Elo Rating Calculator
Enter two ratings to see each side's win probability and exactly how many points they gain or lose. Adjust the K-factor and other settings to simulate any match, 1v1 or free-for-all.
Match Setup
Controls rating change volatility
Affects expected outcome probability
Reduces rating losses by this %
Match Preview
Adjust settings and calculate to see rating changes
Rating formula
d′ = d, limited to ±400
Largest change from one match: +29.1 / −29.1
Rating change vs opponent rating difference (updates live as you edit settings, includes capping)
The same engine runs every Team Up Discord leaderboard, with these settings per board.
How It Works
Expected Outcome
The system calculates the probability of each player winning based on their rating difference. Higher-rated players are expected to win more often.
Rating Adjustment
Upsets (underdog wins) cause larger rating swings. Expected outcomes cause smaller changes. The K-factor controls the overall magnitude.
Quick Guide
- 1 Enter Player and Opponent Ratings, or switch to FFA mode to add multiple players
- 2 Select the Match Result (1v1) or assign Placements (FFA)
- 3 Click Calculate to see the rating changes for all players
- 4 Expand Core Elo Settings to adjust K-factor, influence range, and more
Learn How Elo Works
Watch these videos for an in-depth look at the math and theory behind the Elo rating system. Prefer to read? What Elo means in gaming covers the basics, from the formula to "elo hell".
Key Parameters
K-Factor
Default: 32Controls rating volatility. Higher values mean bigger rating swings per match.
Influence Range
Default: 400The rating difference at which win probability shifts significantly. A 400-point difference gives the favorite ~91% expected win rate.
Loss Dampen
Default: 0%Reduces rating losses by a percentage. Useful if you want to soften the impact of losses while keeping win rewards the same.
FFA Distribution
Default: OnIn Free-for-All matches, each player is compared against every opponent pairwise. When enabled, the total rating change is divided by the number of opponents, keeping FFA rating changes on the same scale as 1v1 matches.
Rating Capping
Default: On (±400)Limits extreme rating changes when skill gaps are large. Prevents favorites from gaining almost nothing for expected wins or underdogs from losing almost nothing for expected losses.
Example Scenarios
Even Match
Two players with equal ratings (1200 vs 1200)
Expected Win
Favorite wins (1400 beats 1200, 76% expected)
Upset
Underdog wins (1200 beats 1400, 24% expected)
Values shown use default settings (K-factor: 32, Influence Range: 400), rounded as the bot records them
Elo Win Probability by Rating Difference
How likely the higher-rated side is to win, and how many points each side gains for winning.
| Rating difference | Favorite wins | Underdog wins | Favorite gains | Underdog gains |
|---|---|---|---|---|
| 0 | 50% | 50% | +16 | +16 |
| 50 | 57.1% | 42.9% | +14 | +18 |
| 100 | 64% | 36% | +12 | +20 |
| 150 | 70.3% | 29.7% | +9 | +23 |
| 200 | 76% | 24% | +8 | +24 |
| 250 | 80.8% | 19.2% | +6 | +26 |
| 300 | 84.9% | 15.1% | +5 | +27 |
| 400 | 90.9% | 9.1% | +3 | +29 |
| 500 | 94.7% | 5.3% | +2 | +30 |
| 600 | 96.9% | 3.1% | +1 | +31 |
| 800 | 99% | 1% | 0 | +32 |
K-factor 32, influence range 400, no rating cap, rounded to whole points. Win chances count a draw as half a win.
Elo Calculator FAQ
How does the Elo calculator work out win probability?
Each side's expected score is 1 / (1 + 10^(rating difference / 400)), where the difference is the opponent's rating minus yours. Two equal ratings give 50% each; a 200-point favorite is expected to win 76% of the time. Draws count as half a win in this figure, so read it as an expected score rather than a strict win rate.
What is the win probability for a 400-point Elo difference?
About 90.9% for the higher-rated player and 9.1% for the lower-rated one. 400 points is the scale of the formula: every 400 points of gap multiplies the favorite's odds by ten. A 100-point gap is 64%, 200 is 76%, and 800 is 99%.
How many Elo points do you gain or lose per match?
The change is K × (actual score − expected score). With K = 32, an even match moves both players 16 points. A 200-point favorite who wins gains only 8, because the win was expected; if the underdog wins instead, they gain 24. The two players' changes always add up to zero in a 1v1.
What K-factor should I use?
K sets how far one result moves a rating. 32 is the common default and suits most gaming communities. Use 16–24 for an established ladder where ratings should be stable, and 40 or more for new players whose rating is still finding its level. Chess federations use a similar idea, starting new players on a higher K and lowering it as they play more games.
Can I use this Elo calculator for football, chess or esports?
Yes — the core formula is the same everywhere. Football Elo systems such as the World Football Elo Ratings add a goal-difference multiplier and home advantage on top, which this calculator does not model; raise K for more important matches to approximate it. For chess and esports, the standard settings here are a close match.
How are draws calculated?
A draw scores 0.5 for each side. Between equal ratings it changes nothing. Between unequal ratings the favorite loses points and the underdog gains them: a 200-point favorite who draws moves −8.
Read up
Rating-system guides
What Does Elo Mean in Gaming? The Elo Rating System Explained
Elo is a skill rating: you gain points for wins and lose them for losses, more for upsets. What low elo, high elo and elo hell mean, and how the math works.
Read it →Elo Rating Calculator: Calculate & Understand Your Rating Changes
Use the free Elo calculator to see how results change ratings. The formulas, K-factor, and advanced options like loss dampening and FFA distribution.
Read it →Glicko-2 vs Elo vs TrueSkill: Choosing a Rating System for Your Community
Elo, Glicko-2 and TrueSkill compared: how each handles uncertainty, team games and inactivity, and which rating system fits your gaming community.
Read it →Elo K-Factor Curves: How Fast Should Ratings Move?
K-factor sets how fast ratings converge and how much they wobble after. Simulated numbers for K 16 to 64, and how a provisional K curve gets you both.
Read it →The Attribution Problem: Rating Individual Players in Team Games
A 5v5 win is one bit of information split five ways. How Elo gives individual credit from a team result, and why an even split is wrong.
Read it →Scoring Free-For-Alls: Rating Battle Royales and Multi-Team Matches
Elo is defined for pairs, and a free-for-all has none. How a placement becomes rating changes, why a 10-player FFA needs scaling, and where it breaks.
Read it →Put these ratings to work
This calculator runs the exact same Elo engine as the Team Up bot. Add it to your Discord server and every setting above is yours to tune per leaderboard.