Olympic Track & Field Body Composition: Event-Level Patterns

Height–weight patterns of Olympic track and field medalists by event, using #TidyTuesday data (2021-07-27).

Keywords

TidyTuesday, R, ggplot2, sports analytics, Olympics, track and field, body composition, data visualization

Track and field athletes bring different body types to their events, and those differences often reflect the physical demands of each discipline. In this project, I analyzed medalists from Olympic track and field using the TidyTuesday dataset (July 27, 2021). The goal was to explore how height and weight vary across events and to see whether distinct patterns emerge between sprinters, distance runners, jumpers, and throwers.

Workflow

1. Load and prepare the dataset
   • Pulled Olympic athlete data from the TidyTuesday repository
   • Filtered for track and field (athletics) medalists.
   • Converted sex to a factor and standardized column labels for clarity.
2. Initial exploration
   • Plotted raw height and weight values by event to get a sense of variation.
   • Noticed dense overlaps across events, suggesting the need for grouping.
3. Group events into categories
   • Distance: 5,000 m, 10,000 m
   • Mid-distance: 800 m, 1,500 m
   • Sprints: 100 m, 400 m
   • Jumps: long jump, triple jump, high jump
   • Throws: shot put, discus, javelin
4. Subset by sex
   • Created separate data frames for female (athletics_f) and male (athletics_m) athletes.
   • Allowed for side-by-side comparison of body composition trends.
5. Refine visualizations
   • Built scatterplots of height vs. weight, colored by event category.
   • Applied transparency (alpha) to handle overlapping points.
   • Improved readability with theme_minimal() and color palettes from colorspace.
6. Final output
   • Produced two plots: one for female medalists, one for male medalists.

Code & Results

# load packages and read in data from tidyTuesday
library(tidyverse)
library(colorspace)
library(patchwork)
library(DT)
olympics <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-07-27/olympics.csv')
# head(olympics)

# I am interested in looking at track and field (athletics) medalists
athletics <- olympics %>% 
  filter(sport=="Athletics",
         !is.na(medal))

athletics$sex <- as.factor(athletics$sex)

athletics_colnames <- c("ID", "Name", "Sex", "Age", "Height",
                        "Weight", "Team", "NOC", "Games", "Year",
                        "Season", "City", "Sport", "Event",
                        "Medal")

DT::datatable(athletics, colnames = athletics_colnames, caption = htmltools::tags$caption(style = "caption-side: bottom; text-align: center;", htmltools::strong("Table 1: Olympic Track & Field Medalists")), filter = "top", options = list(pageLength = 5, autoWidth = TRUE))

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Table 1: Olympic Track & Field Medalists

	ID	Name	Sex	Age	Height	Weight	Team	NOC	Games	Year	Season	City	Sport	Event	Medal
	67 135563			15 48	150 207	40.0 154.0				1896 2016
1	67	Mariya Vasilyevna Abakumova (-Tarabina)	F	22	179	80	Russia	RUS	2008 Summer	2008	Summer	Beijing	Athletics	Athletics Women's Javelin Throw	Silver
2	86	Jos Manuel Abascal Gmez	M	26	182	67	Spain	ESP	1984 Summer	1984	Summer	Los Angeles	Athletics	Athletics Men's 1,500 metres	Bronze
3	359	Andrey Khakimovich Abduvaliyev	M	26	190	125	Unified Team	EUN	1992 Summer	1992	Summer	Barcelona	Athletics	Athletics Men's Hammer Throw	Gold
4	379	Addis Abebe	M	21	160	50	Ethiopia	ETH	1992 Summer	1992	Summer	Barcelona	Athletics	Athletics Men's 10,000 metres	Bronze
5	411	Gezahgne Abera	M	22	166	58	Ethiopia	ETH	2000 Summer	2000	Summer	Sydney	Athletics	Athletics Men's Marathon	Gold

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After filtering the data to focus on track and field medalists, I separated the athletes by sex and visualized their height and weight across different events.

# initial exploration
athletics %>% 
  filter(sex=="F") %>% 
  ggplot(aes(height, weight, color=event))+
  geom_point()

To simplify the analysis, I selected a few events and grouped them into categories such as Distance, Mid-Distance, Sprints, Jumps, and Throws. I then refined the visualizations, resulting in a cleaner and more understandable plot.

# there are so many events so lets choose a few events that represent some categories
# distance: 10k, 5k
# mid-distance: 800m, 1500m
# sprints: 100m, 400m
# jumps: long jump, triple jump
# throws: shot put, discus, javelin
athletics$event <- gsub("Athletics Women's ", "", athletics$event)
athletics$event <- gsub("Athletics Men's ", "", athletics$event)

athletics <- athletics %>% 
  filter(event == c("10,000 metres", "5,000 metres", "800 metres","1,500 metres", "100 metres", "400 metres", "Long Jump", "Triple Jump", "Shot Put", "Discus Throw", "Javelin Throw"))

athletics %>% 
  ggplot(aes(height, weight, color=event))+
  geom_point(alpha=0.5)

# this is still too much, let's group them
athletics <- athletics %>% 
  mutate(event_type = case_when( 
  event %in% c("10,000 metres", "5,000 metres") ~ "Distance",
  event %in% c("800 metres","1,500 metres") ~ "Mid-distance",
  event %in% c("100 metres", "400 metres") ~ "Sprints",
  event %in% c("Long Jump", "Triple Jump", "High Jump") ~ "Jumps",
  event %in% c("Shot Put", "Discus Throw", "Javelin Throw") ~ "Throws",
))

# now it's time to subset by sex
athletics_f <- athletics %>% 
  filter(sex=="F")

athletics_m <- athletics %>% 
  filter(sex=="M")

# plot female
athletics_f %>% 
  ggplot(aes(height, weight, color=event_type))+
  geom_point(alpha=0.5)

This is a much easier graphic to understand so I decided to beautify it.

# putting it together
(( p1 <- athletics_f %>% 
  ggplot(aes(height, weight, color=event_type))+
  geom_point(alpha=0.75)+
  theme_minimal()+
  scale_color_discrete_qualitative("Dark 3")+
  labs(title="Body Composition of Female \nOlympic Medalists by Event\n", x="Height (cm)", y="Weight (kg)", color="Type of Track & Field Event") ))

The findings reveal interesting trends. Female throwers generally exhibit larger body compositions, both in terms of weight and height, while distance runners tend to be smaller and lighter compared to other athletes. Jumpers, on the other hand, show either tall stature (likely due to high jumpers) or body compositions similar to sprinters. Short sprinters demonstrate a balanced distribution without skew in either height or weight, while mid-distance runners appear slightly lighter and shorter than their short sprinter counterparts. The sprinters, as a group, exhibit similar body types, with an average height of around 170 cm and weight of 60 kg.

# putting it together
(( p2 <- athletics_m %>% 
  ggplot(aes(height, weight, color=event_type))+
  geom_point(alpha=0.75)+
  theme_minimal()+
  scale_color_discrete_qualitative("Dark 3")+
  labs(title="Body Composition of Male \nOlympic Medalists by Event\n", x="Height (cm)", y="Weight (kg)", color="Type of Track & Field Event") ))

Similar patterns emerge among male Olympic medalists. Throwers dominate the upper right quadrant, indicating their tendency to be taller and heavier than other athletes. Like their female counterparts, male distance runners tend to have lighter and shorter body compositions. Jumpers and sprinters showcase comparable body types, with mid-distance runners falling between short sprinters and distance runners.

Discussion

Interestingly, I was surprised to discover that throwers tend to be significantly taller than other athletes, as I initially assumed jumpers would be the tallest, given the specific demands of high jump. The realization that distance runners tend to be shorter, which hadn’t caught my attention previously, could be due to height-to-weight ratio. Being lighter appears advantageous for distance runners, and a shorter stature could contribute to achieving this goal.

These data align with my anectodal observations of track and field events. The trends indicate that specific events attract athletes with distinct body sizes, likely due to inherent advantages associated with their builds. This project has been a fun way to combine my passion for track and field with data science techniques. Please reach out if you have any project ideas or would like to collaborate.

FAQ: Olympic Body Composition Analysis

Where does the dataset come from?

The data was published as part of the TidyTuesday project (July 27, 2021) and includes Olympic athletes across many sports and years.

How were the events grouped into categories?

Events were grouped into five categories: Distance (5,000 m, 10,000 m), Mid-distance (800 m, 1,500 m), Sprints (100 m, 400 m), Jumps (long, triple, high jump), and Throws (shot put, discus, javelin).

Why focus only on medalists?

Filtering to medalists provides a focused view of athletes who reached the highest level of performance, making body composition patterns clearer.

What trends stand out between male and female athletes?

In both groups, throwers are taller and heavier, distance runners are shorter and lighter, and sprinters and jumpers cluster in between.

Can this analysis predict performance?

No. The visualizations highlight correlations but do not establish causation. Many factors beyond height and weight, such as training, technique, and physiology affect athletic outcomes.

Oliver F. Anderson, MS – Computational Biologist, Data Scientist, and Research Consultant based in Portland, Oregon. I design data-driven solutions in bioinformatics, machine learning, and AI automation for research and biotech.