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Elo rating system

The Elo[a] rating system is a method for calculating the
relative skill levels of players in zero-sum games such as chess. It
is named after its creator Arpad Elo, a Hungarian-American
physics professor.

The Elo system was originally invented as an improved chess-

rating system over the previously used Harkness system, but is
also used as a rating system for multiplayer competition in a
number of video games,[1] association football, American
football, basketball,[2] Major League Baseball, table tennis, board
games such as Scrabble and Diplomacy, and other games.

The difference in the ratings between two players serves as a

predictor of the outcome of a match. Two players with equal
ratings who play against each other are expected to score an
equal number of wins. A player whose rating is 100 points greater Arpad Elo, the inventor of the Elo
than their opponent's is expected to score 64%; if the difference rating system
is 200 points, then the expected score for the stronger player is
76%.

A player's Elo rating is represented by a number which may change depending on the outcome of
rated games played. After every game, the winning player takes points from the losing one. The
difference between the ratings of the winner and loser determines the total number of points gained
or lost after a game. If the high-rated player wins, then only a few rating points will be taken from the
low-rated player. However, if the lower-rated player scores an upset win, many rating points will be
transferred. The lower-rated player will also gain a few points from the higher rated player in the
event of a draw. This means that this rating system is self-correcting. Players whose ratings are too
low or too high should, in the long run, do better or worse correspondingly than the rating system
predicts and thus gain or lose rating points until the ratings reflect their true playing strength.

An Elo rating is a comparative rating only, and is valid only within the rating pool where it was
established.

Contents
History
Implementing Elo's scheme
Different ratings systems
FIDE ratings
Performance rating
FIDE tournament categories
Live ratings
United States Chess Federation ratings
The K-factor used by the USCF
Rating floors
Theory

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Mathematical details
Mathematical issues
Most accurate distribution model
Most accurate K-factor
Practical issues
Game activity versus protecting one's rating
Selective pairing
Ratings inflation and deflation
Combating deflation
Ratings of computers
Use outside of chess
Athletic sports
Other board and card games
Video games and online games
Other usage
References in the media
See also
Notes
References
Further reading
External links

History
Arpad Elo was a master-level chess player and an active participant in the United States Chess
Federation (USCF) from its founding in 1939.[3] The USCF used a numerical ratings system, devised
by Kenneth Harkness, to allow members to track their individual progress in terms other than
tournament wins and losses. The Harkness system was reasonably fair, but in some circumstances
gave rise to ratings which many observers considered inaccurate. On behalf of the USCF, Elo devised
a new system with a more sound statistical basis.

Elo's system replaced earlier systems of competitive rewards with a system based on statistical
estimation. Rating systems for many sports award points in accordance with subjective evaluations of
the 'greatness' of certain achievements. For example, winning an important golf tournament might be
worth an arbitrarily chosen five times as many points as winning a lesser tournament.

A statistical endeavor, by contrast, uses a model that relates the game results to underlying variables
representing the ability of each player.

Elo's central assumption was that the chess performance of each player in each game is a normally
distributed random variable. Although a player might perform significantly better or worse from one
game to the next, Elo assumed that the mean value of the performances of any given player changes
only slowly over time. Elo thought of a player's true skill as the mean of that player's performance
random variable.

A further assumption is necessary because chess performance in the above sense is still not
measurable. One cannot look at a sequence of moves and derive a number to represent that player's
skill. Performance can only be inferred from wins, draws and losses. Therefore, if a player wins a

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game, they are assumed to have performed at a higher level than their opponent for that game.
Conversely, if the player loses, they are assumed to have performed at a lower level. If the game is a
draw, the two players are assumed to have performed at nearly the same level.

Elo did not specify exactly how close two performances ought to be to result in a draw as opposed to a
win or loss. And while he thought it was likely that players might have different standard deviations
to their performances, he made a simplifying assumption to the contrary.

To simplify computation even further, Elo proposed a straightforward method of estimating the
variables in his model (i.e., the true skill of each player). One could calculate relatively easily from
tables how many games players would be expected to win based on comparisons of their ratings to
those of their opponents. The ratings of a player who won more games than expected would be
adjusted upward, while those of a player who won fewer than expected would be adjusted downward.
Moreover, that adjustment was to be in linear proportion to the number of wins by which the player
had exceeded or fallen short of their expected number.

From a modern perspective, Elo's simplifying assumptions are not necessary because computing
power is inexpensive and widely available. Moreover, even within the simplified model, more efficient
estimation techniques are well known. Several people, most notably Mark Glickman, have proposed
using more sophisticated statistical machinery to estimate the same variables. On the other hand, the
computational simplicity of the Elo system has proven to be one of its greatest assets. With the aid of
a pocket calculator, an informed chess competitor can calculate to within one point what their next
officially published rating will be, which helps promote a perception that the ratings are fair.

Implementing Elo's scheme

The USCF implemented Elo's suggestions in 1960,[4] and the system quickly gained recognition as
being both fairer and more accurate than the Harkness rating system. Elo's system was adopted by
the World Chess Federation (FIDE) in 1970. Elo described his work in some detail in the book The
Rating of Chessplayers, Past and Present, published in 1978.

Subsequent statistical tests have suggested that chess performance is almost certainly not distributed
as a normal distribution, as weaker players have greater winning chances than Elo's model predicts.
Therefore, the USCF and some chess sites use a formula based on the logistic distribution. Significant
statistical anomalies have also been found when using the logistic distribution in chess.[5] FIDE
continues to use the rating difference table as proposed by Elo. The table is calculated with
expectation 0, and standard deviation 200.

The normal and logistic distribution points are, in a way, arbitrary points in a spectrum of
distributions which would work well. In practice, both of these distributions work very well for a
number of different games.

Different ratings systems

The phrase "Elo rating" is often used to mean a player's chess rating as calculated by FIDE. However,
this usage is confusing and misleading because Elo's general ideas have been adopted by many
organizations, including the USCF (before FIDE), many other national chess federations, the short-
lived Professional Chess Association (PCA), and online chess servers including the Internet Chess
Club (ICC), Free Internet Chess Server (FICS), and Yahoo! Games. Each organization has a unique
implementation, and none of them follows Elo's original suggestions precisely. It would be more
accurate to refer to all of the above ratings as Elo ratings and none of them as the Elo rating.

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Instead one may refer to the organization granting the rating. For example: "As of August 2002,
Gregory Kaidanov had a FIDE rating of 2638 and a USCF rating of 2742." The Elo ratings of these
various organizations are not always directly comparable. For example, someone with a FIDE rating
of 2500 will generally have a USCF rating near 2600 and an ICC rating in the range of 2500 to 3100.

FIDE ratings

For top players, the most important rating is their FIDE rating. FIDE has issued the following lists:

From 1971 to 1980, one list a year was issued.

From 1981 to 2000, two lists a year were issued, in January and July.
From July 2000 to July 2009, four lists a year were issued, at the start of January, April, July and
October.
From July 2009 to July 2012, six lists a year were issued, at the start of January, March, May,
July, September and November.
Since July 2012, the list has been updated monthly.

The following analysis of the July 2015 FIDE rating list gives a rough impression of what a given
FIDE rating means in terms of world ranking:

5323 players had an active rating in the range 2200 to 2299, which is usually associated with the
Candidate Master title.
2869 players had an active rating in the range 2300 to 2399, which is usually associated with the
FIDE Master title.
1420 players had an active rating between 2400 and 2499, most of whom had either the
International Master or the International Grandmaster title.
542 players had an active rating between 2500 and 2599, most of whom had the International
Grandmaster title.
187 players had an active rating between 2600 and 2699, all of whom had the International
Grandmaster title.
40 players had an active rating between 2700 and 2799.
4 players had an active rating of over 2800. (Magnus Carlsen was rated 2853, and 3 players were
rated between 2814 and 2816).

The highest ever FIDE rating was 2882, which Magnus Carlsen had on the May 2014 list. A list of the
highest-rated players ever is at Comparison of top chess players throughout history.

Performance rating

Performance rating is a hypothetical rating that would result from the games of a single event only.
Some chess organizations use the "algorithm of 400" to calculate performance rating. According to
this algorithm, performance rating for an event is calculated in the following way:

1. For each win, add your opponent's rating plus 400,

2. For each loss, add your opponent's rating minus 400,
3. And divide this sum by the number of played games.

Example: 2 wins, 2 losses

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1.00 +800
0.99 +677
0.9 +366

This can be expressed by the following formula: 0.8 +240

0.7 +149
0.6 +72
0.5 0
0.4 −72
0.3 −149
0.2 −240
0.1 −366
0.01 −677
0.00 −800

Example: If you beat a player with an Elo rating of 1000,

If you beat two players with Elo ratings of 1000,

If you draw,

This is a simplification, but it offers an easy way to get an estimate of PR (performance rating).

FIDE, however, calculates performance rating by means of the formula: Opponents' Rating Average +
Rating Difference. Rating Difference is based on a player's tournament percentage score , which
is then used as the key in a lookup table where is simply the number of points scored divided by the
number of games played. Note that, in case of a perfect or no score is 800. The full table can be
found in the Manual de la FIDE, B. Permanent Commissions, 02. FIDE Rating Regulations
(Qualification Commission), FIDE Rating Regulations effective from 1 July 2017, 8.1a (https://handb
ook.fide.com/chapter/B022017) online. A simplified version of this table is on the right.

FIDE tournament categories

FIDE classifies tournaments into categories according to the average rating of the players. Each
category is 25 rating points wide. Category 1 is for an average rating of 2251 to 2275, category 2 is
2276 to 2300, etc. For women's tournaments, the categories are 200 rating points lower, so a

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Category 1 is an average rating of 2051 to 2075, etc.[6] The highest- Average rating
rated tournament has been category 23, with an average from 2801 Category
Minimum Maximum
to 2825. The top categories are in the table.
14 2576 2600
15 2601 2625
Live ratings
16 2626 2650
FIDE updates its ratings list at the beginning of each month. In 17 2651 2675
contrast, the unofficial "Live ratings" calculate the change in
18 2676 2700
players' ratings after every game. These Live ratings are based on
the previously published FIDE ratings, so a player's Live rating is 19 2701 2725
intended to correspond to what the FIDE rating would be if FIDE 20 2726 2750
were to issue a new list that day.
21 2751 2775
Although Live ratings are unofficial, interest arose in Live ratings 22 2776 2800
in August/September 2008 when five different players took the
23 2801 2825
"Live" No. 1 ranking.[7]

The unofficial live ratings of players over 2700 were published and maintained by Hans Arild Runde
at the Live Rating website (https://web.archive.org/web/20080603001814/http://chess.liverating.o
rg/) until August 2011. Another website, 2700chess.com (http://www.2700chess.com), has been
maintained since May 2011 by Artiom Tsepotan, which covers the top 100 players as well as the top
50 female players.

Rating changes can be calculated manually by using the FIDE ratings change calculator.[8] All top
players have a K-factor of 10, which means that the maximum ratings change from a single game is a
little less than 10 points.

United States Chess Federation ratings

The United States Chess Federation (USCF) uses its own classification of players:[9]

2400 and above: Senior Master

2200–2399: National Master
2200–2399 plus 300 games above 2200: Original Life Master[10]
2000–2199: Expert or Candidate Master
1800–1999: Class A
1600–1799: Class B
1400–1599: Class C
1200–1399: Class D
1000–1199: Class E
800–999: Class F
600–799: Class G
400–599: Class H
200–399: Class I
100–199: Class J

In general, a beginner (non-scholastic) is 800, the average player is 1500, and professional level is
2200.

The K-factor used by the USCF

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The K-factor, in the USCF rating system, can be estimated by dividing 800 by the effective number of
games a player's rating is based on (Ne) plus the number of games the player completed in a
tournament (m).[11]

Rating floors

The USCF maintains an absolute rating floor of 100 for all ratings. Thus, no member can have a
rating below 100, no matter their performance at USCF-sanctioned events. However, players can
have higher individual absolute rating floors, calculated using the following formula:

where is the number of rated games won, is the number of rated games drawn, and is
the number of events in which the player completed three or more rated games.

Higher rating floors exist for experienced players who have achieved significant ratings. Such higher
rating floors exist, starting at ratings of 1200 in 100-point increments up to 2100 (1200, 1300, 1400,
..., 2100). A rating floor is calculated by taking the player's peak established rating, subtracting 200
points, and then rounding down to the nearest rating floor. For example, a player who has reached a
peak rating of 1464 would have a rating floor of 1464 − 200 = 1264, which would be rounded down to
1200. 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 150.

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 2200. The
achievement of this title is unique in that no other recognized USCF title will result in a new floor. For
players with ratings below 2000, winning a cash prize of $2,000 or more raises that player's rating
floor to the closest 100-point level that would have disqualified the player for participation in the
tournament. For example, if a player won $4,000 in a 1750-and-under tournament, they would now
have a rating floor of 1800.

Theory
Pairwise comparisons form the basis of the Elo rating methodology.[12] Elo made references to the
papers of Good,[13] David,[14] Trawinski and David,[15] and Buhlman and Huber.[16]

Mathematical details

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 200 rating points in chess would mean that the stronger player has an
expected score (which basically is an expected average score) of approximately 0.75, and the USCF
initially aimed for an average club player to have a rating of 1500.

A player's expected score is their probability of winning plus half their probability of drawing. Thus,
an expected score of 0.75 could represent a 75% chance of winning, 25% chance of losing, and 0%
chance of drawing. On the other extreme it could represent a 50% chance of winning, 0% chance of
losing, and 50% chance of drawing. The probability of drawing, as opposed to having a decisive
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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.

If Player A has a rating of and Player B a rating of , the exact formula (using the logistic
curve) [12] for the expected score of Player A is

Similarly the expected score for Player B is

This could also be expressed by

and

where and . Note that in the latter case, the same denominator
applies to both expressions, and it is plain that . This means that by studying only the
numerators, we find out that the expected score for player A is times greater than the
expected score for player B. It then follows that for each 400 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 maximum possible adjustment per game, called the K-factor, was set at K = 16 for masters
and K = 32 for weaker players.

Supposing Player A was expected to score points but actually scored points. 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 1613 and plays in a five-
round tournament. He loses to a player rated 1609, draws with a player rated 1477, defeats a player
rated 1388, defeats a player rated 1586, and loses to a player rated 1720. The player's actual score is
(0 + 0.5 + 1 + 1 + 0) = 2.5. The expected score, calculated according to the formula above, was (0.51 +
0.69 + 0.79 + 0.54 + 0.35) = 2.88. Therefore, the player's new rating is (1613 + 32(2.5 − 2.88)) =
1601, assuming that a K-factor of 32 is used. Equivalently, each game the player can be said to have
put an ante of K times their expected score for the game into a pot, the opposing player does likewise,
and the winner collects the full pot of value K; in the event of a draw, the players split the pot and
receive K/2 points each.
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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. If Player A had scored two wins, one loss, and two draws, for a total score of three
points, that would have been slightly better than expected, and the player's new rating would have
been (1613 + 32(3 − 2.88)) = 1617.

This updating procedure is at the core of the ratings used by FIDE, USCF, Yahoo! Games, the Internet
Chess Club (ICC) and the Free Internet Chess Server (FICS). However, each organization has taken a
different route to deal with the uncertainty inherent in the ratings, particularly the ratings of
newcomers, and to deal with the problem of ratings inflation/deflation. 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.

Elo ratings have also been applied to games without the possibility of draws, and to games in which
the result can also have a quantity (small/big margin) in addition to the quality (win/loss). See Go
rating with Elo for more.

Mathematical issues

There are three main mathematical concerns relating to the original work of Elo, namely the correct
curve, the correct K-factor, and the provisional period crude calculations.

Most accurate distribution model

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. FIDE also uses an approximation to the logistic
distribution.[17]

Most accurate K-factor

The second major concern is the correct "K-factor" used. The chess statistician Jeff Sonas believes
that the original K=10 value (for players rated above 2400) is inaccurate in Elo's work. 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 2400) may be more accurate
both as a predictive tool of future performance, and also more sensitive to performance.[18]

Certain Internet chess sites seem to avoid a three-level K-factor staggering based on rating range. For
example, the ICC seems to adopt a global K=32 except when playing against provisionally rated
players.

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:

Players below 2100: K-factor of 32 used

Players between 2100 and 2400: K-factor of 24 used
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Players above 2400: K-factor of 16 used.

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.[19]

FIDE uses the following ranges:[20]

K = 40, for a player new to the rating list until the completion of events with a total of 30 games
and for all players until their 18th birthday, as long as their rating remains under 2300.
K = 20, for players with a rating always under 2400.
K = 10, for players with any published rating of at least 2400 and at least 30 games played in
previous events. Thereafter it remains permanently at 10.

FIDE used the following ranges before July 2014:[21]

K = 30 (was 25), for a player new to the rating list until the completion of events with a total of 30
games.[22]
K = 15, for players with a rating always under 2400.
K = 10, for players with any published rating of at least 2400 and at least 30 games played in
previous events. Thereafter it remains permanently at 10.

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. When playing online, it may simply
be the selection of highly rated opponents that enables 2800+ players to further increase their
rating,[23] since a grandmaster on the ICC playing site can play a string of different opponents who
are all rated over 2700. In over-the-board events, it would only be in very high level all-play-all events
that a player would be able to engage that number of 2700+ opponents, while in a normal open
Swiss-paired chess tournament, frequently there would be many opponents rated less than 2500,
reducing the ratings gains possible from a single contest.

Practical issues

Game activity versus protecting one's rating

In some cases the rating system can discourage game activity for players who wish to protect their
rating.[24] In order to discourage players from sitting on a high rating, a 2012 proposal by British
Grandmaster John Nunn for choosing qualifiers to the chess world championship included an activity
bonus, to be combined with the rating.[25]

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".[26][27]

Selective pairing

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. Particular
examples of players rated 2800+ choosing opponents with minimal risk and maximum possibility of
rating gain include: choosing computers that they know they can beat with a certain strategy;
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choosing opponents that they think are overrated; or avoiding playing strong players who are rated
several hundred points below them, but may hold chess titles such as IM or GM. 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 3000, for example. This is a separate rating in itself, and is
under "1-minute" and "5-minute" rating categories. Maximum ratings achieved over 2500 are
exceptionally rare.

Ratings inflation and deflation

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 2500 means less than a historical rating of 2500, 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 2009, "The recent ChessBase article on rating inflation by Jeff Sonas would
suggest that my rating in the late 1980s would be approximately equivalent to 2750 in today's much
debauched currency".[28] (Short's highest rating in the 1980s was 2665 in July 1988, which was equal
to third in the world. When he made this comment, 2665 would have ranked him 65th, while 2750
would have ranked him equal 10th. In the September 2012 FIDE rating list, 2665 would have ranked
him equal 86th, while 2750 would have ranked him 13th.)

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.[29]

The number of people with ratings over 2700 has increased. Around 1979 there was only one active
player (Anatoly Karpov) with a rating this high. In 1992 Viswanathan Anand was only the 8th player
in chess history to reach the 2700 mark at that point of time.[30] This increased to 15 players by 1994.
33 players had a 2700+ rating in 2009 and 44 as of September 2012. The current benchmark for elite
players lies beyond 2800.

One possible cause for this inflation was the rating floor, which for a long time was at 2200, 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.[29] In July 2000 the average rating of the top 100 was 2644.
By July 2012 it had increased to 2703.[30]

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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. Therefore, in the long run a
system with strictly equal transactions tends to result
in rating deflation.[31]

In 1995, the USCF acknowledged that several young

scholastic players were improving faster than the
rating system was able to track. As a result,
established players with stable ratings started to lose
rating points to the young and underrated players.
Several of the older established players were
Graphs of probabilities and Elo rating changes
frustrated over what they considered an unfair rating
(for K=16 and 32) of expected outcome (solid
decline, and some even quit chess over it.[32]
curve) and unexpected outcome (dotted curve) vs
initial rating difference. For example, player A
Combating deflation starts with a 1400 rating and B with 1800 in a
tournament using K = 32 (brown curves). The
Because of the significant difference in timing of when blue dash-dot line denotes the initial rating
inflation and deflation occur, and in order to combat difference of 400 (1800 − 1400). The probability of
B winning, the expected outcome, is 0.91
deflation, most implementations of Elo ratings have a
(intersection of black solid curve and blue line); if
mechanism for injecting points into the system in
this happens, A's rating decreases by 3
order to maintain relative ratings over time. FIDE has
(intersection of brown solid curve and blue line) to
two inflationary mechanisms. First, performances
1397 and B's increases by the same amount to
below a "ratings floor" are not tracked, so a player 1803. Conversely, the probability of A winning, the
with true skill below the floor can only be unrated or unexpected outcome, is 0.09 (intersection of
overrated, never correctly rated. Second, established black dotted curve and blue line); if this happens,
and higher-rated players have a lower K-factor. New A's rating increases by 29 (intersection of brown
players have a K = 40, which drops to K = 20 after 30 dotted curve and blue line) to 1429 and B's
played games, and to K = 10 when the player reaches decreases by the same amount to 1771.
2400.[20] The current system in the United States
includes a bonus point scheme which feeds rating
points into the system in order to track improving players, and different K-values for different
players.[32] Some methods, used in Norway for example, differentiate between juniors and seniors,
and use a larger K factor for the young players, even boosting the rating progress by 100% for when
they score well above their predicted performance.[33]

Rating floors in the United States work by guaranteeing that a player will never drop below a certain
limit. This also combats deflation, but the chairman of the USCF Ratings Committee has been critical
of this method because it does not feed the extra points to the improving players. A possible motive
for these rating floors is to combat sandbagging, i.e., deliberate lowering of ratings to be eligible for
lower rating class sections and prizes.[32]

Ratings of computers

Human–computer chess matches between 1997 (Deep Blue versus Garry Kasparov) and 2006
demonstrated that chess computers are capable of defeating even the strongest human players.
However, chess engine ratings are difficult to quantify, due to variable factors such as the time

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control and the hardware the program runs on. Published engine rating lists such as CCRL are based
on engine-only games on standard hardware configurations and are not directly comparable to FIDE
ratings.

For some ratings estimates, see Chess engine § Ratings.

Use outside of chess

Athletic sports

The Elo rating system is used in the chess portion of chess boxing. In order to be eligible for
professional chess boxing, one must have an Elo rating of at least 1600, as well as competing in 50 or
more matches of amateur boxing or martial arts.

American college football used the Elo method as a portion of its Bowl Championship Series rating
systems from 1998 to 2013 after which the BCS was replaced by the College Football Playoff. Jeff
Sagarin of USA Today publishes team rankings for most American sports, which includes Elo system
ratings for college football. The use of rating systems was effectively scrapped with the creation of the
College Football Playoff in 2014; participants in the CFP and its associated bowl games are chosen by
a selection committee.

In other sports, individuals maintain rankings based on the Elo algorithm. These are usually
unofficial, not endorsed by the sport's governing body. The World Football Elo Ratings is an example
of the method applied to men's football.[34] In 2006, Elo ratings were adapted for Major League
Baseball teams by Nate Silver, then of Baseball Prospectus.[35] Based on this adaptation, both also
makes Elo-based Monte Carlo simulations of the odds of whether teams will make the playoffs.[36] In
2014, Beyond the Box Score, an SB Nation site, introduced an Elo ranking system for international
baseball.[37]

In tennis, the Elo-based Universal Tennis Rating (UTR) rates players on a global scale, regardless of
age, gender, or nationality. It is the official rating system of major organizations such as the
Intercollegiate Tennis Association and World TeamTennis and is frequently used in segments on the
Tennis Channel. The algorithm analyzes more than 8 million match results from over 800,000 tennis
players worldwide. On May 8, 2018, Rafael Nadal – having won 46 consecutive sets in clay court
matches – had a near-perfect clay UTR of 16.42.[38]

One of the few Elo-based rankings endorsed by a sport's governing body is the FIFA Women's World
Rankings, based on a simplified version of the Elo algorithm, which FIFA uses as its official ranking
system for national teams in women's football.

From the first ranking list after the 2018 FIFA World Cup, FIFA has used Elo for their FIFA World
Rankings.[39]

In 2015, Nate Silver, editor-in-chief of the statistical commentary website FiveThirtyEight, and
Reuben Fischer-Baum produced Elo ratings for every National Basketball Association team and
season through the 2014 season.[40] In 2014 FiveThirtyEight created Elo-based ratings and win-
projections for the American professional National Football League.[41]

The English Korfball Association rated teams based on Elo ratings, to determine handicaps for their
cup competition for the 2011/12 season.

An Elo-based ranking of National Hockey League players has been developed.[42] The hockey-Elo
metric evaluates a player's overall two-way play: scoring AND defense in both even strength and
power-play/penalty-kill situations.
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Rugbyleagueratings.com uses the Elo rating system to rank international and club rugby league
teams.

Other board and card games

National Scrabble organizations compute normally distributed Elo ratings except in the United
Kingdom, where a different system is used. The North American Scrabble Players Association has the
largest rated population of active members, numbering about 2,000 as of early 2011. Lexulous also
uses the Elo system.

The popular First Internet Backgammon Server (FIBS) calculates ratings based on a modified Elo
system. New players are assigned a rating of 1500, with the best humans and bots rating over 2000.
The same formula has been adopted by several other backgammon sites, such as Play65,
DailyGammon, GoldToken and VogClub. VogClub sets a new player's rating at 1600. The UK
Backgammon Federation uses the FIBS formula for its UK national ratings.[43]

The European Go Federation adopted an Elo-based rating system initially pioneered by the Czech Go
Federation.

Despite questions of the appropriateness of using the Elo system to rate games in which luck is a
factor, trading-card game manufacturers often use Elo ratings for their organized play efforts. The
DCI (formerly Duelists' Convocation International) used Elo ratings for tournaments of Magic: The
Gathering and other Wizards of the Coast games. However, the DCI abandoned this system in 2012
in favour of a new cumulative system of "Planeswalker Points", chiefly because of the above-noted
concern that Elo encourages highly rated players to avoid playing to "protect their rating".[26][27]
Pokémon USA uses the Elo system to rank its TCG organized play competitors.[44] Prizes for the top
players in various regions included holidays and world championships invites until the 2011–2012
season, where awards were based on a system of Championship Points, their rationale being the same
as the DCI's for Magic: The Gathering. Similarly, Decipher, Inc. used the Elo system for its ranked
games such as Star Trek Customizable Card Game and Star Wars Customizable Card Game.

Video games and online games

The Esports game Overwatch, the basis of the unique Overwatch League professional sports
organization, uses a derivative of the Elo system to rank competitive players with various adjustments
made between competitive seasons.[45]

Leagues and match-makers for the skill-based game Counter-Strike: Global Offensive use Elo ratings
such as ESEA League and Faceit; however, the game's own matchmaking system uses Glicko-2.

Various online games use Elo ratings for player-versus-player rankings. Since 2005, Golden Tee Live
has rated players based on the Elo system. New players start at 2100, with top players rating over
3000.[46] In Guild Wars, Elo ratings are used to record guild rating gained and lost through guild
versus guild battles, which are two-team fights. The initial K-value was 30, but was changed to 5 in
January 2007, then changed to 15 in July 2009.[47] World of Warcraft formerly used the Elo rating
system when teaming up and comparing Arena players, but now uses a system similar to Microsoft's
TrueSkill.[48] The MOBA game League of Legends used an Elo rating system prior to the second
season of competitive play.[49] The game Puzzle Pirates uses the Elo rating system to determine the
standings in the various puzzles. Roblox introduced the Elo rating in 2010.[50] The browser game
Quidditch Manager uses the Elo rating to measure a team's performance.[51] Another recent game to
start using the Elo rating system is AirMech, using Elo[52] ratings for 1v1, 2v2, and 3v3 random/team

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matchmaking. RuneScape 3 was to use the Elo system for the rerelease of bounty hunter in 2016.[53]
Mechwarrior Online instituted an Elo system for its new "Comp Queue" mode, effective with the Jun
20, 2017 patch.[54]

In 1998[55] an online gaming ladder called Clanbase[56] was launched, who used the Elo scoring
system to rank teams. The site later went offline in 2013.[57] A similar alternative site was launched in
2016 under the name Scrimbase[58] and they are also using the Elo scoring system for ranking teams.

Other usage

The Elo rating system has been used in soft biometrics,[59] which concerns the identification of
individuals using human descriptions. Comparative descriptions were utilized alongside the Elo
rating system to provide robust and discriminative 'relative measurements', permitting accurate
identification.

The Elo rating system has also been used in biology for assessing male dominance hierarchies,[60]
and in automation and computer vision for fabric inspection.[61]

Moreover, online judge sites are also using Elo rating system or its derivatives. For example,
Topcoder is using a modified version based on normal distribution,[62] while Codeforces is using
another version based on logistic distribution.[63][64][65]

Elo rating system has also been noted in dating apps, such as in the matchmaking app Tinder, which
uses a variant of the Elo rating system.[66]

References in the media

The Elo rating system was featured prominently in The Social Network during the algorithm scene
where Mark Zuckerberg released Facemash. In the scene Eduardo Saverin writes mathematical
formulas for the Elo rating system on Zuckerberg's dormitory room window. Behind the scenes, the
movie claims, the Elo system is employed to rank girls by their attractiveness. The equations driving
the algorithm are shown briefly, written on the window;[67] however, they are slightly incorrect.

See also
Chess rating system, discusses other chess rating systems
Glicko rating system, the rating methods developed by Mark Glickman
Elo hell
Bradley–Terry model

Notes
a. In English, this is variously pronounced as /ˈiːloʊ/, /ˈɛloʊ/, or even /ˌiːɛlˈoʊ/ as if it were the
initialism E-L-O. The original name Élő is pronounced [ˈeːløː] ( listen) in Hungarian.

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Further reading
Elo, Arpad (1978). The Rating of Chessplayers, Past and Present. Arco. ISBN 978-0-668-04721-
0.
Harkness, Kenneth (1967). Official Chess Handbook. McKay.

External links
Mark Glickman's research page, with a number of links to technical papers on chess rating
systems (http://www.glicko.net/research.html)

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