What country did best in Tokyo Olympics?
Traditional medal lists, as a realistic measure of every country’s success in the Olympic Games, are questioned. How can we know who really succeded? In this study about Tokyo 2020’s olympic games medal score, I propose several #DataViz solutions in order to reply to this question.
Introduction: the old and new medal lists
It’s been several months since Tokyo Olympic Games (performed on 2021) ended and, as usual, a medal ranking is published; where we can read a summary about how many gold, silver and bronze medals every country’s delegation achieved, in total.
As the Olympic Games are still an excellent political showcase where nations that compete in other fields (military, commercial, etc) can compare and exhibit their muscle, these medal’s lists represent, in some sort, a kind of meter of the degree of civilization and competitivity of a nation.
The simplest way to order these medal lists are, tipically, by counting the total amount of medals, from the biggest to the lowest. But just at that point, complexity begins. If, for instance, two countries are tied to 20 medals… who’s first in the list? Then, a new criterium is added about the quality of those medals, in order to break the tie: if one country won more golden and silver medals than the other, this one goes first.
But then after, other criteria appear that claim about the goodness of that simple sorting method. Many people think, not without reason, that a country that gets half the medals than other one, but it has half the population; this one is a better prepared nation. That’s why some alternative rankings are created, where the number of medals are divided between the total population. These new rankings are catchy in the press, because they don’t show classic sport superpowers (China, USA, Russia, UK, Germany…) on the top, but tiny nations like Bahamas, Bermuda, Jamaica or some Polynesia microstates.
This sorted raking makes all sense because of a logical bias: in some ultra small nations, one or two excellent athletes may arise, that get a couple of medals each. This is far more than enough to rocket their nations up in the rank. A typical phenomenon we can see in every Olympic Games with caribbean or kenyan/ethiopian runners.
But that doesn’t mean that these countries are real great sport superpowers that invest large sums of money in a sophisticated sport education plan. It simply means that they just got a few talented athletes in some very specific discipline. So just diving medals between total population does not suffice to identify the sport policy of a country as successful.
My proposal: Visualize success per population and disciplines
At this point, I wonder if there’s some way to analyse medal ranks from another point of view, searching for the most “olympic”. For that sake, I started with the next seminal idea: a powerful country that invest in education, sport facilities and supports athletes, should do that in a wide range of sports, instead of wait that a few brilliant individuals break some records in an arbitrary discipline.
From the final lists of medals achieved in each and every of the olympc disciplines published by the official Tokyo 2020 website, I cooked a dataset, where I recorded the amount of medals of every country, separated in nine categories:
- Individual sports (badminton, tennis, weightlifting
- Team sports (soccer, basket…)
- Shooting (Bow, rifle…)
- Fighting sports (karate, judo, wrestling…)
- Aquatic sports (Sailing, Kayak…)
- Swimming
- Athletes
- Gymnastics
- Cycling
Then, with the data of the medals per category, and then normalized by the total population of the first 48 countries in the official medal rank, I made this interactive data visualization using a radar graph:
In these plots it is possible to select, by one hand, one of those 48 countries from the rank. The radar graph shows the total amount of medals achieved in each of the nine selected categories.
A balanced plot (regularly scattered throughout the radar angles) shows a country that succeded in a multiple sport disciplines.
An unbalanced graph (with few spikes) shows the profile of a country that concentrates its success in a few sports, but performs poorly in the rest.
But there’s more. There are three ways to plot the radar:
- Absolute: the scale of medals is total amount, not normalized, meaning that concentric divisions go from 0 to 30 medals (the top amount of medals achieved by one single country in one category). This render makes the radar plot almost invisible for modest countries.
- Relative: just the same radar, but normalizing data to the average of medals achieved by the selected country, so no matter if they won a lot of a few medals, it makes much easier to visualize the category share.
- Weighted on population: again the same plot, but this time taking the country’s population into account. This type of render makes that countries like India or China (more than 1 billion each) appear like a tiny graph, while small countries get significant graphs in size.
I invite you to play with the interactive graph, so you can explore the information from it and get your own insights.
Now, the analysis
The goal of a data visualization is always qualitative, but using the same dataset I created looking for the best olympic country, I made some treatment and analysis. Here’s my results:
- To know the bias in the medals between different categories, I chose the variance among the nine categories as a measure of balance. The lower the variance, the more balanced that country is.
- I made up a new variable, thought as a kind of score, that took countrie’s population into account, the total amount of medals achieved, and the variance. For that sake, I established this simple formula:
This way, according to this new rank criterium, a small country that got medals in several categories scored better than a big country doing the same, or than a small country that got more medals but in only in one sport.
Following this criteria, the medal ranking gets modified in the same way:
Here’s the classical, regular medal rank of Tokyo 2020 olympics:
and this is the result after applying my weighting formula:
New Zealand, Bahamas and Slovenia would be the countries with more olympic prestige, according to this essay. Are you surprised? Try yourself playing with the interactive graph, and get your own conclusions:
