FIDE Chess Player Ratings

This week we’re exploring August and September chess player rating data from FIDE (the International Chess Federation). Monthly data files are published on the FIDE website.

A chess rating (Elo) is an estimate of a player’s strength relative to other players. If a player performs better or worse than expected, their rating increases or decreases accordingly.

The September 2025 rating list was shaped primarily by results from the Sinquefield Cup, Quantbox Chennai Grand Masters, 61st International Akiba Rubinstein Chess Festival, and the Spanish League Honor Division 2025 – a Swiss team tournament held in Linares.

Which players showed the greatest improvement from August to September?
Which federations have the most number of titled players?
How did the rankings of the top male or female players change?
Who are the top youth players?

Thank you to Jessica Moore for curating this week’s dataset.

The Data

# Using R
# Option 1: tidytuesdayR R package 
## install.packages("tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2025-09-23')
## OR
tuesdata <- tidytuesdayR::tt_load(2025, week = 38)

fide_ratings_august <- tuesdata$fide_ratings_august
fide_ratings_september <- tuesdata$fide_ratings_september

# Option 2: Read directly from GitHub

fide_ratings_august <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-23/fide_ratings_august.csv')
fide_ratings_september <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-23/fide_ratings_september.csv')

# Using Python
# Option 1: pydytuesday python library
## pip install pydytuesday

import pydytuesday

# Download files from the week, which you can then read in locally
pydytuesday.get_date('2025-09-23')

# Option 2: Read directly from GitHub and assign to an object

fide_ratings_august = pandas.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-23/fide_ratings_august.csv')
fide_ratings_september = pandas.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-23/fide_ratings_september.csv')

# Using Julia
# Option 1: TidierTuesday.jl library
## Pkg.add(url="https://github.com/TidierOrg/TidierTuesday.jl")

using TidierTuesday

# Download files from the week, which you can then read in locally
download_dataset('2025-09-23')

# Option 2: Read directly from GitHub and assign to an object with TidierFiles

fide_ratings_august = read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-23/fide_ratings_august.csv")
fide_ratings_september = read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-23/fide_ratings_september.csv")

# Option 3: Read directly from Github and assign without Tidier dependencies
fide_ratings_august = CSV.read("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-23/fide_ratings_august.csv", DataFrame)
fide_ratings_september = CSV.read("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-23/fide_ratings_september.csv", DataFrame)

How to Participate

Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.
Create a visualization, a model, a Quarto report, a shiny app, or some other piece of data-science-related output, using R, Python, or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.
Submit your own dataset!

PydyTuesday: A Posit collaboration with TidyTuesday

Exploring the TidyTuesday data in Python? Posit has some extra resources for you! Have you tried making a Quarto dashboard? Find videos and other resources in Posit’s PydyTuesday repo.
Share your work with the world using the hashtags #TidyTuesday and #PydyTuesday so that Posit has the chance to highlight your work, too!
Deploy or share your work however you want! If you’d like a super easy way to publish your work, give Connect Cloud a try.

Data Dictionary

`fide_ratings_august.csv`

variable	class	description
id	integer	Identification number of a player within FIDE database.
name	character	Player name.
fed	character	Federation of player. Similar, but not identical to, IOC country codes. An unofficial list is available here
sex	character	Sex of player (M - male, F - female)
title	character	Title of a player (GM - Grand Master, WGM - Woman Grand Master, IM - International Master, WIM - Woman International Master, FM - FIDE Master, WFM - Woman FIDE Master, CM - Candidate Master, WCM - Woman Candidate Master)
wtitle	character	Woman title of a player (WGM - Woman Grand Master, WIM - Woman International Master, WFM - Woman FIDE Master, WCM - Woman Candidate Master)
otitle	character	Comma-separated list. Other titles of a player (IA - International Arbiter, FA - FIDE Arbiter, NA - National Arbiter, IO - International Organizer, FT - FIDE Trainer, FST - FIDE Senior Trainer, DI - Developmental Instructor, NI - National Instructor)
foa	character	FIDE Online Arena title of a player (AGM - Arena Grand Master, AIM - Arena International Master, AFM - Arena FIDE Master, ACM - Arena Candidate Master)
rating	integer	Standard chess rating (Elo).
games	integer	Number of games played in the month.
k	integer	Rating K factor (Value influencing how much a player’s rating changes after a game)
bday	integer	Year of birth of a player.

`fide_ratings_september.csv`

variable	class	description
id	integer	Identification number of a player within FIDE database.
name	character	Player name.
fed	character	Federation of player. Similar, but not identical to, IOC country codes. An unofficial list is available here
sex	character	Sex of player (M - male, F - female)
title	character	Title of a player (GM - Grand Master, WGM - Woman Grand Master, IM - International Master, WIM - Woman International Master, FM - FIDE Master, WFM - Woman FIDE Master, CM - Candidate Master, WCM - Woman Candidate Master)
wtitle	character	Woman title of a player (WGM - Woman Grand Master, WIM - Woman International Master, WFM - Woman FIDE Master, WCM - Woman Candidate Master)
otitle	character	Comma-separated list. Other titles of a player (IA - International Arbiter, FA - FIDE Arbiter, NA - National Arbiter, IO - International Organizer, FT - FIDE Trainer, FST - FIDE Senior Trainer, DI - Developmental Instructor, NI - National Instructor)
foa	character	FIDE Online Arena title of a player (AGM - Arena Grand Master, AIM - Arena International Master, AFM - Arena FIDE Master, ACM - Arena Candidate Master)
rating	integer	Standard chess rating (Elo).
games	integer	Number of games played in the month.
k	integer	Rating K factor (Value influencing how much a player’s rating changes after a game)
bday	integer	Year of birth of a player.

Cleaning Script

library(tidyverse)
library(withr)
conflicted::conflicts_prefer(dplyr::filter)

# download, unzip and read the file #
august_zipped <- withr::local_tempfile(fileext = ".zip")
september_zipped <- withr::local_tempfile(fileext = ".zip")

download.file(
  "https://ratings.fide.com/download/standard_aug25frl.zip",
  destfile = august_zipped,
  mode = "wb"
)
download.file(
  "https://ratings.fide.com/download/standard_sep25frl.zip",
  destfile = september_zipped,
  mode = "wb"
)

august_unzipped <- unzip(august_zipped, exdir = tempdir())
september_unzipped <- unzip(september_zipped, exdir = tempdir())

fide_ratings_august <- readr::read_fwf(
  file = august_unzipped,
  skip = 1,
  col_types = c("dcccccccdddcc"),
  fwf_widths(
    c(8, 68, 4, 4, 5, 5, 15, 4, 6, 4, 3, 6, 4),
    c(
      "id",
      "name",
      "fed",
      "sex",
      "title",
      "wtitle",
      "otitle",
      "foa",
      "rating",
      "games",
      "k",
      "bday",
      "flag"
    )
  )
) %>%
  # cleaning up player names
  mutate(name = str_remove(name, "^\\d+\\s+")) %>%
  # filter to only players who were active in the last 12 months
  filter(flag %in% c(NA, "w"), bday > 0) %>%
  select(-flag)

fide_ratings_september <- readr::read_fwf(
  file = september_unzipped,
  skip = 1,
  col_types = c("dcccccccdddcc"),
  fwf_widths(
    c(8, 68, 4, 4, 5, 5, 15, 4, 6, 4, 3, 6, 4),
    c(
      "id",
      "name",
      "fed",
      "sex",
      "title",
      "wtitle",
      "otitle",
      "foa",
      "rating",
      "games",
      "k",
      "bday",
      "flag"
    )
  )
) %>%
  # cleaning up player names
  mutate(name = str_remove(name, "^\\d+\\s+")) %>%
  # filter to only players who were active in the last 12 months
  filter(flag %in% c(NA, "w"), bday > 0) %>%
  select(-flag)