Analytics & Olympics: UW researchers using data to predict athletic success

A team of researchers at the University of Waterloo (UW) have been pouring over the data, trying to determine if there is way to predict when an athlete will reach peak performance.

Specifically, they examined every track-and-field competitor that has competed at the Olympics dating back to the 1996 Games in Atlanta, GA.

The looked at five key factors: Gender, nationality, event type, how long an athlete has trained at an elite level and whether or not it was an Olympic year.

The results showed that for both male and female track-and-field athletes, the average age of olympic competitors was just under 27. It also found that the median peak age was also 27.

In a press release, study co-author and undergrad economics student Matthew Chow said, “Age, however isn’t the only factor in an athlete’s peak. What’s really exciting is that we also found out that knowing it’s an Olympic year actually helps predict an athlete’s performance.”

David Awosoga, a master’s student in data science and the study‘s lead author said, “Because the Olympics occur only once every four years, track-and-field athletes must carefully consider when and how they should train to maximize their probability of qualifying for the Olympics while at their personal peak.”

The authors believe the research could be used to help ensure training regiments are timed in a way that allows the athletes to be at the best when major competitions come around, increasing the chance of a medal performance.