TidyTuesday: Olympic Schedule 2026 — When Complexity Peaks

TidyTuesday

Sports

System

Mapping the density, rhythm, and structural imbalance of 1,866 events across the Milan-Cortina 2026 Winter Olympics

Author

chokotto

Published

March 24, 2026

Overview

An Olympic schedule is not just a timetable — it’s a resource allocation problem made visible. This week’s TidyTuesday uses the complete event schedule for the 2026 Milan-Cortina Winter Olympics (1,866 events across 16 disciplines) to reveal the hidden structure: which days carry the heaviest load, where medal events cluster, and how training and competition rhythms overlap.

Data Source: TidyTuesday 2026-02-10 / Daniel Chen, Posit PBC
Events: 1,866 total (competition + training)
Disciplines: 16 winter sports
Period: February 4 – February 23, 2026
Visualization: ggplot2 + patchwork (static panels)

Data

Show code

library(tidyverse)
library(scales)
library(glue)
library(patchwork)

Show code

data_dir <- file.path(getwd(), "data")

schedule <- read_csv(file.path(data_dir, "schedule.csv"),
                     show_col_types = FALSE)

schedule <- schedule |>
  mutate(
    date = as.Date(date),
    is_medal_event = as.logical(is_medal_event),
    is_training = as.logical(is_training)
  )

cat(glue(
  "Total events: {nrow(schedule)}\n",
  "Disciplines: {n_distinct(schedule$discipline_name)}\n",
  "Venues: {n_distinct(schedule$venue_name)}\n",
  "Date range: {min(schedule$date)} to {max(schedule$date)}\n",
  "Medal events: {sum(schedule$is_medal_event)}\n",
  "Training sessions: {sum(schedule$is_training)}"
))

Total events: 1866
Disciplines: 16
Venues: 14
Date range: 2026-02-04 to 2026-02-22
Medal events: 344
Training sessions: 246

Show code

NOTE_TEXT   <- "Full event schedule including training and competition"
SOURCE_TEXT <- "TidyTuesday 2026-02-10 / Daniel Chen"
CAPTION     <- glue("Note: {NOTE_TEXT}  |  Source: {SOURCE_TEXT}  |  \u00A9 2026 chokotto")

theme_fm <- theme_minimal(base_size = 12) +
  theme(
    plot.background = element_rect(fill = "white", color = NA),
    panel.background = element_rect(fill = "#f8fafc", color = NA),
    panel.grid.major = element_line(color = "#e2e8f0", linewidth = 0.3),
    panel.grid.minor = element_blank(),
    text = element_text(color = "#334155"),
    axis.text = element_text(color = "#475569"),
    plot.title = element_text(color = "#1e293b", face = "bold", size = 14),
    plot.subtitle = element_text(color = "#64748b", size = 10),
    plot.caption = element_text(
      face = "italic", color = "#94a3b8", size = 9,
      hjust = 0, margin = margin(t = 12)
    ),
    plot.caption.position = "plot",
    strip.text = element_text(color = "#1e293b", face = "bold"),
    legend.background = element_rect(fill = "white", color = NA),
    legend.text = element_text(color = "#475569"),
    plot.margin = margin(15, 15, 15, 15)
  )

Visualizations

1. Event Density Heatmap: Date × Discipline

Where does the schedule concentrate? This heatmap shows total events per day per discipline — revealing the rhythm of the Games.

Show code

heatmap_data <- schedule |>
  count(date, discipline_name, name = "n_events") |>
  complete(date, discipline_name, fill = list(n_events = 0))

discipline_order <- schedule |>
  count(discipline_name) |>
  arrange(desc(n)) |>
  pull(discipline_name)

heatmap_data <- heatmap_data |>
  mutate(discipline_name = factor(discipline_name, levels = rev(discipline_order)))

p1 <- ggplot(heatmap_data, aes(x = date, y = discipline_name, fill = n_events)) +
  geom_tile(color = "white", linewidth = 0.4) +
  scale_fill_gradient(
    low = "#f1f5f9", high = "#e63946",
    name = "Events",
    breaks = c(0, 10, 20, 30, 40)
  ) +
  scale_x_date(
    date_labels = "%b %d",
    date_breaks = "2 days",
    expand = c(0, 0)
  ) +
  labs(
    title = "Olympic Event Density: Some Disciplines Run Every Day, Others Concentrate",
    subtitle = "Total events per day per discipline \u2014 darker = more events scheduled",
    caption = CAPTION,
    x = NULL,
    y = NULL
  ) +
  theme_fm +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1, size = 9),
    legend.position = "right",
    legend.key.height = unit(1.2, "cm")
  )

p1

2. Medal Event Concentration: When Are Medals Decided?

Show code

medal_by_date <- schedule |>
  filter(is_medal_event) |>
  count(date, name = "medal_events")

daily_total <- schedule |>
  filter(!is_training) |>
  count(date, name = "total_comp")

medal_context <- daily_total |>
  left_join(medal_by_date, by = "date") |>
  mutate(medal_events = replace_na(medal_events, 0),
         non_medal = total_comp - medal_events) |>
  pivot_longer(cols = c(medal_events, non_medal),
               names_to = "type", values_to = "count") |>
  mutate(type = factor(type, levels = c("non_medal", "medal_events"),
                       labels = c("Non-medal competition", "Medal events")))

p2 <- ggplot(medal_context, aes(x = date, y = count, fill = type)) +
  geom_col(width = 0.8) +
  scale_fill_manual(
    values = c("Non-medal competition" = "#94a3b8", "Medal events" = "#e63946"),
    name = NULL
  ) +
  scale_x_date(date_labels = "%b %d", date_breaks = "2 days") +
  scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +
  labs(
    title = "Medal events peak mid-Games \u2014 the opening and closing days are lighter",
    subtitle = "Daily competition events split by medal / non-medal status",
    caption = CAPTION,
    x = NULL,
    y = "Events"
  ) +
  theme_fm +
  theme(
    legend.position = "top",
    legend.justification = "left"
  )

p2

3. Training vs. Competition Balance

Show code

daily_type <- schedule |>
  mutate(session_type = if_else(is_training, "Training", "Competition")) |>
  count(date, session_type) |>
  mutate(session_type = factor(session_type, levels = c("Training", "Competition")))

p3 <- ggplot(daily_type, aes(x = date, y = n, fill = session_type)) +
  geom_area(alpha = 0.8, position = "stack") +
  scale_fill_manual(
    values = c("Training" = "#f59e0b", "Competition" = "#3b82f6"),
    name = NULL
  ) +
  scale_x_date(date_labels = "%b %d", date_breaks = "2 days") +
  scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +
  labs(
    title = "Training sessions dominate early days, then competition takes over",
    subtitle = "Daily event volume by session type (stacked area)",
    caption = CAPTION,
    x = NULL,
    y = "Events"
  ) +
  theme_fm +
  theme(
    legend.position = "top",
    legend.justification = "left"
  )

p3

4. Venue Load Distribution

Show code

venue_summary <- schedule |>
  count(venue_name, sort = TRUE) |>
  mutate(venue_name = fct_reorder(venue_name, n))

venue_colors <- if_else(
  venue_summary$n == max(venue_summary$n), "#e63946", "#94a3b8"
)

p4 <- ggplot(venue_summary, aes(x = n, y = venue_name)) +
  geom_col(fill = venue_colors, width = 0.7) +
  geom_text(aes(label = n), hjust = -0.2, color = "#475569", size = 3.5) +
  scale_x_continuous(expand = expansion(mult = c(0, 0.15))) +
  labs(
    title = "Venue load is highly uneven \u2014 one venue hosts far more events than others",
    subtitle = "Total scheduled events by venue",
    caption = CAPTION,
    x = "Events",
    y = NULL
  ) +
  theme_fm

p4

5. Combined Panel

Show code

combined <- (p1 + labs(caption = NULL)) /
  (p2 + labs(caption = NULL)) /
  (p3 + labs(caption = NULL)) /
  (p4 + labs(caption = NULL)) +
  plot_layout(heights = c(2, 1, 1, 1)) +
  plot_annotation(
    title = "Milan-Cortina 2026: The Structure Behind the Schedule",
    subtitle = "1,866 events across 16 disciplines, 20 days, and multiple venues \u2014 when complexity peaks",
    caption = CAPTION,
    theme = theme(
      plot.background = element_rect(fill = "white", color = NA),
      plot.title = element_text(color = "#1e293b", face = "bold", size = 18),
      plot.subtitle = element_text(color = "#64748b", size = 12),
      plot.caption = element_text(
        face = "italic", color = "#94a3b8", size = 9,
        hjust = 0, margin = margin(t = 12)
      ),
      plot.caption.position = "plot"
    )
  )

combined

Key Findings

Event density is not uniform: Certain disciplines (e.g., Curling, Alpine Skiing) span nearly the entire Games, while others cluster into a few intense days
Medal events peak mid-Games: The opening and closing days carry fewer medal decisions — the real “pressure peak” comes in the second week
Training fades as competition rises: The schedule shifts from training-heavy early days to competition-dominant mid-to-late Games, reflecting the operational rhythm of Olympic preparation
Venue load is highly concentrated: A small number of venues shoulder a disproportionate share of events, creating logistical bottleneck risk

This post is part of the TidyTuesday weekly data visualization project.

Disclaimer

This analysis is for educational and practice purposes only. Schedule data reflects the planned event timetable and may not account for weather delays, cancellations, or last-minute changes.

--- title: "TidyTuesday: Olympic Schedule 2026 — When Complexity Peaks" description: "Mapping the density, rhythm, and structural imbalance of 1,866 events across the Milan-Cortina 2026 Winter Olympics" date: "2026-03-24" x-posted: true author: "chokotto" categories: - TidyTuesday - R - Sports - System image: "thumbnail.svg" engine: knitr code-fold: true code-tools: true code-summary: "Show code" twitter-card: card-type: summary_large_image image: "thumbnail.png" title: "TidyTuesday: Olympic Schedule 2026" description: "1,866 events, 16 disciplines, 20 days — visualizing when the Olympics peaks" --- ## Overview An Olympic schedule is not just a timetable — it's a **resource allocation problem** made visible. This week's TidyTuesday uses the complete event schedule for the **2026 Milan-Cortina Winter Olympics** (1,866 events across 16 disciplines) to reveal the hidden structure: which days carry the heaviest load, where medal events cluster, and how training and competition rhythms overlap. - **Data Source**: [TidyTuesday 2026-02-10](https://github.com/rfordatascience/tidytuesday/blob/main/data/2026/2026-02-10/readme.md) / [Daniel Chen, Posit PBC](https://github.com/chendaniely) - **Events**: 1,866 total (competition + training) - **Disciplines**: 16 winter sports - **Period**: February 4 – February 23, 2026 - **Visualization**: ggplot2 + patchwork (static panels) ## Data ```{r} #| label: load-packages #| message: false #| warning: false library(tidyverse) library(scales) library(glue) library(patchwork) ``` ```{r} #| label: load-data #| message: false data_dir <- file.path(getwd(), "data") schedule <- read_csv(file.path(data_dir, "schedule.csv"), show_col_types = FALSE) schedule <- schedule |> mutate( date = as.Date(date), is_medal_event = as.logical(is_medal_event), is_training = as.logical(is_training) ) cat(glue( "Total events: {nrow(schedule)}\n", "Disciplines: {n_distinct(schedule$discipline_name)}\n", "Venues: {n_distinct(schedule$venue_name)}\n", "Date range: {min(schedule$date)} to {max(schedule$date)}\n", "Medal events: {sum(schedule$is_medal_event)}\n", "Training sessions: {sum(schedule$is_training)}" )) ``` ```{r} #| label: setup-theme #| message: false NOTE_TEXT <- "Full event schedule including training and competition" SOURCE_TEXT <- "TidyTuesday 2026-02-10 / Daniel Chen" CAPTION <- glue("Note: {NOTE_TEXT} | Source: {SOURCE_TEXT} | \u00A9 2026 chokotto") theme_fm <- theme_minimal(base_size = 12) + theme( plot.background = element_rect(fill = "white", color = NA), panel.background = element_rect(fill = "#f8fafc", color = NA), panel.grid.major = element_line(color = "#e2e8f0", linewidth = 0.3), panel.grid.minor = element_blank(), text = element_text(color = "#334155"), axis.text = element_text(color = "#475569"), plot.title = element_text(color = "#1e293b", face = "bold", size = 14), plot.subtitle = element_text(color = "#64748b", size = 10), plot.caption = element_text( face = "italic", color = "#94a3b8", size = 9, hjust = 0, margin = margin(t = 12) ), plot.caption.position = "plot", strip.text = element_text(color = "#1e293b", face = "bold"), legend.background = element_rect(fill = "white", color = NA), legend.text = element_text(color = "#475569"), plot.margin = margin(15, 15, 15, 15) ) ``` ## Visualizations ### 1. Event Density Heatmap: Date × Discipline Where does the schedule concentrate? This heatmap shows total events per day per discipline — revealing the rhythm of the Games. ```{r} #| label: heatmap #| fig-width: 14 #| fig-height: 8 #| warning: false heatmap_data <- schedule |> count(date, discipline_name, name = "n_events") |> complete(date, discipline_name, fill = list(n_events = 0)) discipline_order <- schedule |> count(discipline_name) |> arrange(desc(n)) |> pull(discipline_name) heatmap_data <- heatmap_data |> mutate(discipline_name = factor(discipline_name, levels = rev(discipline_order))) p1 <- ggplot(heatmap_data, aes(x = date, y = discipline_name, fill = n_events)) + geom_tile(color = "white", linewidth = 0.4) + scale_fill_gradient( low = "#f1f5f9", high = "#e63946", name = "Events", breaks = c(0, 10, 20, 30, 40) ) + scale_x_date( date_labels = "%b %d", date_breaks = "2 days", expand = c(0, 0) ) + labs( title = "Olympic Event Density: Some Disciplines Run Every Day, Others Concentrate", subtitle = "Total events per day per discipline \u2014 darker = more events scheduled", caption = CAPTION, x = NULL, y = NULL ) + theme_fm + theme( axis.text.x = element_text(angle = 45, hjust = 1, size = 9), legend.position = "right", legend.key.height = unit(1.2, "cm") ) p1 ``` ### 2. Medal Event Concentration: When Are Medals Decided? ```{r} #| label: medal-density #| fig-width: 12 #| fig-height: 5 #| warning: false medal_by_date <- schedule |> filter(is_medal_event) |> count(date, name = "medal_events") daily_total <- schedule |> filter(!is_training) |> count(date, name = "total_comp") medal_context <- daily_total |> left_join(medal_by_date, by = "date") |> mutate(medal_events = replace_na(medal_events, 0), non_medal = total_comp - medal_events) |> pivot_longer(cols = c(medal_events, non_medal), names_to = "type", values_to = "count") |> mutate(type = factor(type, levels = c("non_medal", "medal_events"), labels = c("Non-medal competition", "Medal events"))) p2 <- ggplot(medal_context, aes(x = date, y = count, fill = type)) + geom_col(width = 0.8) + scale_fill_manual( values = c("Non-medal competition" = "#94a3b8", "Medal events" = "#e63946"), name = NULL ) + scale_x_date(date_labels = "%b %d", date_breaks = "2 days") + scale_y_continuous(expand = expansion(mult = c(0, 0.05))) + labs( title = "Medal events peak mid-Games \u2014 the opening and closing days are lighter", subtitle = "Daily competition events split by medal / non-medal status", caption = CAPTION, x = NULL, y = "Events" ) + theme_fm + theme( legend.position = "top", legend.justification = "left" ) p2 ``` ### 3. Training vs. Competition Balance ```{r} #| label: training-competition #| fig-width: 12 #| fig-height: 5 #| warning: false daily_type <- schedule |> mutate(session_type = if_else(is_training, "Training", "Competition")) |> count(date, session_type) |> mutate(session_type = factor(session_type, levels = c("Training", "Competition"))) p3 <- ggplot(daily_type, aes(x = date, y = n, fill = session_type)) + geom_area(alpha = 0.8, position = "stack") + scale_fill_manual( values = c("Training" = "#f59e0b", "Competition" = "#3b82f6"), name = NULL ) + scale_x_date(date_labels = "%b %d", date_breaks = "2 days") + scale_y_continuous(expand = expansion(mult = c(0, 0.05))) + labs( title = "Training sessions dominate early days, then competition takes over", subtitle = "Daily event volume by session type (stacked area)", caption = CAPTION, x = NULL, y = "Events" ) + theme_fm + theme( legend.position = "top", legend.justification = "left" ) p3 ``` ### 4. Venue Load Distribution ```{r} #| label: venue-load #| fig-width: 12 #| fig-height: 5 #| warning: false venue_summary <- schedule |> count(venue_name, sort = TRUE) |> mutate(venue_name = fct_reorder(venue_name, n)) venue_colors <- if_else( venue_summary$n == max(venue_summary$n), "#e63946", "#94a3b8" ) p4 <- ggplot(venue_summary, aes(x = n, y = venue_name)) + geom_col(fill = venue_colors, width = 0.7) + geom_text(aes(label = n), hjust = -0.2, color = "#475569", size = 3.5) + scale_x_continuous(expand = expansion(mult = c(0, 0.15))) + labs( title = "Venue load is highly uneven \u2014 one venue hosts far more events than others", subtitle = "Total scheduled events by venue", caption = CAPTION, x = "Events", y = NULL ) + theme_fm p4 ``` ### 5. Combined Panel ```{r} #| label: combined-panel #| fig-width: 14 #| fig-height: 20 #| warning: false combined <- (p1 + labs(caption = NULL)) / (p2 + labs(caption = NULL)) / (p3 + labs(caption = NULL)) / (p4 + labs(caption = NULL)) + plot_layout(heights = c(2, 1, 1, 1)) + plot_annotation( title = "Milan-Cortina 2026: The Structure Behind the Schedule", subtitle = "1,866 events across 16 disciplines, 20 days, and multiple venues \u2014 when complexity peaks", caption = CAPTION, theme = theme( plot.background = element_rect(fill = "white", color = NA), plot.title = element_text(color = "#1e293b", face = "bold", size = 18), plot.subtitle = element_text(color = "#64748b", size = 12), plot.caption = element_text( face = "italic", color = "#94a3b8", size = 9, hjust = 0, margin = margin(t = 12) ), plot.caption.position = "plot" ) ) combined ``` ## Key Findings 1. **Event density is not uniform**: Certain disciplines (e.g., Curling, Alpine Skiing) span nearly the entire Games, while others cluster into a few intense days 2. **Medal events peak mid-Games**: The opening and closing days carry fewer medal decisions — the real "pressure peak" comes in the second week 3. **Training fades as competition rises**: The schedule shifts from training-heavy early days to competition-dominant mid-to-late Games, reflecting the operational rhythm of Olympic preparation 4. **Venue load is highly concentrated**: A small number of venues shoulder a disproportionate share of events, creating logistical bottleneck risk *** _This post is part of the [TidyTuesday](https://github.com/rfordatascience/tidytuesday) weekly data visualization project._ :::{.callout-caution collapse="false" appearance="minimal" icon="false"} ## Disclaimer ::: {style="font-size: 0.85em; color: #64748b; line-height: 1.6;"} This analysis is for educational and practice purposes only. Schedule data reflects the planned event timetable and may not account for weather delays, cancellations, or last-minute changes. ::: :::