Elo Rating Change
Calculator
Results
- Rating change
- 24.311901
- New rating
- 1,524.311901
- Expected score
- 0.240253
Gaming results
| Rating change | 24.311901 |
| New rating | 1,524.311901 |
| Expected score | 0.240253 |
formula-map diagram
- Rating change
- 24.311901
- New rating
- 1,524.311901
- Expected score
- 0.240253
Game math relationship
Formula
R' = R + K × (S − E)= 24.311901652735
Note
This is a simplified model: it applies the standard probability or game-math formula to the numbers you entered. Real games add pity systems, drop-rate tiers, rounding, server-side variance and balance patches, so treat the result as an estimate rather than a guarantee.
More in Gaming and probability
See all →Frequently asked questions
How is the rating change after a match calculated?+
Rating change equals the K-factor multiplied by the difference between the actual result (1 for a win, 0.5 for a draw, 0 for a loss) and the expected score calculated before the match; this is the core Elo update formula.
What is the K-factor and why does it matter?+
The K-factor controls how much a single result can move your rating — a higher K-factor makes ratings react faster to recent results (common for newer or provisional players), while a lower K-factor produces more stable, slowly changing ratings for established players.
Why does beating a much higher-rated opponent gain more points than beating an equally-rated one?+
Beating a stronger opponent means your actual result (a win) is far above what was expected going in, so the gap between actual and expected result is larger, producing a bigger rating change under the same K-factor.
Can rating change be negative even after a win?+
In standard Elo, no — a win always produces a non-negative rating change since a result of 1 is always greater than or equal to any possible expected score (which maxes out just under 1), though some rating systems built on Elo's concept do allow more complex outcomes.
Why do both players' rating changes sum to zero in a standard Elo system?+
Elo is a zero-sum system by design — the winner's expected-score deficit exactly equals the loser's expected-score surplus, so points gained by one player are mathematically identical to points lost by the other when using the same K-factor for both.