Glicko-2
Same skill estimate. Different certainty.
Interactive example · no real player data
The centre μ stays at 25. A narrower curve means more confidence. The dashed curve shows σ = 9. Glicko also tracks rating deviation and volatility; this is a confidence illustration.
Educational preview · this method is coming soon
How it works About 1 min read
Glicko-2 extends Elo with two additional parameters: Rating Deviation (RD, measuring uncertainty) and Volatility (measuring consistency). It converges faster for new players and grows uncertain when players are inactive.
How it works: Like Elo, but with a confidence interval. A new player might be 1500 ± 350, while an established player is 1800 ± 50. Rating changes are larger when uncertainty is high, allowing the system to quickly find a new player's true skill. Inactivity gradually increases uncertainty.
When to use it: For competitive ladders where players come and go. Glicko-2 handles uneven activity gracefully -- a player who hasn't played in months will see faster rating adjustments when they return.
Watch out for: More complex to implement and explain than Elo. The volatility parameter (tau) needs tuning for your player pool. Only supports 1v1 and team-vs-team.