, Aktiv (CElo), Aktiv Fide, Turnier (CElo), Turnier Fide. Millennium The King Element ARM Cortex M7 MHz, , Millennium ChessGenius. Author: Thomas Beck. Falls es Probleme mit dem Java-Plugin geben sollte, existiert auch eine rudimentäre HTML-Version. Navigation ueberspringen. Turniere. The Rating of Chess Players, Past and Present | Elo, Arpad E., Sloan, Sam | ISBN: | Kostenloser Versand für alle Bücher mit Versand und.
Liste der Schachspieler mit einer Elo-Zahl von 2700 oder mehrAuthor: Thomas Beck. Falls es Probleme mit dem Java-Plugin geben sollte, existiert auch eine rudimentäre HTML-Version. Navigation ueberspringen. Turniere. , Aktiv (CElo), Aktiv Fide, Turnier (CElo), Turnier Fide. Millennium The King Element ARM Cortex M7 MHz, , Millennium ChessGenius. Die Elo-Zahl ist eine Wertungszahl, die die Spielstärke von Schach- und Gospielern beschreibt. Bei der Zürich Chess Challenge wurde im Januar erstmals Kategorie 23 (mit einem Elo-Durchschnitt von ) erreicht.
Elo Chess Navigation menu VideoThe Elo Rating System for Chess and Beyond
In turn, when the lower-rated player wins, this achievement is considered much more significant, and that player's reward is more points added to their rating.
The higher-rated player, though, is penalized accordingly. To determine the exact amount of points a player would win or lose after a game, several complex mathematical calculations are needed.
Do not worry, though, because Chess. After every rated game, your rating is updated instantly. Almost all chess federations and websites around the world use the Elo rating system or a variation of it, such as the Glicko system.
This measurement of a player's strength has become the standard in the chess world, so it is the easiest way to assess someone's level of play.
In addition, the Elo system is a statistical model that operates solely based on the outcomes of the games played. As a result, this measurement is more precise than merely judging a player's strength based on subjective and arbitrary elements of the game.
If a person makes "the most beautiful sacrifices" or plays "the most impressive defensive moves," for example, this achievement is not reflected in their rating unless they win.
Although this mathematical approach for measuring how good players are is more accurate than ones based on opinion, it is essential to note that it does have its limitations.
As of April , the Hydra supercomputer was possibly the strongest "over the board" chess player in the world; its playing strength is estimated by its creators to be over on the FIDE scale.
This is consistent with its six game match against Michael Adams in in which the then seventh-highest-rated player in the world only managed to score a single draw.
However, six games are scant statistical evidence and Jeff Sonas suggested that Hydra was only proven to be above by that single match taken in isolation.
On a slightly firmer footing is Rybka. As of January , Rybka is rated by several lists within , depending on the hardware it is run on and the version of software used.
Without such calibration, different rating pools are independent, and can only be used for relative comparison within the pool. The primary goal of Elo ratings is to accurately predict game results between contemporary competitors, and FIDE ratings perform this task relatively well.
A secondary, more ambitious goal is to use ratings to compare players between different eras. It would be convenient if a FIDE rating of meant the same thing in that it meant in If the ratings suffer from inflation , then a modern rating of means less than a historical rating of , while if the ratings suffer from deflation , the reverse will be true.
Unfortunately, even among people who would like ratings from different eras to "mean the same thing", intuitions differ sharply as to whether a given rating should represent a fixed absolute skill or a fixed relative performance.
Those who believe in absolute skill including FIDE would prefer modern ratings to be higher on average than historical ratings, if grandmasters nowadays are in fact playing better chess.
By this standard, the rating system is functioning perfectly if a modern rated player would have a fifty percent chance of beating a rated player of another era, were it possible for them to play.
Time travel is widely believed to be impossible, but the advent of strong chess computers allows a somewhat objective evaluation of the absolute playing skill of past chess masters, based on their recorded games.
Those who believe in relative performance would prefer the median rating or some other benchmark rank of all eras to be the same.
By one relative performance standard, the rating system is functioning perfectly if a player in the twentieth percentile of world rankings has the same rating as a player in the twentieth percentile used to have.
Ratings should indicate approximately where a player stands in the chess hierarchy of his own era. The average FIDE rating of top players has been steadily climbing for the past twenty years, which is inflation and therefore undesirable from the perspective of relative performance.
However, it is at least plausible that FIDE ratings are not inflating in terms of absolute skill. Perhaps modern players are better than their predecessors due to a greater knowledge of openings and due to computer-assisted tactical training.
In any event, both camps can agree that it would be undesirable for the average rating of players to decline at all, or to rise faster than can be reasonably attributed to generally increasing skill.
Both camps would call the former deflation and the latter inflation. Not only do rapid inflation and deflation make comparison between different eras impossible, they tend to introduce inaccuracies between more-active and less-active contemporaries.
If the winner gains N rating points, the loser should drop by N rating points. The intent is to keep the average rating constant, by preventing points from entering or leaving the system.
Unfortunately, this simple approach typically results in rating deflation, as the USCF was quick to discover.
Rating points enter the system every time a previously unrated player gets an initial rating. Likewise rating points leave the system every time someone retires from play.
Most players are significantly better at the end of their careers than at the beginning, so they tend to take more points away from the system than they brought in, and the system deflates as a result.
In order to combat deflation, most implementations of Elo ratings have a mechanism for injecting points into the system.
FIDE has two inflationary mechanisms. First, performances below a "ratings floor" are not tracked, so a player with true skill below the floor can only be unrated or overrated, never correctly rated.
Second, established and higher-rated players have a lower K-factor. There is no theoretical reason why these should provide a proper balance to an otherwise deflationary scheme; perhaps they over-correct and result in net inflation beyond the playing population's increase in absolute skill.
On the other hand, there is no obviously superior alternative. Performance can't be measured absolutely; it can only be inferred from wins and losses.
Ratings therefore have meaning only relative to other ratings. Therefore, 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 of approximately 0.
In practice, since the true strength of each player is unknown, the expected scores are calculated using the player's current ratings.
When a player's actual tournament scores exceed his 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 his 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 his expected score.
The formula for updating his rating is. This update can be performed after each game or each tournament, or after any suitable rating period.
An example may help 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 His expected score, calculated according the formula above, was 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.
In some cases the rating system can discourage game activity for players who wish to protect their rating. Beyond the chess world, concerns over players avoiding competitive play to protect their ratings caused Wizards of the Coast to abandon the Elo system for Magic: the Gathering tournaments in favour of a system of their own devising called "Planeswalker Points".
A more subtle issue is related to pairing. When players can choose their own opponents, they can choose opponents with minimal risk of losing, and maximum reward for winning.
In the category of choosing overrated opponents, new entrants to the rating system who have played fewer than 50 games are in theory a convenient target as they may be overrated in their provisional rating.
The ICC compensates for this issue by assigning a lower K-factor to the established player if they do win against a new rating entrant.
The K-factor is actually a function of the number of rated games played by the new entrant. Therefore, Elo ratings online still provide a useful mechanism for providing a rating based on the opponent's rating.
Its overall credibility, however, needs to be seen in the context of at least the above two major issues described — engine abuse, and selective pairing of opponents.
The ICC has also recently introduced "auto-pairing" ratings which are based on random pairings, but with each win in a row ensuring a statistically much harder opponent who has also won x games in a row.
With potentially hundreds of players involved, this creates some of the challenges of a major large Swiss event which is being fiercely contested, with round winners meeting round winners.
This approach to pairing certainly maximizes the rating risk of the higher-rated participants, who may face very stiff opposition from players below , for example.
This is a separate rating in itself, and is under "1-minute" and "5-minute" rating categories. Maximum ratings achieved over are exceptionally rare.
An increase or decrease in the average rating over all players in the rating system is often referred to as rating inflation or rating deflation respectively.
For example, if there is inflation, a modern rating of means less than a historical rating of , while the reverse is true if there is deflation.
Using ratings to compare players between different eras is made more difficult when inflation or deflation are present. See also Comparison of top chess players throughout history.
It is commonly believed that, at least at the top level, modern ratings are inflated. For instance Nigel Short said in September , "The recent ChessBase article on rating inflation by Jeff Sonas would suggest that my rating in the late s would be approximately equivalent to in today's much debauched currency".
By when he made this comment, would only have ranked him 65th, while would have ranked him equal 10th. It has been suggested that an overall increase in ratings reflects greater skill.
The advent of strong chess computers allows a somewhat objective evaluation of the absolute playing skill of past chess masters, based on their recorded games, but this is also a measure of how computerlike the players' moves are, not merely a measure of how strongly they have played.
The number of people with ratings over has increased. Around there was only one active player Anatoly Karpov with a rating this high.
In Viswanathan Anand was only the 8th player in chess history to reach the mark at that point of time. The current benchmark for elite players lies beyond One possible cause for this inflation was the rating floor, which for a long time was at , and if a player dropped below this they were stricken from the rating list.
As a consequence, players at a skill level just below the floor would only be on the rating list if they were overrated, and this would cause them to feed points into the rating pool.
By July it had increased to In a pure Elo system, each game ends in an equal transaction of rating points. If the winner gains N rating points, the loser will drop by N rating points.
This prevents points from entering or leaving the system when games are played and rated. However, players tend to enter the system as novices with a low rating and retire from the system as experienced players with a high rating.
I feel much better than this and I think my endgame is my strong-point and I have a firm grasp on middle-game tactics, and have noticed a lot of my losses can be attributed to openings blunders or just overall lack of opening knowledge so I've begun to study openings, which I know most don't recommend for "beginners" which I am not, and have been playing chess since I was 5, but I realize I'm not a very strong player and I am dead-set on improving.
So I need to have an accurate idea of my rating so I can both study accordingly and see results which is key to improvement in anything.
So you're basing your study plan on a mysterious number so that you can filter out material that's correct for your level? I don't see why you need a number to do that.
Pursue material suitable for and if you find it too rudimentary, move to books recommended for the next rating class. Rinse and repeat.
It isn't too hard. IT doesn't take too long to figure out what's over your head and what isn't. I don't see a phenomenal jump in the efficiency of your study-time by hunting down this mythical number and THEN filtering the quality of material flowing into your cranium Or better yet, taking a closer look at your lost games and having a strong-er player go over them with you.
My CFC rating used to about On here, my blitz rating fluctuates anywhere between and admittedly on the lower end right now. Shivsky, thanks for your input and while I can't help but agree with your sentiments I do think there is some value to knowing how one ranks up with other players and because I am pursuing a stronger game I can't help but look to others for suggestions.
So yes I could figure out for myself what is and isn't beneficial for me to learn - whether it's too elementary or over my head, when starting out a study plan I'd rather take a tried and true r approach rather than follow my own unorganized study plan.
This helps me personally with staying on track rather than getting distracted and jumping from study topic to study topic and I can remain focused.
All in all, I'm not one to conform to trodding the beaten path, but at the same time I want to avoid going it freestyle on my own, and just wanted to better understand my skill level so I can plot my study accordingly.
That said, I'm not trying to "filter out" anything based on the number, but I'm trying to "filter out" the things based on what the number represents.
I am not following my number blindly, I know to take statistics with a grain of salt. Knowing where one stands against others can not be ignored when competing with others.
My best example of all of this would be if I asked members here on the forum what they recommend I study, the first question they'd ask, as information they'd need to base their answer on, would likely be my rating.
Fair enough As far as books go, there's the Novice Test in Danny Kopec's Test, Evaluate and Improve your Chess and the very comprehensive Igor Khelmnitsky Chess Rating Exam if you want to get a good approximation without actually playing a Federation rated tournament game.
The other way out is for you to post one of your losses in this thread and you'll find most of the decent folk here who play rated tournaments could size you up rather quickly.
FIDE tournements is 2 hours each player each game. There is alot of difference between both 5 mins and 3 days. You cant find your elo without playing in a elo rated tournement.