Project Introduction

The Olympic Games are held once every four years in a different city around the world. The games bring together all of the world's nations to compete across a variety of sporting events in order to determine who the best in the world at each one is. Teams invest huge funding into their athletes in the hope that they will be able to bring home Olympic medals to their nation, it brings a sense of pride to smaller nations and bragging rights to larger nations who top the medal table.

However, despite all of the training and investment that athletes out in, there may be factors beyond their control at play at the games. Our thesis is that temperature, and specifically temperature difference between an athlete's home country and the country where the games are being held, brings an outsized impact on peformance. And it interestingly, we believe that this effect is not uniform, meaning the effect of temperature is different between genders, age groups, or particular countries. Therefore, the goal of our project was to analyze and visualize the effect of temperature on athlete performance, if indeed there is any at all..

Group 17 Introduction

I'm Alan Gordon, a junior at Notre Dame and a business analytics major. I'm from a small town called Greystones just outside Dublin, Ireland. I've played sports since I was little and have watched the Olympic Games for as long as I can remember, I even got the chance to attend the London 2012 Olympics when I was in elementary school! I enjoyed completing this project as I found it interesting to dive into a question I have wondered about for years.

I'm JD Brown, a junior at Notre Dame and an accounting major. I'm from Spencer County, outside Louisville. I played basketball through school and have also watched the Olympic Games year after year. I found it super interesting to analyze the effect of temperature on Olympic Performance, as I've always wondered whether athletes who are used to colder temperatures would suffer when going to compete in places like Rio or Africa.

Data Example and Code Walkthrough

Athlete Sex Age Height Weight Team City Temperature Diff Medal
Dijang M 24 180 80 China Barcelona 3.7 No
Bai M 21 184 83 China Barcelona 3.7 No
Zhong M 23 188 110 China Barcelona 3.7 Yes
Yanshu M 28 169 79 China Barcelona 3.7 No

During analysis, we used many packages to our advantage. First, we used the csv library to read the source csv files: athlete_events.csv, city_temperature.csv, and iso3.csv. Each source had a country column which made merging simple. We formed and examined data frames using pandas and numpy, sliced them using data frame filtering techniques, and visuzliazed results and conclusions using plotly express and matplotlib. Because our codebase is too long to go through live, we recorded the video to the right which allows you to walk through the code with us which will hopefully help you to understand our thought process.

The following are a sample of the questions we hoped our project would answer, more can be found in the FAQ section on the contact us page. 1. Which nationalities achieve the highest number of medals at the Olympic Games and how does temperature affect this? 2. What is the optimal temperature or temperature difference for Olympic athletes to compete at? 3. Do certain nationalities perform better in certain events compared to others or do larger countries simply dominate the medal table across the board? 4. Does temperature affect genders and ages equally or differently for Olympic athletes? 5. Do athletes tend to perform better in cities that are closer in distance to their home country i.e does travel impact perfomance at the Olympic Games? 6. Do countries with similar temperature tend to comepete well at the same Olympic Games or is performance more random in nature? 7. Does the host country tend to outperform their average performance across time at their home Olympic Games?

The only major caveat to bear in mind with these code sources is that there were certain major coountries missing from the data which might mean that important data was missing, the US is the prime example here. Aside from that, we know there is a lot of information to consume on this website and that it might be a slight overload at the beginning. So, we created the demo video to the right as a brief snapshot of the key objectives and takeaways from the project.