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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'

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:68.40%68.93%66.91%
NameUseridGCRPercent wonGCR1GCR2
Hexa Sakkbosa601847136.5/151 = 90.40%18181876
Francis Fahystamandua1837216.0/262 = 82.44%18261849
Kevin Paceypanther1767299.0/372 = 80.38%17691766
dax00dax00174485.0/90 = 94.44%17291758
Carlos Cetinasissa1712516.5/831 = 62.15%16971727
Cameron Milesshatteredglass170115.0/17 = 88.24%16901712
Jochen Muellerleopold_stotch169355.0/92 = 59.78%16831703
H Spetyura167513.0/13 = 100.00%16631687
Gary Giffordpenswift167060.5/85 = 71.18%15761765
Fergus Dunihofergus165759.5/97 = 61.34%16691645
Jose Carrilloj_carrillo_vii165485.5/151 = 56.62%16591650
Play Testerplaytester165418.5/25 = 74.00%16521655
David Paulowichdavid_64162111.0/13 = 84.62%16241617
Vitya Makovmakov16117.5/8 = 93.75%16051618
Homo Simiaalienum16097.0/8 = 87.50%15961622
shift2shiftshift2shift160811.0/19 = 57.89%16151601
Tim O'Lenatim_olena16086.5/8 = 81.25%16131603
Andreas Kaufmannandreas16077.0/7 = 100.00%16081605
Charles Danielfrozen_methane160435.0/64 = 54.69%15701639
John Gallantbigjohn158912.0/16 = 75.00%15571621
ctzctz158112.0/17 = 70.59%15491613
Vitya Makovmakov3331576271.0/646 = 41.95%15261625
kokoszkokosz15757.0/8 = 87.50%15601590
Abdul-Rahman Sibahisibahi157316.0/23 = 69.57%15721573
attack hippoattackhippo15725.5/7 = 78.57%15641580
je jujejujeju157136.5/60 = 60.83%15631579
Alexander Trotterqilin15684.0/4 = 100.00%15651570
Stephen Stockmanstevestockman156610.0/16 = 62.50%15691562
TH6notath615607.0/12 = 58.33%15551564
Raymond Dlewel155913.0/22 = 59.09%15751543
pallab basupallab155928.0/44 = 63.64%15831535
Greg Strongmageofmaple155879.0/163 = 48.47%16201497
Jenard Cabilaomgawalangmagawa155811.0/23 = 47.83%15651551
Nicola Caridiniccar15543.0/3 = 100.00%15571550
Nicholas Wolffnwolff15509.0/15 = 60.00%15711528
Roberto Lavierirlavieri200315503.0/3 = 100.00%15451555
erikerik1548106.5/193 = 55.18%15551541
S Ssim15436.0/9 = 66.67%15311554
carlos carloscarlos153816.0/27 = 59.26%15111566
Tom e4ktome4k15372.0/2 = 100.00%15381536
Eric Greenwoodcavalier15344.0/6 = 66.67%15441524
Todd Witterstoddw15342.0/2 = 100.00%15321535
Neil Spargospargo15323.0/4 = 75.00%15211543
Matthew Montchalinmatthew_montchal15313.0/4 = 75.00%15301533
Jake Palladinocerebralassassin15312.0/2 = 100.00%15281534
Julien Coll Moratfacteurix15292.0/3 = 66.67%15281530
Joseph DiMurotrojh15281.0/1 = 100.00%15321524
Isaac Felpsattacker14415283.0/4 = 75.00%15281528
Fred Koktangram15282.0/3 = 66.67%15291527
Uwe Kreuzercaissus15282.0/2 = 100.00%15261529
joe rosenbloombootzilla15272.0/3 = 66.67%15261529
Nicholas Wolffmaeko152665.5/142 = 46.13%15461506
Adrian Alvarez de la Campaadrian15243.5/6 = 58.33%15251523
Yeinzon Rodríguez Garcíayeinzon15231.0/1 = 100.00%15271520
Anthony Viensstarkiller15232.0/3 = 66.67%15231524
von raidervonraider15201.0/1 = 100.00%15221518
Larry Wheelerbrainburner15191.0/1 = 100.00%15201519
Chuck Leegyw6t151917.5/39 = 44.87%15061532
michirmichir15191.0/1 = 100.00%15191518
dicepawndicepawn15191.0/1 = 100.00%15191519
Todor Tchervenkovtchervenkov15181.0/1 = 100.00%15171519
Richard Titlertitle15181.0/1 = 100.00%15181518
David Levinsmidrael15181.0/1 = 100.00%15181518
Antonio Bruzzitotonno_janggi15181.0/1 = 100.00%15181518
calebblazecalebblaze15181.0/1 = 100.00%15181518
jj15181.0/1 = 100.00%15181518
eunchong leeeunchong15181.0/1 = 100.00%15181518
Angel47 Usmanangel4715181.0/1 = 100.00%15181518
whitenerdy53whitenerdy5315181.0/1 = 100.00%15181518
Trevor Savagesavage15181.0/1 = 100.00%15181518
yas kumkumagai15181.0/1 = 100.00%15181518
Jan Żmudajanzmuda15171.0/1 = 100.00%15181517
Titus Ledbettertbl215171.0/1 = 100.00%15181517
Hesham Husseinegy_sniper15171.0/1 = 100.00%15181516
M Wintherkalroten15171.0/1 = 100.00%15161518
bosa6bosa615171.0/1 = 100.00%15151519
Garrett Smithgmsmith15161.0/2 = 50.00%15241509
Antonio Barratotonno15161.0/1 = 100.00%15131519
Georges-Clounet Jesuispartoutgeorgesclounet15151.0/1 = 100.00%15131518
Aaron Smithzirtoc15152.5/5 = 50.00%15101520
pink sockpickett_aaron15152.0/3 = 66.67%15151515
Simon Langley-Evansslangers15151.5/2 = 75.00%15131516
Antoine Fourrièreantoinefourriere15111.5/2 = 75.00%15071515
xxmanxxman15091.0/2 = 50.00%15181501
mystery playercentipede15082.0/5 = 40.00%15101505
Joe Joycejoejoyce150720.5/54 = 37.96%14861528
Zachary Wadeazost1215063.0/5 = 60.00%15001513
Nathanlokor15051.0/2 = 50.00%15111500
xeongreyxeongrey15058.0/17 = 47.06%15121498
pheko Motaungcouriermabovini150435.5/70 = 50.71%15641443
Gee Beegdimension15031.0/2 = 50.00%15031502
Christine Bagley-Joneszcherryz15020.5/1 = 50.00%15051500
Colin Adamslionhawk15021.0/2 = 50.00%15051500
Tom Trenchtomdench9515020.5/1 = 50.00%15041500
As Bardhiasbardhi15021.0/2 = 50.00%15041500
Graeme Neathamgrayhawke15011.0/2 = 50.00%14981505
Albert Vámosiblackrider_4815011.0/4 = 25.00%15141488
Hans Henrikssonhasurami15002.0/4 = 50.00%14891511
Aurelian Floreacatugo1500218.5/535 = 40.84%16051395
noy noynoy14993.0/7 = 42.86%14881509
Thom Dimentunwiseowl14982.0/5 = 40.00%14981497
Juan Pablo Schweitzer Kirsingerdefender14981.0/2 = 50.00%14951500
Kent Weschlerperplexedibex14971.0/3 = 33.33%14951500
Georg Spengleravunjahei14979.0/28 = 32.14%14861508
Colin Weaveruselessgit14971.0/4 = 25.00%14951498
Max Fengwowimbob111214941.0/3 = 33.33%14971491
arxarx149211.0/24 = 45.83%14951489
jesus babyboypokechamp14920.0/1 = 0.00%14961487
Hsa Saidh14910.0/1 = 0.00%14961487
John Smithultimatecoolster14913.0/9 = 33.33%14881494
don anezdonanez14910.0/1 = 0.00%14951488
Michael Christensenjustsojazz14910.0/1 = 0.00%14951487
hubergerdhubergerd14910.0/1 = 0.00%14951486
Eni Lienili149111.5/46 = 25.00%15031478
Bob Brownbobhihih14900.0/1 = 0.00%14951486
wyatt wyattquimssarcasm14900.0/1 = 0.00%14951485
DFA Productions70nyd014900.0/1 = 0.00%14961484
vikvik14900.0/1 = 0.00%14961484
Hugo Mendes-Nuneshugo199514890.0/1 = 0.00%14961483
kunkunkunkun14890.0/1 = 0.00%14961482
makomako14890.0/1 = 0.00%14951482
Fabner Cruz Gracilianofabner14890.0/1 = 0.00%14961481
Hafsteinn Kjartanssonhnr0114880.0/1 = 0.00%14961481
Jason Stehlyjasonstehly14880.0/1 = 0.00%14931484
loveokenloveoken14880.0/1 = 0.00%14931484
Steve Polleychessfan5914880.0/1 = 0.00%14931483
Éric Manálangedubble1914880.0/1 = 0.00%14931482
xerisianxxerisianx14870.0/1 = 0.00%14931481
John Badgerjbadger14870.0/1 = 0.00%14911482
ugo judeugojude14860.0/1 = 0.00%14911481
DJ Linickdjlinick14860.0/1 = 0.00%14901483
Ivan Velascoswordandsilver14860.0/1 = 0.00%14901482
Rob Brownsteelhead14860.0/1 = 0.00%14901481
Mike Smolowitzmjs170114850.0/1 = 0.00%14891482
Gus Dunihoduniho14850.0/1 = 0.00%14881483
Bradlee Kingstonbrad1914850.0/1 = 0.00%14891481
Andy Thomasandy_thomas14850.0/1 = 0.00%14881482
Luis Menendezpleyades2114850.0/1 = 0.00%14871483
Nasmichael Farrismichaeljay14850.0/1 = 0.00%14891481
Brock Sampsonthe_iron_kenyan14850.0/1 = 0.00%14881482
Travis Comptonironlance14850.0/1 = 0.00%14881481
Erlang Shenerlangshen14850.0/1 = 0.00%14881481
Derek Mooseelevatorfarter14841.0/3 = 33.33%14841484
James Sprattwhittlin14840.0/1 = 0.00%14871481
Talen Storlatalen3141593141514840.0/1 = 0.00%14861481
manolo manolomanolo14840.0/1 = 0.00%14851482
Jeremy Goodyamorezu14840.0/1 = 0.00%14851482
Antony Vailevichjabberw0cky114840.0/1 = 0.00%14861481
yi fang liuliuyifang14830.0/1 = 0.00%14861481
andy lewickiherlocksholmes14830.0/1 = 0.00%14851481
trtztrtz gfghtrtztrtz14830.0/1 = 0.00%14851481
Thomas Meehanorangeaurochs14830.0/1 = 0.00%14831483
Joseph Grangercdafan14830.0/1 = 0.00%14841482
luigi mattagigino4214830.0/1 = 0.00%14841481
Jose Canceljoche14830.0/1 = 0.00%14821483
MichaÅ‚ Jarskihookz14830.0/1 = 0.00%14831482
Roberto Cassanotamerlano14830.0/1 = 0.00%14831482
btstwbtstw14830.0/1 = 0.00%14841481
Andreas Bunkahlebunkahle14830.0/1 = 0.00%14841481
Dan Kellydankelly14830.0/1 = 0.00%14841481
Tony Quintanillatony_quintanilla14830.0/1 = 0.00%14851480
Hung Daobyteboy14820.0/1 = 0.00%14841481
sixtysixty14820.0/3 = 0.00%14851480
cdpowercdpower14820.0/1 = 0.00%14831481
anna colladoapatura_iris14820.0/1 = 0.00%14811482
Ronald Brierleybenwb14820.0/1 = 0.00%14821481
Paolo Porsiapillau14820.0/1 = 0.00%14821481
Robin Sneijderrobinwooter214820.0/1 = 0.00%14821481
Minh Dangminhdang14820.0/1 = 0.00%14821481
Виктор Байгужаковbajvik14820.0/1 = 0.00%14811482
Babo Jeffbabojeff14810.0/1 = 0.00%14811481
ben chewben558214810.0/1 = 0.00%14811481
14810.0/1 = 0.00%14811481
Ryan Schwartzshunoshi14810.0/1 = 0.00%14811481
Vitali Maslanskivitali_1014810.0/1 = 0.00%14811481
y kumyasuhiro14810.0/1 = 0.00%14811481
Harry Gaoharrygao14810.0/1 = 0.00%14811481
wonsang leewonsang14810.0/1 = 0.00%14811481
paulblazepaulblaze14810.0/1 = 0.00%14811481
Mark Thompsonmarkthompson14810.0/2 = 0.00%14911472
blundermanblunderman14810.0/1 = 0.00%14811481
Abe Anonapostateabe14810.0/1 = 0.00%14801482
Uri Bruckbruck14800.0/2 = 0.00%14921469
arcasorarcasor14800.0/1 = 0.00%14791481
Giuseppe Acciarocoopwie14802.0/5 = 40.00%14731487
Nicholas Archerchess_hunter14800.0/2 = 0.00%14871473
rederikrederik14800.0/1 = 0.00%14781481
Jun Ocampojunpogi14800.0/2 = 0.00%14861473
Julianredpanda147917.0/35 = 48.57%14611496
László Gadosdani198314791.0/4 = 25.00%14721485
voicantvoicant14790.0/1 = 0.00%14761481
Bn Emnelk11414780.0/2 = 0.00%14831474
Ivan Kosintsevbombino14780.0/1 = 0.00%14741481
Francesco Casalinofrancesco14780.0/2 = 0.00%14831472
ologyology14770.0/1 = 0.00%14731481
Diego M.diego14770.0/3 = 0.00%14831471
Alexander Krutikovlonewolf14771.0/4 = 25.00%14751479
Frank Istvánistvan6014760.0/2 = 0.00%14841468
Ivan Ivankillbill22514760.0/1 = 0.00%14701481
andres fuentesxabyer14760.0/2 = 0.00%14751476
championchampion14750.0/2 = 0.00%14841467
wdtrwdtr14730.0/3 = 0.00%14771469
Pablo Denegrideep_thinker14730.0/2 = 0.00%14731473
Charles Gilmancharles_gilman14730.0/2 = 0.00%14751471
Szling Ozecszling_ozec14720.0/3 = 0.00%14741470
dfe6631dfe663114720.0/2 = 0.00%14691475
andrewthepawnandrewthepawn14720.0/2 = 0.00%14701473
Michael Huntkronsteen3314710.0/2 = 0.00%14721470
Lennon Figueiredogiwseppe14711.0/4 = 25.00%14711471
Adam DeWittchessshogi14700.0/2 = 0.00%14751466
John Twycrossjt14700.0/2 = 0.00%14741467
Kacper Rutkowskikacperrutkowski14700.0/2 = 0.00%14741466
Travis Comptonblackrood14690.0/2 = 0.00%14651474
Zoli M Zoltánbaltazarprof14680.0/5 = 0.00%14801457
Steve Hsteve_201014670.0/2 = 0.00%14651469
Zac Sparxkrinid14670.0/2 = 0.00%14681465
Diogen Abramelindanko14670.0/4 = 0.00%14701463
cherokee malansailorhertzog14670.0/2 = 0.00%14691464
vitaliy ravitztalsterch14660.0/5 = 0.00%14621471
jeremy diniericharles_bukowski14660.0/2 = 0.00%14651468
iuchi45iuchi4514660.0/2 = 0.00%14671465
Daniel MacDuffdanielmacduff14660.0/3 = 0.00%14661466
Boyko Ahtarovzdra4146610.0/23 = 43.48%14561475
Donut Donutdonutdonut14650.0/2 = 0.00%14651465
Pat Quexionezsuperpatzermaste14650.0/4 = 0.00%14671463
Michael Nelsonmikenels14640.0/2 = 0.00%14621466
playshogiplayshogi14640.0/2 = 0.00%14661463
Sergey Biryukovsbiryukov14640.0/4 = 0.00%14691459
Namik Zadenamik14630.0/2 = 0.00%14611465
A tomiatomi14634.5/16 = 28.12%14551471
Scott Crawfordmathemagician14630.0/7 = 0.00%14701455
andy lewickietaoni14630.0/2 = 0.00%14611464
michael collinsverderben14621.0/5 = 20.00%14661457
louisvlouisv14550.0/3 = 0.00%14571453
Graemegraemecn14530.0/3 = 0.00%14491458
John Langleyjonners14520.5/4 = 12.50%14521451
Andy Lewickiondraszek14510.0/3 = 0.00%14441459
Dayrom Gilallahukbar14510.0/3 = 0.00%14501453
Armin Liebhartlunaris145016.0/37 = 43.24%14621439
Michael Schmahlmschmahl14505.0/15 = 33.33%14551445
Linn Russellfreakat14490.0/3 = 0.00%14481449
boukineboukine14494.0/11 = 36.36%14301467
Adalbertus Kchewoj14471.0/5 = 20.00%14411454
Николай Сокольскийalexich14470.0/4 = 0.00%14461447
Aaron Maynardvopi14451.0/6 = 16.67%14381452
Scott McGrealagentofchaos14457.0/19 = 36.84%14431446
dmitarzvonimirdmitarzvonimir14420.0/4 = 0.00%14361448
heche60heche6014392.0/12 = 16.67%14371442
Nick Wolffwolff143725.0/71 = 35.21%14211453
Evan Jorgensonsabataegalo14370.0/7 = 0.00%14271446
Joshua Tsamraku14364.5/12 = 37.50%14131459
Evert Jan Karmanevertvb14362.5/11 = 22.73%14201451
John Davischappy14323.0/15 = 20.00%14211444
Phoenix TKartkr10101014322.0/9 = 22.22%14331432
Sagi Gabaysagig7214320.5/16 = 3.12%14111452
Jon Dannjon_dann14300.0/4 = 0.00%14271433
Jeremy Goodjudgmentality142943.5/127 = 34.25%14251432
juan rodriguezrodriguez142811.5/38 = 30.26%14401416
Jack Zavierubersketch14250.0/6 = 0.00%14241426
Matthew La Valleesherman10114226.0/23 = 26.09%14041439
Arthur Yvrardtorendil14160.0/7 = 0.00%14111421
Alan Galetornadic14153.0/20 = 15.00%14081422
Daniil Frolovflowermann14133.0/16 = 18.75%14001427
yellowturtleyellowturtle14070.0/10 = 0.00%14071408
Evan Jorgensonejorgens14050.0/7 = 0.00%13921418
Jeremy Hook10011013982.0/30 = 6.67%13901405
George Dukegwduke139742.5/116 = 36.64%13681425
darren paullramalam137210.5/87 = 12.07%13461398
Bogot Bogotolbog136912.0/44 = 27.27%13611378
Jarid Carlsonsacredchao136912.0/57 = 21.05%13371401
sxgsxg134431.5/139 = 22.66%13161373
per hommerbergper3112951.0/36 = 2.78%12931297
Сергей Бугаевскийbugaevsky12793.0/55 = 5.45%12741284
wdtr2wdtr2127313.5/98 = 13.78%12371308

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