How Olympic Swimmer Kate Douglass Uses Data to Win — and You Can Too

When U.S. swimmer Kate Douglass steps onto the starting block at the Paris Olympics Aquatics Centre next week, she’ll have data analytics — in part — to thank for getting her there. 

Don’t get us wrong. Kate has logged plenty of hours in the pool. A swimmer since she was 7, she’s driven herself to become one of the most versatile and successful swimmers anywhere, claiming 15 U.S. collegiate titles, 14 world championship medals, and one bronze medal at the Tokyo Olympics.

In Paris, she’ll compete in the 200-meter breaststroke, 200-meter individual medley, and three relays. Kate is so adept at so many different strokes — she also qualified for the 100-meter freestyle race, but opted out — that Sports Illustrated called her the “USA’ s Swiss Army knife in Paris.” 

But the 22-year-old, who earned a degree in statistics from the University of Virginia, also brings something else to the table: data analytics. Four years ago, while still an undergrad, Kate and the entire University of Virginia (UVA) swim team started working with the school’s renowned theoretical mathematician Ken Ono, applying data analytics to their strokes. 

Working with Ken, Kate improved her breaststroke technique enough to break the U.S. record in the 200-meter breaststroke this year by .29 seconds, and she later qualified to swim the event in the Olympics.

You may be scratching your head, wondering what this has to do with HR, talent development, or talent acquisition. The answer is: plenty. Data analytics is among the most important skills HR professionals can have right now. Talent leaders are increasingly being called upon to analyze data and identify patterns that could lead to budget and workforce efficiencies, better hiring outcomes, and smarter decision-making around upskilling and reskilling.

Data analytics will take center stage in Paris, with athletes in swimming, judo, and track and field, among many other sports, using it to better their performances. Even the Games themselves are relying on analytics to help with scheduling, transportation, and food and beverage operations. But Kate Douglass’s story, in particular, holds lessons for talent pros. Let’s dive into the deep end and see what they are. 

1. Even small gains from data analytics can deliver huge results 

The UVA swim team’s process for gathering data is, in some ways, similar to the one used by talent pros: They measure for the problems they are trying to solve. While talent professionals may take surveys or conduct exit interviews to understand trends, UVA attaches high-tech equipment called accelerometers to swimmers to measure different aspects of their strokes. Then, they analyze this data and make recommendations to improve swimmers’ technique and race strategy.

When Kate reviewed her own data, she discovered, among other things, that her head position in the breaststroke was creating drag, particularly when she compared her technique to that of five-time Olympic medalist Lilly King. So, Kate worked on improving her technique until she had shaved an average of .11 seconds off every streamline glide, which is what swimmers call the underwater glide they take after their initial dive and their three subsequent pushes off the wall. That added up to nearly half a second over a 200-meter breaststroke race.

Kate — who is now getting a master’s degree in statistics — wrote about this as the lead author in a recent academic paper in The Mathematical Intelligencer. While that’s an impressive feat, she also beat out Lilly King for the top spot in the 200 breaststroke race at the U.S. Olympic Trials. By more than two seconds. 

The lesson here? Even small gains from data analytics can deliver huge results. 

2. Don’t just apply data analytics to processes that are broken. Apply them to your strengths too

Before improving her technique, Kate was already a world-class breaststroker. She used data analytics to become better. 

In other words, she made tiny changes to improve on something that was already working well. 

The same can be applied to companies. Some HR professionals think they need to use data analytics to fix the broken parts of their processes or organizations. But you can apply data analytics to strengths too.

Say, for example, your company has a well-deserved reputation for work-life balance and flexibility. Could you use data analytics to make it even stronger? You might, for example, conduct surveys of current employees to find out where you could improve or conduct exit interviews to find out what’s working and what’s not. After compiling the data, you can identify places to improve and implement changes.

When you do this, you’ll make the company even more attractive to job seekers than it already is. Just like a few seconds in a tight race, it could make all the difference in advancing to the finals — or getting a “yes” from a candidate.

3. Focus on the process and improvements, rather than the outcomes 

When talent pros implement changes based on data analytics, they’re often anxious to achieve lofty outcomes — and quickly. You might be better off focusing on the process and celebrating improvements, even if they’re small.

Kate learned this at her first Olympics, in Tokyo. “Going to Tokyo helped boost my confidence,” she said shortly before the U.S. Olympic Trials earlier this year. She explained that she used to get really nervous at the big events. But now, she said, “I’m just really excited to see how I’ve improved in my training and to see what I can do.” 

In other words, she’s focusing on process and progress rather than the outcome. While that’s a good idea for life in general, it’s particularly useful in the talent world. After you’ve analyzed data and started to take action, you can focus on improvements or progress. 

What would this look like at an organization? If you were using data analytics to reduce attrition, you might take an actionable step every day or week to increase employee engagement or offer more development opportunities. Sometimes, when you focus on the process and improvements — what you can today to reduce attrition — it becomes easier to achieve your goals. It’s about enjoying the journey rather than focusing solely on the destination. 

To put it into perspective, it took Kate 36 months and countless thousands of laps to shave 44 hundredths of a second off her 200-meter breaststroke. 

Final thoughts

In her academic paper in The Mathematical Intelligencer, Kate notes that recent leaps in data analytics will continue to help athletes make “equally magnificent” leaps in performance. “These athletes, armed with troves of data, refined training techniques, and complex analytics,” she writes, “demonstrate the beauty of the games, as both a driver and display of what humans and technology can achieve.” 

And if data analytics can help athletes win Olympic medals, imagine what it can do for HR.