Previously, we have written a blog post on Lichess to FIDE Elo Rating Conversion. The formulae there still hold to some extent, but it is slightly outdated. We have updated the formula (using linear regression), using recent data in **Feb 2020**.

## Conversion Formulae

The formula is:

**FIDE rating = (0.8399)*(Lichess Blitz Rating) + 179.8890**

For example, if your Lichess Blitz Rating is 1800, then your estimated FIDE rating is:

(0.8399)*1800+179.8890 = 1692

## Methodology

We use 30 data points obtained from Lichess.org website through searching the keywords: “FIDE rating:” site:https://lichess.org/@.

We then use R to perform linear regression. The best fit line is shown below.

## Code and Output

df <- read.csv("lichessfide.csv") head(df) ## X Lichess..Blitz. FIDE ## 1 1 2425 2390 ## 2 2 2215 1899 ## 3 3 2521 2550 ## 4 4 2834 2554 ## 5 5 1498 1597 ## 6 6 2943 2612 #scatter.smooth(x=df$Lichess..Blitz., y=df$FIDE, main="FIDE rating against Lichess Blitz") # scatterplot cor(df$Lichess..Blitz., df$FIDE) # calculate correlation ## [1] 0.8609222 # 0.8609222 linearMod <- lm(FIDE ~ Lichess..Blitz., data=df) # build linear regression model on full data print(linearMod) ## ## Call: ## lm(formula = FIDE ~ Lichess..Blitz., data = df) ## ## Coefficients: ## (Intercept) Lichess..Blitz. ## 179.8890 0.8399 summary(linearMod) ## ## Call: ## lm(formula = FIDE ~ Lichess..Blitz., data = df) ## ## Residuals: ## Min 1Q Median 3Q Max ## -318.89 -132.16 -0.55 98.59 294.14 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 179.88901 228.51972 0.787 0.438 ## Lichess..Blitz. 0.83989 0.09379 8.955 1.04e-09 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 152.5 on 28 degrees of freedom ## Multiple R-squared: 0.7412, Adjusted R-squared: 0.7319 ## F-statistic: 80.19 on 1 and 28 DF, p-value: 1.038e-09 plot(df$Lichess..Blitz, df$FIDE) abline(lm(FIDE ~ Lichess..Blitz.,data=df))

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