwttei27fmrw7de14750.0/1 = 0.00%14681481
Francisco Magalhãeslowcarbknight14750.0/1 = 0.00%14671482
Charles Gilmancharles_gilman14740.0/2 = 0.00%14731474
Pablo Denegrideep_thinker14730.0/2 = 0.00%14751472
Lennon Figueiredogiwseppe14731.0/4 = 25.00%14711476
John Twycrossjt14730.0/2 = 0.00%14741473
Szling Ozecszling_ozec14730.0/3 = 0.00%14771469
Kacper Rutkowskikacperrutkowski14720.0/2 = 0.00%14741469
Sergey Biryukovsbiryukov14710.0/4 = 0.00%14711471
Pat Quexionezsuperpatzermaste14710.0/4 = 0.00%14731468
andrewthepawnandrewthepawn14700.0/2 = 0.00%14671473
Travis Comptonblackrood14700.0/2 = 0.00%14721467
dfe6631dfe663114690.0/2 = 0.00%14691470
Zoli M Zoltánbaltazarprof14690.0/5 = 0.00%14821457
Steve Hsteve_201014690.0/2 = 0.00%14691469
Daniel MacDuffdanielmacduff14690.0/3 = 0.00%14691468
iuchi45iuchi4514680.0/2 = 0.00%14671469
Adam DeWittchessshogi14680.0/3 = 0.00%14741462
jeremy diniericharles_bukowski14680.0/2 = 0.00%14661469
cherokee malansailorhertzog14670.0/2 = 0.00%14711464
Memedes Lulagiwseppe314670.0/2 = 0.00%14691466
Zac Sparxkrinid14660.0/2 = 0.00%14681464
A tomiatomi14664.5/16 = 28.12%14611471
Donut Donutdonutdonut14660.0/2 = 0.00%14661465
playshogiplayshogi14640.0/2 = 0.00%14641464
Scott Crawfordmathemagician14640.0/7 = 0.00%14731455
Michael Nelsonmikenels14630.0/2 = 0.00%14611466
michael collinsverderben14631.0/5 = 20.00%14691457
Namik Zadenamik14630.0/2 = 0.00%14611465
andy lewickietaoni14630.0/2 = 0.00%14621463
Michael Huntkronsteen3314590.0/3 = 0.00%14501467
Paul Rapoportnumerist14580.0/3 = 0.00%14581457
Graemegraemecn14560.0/3 = 0.00%14541458
louisvlouisv14550.0/3 = 0.00%14571453
Andy Lewickiondraszek14550.0/3 = 0.00%14491460
Николай Сокольскийalexich14520.0/4 = 0.00%14551449
Nick Wolffwolff145226.0/72 = 36.11%14151489
John Langleyjonners14520.5/4 = 12.50%14521451
Dayrom Gilallahukbar14520.0/3 = 0.00%14511453
Michael Schmahlmschmahl14515.0/15 = 33.33%14601442
Joshua Tsamraku14495.0/12 = 41.67%14251473
Linn Russellfreakat14490.0/3 = 0.00%14491449
Scott McGrealagentofchaos14497.0/19 = 36.84%14541444
Adalbertus Kchewoj14491.0/5 = 20.00%14451452
Aaron Maynardvopi14481.0/6 = 16.67%14441453
Jeremy Goodjudgmentality144743.5/127 = 34.25%14391454
vitaliy ravitztalsterch14462.0/15 = 13.33%14371456
heche60heche6014422.0/12 = 16.67%14411442
boukineboukine14404.0/12 = 33.33%14191461
dmitarzvonimirdmitarzvonimir14390.0/5 = 0.00%14351444
Sagi Gabaysagig7214390.5/16 = 3.12%14211456
Evan Jorgensonsabataegalo14370.0/7 = 0.00%14241450
Evert Jan Karmanevertvb14342.5/11 = 22.73%14201449
Phoenix TKartkr10101014332.0/9 = 22.22%14361430
juan rodriguezrodriguez143011.5/38 = 30.26%14371424
Jon Dannjon_dann14290.0/4 = 0.00%14261433
Jack Zavierubersketch14220.0/6 = 0.00%14171426
Alan Galetornadic14213.0/20 = 15.00%14181424
Daniil Frolovflowermann14203.0/16 = 18.75%14041436
Matthew La Valleesherman10114196.0/23 = 26.09%14031434
Arthur Yvrardtorendil14160.0/7 = 0.00%14111421
George Dukegwduke141342.5/117 = 36.32%13501475
John Davischappy14113.0/17 = 17.65%14001422
Samuel de Souzasamsou14110.0/8 = 0.00%14111411
Jeremy Hook10011014112.0/30 = 6.67%14101412
yellowturtleyellowturtle14100.0/10 = 0.00%14131408
Evan Jorgensonejorgens14090.0/7 = 0.00%14001418
Митя Стрелецкийsocrat8313970.0/10 = 0.00%13951399
darren paullramalam139413.5/100 = 13.50%13681420
Bogot Bogotolbog138512.0/44 = 27.27%13621407
Jarid Carlsonsacredchao137713.0/68 = 19.12%13411413
Сергей Маэстроfantomas13430.0/30 = 0.00%13541331
Diogen Abramelindanko13330.0/35 = 0.00%13091357
Oisín D.sxg130642.0/189 = 22.22%12791333
per hommerbergper3113012.0/48 = 4.17%12831319
Сергей Бугаевскийbugaevsky12893.0/56 = 5.36%12721306
Alisher Bolsaniraja8512890.0/33 = 0.00%12801297
wdtr2wdtr2126718.5/138 = 13.41%12431291

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