Chess Dreams and Breakthroughs: A Global Perspective

Individual excellence emerges from active engagement within strong chess ecosystems

TidyTuesday

2025

Analysis of FIDE chess ratings data revealing patterns in player activity, breakthrough performances, and global chess ecosystems. Explores how individual excellence emerges from engagement within strong national chess federations.

Author

Steven Ponce

Published

September 21, 2025

Figure 1: Multi-panel visualization showing chess player activity and achievements from FIDE data (August-September 2025). The top panel displays four histograms of game activity levels, showing that most players are casual (1-3 games) while fewer are highly active (16+ games). Bottom left shows top 20 rating improvements, led by Plzak, David, with +362 points, with a median of 255.5. Bottom right shows countries with the most titled players, led by Germany (1,004), Spain (702), and Russia (469), with a median of 391 players.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
  tidyverse,   # Easily Install and Load the 'Tidyverse'
  ggtext,      # Improved Text Rendering Support for 'ggplot2'
  showtext,    # Using Fonts More Easily in R Graphs
  janitor,     # Simple Tools for Examining and Cleaning Dirty Data
  scales,      # Scale Functions for Visualization
  glue,        # Interpreted String Literals,
  patchwork    # The Composer of Plots
  )})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 12,
  height = 12,
  units  = "in",
  dpi    = 300
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2025, week = 38)

fide_ratings_august <- tt$fide_ratings_august |> clean_names()
fide_ratings_september <- tt$fide_ratings_september |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)
```

3. Examine the Data

Show code

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(fide_ratings_august)
glimpse(fide_ratings_september)
```

4. Tidy Data

Show code

```{r}
#| label: tidy
#| warning: false

# helper function
clean_fide_data <- function(data, min_rating = 1000, max_rating = 3500) {
  data |>
    group_by(id) |>
    slice_max(rating, n = 1, with_ties = FALSE) |>
    ungroup() |>
    filter(!is.na(rating), rating >= min_rating, rating <= max_rating, !is.na(id)) |>
    mutate(
      name = str_squish(name),
      fed = str_squish(fed),
      sex = factor(sex, levels = c("M", "F")),
      title = na_if(str_squish(title), ""),
      wtitle = na_if(str_squish(wtitle), ""),
      rating = as.numeric(rating),
      games = as.numeric(games),
      k = as.numeric(k),
      bday = as.numeric(bday)
    ) |>
    filter(!is.na(name), !is.na(fed))
}

# Data prep
fide_august_clean <- clean_fide_data(fide_ratings_august)
fide_september_clean <- clean_fide_data(fide_ratings_september)

# Rating comparison
cur_year <- 2025

rating_comparison <- fide_august_clean |>
  select(id, name, fed, sex, title, wtitle, bday,
    rating_aug = rating, games_aug = games
  ) |>
  inner_join(
    fide_september_clean |>
      select(id, rating_sep = rating, games_sep = games),
    by = "id"
  ) |>
  mutate(
    rating_change = rating_sep - rating_aug,
    total_games = games_aug + games_sep,
    has_title = !is.na(title) | !is.na(wtitle),
    current_age = ifelse(!is.na(bday), cur_year - bday, NA_real_)
  ) |>
  filter(total_games > 0)

# Activity (cap at 30 to keep facets readable)
activity_data <- fide_september_clean |>
  filter(games > 0, games <= 30) |>
  mutate(activity_level = case_when(
    games <= 3 ~ "Low (1–3)",
    games <= 7 ~ "Moderate (4–7)",
    games <= 15 ~ "High (8–15)",
    TRUE ~ "Very High (16+)"
  )) |>
  mutate(
    activity_level = factor(activity_level,
      levels = c(
        "Low (1–3)",
        "Moderate (4–7)",
        "High (8–15)",
        "Very High (16+)"
      )
    )
  )

# Federations (titled players)
federation_data <- fide_september_clean |>
  filter(!is.na(title) | !is.na(wtitle)) |>
  count(fed, name = "titled_players") |>
  arrange(desc(titled_players)) |>
  slice_head(n = 15)
```

5. Visualization Parameters

Show code

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get basic theme colors
colors <- get_theme_colors(
  palette = list(
    ink = "#243245",
    text = "#435369",
    grid = "#E4E9F0",
    edge = "#C9D3E1",
    bg = "#F7FAFC",
    panel = "#FFFFFF",
    p1 = "#00A0E3", 
    p2 = "#2B3FB7",
    p3 = "#0B1F2A"
  )
)

### |- titles and caption ----
title_text <- str_glue("Chess Dreams and Breakthroughs: A Global Perspective")

subtitle_text <- str_glue(
  "Individual excellence emerges from active engagement within strong chess ecosystems"
)

caption_text <- create_social_caption(
  tt_year = 2025,
  tt_week = 38,
  source_text = "Data: FIDE Rating Lists (Aug–Sep 2025)"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.2), color = colors$title, margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, lineheight = 1.2, color = colors$subtitle, size = rel(0.78), margin = margin(b = 20)),

    # Axis elements
    axis.line = element_blank(),
    axis.ticks = element_blank(),

    # Grid elements
    panel.grid.major = element_line(color = "gray90", linetype = "solid", linewidth = 0.3),
    # panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.minor.y = element_blank(),

    # Axis elements
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    axis.title.x = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(t = 15)),
    axis.title.y = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(r = 10)),

    # Legend elements
    legend.position = "plot",
    legend.title = element_text(family = fonts$tsubtitle, color = colors$text, size = rel(0.8), face = "bold"),
    legend.text = element_text(family = fonts$tsubtitle, color = colors$text, size = rel(0.7)),
    legend.margin = margin(t = 15),

    # Plot margin
    plot.margin = margin(t = 15, r = 15, b = 15, l = 15),
  )
)

# Set theme
theme_set(weekly_theme)

# helpers for medians & formatting
num_lab <- label_number(accuracy = 1, big.mark = ",")
add_median <- function(y, label) {
  list(
    geom_hline(yintercept = y, linetype = "22", linewidth = 0.5, color = "gray50"),
    annotate(
      "label",
      x = Inf, y = y, hjust = 1, vjust = -0.2,
      label = label, size = 3, label.size = 0,
      family = fonts$text, fill = colors$palette$panel, color = colors$palette$text
    )
  )
}
```

6. Plot

Show code

```{r}
#| label: plot
#| warning: false

### |-  P1: Breakthroughs — top 20 improvers ----
break_df <- rating_comparison |>
  filter(rating_change > 0) |>
  arrange(desc(rating_change)) |>
  slice_head(n = 20) |>
  mutate(name_short = str_trunc(name, 28))

med_improve <- median(break_df$rating_change)

p1 <-
  ggplot(break_df, aes(x = reorder(name_short, rating_change), y = rating_change)) +
  # geoms
  geom_col(fill = colors$palette$p2, width = 0.7, alpha = 0.95) +
  geom_text(aes(label = paste0("+", rating_change)),
    hjust = -0.15, family = fonts$text, color = colors$palette$text, size = 3.2
  ) +
  add_median(med_improve, paste0("Median ", med_improve)) +
  # scales
  scale_x_discrete() +
  scale_y_continuous(labels = num_lab, expand = expansion(mult = c(0, 0.10))) +
  coord_flip(clip = "off") +
  labs(
    title = "Chess Rating Breakthroughs",
    subtitle = "Top 20 rating gains, August to September 2025",
    x = NULL, y = "Rating improvement (Elo)"
  ) +
  # theme
  theme(
    panel.grid.major.y = element_blank(),
    plot.margin = margin(10, 20, 10, 10)
  )

### |-  P2: Activity — faceted histograms ----
p2 <-
  ggplot(activity_data, aes(games)) +
  # geoms
  geom_histogram(bins = 18, fill = colors$palette$p1, color = "white", linewidth = 0.25, alpha = 0.95) +
  # scales
  scale_x_continuous(breaks = pretty_breaks(4)) +
  scale_y_continuous(labels = num_lab) +
  # labs
  labs(
    title = "Player Activity Patterns",
    subtitle = "Distribution of games played in September 2025 by activity level",
    x = "Games played (September)", y = "Number of players"
  ) +
  # facets
  facet_wrap(~activity_level, ncol = 4, scales = "free_y") +
  # theme
  theme(
    strip.text = element_text(face = "bold", color = colors$palette$ink),
    strip.background = element_rect(fill = colors$palette$bg, color = colors$palette$edge),
    panel.grid.major.x = element_line(color = colors$palette$grid),
    plot.margin = margin(10, 10, 5, 10)
  )

### |-  P3:  Federations — titled players (top 15) ----
med_titled <- median(federation_data$titled_players)

p3 <-
  ggplot(federation_data, aes(x = reorder(fed, titled_players), y = titled_players)) +
  # geoms
  geom_col(fill = alpha(colors$palette$p3, 0.92), width = 0.7) +
  geom_text(aes(label = num_lab(titled_players)),
    hjust = -0.15,
    family = fonts$text, color = colors$palette$text, size = 3.2
  ) +
  add_median(med_titled, paste0("Median ", med_titled)) +
  # scales
  scale_x_discrete() +
  scale_y_continuous(labels = num_lab, expand = expansion(mult = c(0, 0.10))) +
  coord_flip(clip = "off") +
  # labs
  labs(
    title = "Chess Federation Powerhouses",
    subtitle = "Countries with the most titled players",
    x = NULL, y = "Number of titled players"
  ) +
  # theme
  theme(
    legend.position = "none",
    panel.grid.major.y = element_blank(),
    plot.margin = margin(10, 20, 10, 10)
  )

### |-  Combined plots ----
combined_plots <- p2 / (p1 | p3) +
  plot_layout(heights = c(1.1, 1.3))

combined_plots <- combined_plots +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.8),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_markdown(
        size = rel(0.95),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 1.2,
        margin = margin(t = 5, b = 10)
      ),
      plot.caption = element_markdown(
        size = rel(0.65),
        family = fonts$caption,
        color = colors$caption,
        hjust = 0.5,
        margin = margin(t = 10)
      )
    )
  )
```

7. Save

Show code

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "tidytuesday", 
  year = 2025, 
  week = 38, 
  width  = 12,
  height = 12
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      patchwork_1.3.0 glue_1.8.0      scales_1.3.0   
 [5] janitor_2.2.0   showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       httr2_1.0.6        xfun_0.49          htmlwidgets_1.6.4 
 [5] gh_1.4.1           tzdb_0.5.0         yulab.utils_0.1.8  vctrs_0.6.5       
 [9] tools_4.4.0        generics_0.1.3     parallel_4.4.0     curl_6.0.0        
[13] gifski_1.32.0-1    fansi_1.0.6        pkgconfig_2.0.3    ggplotify_0.1.2   
[17] lifecycle_1.0.4    compiler_4.4.0     farver_2.1.2       munsell_0.5.1     
[21] codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10       
[25] crayon_1.5.3       pillar_1.9.0       camcorder_0.1.0    magick_2.8.5      
[29] commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4     
[33] labeling_0.4.3     rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0     
[37] grid_4.4.0         colorspace_2.1-1   cli_3.6.4          magrittr_2.0.3    
[41] utf8_1.2.4         withr_3.0.2        rappdirs_0.3.3     bit64_4.5.2       
[45] timechange_0.3.0   rmarkdown_2.29     tidytuesdayR_1.1.2 gitcreds_0.1.2    
[49] bit_4.5.0          hms_1.1.3          evaluate_1.0.1     knitr_1.49        
[53] markdown_1.13      gridGraphics_0.5-1 rlang_1.1.6        gridtext_0.1.5    
[57] Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3         vroom_1.6.5       
[61] svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9     R6_2.5.1          
[65] fs_1.6.5           systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_2025_38.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:

TidyTuesday 2025 Week 38: [FIDE Chess Player Ratings](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-09-23