A short Two Minute Papers video (2016) by Károly Zsolnai-Fehér explaining how scientific models can predict aggregate Olympic medal counts more reliably than individual athlete outcomes. It highlights a simple macro-level model by Professor Daniel Johnson that reportedly achieved ~94% agreement with actual national medal counts across five Olympic Games using only a handful of variables (GDP per capita, population, political system, climate, host advantage, plus a proximity factor). The video emphasizes that larger-group predictions require fewer explanatory variables and points viewers to the original paper and related links in the description.
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Dear fellow scholars, this is two minute papers with the 2016 Rio Olympic Games is right around the corner, so it is the perfect time to talk a bit about how we can use science to predict the results. Before we start, I'd like to mention that we won't be showcasing any predictions for this year's Olympics. Instead, we are going to talk about a model that was used to predict the results of previous Olympic Games events. So the following very simple question can we predict the future? And the answer is very no, we can't. End of video. Thanks for watching. Well, jokes aside, we cannot predict the future itself, but we can predict what is likely to happen based on our experience of what happened so far. In mathematics, this is what we call extrapolation. There is also a big difference between trying to extrapolate the results of one athlete or the aggregated number of men medals for many athletes, usually an entire nation. To bring up an example about traffic, if we were to predict where one individual car is heading, we would obviously fail most of the time. However, whichever city we live in, we know exactly the hotspots where there are traffic jams every single morning of the year. We cannot accurately predict the behavior of one individual. But if we increase the size of the problem and predict for a group of people, it suddenly gets easier. Going back to the Olympics, Will Usain Bolt win the gold on the 100 meter sprint this year? Predicting the results of one athlete is usually hopeless, and we bravely call such endeavors to be mere speculation. The guy whose results we are trying to predict may not even show up this year. As many of you have heard, many of the Russian athletes have been banned from the Olympic Games. Our model would sure as hell not be able to predict this. Or would it? We'll see in a second, but hopefully it is easy to see that macro level predictions are much more feasible than predicting on an individual level. In fact, to demonstrate how much of an understatement it is to say feasible, hold on to your seatbelts because Daniel Johnson, a professor of microeconomics at the Colorado College, created a simple prediction model that over the past five Olympic Games was able to achieve 94% agreement between the predicted and the actual medal counts per nation. What is even more amazing is that the model doesn't even take into consideration the athletic abilities of any of these contenders. Wow. Media articles report that his model uses only five simple a country's per capita income, population, political structure, climate, and host nation advantage. Now I'd first like to mention that GDP per capita means the gross domestic product of one person in a given country. Therefore it is independent of the population of the country. If we sit down and read the paper, which is a great and very easy read and you should definitely have a look, it's in the video description box. So upon reading the paper we realize there are more variables that are subject to scrutiny. For instance, a proximity factor which encodes the distance from the hosting nation. Not only the hosting nation itself, but its neighbors are also enjoying significant advantages in the form of lower transportation costs and being used to the climate of the venue. Unfortunately, I haven't found his predictions for this year's Olympics, but based on the simplicity of the model, it should be quite easy to run the predictions provided that the sufficient data is available. The take home message is that usually the bigger the group we are trying to predict results for the lesser the number of variables that are enough to explain their behavior. If we are talking about the Olympics, five or six variables are enough to faithfully predict nationwide medal counts. These are amazing results that are also a nice testament to the power of mathematics. I also really like how the citation count of the paper gets a big bump every four years. I wonder why? If you are interested in how the Olympic Games unfold, make sure to have a look at the Olympics Reddit. I found it to be second to none. As always, the link is available in the description box. Thanks for watching and for your generous support and I'll see you next time.
