Olympic Medals and the Power of Wealth & Population
Beyond the Medal Count: How a Country's Resources and Population Shape Olympic Success. A three part study of the Paris 2024 Summer Olympic medal results.
The 2024 Paris Summer Olympics came to a close in early August, leaving us in awe of the athletes' achievements. While we await the upcoming Paralympic Games, it’s a great moment to delve into the numbers and explore how countries performed in terms of Olympic medals. The traditional medal count, with the U.S. leading the tally at 126, might suggest a straightforward ranking of the best-performing nations.
However, is that the full story? For example, should a “small” nation winning 50 medals be considered more impressive than a much “larger” country winning 70? Medal counts alone don’t always reflect the unique challenges and advantages faced by each nation.
I created and analyzed an Olympic medal dataset, comparing the performances of various countries and territories against their population, economy and even number of conflicts.
In this post, I dive into the first part of the analysis: Is a country's Olympic success more closely tied to its economy or the size of its population?
Clearly, Olympic success is a much more complex than just the economy and population. Nevertheless, getting directional insights into this matter will be informative, and not to mention, fun!
The Relationship Between GDP, Population, and Olympic Medals
To start the exploration, I first calculated the correlation between the number of medals won by each country and three key factors: GDP, population, and the number of conflicts. By visualizing these relationships, we can see which factors most strongly influence a country’s Olympic performance.
The chart below illustrates those correlation coefficients, with higher bars representing stronger correlations. The results are clear: GDP has a far stronger relationship with total medals than population.
In fact, the correlation between GDP and medals is more than twice as strong as that between population and medals. This suggests that a country's economic power plays a more crucial role in determining Olympic success than its population size.
To further test and understand these relationships, I ran a simple linear regression to see how well GDP and population predict Olympic success. The results were, again, clear:
GDP alone explained 73% of the variation in total medals (R² = 0.73).
Population alone explained only 18% of the variation (R² = 0.18).
Of course, GDP is itself correlated with population as well. So to account for that as well, I run a regression with all the factors included, with GDP consistently emerging as more positively related to the medals than population.
In fact, population consistently emerges as negatively correlated with the number of medals, when accounting for GDP and conflicts. This might be also related to the fact that the correlation between total medal count and GDP per capita (which is just GDP that incorporates population information) is much lower than the correlation between the medals and just the GDP.
Note also the the number of conflicts (similar to what we saw in the correlation plot above) has a positive coefficient but a large p-value.
This is likely driven by correlations between a country’s economy and population and the number of conflicts they might be involved in. For example, countries like the US and Mexico have strong economies and large number of conflicts. Ukraine, the country with the largest number of conflicts since August 2022, still ranked in the Top 10% of total medals (22nd out of ~200, with 12 medals)) further weakening that correlation.
The Influence of Outliers: U.S. and India.
Not surprisingly, the United States and India heavily influence the dataset, but in opposite ways. The U.S., with the highest GDP globally, also secured the most medals—126 in total. This skews the correlation toward GDP. Conversely, India, with the largest population (1.45 billion in 2022), won only 6 medals. Representing 18% of the global population but winning just 0.6% of the total medals, India significantly weakens the correlation between population and medal count.
So I wanted to further stress-test the results by seeing what happens when we remove some or all of these outliers. This led to some shifts in the correlations, but didn’t change the narrative:
The correlation between GDP and medals decreased slightly, from 0.87 to 0.78—a modest 8% drop.
The correlation between population and medals, however, dropped substantially, falling by 35%, from 0.42 to 0.27.
When running a linear regression, GDP now explained 61% (R²=0.61, a 21% drop from 0.78) of the total medal count variation, and population now explained 31%. (R²=0.31, a 72% increase from 0.18)
So even after adjusting for these extreme cases, GDP remains a stronger predictor of a country's total medal count than population.
Further stress-testing still keeps the main finding unchanged. Even when removing China too, and breaking down by each type of medal, GDP still explains more of the total medal variance than population. See Appendix II, III and IV for all the analysis.
What’s next
This post highlighted that a country's total medal count in the Paris Summer Olympics is more strongly linked to its economy than its population. As mentioned earlier, future analyses could introduce additional variables about the country and apply more complex statistical tests to gain a deeper understanding of the factors influencing Olympic success.
Extending this analysis to the Paralympic Games and Winter Olympics could also provide valuable insights.
Additionally, breaking down the analysis by individual sports categories (e.g., track and field, swimming, gymnastics) would be interesting to see if economic strength or population size plays a more significant role in different types of sports. For example, GDP might have a stronger correlation with medal success in sports like golf, while population could be more influential in sports like football (or soccer for American readers).
In the next post, I will continue the Paris 2024 Summer theme by examining which countries over- or underperformed in the medal count relative to their economy and population sizes.
Can you guess which large GDP country (over $1 trillion) won no medals? Or which country with the largest population went home empty-handed?
Dataset
As part of this work, I’m also making the dataset available to anyone interested in using it for further research or analysis. Feel free to contact me if you'd like access.
For more information on the sources and dataset columns, please refer to Appendix I.
I had a lot of fun doing this work - thank you for reading!
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Appendix
I. Dataset
There are various places on the web that have the total medal count by country for the Paris 2024 Olympics, mainly the official Olympics page. I used this ESPN website, only because it was easier to use.
I used this worldometers website to collect GDP and population data for 2022. Even though the athletes that competed in 2024 were trained earlier than 2022, it will still be directionally correct regarding the conditions in the athlete’s country.
Finally, I added a dataset with conflicts in the country since 2022, I used the ACLED conflict index dataset. I will use this dataset more heavily in the next post when I will take this variable into account to produce a new ranking of performance at the Olympics.
Shortcomings of the data
The dataset has some clear shortcomings since it doesn’t take into account athletes who perhaps train abroad, such as the French swimmer Léon Marchand who’s coach is American and trains in the US.
Dual national Kaylia Nemour who won Gold in gymnastics forAlgeria is another example of the dataset not capturing the whole story. Even though she won Gold for Algeria, Nemour was competing for France until 2021.
II. Removing US, India and China
Removing China (in addition to the US and India), drops the correlation between medals and population even lower than overall. This is because, after removing US and India, China was an outlier with both a big population and large medal count.
Removing the US, India and China, actually decreases the R² for both GDP and population.
And it still keeps the population coefficient negative, and the number of conflicts positive.
III. Gold, Silver and Bronze medals
Finally, I wanted to test whether there are any variability when we look at gold, silver and bronze medals.
Perhaps one interesting observation is that population is the noticeably more correlated with Gold medals (0.47) than Silver or Bronze (0.38).
IV: Median GDP and population by medal count
At this point, I’m honestly just having fun.
