Dies war insbesondere früher der Fall, als der Weltschachbund FIDE Schachspieler erst ab einer Wertungszahl von in die Rangliste aufnahm. Da die Elo-. Name, Title, Fed, Rating, G, B-Year. 1, Bluebaum, Matthias, g, GER, , 8, 2, Donchenko, Alexander, g, GER, , 8, Im Dezember erreichte der norwegische Schachspieler Magnus Carlsen mit einer Elo-Zahl von Punkten die höchste Wertungszahl weltweit. <
Weltrangliste der besten Schachspieler nach Elo-Punkten 2020Fabiano Caruana. Name, Title, Fed, Rating, G, B-Year. 1, Bluebaum, Matthias, g, GER, , 8, 2, Donchenko, Alexander, g, GER, , 8, ÖSB-Eloliste: txpinball.com Fide-Eloliste: txpinball.com ICCF-Eloliste: txpinball.com
Elo Rangliste FIDE-Turnier veranstalten VideoThe history of the top chess players over time
Rybka 3 Human bit 4CPU. Strelka 5. Fritz 16 bit. Beef 0. Komodo 4 bit. Naum 4. Nemorino 4. Stockfish 3 bit.
Fritz 15 bit. Komodo 3 bit. Chiron 2 bit 4CPU. Critter 0. Rybka 4 bit. Demolito bit 4CPU. Amoeba 3.
Deep Fritz 14 bit 4CPU. Nemorino 3. Wasp 4. Rodent III 0. Defenchess 1. Topple 0. Koivisto 4. Minic 2. Hiarcs 14 4CPU. Ethereal 9.
Marvin 4. Rybka 3 bit. Chiron 1. DeepSaros 3. Komodo 2. Naum 4 bit 4CPU. Sting SF 5 bit. Sting SF 8. Gull R bit 4CPU. Wasp 2. Sting SF 3 bit.
Sting SF 6. Deep Fritz 13 4CPU. Sting SF 15 bit. Sting SF 9. BlackMamba 1. Deep Junior 13 bit 4CPU. Fat Fritz Junior w bit.
Bobcat 8. SmarThink 1. Pirarucu 2. Sting SF 4 bit. Senpai 1. DeepSaros 2. Pirarucu 3. Rybka 3 Dynamic bit. Sting SF 2 bit.
Deep Junior Deep Fritz 14 bit 1CPU. Deep Shredder 12 bit 4CPU. Rybka 2. Spike 1. Chiron 2 bit. Rybka 3 Human bit. Crafty Marvin 3. Nemorino 2. Sting SF Cheng 4.
Sting SF 19 bit. Sting SF 7. Monolith 2 bit. Rodent IV 0. Sting SF 9 bit. Hakkapeliitta TCEC v2 bit. Sting SF 24 bit.
Bagatur 2. Gull II b2 bit. Weiss 1. Deep Fritz 11 4CPU. Ololi Alkhazashvili. WIM Anna Dergatschova. WFM Nadia Jussupow. Sandra Ulms.
WIM Olga Kozlova. WFM Heike Vogel. WIM Veronika Kiefhaber. WFM Annelen Siegismund. WGM Natalia Straub. Stefanie Duessler.
WIM Nellya Vidonyak. WIM Ulrike Roessler. WIM Brigitte Burchardt. WFM Anna Endress. WFM Caroline Rieseler. WIM Polina Zilberman.
WIM Antje Goehler. WFM Antonia Ziegenfuss. WFM Alina Zahn. WFM Stefanie Scognamiglio. WFM Alisa Frey. WIM Olena Hess. Irina Braeutigam.
Katja Sommaro. Irena Fliter. WFM Franziska Beltz. Marine Zschischang. WFM Hannah Kuckling. Christina Winterholler.
WFM Jevgenija Leveikina. Stefanie Schenk. Carolin Umpfenbach. WFM Margrit Malachowski. Carina Brandt. Marina Limbourg.
Manuela Gerlach-Buedinger. Alina Rath. WIM Constanze Jahn. Astrid Amelang. Karin Chin. Charlotte Sanati.
WFM Fan Zhang. WIM Kerstin Kunze. Olga Weis. Beate Pfau. Katharina Mehling. Elisa Silz. WIM Luba Kopylov. Helene Giss. Steffi Arnhold. WCM Katharina Ricken.
WFM Sylvia Wolf. Sibylle Heyme. WFM Jade Schmidt. WIM Iris Mai. WFM Heike Germann. WIM Claudia Steinbacher. Johanna Bluebaum. WFM Olga Birkholz.
Tanja Pflug. Jacqueline Kobald. Simona Gheng. Susan Erbs. Svenja Butenandt. WFM Doreen Troyke. Kathrin Sewald. Nathalie Waechter. WFM Ingrid Voigt.
Vanessa Braeuer. WFM Dr. Anita Stangl. David Howell. Jeffery Xiong. Alexei Drejew. Alexander Beliavsky.
Alexander Motyljow. Maxim Rodshtein. Daniil Dubow. Gawain Jones. Sachar Jefymenko. Liviu-Dieter Nisipeanu. Jewgeni Najer.
Ferenc Berkes. Sergei Rublewski. Ivan Sokolov. Boris Gratschow. Jon Ludvig Hammer. Emil Sutovsky. Markus Ragger. The K-factor , in the USCF rating system, can be estimated by dividing by the effective number of games a player's rating is based on N e plus the number of games the player completed in a tournament m.
The USCF maintains an absolute rating floor of for all ratings. Thus, no member can have a rating below , no matter their performance at USCF-sanctioned events.
However, players can have higher individual absolute rating floors, calculated using the following formula:.
Higher rating floors exist for experienced players who have achieved significant ratings. Such higher rating floors exist, starting at ratings of in point increments up to , , , A rating floor is calculated by taking the player's peak established rating, subtracting points, and then rounding down to the nearest rating floor.
Under this scheme, only Class C players and above are capable of having a higher rating floor than their absolute player rating.
All other players would have a floor of at most There are two ways to achieve higher rating floors other than under the standard scheme presented above.
If a player has achieved the rating of Original Life Master, their rating floor is set at The achievement of this title is unique in that no other recognized USCF title will result in a new floor.
Pairwise comparisons form the basis of the Elo rating methodology. Performance is not measured absolutely; it is inferred from wins, losses, and draws against other players.
Players' ratings depend on the ratings of their opponents and the results scored against them. The difference in rating between two players determines an estimate for the expected score between them.
Both the average and the spread of ratings can be arbitrarily chosen. Elo suggested scaling ratings so that a difference of rating points in chess would mean that the stronger player has an expected score which basically is an expected average score of approximately 0.
A player's expected score is their probability of winning plus half their probability of drawing. Thus, an expected score of 0. The probability of drawing, as opposed to having a decisive result, is not specified in the Elo system.
Instead, a draw is considered half a win and half a loss. In practice, since the true strength of each player is unknown, the expected scores are calculated using the player's current ratings as follows.
It then follows that for each rating points of advantage over the opponent, the expected score is magnified ten times in comparison to the opponent's expected score.
When a player's actual tournament scores exceed their expected scores, the Elo system takes this as evidence that player's rating is too low, and needs to be adjusted upward.
Similarly, when a player's actual tournament scores fall short of their expected scores, that player's rating is adjusted downward.
Elo's original suggestion, which is still widely used, was a simple linear adjustment proportional to the amount by which a player overperformed or underperformed their expected score.
The formula for updating that player's rating is. This update can be performed after each game or each tournament, or after any suitable rating period.
An example may help to clarify. Suppose Player A has a rating of and plays in a five-round tournament. He loses to a player rated , draws with a player rated , defeats a player rated , defeats a player rated , and loses to a player rated The expected score, calculated according to the formula above, was 0.
Note that while two wins, two losses, and one draw may seem like a par score, it is worse than expected for Player A because their opponents were lower rated on average.
Therefore, Player A is slightly penalized. New players are assigned provisional ratings, which are adjusted more drastically than established ratings.
The principles used in these rating systems can be used for rating other competitions—for instance, international football matches.
See Go rating with Elo for more. The first mathematical concern addressed by the USCF was the use of the normal distribution.
They found that this did not accurately represent the actual results achieved, particularly by the lower rated players. Instead they switched to a logistic distribution model, which the USCF found provided a better fit for the actual results achieved.
The second major concern is the correct "K-factor" used. If the K-factor coefficient is set too large, there will be too much sensitivity to just a few, recent events, in terms of a large number of points exchanged in each game.
And if the K-value is too low, the sensitivity will be minimal, and the system will not respond quickly enough to changes in a player's actual level of performance.
Elo's original K-factor estimation was made without the benefit of huge databases and statistical evidence. Sonas indicates that a K-factor of 24 for players rated above may be more accurate both as a predictive tool of future performance, and also more sensitive to performance.
Certain Internet chess sites seem to avoid a three-level K-factor staggering based on rating range. The USCF which makes use of a logistic distribution as opposed to a normal distribution formerly staggered the K-factor according to three main rating ranges of:.
Currently, the USCF uses a formula that calculates the K-factor based on factors including the number of games played and the player's rating.
The K-factor is also reduced for high rated players if the event has shorter time controls. FIDE uses the following ranges: .
FIDE used the following ranges before July . The gradation of the K-factor reduces ratings changes at the top end of the rating spectrum, reducing the possibility for rapid ratings inflation or deflation for those with a low K-factor.
This might in theory apply equally to an online chess site or over-the-board players, since it is more difficult for players to get much higher ratings when their K-factor is reduced.
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