Converting Chess.com to Lichess Ratings: A New Data-Driven Method

The Great Elo Mystery

Switching between chess platforms shouldn’t feel like traveling to a parallel universe—but for ratings, it often does. If you play on both Chess.com and Lichess, you’ve probably faced at least one of these headaches:

• Are we even talking about the same rating? So often, someone mentions their Chess.com rating, but you have no idea what that actually means on Lichess. The numbers rarely seem to match up.
• What’s the real conversion, anyway? Maybe you remember, “My 1600 on Chess.com was close to 1850 on Lichess.” But is that true for everyone, and across all rating levels? Does the gap change as you improve? There’s lots of uncertainty, and hardly any clear answers.
• Which of my accounts tells the real story? With accounts on both platforms, you probably want to keep both ratings up to date. But which rating is giving you a truer reflection of your skill? Is one more underrated than the other? Comparing your progress starts to get messy.

All in all, tracking and communicating your real chess strength across sites can quickly become a confusing guessing game.

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Why Old Conversion Charts Miss the Mark

You might have seen rating conversion charts before—but most of them aren’t very reliable. Here’s where the old approach went wrong:

• How did it work? The old method was simple: People on chess forums or survey websites were asked to submit their usernames (or ratings) from both Chess.com and Lichess. This way, you could pair up the two ratings for hundreds or maybe thousands of players, create a giant scatterplot (a “cloud” of points), and look for a correlation between the two platforms.
• Different motivations, different seriousness Some players use one site for their most serious games, but only visit the other site for casual blitz or experiments. It’s hard to compare ratings when people approach each site differently.
• Unmaintained or outdated accounts When players reported their two ratings, often one of the accounts wasn’t very active or hadn’t been updated in months. So the numbers didn’t always reflect a player’s current real ability.
• Voluntary submissions mean unpredictable data The system relied on volunteer players submitting their own ratings—and there’s no guarantee everyone was accurate, honest, or even understood what was being asked. The sample could be biased or simply not reflect the overall population.

In short, the old scatterplot approach gave only a rough idea, and often led to fuzzy or misleading conversion numbers. It was better than nothing—but far from perfect!

A Fresh Solution: Using Chess Engines as The Great Equalizer

Here’s where it gets cool. Instead of asking people to tell us their ratings, what if we use their moves—and how well they play, measured by the engine?

I analyzed over 75,000 games (yes, my computer needed a nap) from both Chess.com and Lichess, always using the same time control (10+0), the same engine depth (13 ), and a fair spread of ratings. For each game, I measured “move precision”—basically, how closely your moves match Stockfish’s top choices.

The magic?
It turns out that both platforms have a tight link between player rating and average move precision. Better players play more precise moves, regardless of which site they’re on. So, if you know your “precision” score on one site, you can find the true equivalent rating on the other.

Experimental Protocol

I applied the same analytical framework to a dataset of 40,000 Chess.com games, maintaining:

1. Consistent Time Controls across both datasets (10+0)
2. Equal Engine Evaluation Depth for all positions (13)
3. Comparable Sampling Methods across rating ranges
4. Identical Precision Calculation Formulas

This consistency was crucial to ensure valid cross‐platform comparisons.
More detail in this article : Do Stronger Players Play More Accurately? I Analyzed 35,000 Games to Find Out

Correlation Findings

The analysis revealed that both Chess.com and Lichess ratings correlate strongly with playing precision:

Two critical observations emerged:

1. Monotonic Relationship Higher precision consistently correlates with higher ratings on both platforms.
2. Different Curves Chess.com ratings are generally lower than Lichess ratings for equivalent precision levels.

These findings confirm that Lichess ratings tend to run higher than Chess.com ratings, providing a quantitative basis for conversion.

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Developing the Conversion System

The Rating Differential

Using precision as our bridge metric, we can calculate the rating differential between platforms. For any given precision level, we observe the corresponding rating on each platform and determine their relationship:

The gap is most pronounced at lower levels (300–400 points difference) and narrows as ratings increase, becoming minimal around the 2,500 level.

Direct Conversion Chart

For practical application, here’s a direct conversion chart for rapid:

Empirical testing confirms these conversions align closely with real‐world observations, though individual variation exists.

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Practical Applications

Online Conversion Tool

To eliminate the need for manual chart consultation, I developed a web‐based tool:

Chess ELO Manager https://lucasbertola.github.io/chessEloManager/

This open‐source calculator provides instant conversions between Chess.com and Lichess ratings.

Developer Implementation

For developers, conversion functions are provided in JavaScript for rapid game:

export const convertLichessToChesscom = (lichessRating) => {
  const x = lichessRating;
  return (
    -1.5643446939104476e-19 * x**7 +
     1.9181728962123964e-15 * x**6 +
    -9.604852714700698e-12  * x**5 +
     2.5310705975897389e-8  * x**4 +
    -3.7767766830985903e-5  * x**3 +
     3.2037977111173628e-2  * x**2 +
    -1.3370078341728121e1   * x     +
     2.2500120508668001e3
  );
};

export const convertChesscomToLichess = (chesscomRating) => {
  const x = chesscomRating;
  return (
     2.188199697721715e-19 * x**7 +
    -2.1952738887873693e-15 * x**6 +
     8.877826440835283e-12 * x**5 +
    -1.8514468258153255e-8  * x**4 +
     2.12073811916615e-5    * x**3 +
    -1.3266256211169091e-2 * x**2 +
     5.1118584788758685    * x     -
     8.7762701063215870e1
  );
};

These polynomials capture the nonlinear relationship, ensuring accurate conversions across the rating spectrum.

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Conclusion

By using move precision as an objective bridge metric, this new method delivers a more reliable conversion between Chess.com and Lichess ratings than previous approaches.
I encourage community feedback—share your experiences and help refine the model further!

What are your experiences with ratings across platforms? Do the conversions match your expectations? Let me know in the comments below!