Olympic Medal Table Modeling

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Problem C: Models for Olympic Medal Tables

In addition to watching individual events during the latest summer Olympic Games in Paris,
2024, fans followed the overall “medal table” for each country. The final results (Table 1)
showed the United States with the most total medals (126), and China and the United States tied
at the top of the standings for the first place Gold medals (40). The host country, France, was

5th in the Gold medal count (16) standings but 4th in terms of total medal count while Great

Britain, 7th with 14 Gold medals finished 3rd in total medals.

Gold Silver Bronze Total
United States 40 44 42 126
China 40 27 24 91
Japan 20 12 13 45
Australia 18 19 16 53
France 16 26 22 64
Netherlands 15 7 12 34
Great Britain 14 22 29 65
Table 1: Paris Olympics (2024) Final Medal Table – Gold Medal Top 7 Countries[1]
The standings at the top of the table are always watched closely, but the medal counts for other

countries are often just as valued. For example, Albania (2 medals), Cabo Verde , Dominica, and
Saint Lucia (2 medals) won their nations' first Olympic medals at the Paris games. Dominica and
Saint Lucia also each earned a Gold medal at these games. More than 60 countries have still yet
to win an Olympic medal.

Predictions of the final medal counts are commonly made, but typically not based on historical
medal counts but closer to the start of an upcoming Olympic games when current athletes

scheduled to compete are known (for example:

https://www.nielsen.com/news-center/2024/virtual-medal-table-forecast/).

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Data is provided of medal tables for all summer Olympic games, host countries, as well as the
number of Olympic events at each games broken down by sport for all summer Olympic games
played. Additionally, data for all individual Olympic competitors with their sport and result
(medal type, or no medal) is provided. Your models and data analysis must ONLY use the
provided data sets. You may use additional resources to provide background and context or help
with interpreting results (be sure to document the sources). Specifically, use the provided data to:

• Develop a model for medal counts for each country (for Gold and total medals at a
minimum). Include estimates of the uncertainty/precision of your model predictions and
measures of how well model performs.
o Based on your model, what are your projections for the medal table in the Los
Angeles, USA summer Olympics in 2028? Include prediction intervals for all
results. Which countries do you believe are most likely to improve? Which will
do worse than in 2024?
o Your model should include countries that have yet to earn medals; what is your
projection for how many will earn their first medal in the next Olympics? What
sort of odds do you give to this estimate?
o Your model should also consider the events (number and types) at a given
Olympics. Explore the relationship between the events and how many medals
countries earn. What sports are most important for various countries? Why? How
do the events chosen by the home country impact results?
• Athletes may compete for different countries, but it is not a simple matter for them to

change due to citizenship requirements. Coaches, however, can easily move from one
country to another as they do not need to be citizens to coach. There is, therefore, the
possibility of a “great coach” effect. Two possible examples of this include Lang Ping[2],
who coached volleyball teams from both the U.S. and China to championships, and the
sometimes-controversial gymnastics coach, Béla Károlyi[3], who coached Romania and
then the U.S. women’s teams with great success. Examine the data for evidence of
changes that might be due to a “great coach” effect. How much do you estimate such an
effect contributes to medal counts? Choose three countries and identify sports where they
should consider investing in a “great” coach and estimate that impact.
• What other original insight(s) about Olympic medal counts does your model reveal?

Explain how these insight(s) can inform country Olympic committees.