This is the last post in a three-part series. Part one, describing the data, is here. Part two gives an overview of the machine learning methods and can be found here. This post presents the results.

To present the results I will use classification matrices, transformed into heatmaps. The rows indicate Olympians' actual sports, and the columns are their predicted sports. A dark value on the diagonal indicates accurate predictions (the athlete is predicted to be in their actual sport) while light values on the diagonal suggest that Olympians in a certain sport are misclassified by the algorithms used. In each case results for the training set are in the left column and results for the test set are on the right. For a higher resolution version, see this pdf.

Classifying Athletes by Sport

For most rows, swimming is the most common predicted sport. That's partially because there are so many swimmers in the data and partially due to the fact that swimmers have a fairly generic body type as measured by height and weight (see the first post). With more features such as arm length and torso length we could better distinguish between swimmers and non-swimmers.

Three out of the four methods perform similarly. The real oddball here is random forest: it classifies the training data very well, but does about as well on the test data as the other methods. This suggests that random forest is overfitting the data, and won't give us great predictions on new data.

Classifying Athletes by Event

The results here are similar to the ones above: all four methods do about equally well for the test data, while random forest overfits the training data. The two squares in each figure represent male and female sports. This is a good sanity check--at least our methods aren't misclassifying men into women's events or vice versa (recall that sex is one of the four features used for classification).

Accuracy

Visualizations are more helpful than looking at a large table of predicted probabilities, but what are the actual numbers? How accurate are the predictions from these methods? The table below presents accuracy for both tasks, for training and test sets.

The various methods classify Olympians into sports and events with about 25-30 percent accuracy. This isn't great performance. Keep in mind that we only had four features to go on, though--with additional data about the participants we could probably do better.

After seeing these results I am deeply skeptical that David Epstein could classify Olympians by event using only their height and weight. Giving him the benefit of the doubt, he probably had in mind the kind of sports and events that we saw were easy to classify: basketball, weightlifting, and high jump, for example. These are the types of competitions that The Sports Gene focuses on. As we have seen, though, there is a wide range of sporting events and a corresponding diversity of body types. Being naturally tall or strong doesn't hurt, by it also doesn't automatically qualify you for the Olympics. Training and hard work play an important role, and Olympic athletes exhibit a wide range of physical characteristics.