2026-10-15

Introduction: The Olympic Gender Gap

BT21

Male athletes have historically dominated the sports industry in terms of sponsorships and viewership.

  • But when it comes to the Olympics: How many medals have males won compared to females and has that gap decreased over the years?

  • Total Data set: 271,116 data points

  • Time Line: 1896 - 2016

Statistics

First, we need to look at if the medal rate for men and women are similar.

  • Point estimate: By looking at our sample group of the data, we can estimate the true population proportion. For a specific sex, we calculate the medal rate by dividing the number of medalists by the total number of athletes using the formula: \(\hat{p} = \frac{x}{n}\)

  • Hypothesis Test: To test for the difference between the men’s medal rate (\(\hat{p}_M\)) and the women’s medal rate (\(\hat{p}_F\)), we use the Two-Proportion Z-test:\[Z = \frac{\hat{p}_M - \hat{p}_F}{\sqrt{\hat{p}(1-\hat{p}) \left(\frac{1}{n_M} + \frac{1}{n_F}\right)}}\]

  • Result: While evaluating this we get the p-value of 4.22e-12. Due to the p-value (4.22e-12) being smaller than 0.05, we reject the null hypothesis and conclude that the overall pooled medal rates for men and women are significantly different. However, as the following visualizations will show, this historical gap has converged over time as female participation increased.

Slide with R Output

- Here, we grouped the data by Year and Sex to count the unique athletes and medalists, then divided them to calculate the medal rate.

medal_summary <- medal_data %>%
  group_by(Year, Sex) %>%
  summarise(
    n_athletes = n_distinct(ID),
    n_medalists = n_distinct(ID[!is.na(Medal)]),
    .groups = "drop"
  ) %>%
  mutate(medal_rate = n_medalists/n_athletes)

head(medal_summary)
## # A tibble: 6 × 5
##    Year Sex   n_athletes n_medalists medal_rate
##   <int> <chr>      <int>       <int>      <dbl>
## 1  1896 M            176          93      0.528
## 2  1900 F             23           8      0.348
## 3  1900 M           1201         461      0.384
## 4  1904 F              6           5      0.833
## 5  1904 M            644         352      0.547
## 6  1906 F              6           5      0.833

Visual #1:

The gold line represents female athletes, whose participation was historically low. But as we approach the end of the 20th century, both lines converge to one another, meaning the ratio of female to male athletes grew closer. This is significant for our next slide, where we will see the medal rate of male and female athletes.

As women were starting to participate in the Olympics, we can see an increase in the number of athletes in general.

Visual #2:

  • In the early 1900s, the yellow line representing female competitors was extremely high, however, this is simply due to the very small sample size of women participating at the time.
  • As female participation increased, the medal rate normalized and became similar to the male athletes.
  • This shows that as the participation gap between men and women closed, the medal earning rate also became equal.

Plotly:

In this visualization, we can identify the specific medal rate for each year. These findings present a clear trend of convergence between male and female athletes.

Conclusions

  • In early 1900s, specifically 1904-1906 we see the woman’s medal rate reach the top of the graph, but that’s because very few women competed in the Olympics. But towards the end of the 1900s, we know and can see in the data set that there is a huge female population.
  • If we take a look at the overall graph, we can see that the two lines between female and male athletes start tightening meaning we can see how many more female athletes we have competing.
  • While men have won more Olympic medals in total, the medal winning rate betweem male and female atheletes has now become equal, due to women being allowed to participate in an equal number of sporting events.

Thank you!

  • Final Thought: The historical gap in Olympic medal rates between men and women has successfully closed.
  • The Data shows: As female participation grew, the medal earning rate normalized and equalized between sexes.
  • Dataset: athlete_events.csv