# Olympic Medals in Context

Every 2 years athletes from around the world take to a single city to compete in either the summer or winter Olympics. It seems, however, that the same countries top the medal count list year after year. So what is it exactly that makes a country succeed in the medal count? How different would the podium be if all countries had the same wealth, population, and environmental factors? Well, here is a quick look at some of those numbers.

Maybe the biggest factor in succeeding in the Olympics is the size of a country's economy. More money means more medals, right? Here is what the medal stand would look like if we equalized the playing field in terms of Gross Domestic Product.

Maybe the biggest factor is population instead. More people means more athletes and more medals. Here is what the medal stand would look like if we equalized the playing field in terms of Population.

Maybe it is a combination of money and people - or more importantly, how much money you have per person. Excess money could mean more medals. Here is what the medal stand would look like if we equalized the playing field in terms of GDP-Per-Capita.

Maybe it has to do with the weather, especially in the winter Olympics. Wouldn't you expect colder countries to do better? Here is what the medal stand would look like if we equalized the playing field in terms of annual mean temperature.

One clear winner in all of this is Norway, which seems to perform well in nearly all of our controls except, perhaps, for temperature. Sure, it is cold up there, but plenty of other Scandinavian countries just don't seem to end up on the podium as often.

Our Methods
The data used to create these graphs was acquired from numerous sources including the official medal counts by the Vancouver Olympic Committee, the Population Reference Bureau, the CIA World Fact Book and other publicly available data sets.

The general model used to determine predicted medal counts at equilibrium was as follows:

• Organize Data from 0 to 1 scale by dividing by the largest datum
* This was different in the case of Temperature, where we expected an indirect rather than direct relationship (ie: lower temperature = more medals). In this case, we inversed the numbers by subtracting each from the highest data point before performing the same 0 to 1 conversion).
• We then divided the medal count earned by each country by their score on this metric.
• These new counts were then turned into percentages by dividing the new count by the total of all counts.
• Finally, these metrics were multiplied against the total number of medals won.

It is important to bear in mind that our goal here was not to see what the medal counts would be if each tested variable were the ONLY variable, but rather what the number would look like if we removed it from the equation altogether. We did not check for correlations of any kind, so any relationship between these variables and the outcomes of the Olympics are purely speculative.

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