What Is ELO Rating? Competitive Rating System Explained

How the ELO System Works

Every competitor in an ELO system starts with a base rating, commonly 1000, 1200, or 1500 depending on the implementation. After each match, ratings are updated using a formula that considers the rating difference between the two competitors. The core formula calculates an expected outcome based on the rating gap. If a 1600-rated player faces a 1400-rated player, the system expects the 1600-rated player to win. If they do win, they gain a small number of rating points. If the 1400-rated player pulls off the upset, they gain a large number of rating points, and the 1600-rated player loses the same large amount. The K-factor determines how many points are exchanged per match. A higher K-factor means ratings change more quickly, while a lower K-factor creates more stable ratings. New players often have a higher K-factor so their rating converges to their true skill level faster. Established players use a lower K-factor to prevent volatile swings from single results. Over time, players' ratings converge to reflect their true skill level relative to the competition. A rating of 1600 does not mean anything in absolute terms — it only indicates that this player is expected to beat players rated lower than 1600 and lose to players rated higher, with specific probabilities determined by the exact rating gap.

ELO in Esports and Online Gaming

While the original ELO system was designed for chess, its principles underpin nearly every ranked matchmaking system in modern gaming. Most competitive games use a hidden matchmaking rating based on ELO concepts to pair players against opponents of similar skill, even when the visible rank uses a different label system like tiers or divisions. Here is how ELO-derived systems appear across competitive gaming:

• Chess.com and Lichess use ELO directly for their online chess ratings
• League of Legends uses an ELO-derived MMR (Matchmaking Rating) behind its visible rank tiers
• Valorant, Overwatch, and Apex Legends use hidden MMR systems based on ELO principles for matchmaking
• Rocket League displays a visible rating number that closely follows ELO calculations
• Fighting games like Street Fighter 6 and Tekken 8 use ELO-based systems for their ranked modes
• Tournament platforms use ELO to seed brackets and create balanced matchups

Advantages and Limitations

The ELO system is elegant and mathematically sound, producing intuitive results with a simple formula that is easy to implement. Its self-correcting nature means ratings improve in accuracy with more matches played. However, the system has limitations that have led to various modifications and alternatives in modern competitive gaming, particularly when applied to team games or environments with large player populations.

• Advantage: Simple and mathematically transparent — the formula is well-understood and produces intuitive results
• Advantage: Self-correcting over time — ratings naturally converge to a player's true skill level with enough matches
• Advantage: Rewards beating strong opponents more than beating weak opponents
• Advantage: Can be applied to any head-to-head competitive activity
• Limitation: Designed for 1v1 competitions — adapting it to team games is imperfect because individual contribution is hard to isolate
• Limitation: Rating inflation and deflation can occur over time as the player pool changes
• Limitation: New players need many games before their rating becomes accurate (the cold start problem)
• Limitation: Does not account for the magnitude of victory, only whether a competitor won or lost
• Limitation: Can be manipulated through selective opponent choice or intentional losing (smurfing, sandbagging)

ELO Variants and Modern Rating Systems

The original ELO system has inspired numerous variants and improvements that address its limitations. Microsoft Research developed TrueSkill for Xbox Live, which extends ELO to handle team games, free-for-all formats, and uncertainty in ratings. TrueSkill 2 further improved on this by incorporating per-match performance data. Glicko and Glicko-2, developed by Mark Glickman, add a rating deviation component that represents uncertainty. A player who has not played recently has a higher rating deviation, meaning their rating will change more dramatically in their next match. This addresses the cold start problem and inactivity issues. In esports, most games use proprietary systems inspired by ELO but heavily modified. League of Legends' matchmaking system considers recency, win streaks, and party size. Valorant's system factors in individual round performance, not just match wins. These modifications help the systems converge faster and produce better matchmaking quality. For tournament organizers, ELO ratings provide excellent data for seeding brackets. If your community plays regularly, tracking ELO ratings across events gives you reliable seeding data that improves bracket quality. ReadyRaider can track player ratings across events in your community, giving you better data for tournament seeding over time.