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Interactive Tool

Elo Rating Calculator

Experiment with different Elo rating parameters to see how they affect rating changes. Adjust player ratings, match outcomes, and system parameters to simulate different scenarios.

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Match Setup

Controls rating change volatility

Affects expected outcome probability

Reduces rating losses by this %

Match Preview

Player
1200
VS
Even Match
Opponent
1200
50% win 50% win
Selected Outcome
Player Wins

Adjust settings and calculate to see rating changes

Rating formula

Δ = 32 × ( S 1 1 +10d/400 )
32 K-Factor 10 Curve Factor 400 Influence Range d rating diff S result (1 win, 0 loss)

Rating change vs opponent rating difference (updates live as you edit settings, includes capping)

+290-29-6000+600opponent rating diffWinLoss

This calculator demonstrates the Elo rating system used by Team Up's leaderboard bot.

01 The basics

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
02 Watch & learn

Learn How Elo Works

Watch these videos for an in-depth look at the math and theory behind the Elo rating system.

03 Tuning dials

Key Parameters

K-Factor

Default: 32

Controls rating volatility. Higher values mean bigger rating swings per match.

16–24: Stable (experienced players)32: Standard40+: Volatile (new players)

Influence Range

Default: 400

The 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: On

In 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: Off

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.

04 See it in action

Example Scenarios

Even

Even Match

Two players with equal ratings (1200 vs 1200)

Winner gains: +16
Loser loses: −16
Expected

Expected Win

Favorite wins (1400 beats 1200)

Favorite gains: +9
Underdog loses: −9
Upset

Upset

Underdog wins (1200 beats 1400)

Underdog gains: +23
Favorite loses: −23

Values shown use default settings (K-factor: 32, Influence Range: 400)

Read up

Rating-system guides

What Is Elo Rating? How Competitive Ranking Works in Gaming

Learn how the Elo rating system works, why competitive games use it, and how to add Elo rankings to your gaming community. Includes examples and a free calculator.

Read it →

Elo Rating Calculator: Calculate & Understand Your Rating Changes

Use our free Elo rating calculator to see exactly how match results affect player ratings. Learn the formulas, understand K-factor, and experiment with advanced parameters like loss dampening, rating capping, and FFA distribution.

Read it →

Glicko vs. Elo vs. TrueSkill: Choosing a Rating System for Your Community

A practical comparison of Elo, Glicko-2, and TrueSkill rating systems — how each handles uncertainty, team games, and inactivity, and which one fits your gaming community.

Read it →

Elo K-Factor Curves: How Fast Should Ratings Move?

K-factor decides how fast ratings converge and how much they wobble forever after. Simulated convergence and steady-state numbers for K 16 through 64, why the two goals conflict, 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 assigns individual credit from a team result, why the naive even split is wrong, and what you can and can't fix with stats.

Read it →

Scoring Free-For-Alls: Rating Battle Royales and Multi-Team Matches

Elo is defined for a pair of players, and a free-for-all has no pairs. Here's how a placement is converted into ratings, why a 10-player FFA would otherwise move ratings nine times too far, and where the approach genuinely breaks down.

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.