Elo Rating Calculator

Calculate expected score, rating change, and new Elo ratings for two players after a win, draw, or loss.

Match result

K factor

How to read this

The calculator applies one Elo update for the result you select. Published ratings may still round or batch several games together, depending on the federation or platform.

The FIDE-style option caps large rating gaps at 400 points for the expectation step, which matches the standard handbook approach for most rated players.

Player A change

+7.2 points

Player A win with K = 20 moves Player A to 1,607.2 and Player B to 1,492.8.

Expected score A
64.01%
Expected score B
35.99%
New rating A
1,607.2
New rating B
1,492.8
Player B change
-7.2
Used rating gap
-100

Interpretation

Player A still gained rating points, but a favourite gains fewer points for the same result.

Formula

Expected score = 1 / (1 + 10^((Opponent rating − Player rating) / 400)). Rating change = K × (actual score − expected score).

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Expected score and Elo rating change after one result

An Elo rating calculator estimates the expected score for two players and the rating change after a win, draw, or loss. This version keeps the inputs transparent: current ratings, a chosen K factor, and an optional FIDE-style cap on very large rating gaps.

How expected score is calculated

Elo starts by turning the rating gap into a scoring expectation. If two players have the same rating, each is expected to score 0.5 on average. As the gap widens, the higher-rated player’s expected score rises and the lower-rated player’s expected score falls.

That expectation is then compared with the actual result. A player who scores above expectation gains rating points, while a player who scores below expectation loses them. The size of the swing depends on the K factor.

Expected score = 1 / (1 + 10^((opponent rating - player rating) / 400))

This is the standard Elo expectation formula used by the calculator.

Rating change = K x (actual score - expected score)

Actual score is 1 for a win, 0.5 for a draw, and 0 for a loss.

Why K factor and rating-gap rules matter

The K factor controls how reactive the system is. Higher K values make ratings move faster, which is useful for new or rapidly improving players. Lower K values make ratings more stable once a player’s established level is clearer.

Federations also decide how to handle very large rating gaps. The FIDE-style option in this calculator caps the expectation step at a 400-point difference, which stops huge mismatches from distorting the update as aggressively as a pure uncapped formula would.

What this estimate is and is not

This is a transparent single-result Elo update, not an official federation rating server. Real published ratings can depend on the governing body, the applicable K-factor rules for that player, whether several games are batched together before rounding, and whether special provisional rules apply.

That still makes the calculator useful. It gives a clean way to test “what if this result happens?” before a game or to audit whether a rating swing looks directionally reasonable after the fact.

Frequently asked questions

What does a higher K factor do?

A higher K factor makes the same result move ratings more. Newer or faster-changing pools often use higher K values, while more established ratings usually use lower ones for stability.

 Why does an upset win gain more points than a favourite win?

Because the underdog was expected to score less. When the actual score beats the expectation by a larger margin, the Elo formula produces a larger positive rating change.

 Why might my federation’s official update differ slightly?

Official systems may batch several games together, round only at the rating-period level, or apply player-specific K-factor rules and provisional-rating adjustments that a generic one-game estimator does not reproduce fully.

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