## Convert Lichess Blitz Rating to FIDE

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 The formulae there still hold to some extent, but it is slightly outdated.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")

##   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))```

## Checkmate Black King on 1st rank (Sicilian Defense, O’Kelly Variation)

I try to play Sicilian in the “Delayed Alapin” style with c3 followed by d4. The black player, though lower rated, is quite an experienced player with over 11,000 rapid games played on Lichess.

The turning point was 14. d5! I did not fully calculate all the lines, but I thought it was worth giving up the isolated d-pawn for a very active position, with my rook lined up against the black queen. The tactics worked out well in the end, in fact it turns out that white can always regain the pawn.

Lichess seems to have rating deflation: Many players (including myself) who were once rated over 2000 for rapid, are now dropping back to 1900 rating range. It seems due to an influx of good players into Lichess. Also see: Lichess to FIDE Elo Rating Conversion.

Final Position:

## Lichess to FIDE Elo Rating Conversion

Lichess is a free Chess Server – one of the best out there in fact. It comes with free engine (Stockfish) analysis, and many other nice features. It is well known that Lichess ratings are inflated compared to FIDE / USCF / most other rating systems. The following are some of the best conversion systems to convert Lichess rating to other ratings.

1) Dudeski_robinson’s Formula

FIDE Rating = 187 + Lichess Classical Rating X 0.38 + Lichess Blitz Rating X 0.48

Dudeski_robinson’s formula is pretty scientific, he actually uses linear regression out of real data to produce the above formula.

A rough estimate would be:
Fide ELO = Lichess Classical – 170
or
Fide ELO = Lichess Blitz – 80

Source: Lichess Forum

2) Chess Rating Comparison 2016 (Google Sheets)

This is also pretty scientific, with the added plus that it also compares between USCF and Chess.com, in addition to FIDE.

 Chess.com Lichess.org USCF FIDE Bullet Blitz Rapid Bullet Blitz Classical Regular Regular 860 1100 1160 900 1125 1200 930 1150 1250 960 1175 1290 1000 1200 1330 1370 1530 1590 1270 1260 1030 1225 1360 1390 1550 1620 1290 1280 1060 1250 1400 1410 1570 1640 1320 1310 1090 1275 1430 1440 1590 1670 1340 1330 1130 1300 1460 1460 1610 1690 1360 1350 1160 1325 1490 1480 1630 1720 1390 1370 1190 1350 1520 1510 1650 1740 1410 1390 1230 1375 1550 1530 1670 1770 1430 1410 1260 1400 1570 1550 1690 1790 1460 1430 1290 1425 1590 1580 1710 1810 1480 1460 1320 1450 1610 1600 1730 1830 1500 1480 1360 1475 1630 1620 1750 1860 1530 1500 1390 1500 1650 1650 1770 1880 1550 1520 1420 1525 1670 1670 1790 1900 1570 1540 1460 1550 1680 1690 1810 1920 1600 1560 1490 1575 1700 1720 1830 1940 1620 1580 1520 1600 1710 1740 1850 1960 1640 1610 1550 1625 1730 1760 1870 1980 1670 1630 1590 1650 1740 1790 1890 2000 1690 1650 1620 1675 1750 1810 1910 2010 1710 1670 1650 1700 1760 1840 1930 2030 1740 1690 1690 1725 1770 1860 1950 2050 1760 1710 1720 1750 1780 1880 1970 2070 1780 1730 1750 1775 1790 1910 1990 2080 1810 1760 1790 1800 1800 1930 2010 2100 1830 1780 1820 1825 1810 1950 2030 2110 1850 1800 1850 1850 1820 1980 2050 2130 1880 1820 1880 1875 1820 2000 2070 2150 1900 1840 1920 1900 1830 2020 2090 2160 1920 1860 1950 1925 1840 2050 2120 2180 1950 1880 1980 1950 1850 2070 2140 2190 1970 1910 2020 1975 1860 2090 2160 2210 1990 1930 2050 2000 1870 2120 2180 2220 2020 1950 2080 2025 1880 2140 2200 2230 2040 1970 2110 2050 1890 2160 2220 2240 2060 1990 2150 2075 1900 2190 2240 2250 2090 2010 2180 2100 1910 2210 2260 2270 2110 2030 2210 2125 1930 2230 2280 2280 2130 2060 2250 2150 1940 2260 2300 2290 2160 2080 2280 2175 1950 2280 2320 2300 2180 2100 2310 2200 1970 2300 2340 2300 2200 2120 2340 2225 1980 2330 2360 2310 2230 2140 2380 2250 2000 2350 2380 2320 2250 2160 2410 2275 2020 2370 2400 2330 2270 2180 2440 2300 2040 2400 2420 2340 2300 2210 2480 2325 2420 2440 2340 2320 2230 2510 2350 2440 2460 2350 2340 2250 2540 2375 2470 2480 2360 2370 2270 2580 2400 2490 2500 2360 2390 2290 2610 2425 2510 2520 2370 2410 2310 2640 2450 2540 2540 2370 2440 2330 2670 2475 2560 2560 2380 2460 2360 2710 2500 2580 2580 2380 2480 2380 2740 2525 2610 2600 2380 2500 2400 2770 2550 2630 2620 2390 2530 2420 2810 2575 2650 2640 2390 2550 2440 2840 2600 2680 2660 2390 2570 2460 2870 2625 2700 2680 2390 2600 2480 2900 2650 2720 2700 2400 2620 2510