How I Forged My Own Role in Sport and Used Data to Win Olympic Medals



  • Gus Kaeding, 37, is the performance data manager for US Ski and Snowboard in Park City, Utah.
  • It uses quantitative data such as speed, injury and sleep to select future Olympic medalists.
  • This is what her job looks like and her advice for getting into sport, told to Claire Turrell.

This essay as told is based on a transcribed conversation with Gus Kaeding, a 37-year-old performance data manager from Park City, Utah, about his career. It has been edited for length and clarity.

In 2015, I was a cross-country ski coach for the US ski and snowboard team. As much as I enjoyed my role as a coach, I felt that a lot of the decisions on the snow were made based on anecdotal evidence.

I knew there was an opportunity to use the data to make better decisions and gain a competitive advantage. Baseball and basketball had provided a roadmap for other sports on how this could be done, so I left the team in 2016 to get an MBA in Finance and Strategy at the University of Boston, then I came back two years later to start the performance data department.

While Major League Baseball, which was one of the first to use performance data managers, had a century of data, we had nothing. It could have been intimidating, but I found it exciting. We had to focus on the problem we were trying to solve and then ask what data we needed.

I started to create an athlete database with three distinct layers: medical data, performance stats and general information, down to athlete’s shoe size.

Collecting this data by myself and my interns was important, but it’s the insight we get from the data that makes it interesting.

I don’t work with athletes anymore, like I did when I was a coach. I now work with their staff, whether it is a doctor, a development coach or an on-snow coach. For example, I’m in regular contact with Jeff Lackie, who is one of Mikaela Shiffrin’s coaches, and he’s very data-driven.

We collect data from many different sources such as competition results, video evidence, medical information and wearable devices such as Oura, Sleep Tracking Ring and Archinisis Performance Monitor which features overlay video to statistics.

The data we revealed that stopped everyone in their tracks was that from 2006 to 2014, we focused on helping star athletes.

Since then we have been focusing on the grassroots and helping athletes develop.

We have also redesigned the selection process. Since we launched the department in 2018, this has helped make the process more quantitative and we have helped coaches make more data-driven decisions regarding athlete selection. Coaches were making decisions that were the same as the data 90% of the time – we just provided them with information for the remaining 10%.

When we launched the department, we identified high potential young developing athletes by comparing their stats to medalists of the same age. We then gave coaches an overview of the developmental stages of medal-winning athletes, which they could apply to their athletes of the same age. We identified a few more athletes earlier and built them into a system with better support, better nutrition, and better training. During Beijing 2022, not only will you see a younger Olympic team from the United States, but you will also see a number of young competitors on the podium.

Although I love the data, the biggest thing I learned during this job is that if you can’t pass the data on to others, it’s useless.

The department consists of me and up to four interns. I advertise internships through sites like Women In Sports Tech and even LinkedIn.

While our Harvard and North Carolina interns may have already passed me with their data skills, the only thing I can still help them with is relaying that data to people whose eyes are turning glassy. when they see a spreadsheet. You can display tables and graphs until you turn blue, but it’s best to boil it down to a specific case and tell that story. You see politicians doing this quite often.

I will tell the story of an athlete and how change has helped him. Then I will say, “So if you were to implement this, this is the expected result”. Your audience is different every time – sometimes it can be a coach and sometimes an administrator speaking in dollars and cents. You have to be flexible to the situation.

At the start of each project, I always do a SWOT analysis (strengths, weaknesses, opportunities and threats) to see how beneficial the results will be for the athlete.

My day starts around 7 a.m., when I wake up, check my emails, then jump on a service call. Next, I’ll go over the projects we’ve been doing over the past week, such as the force plate data, which is a measurement of strength we take in the gym to see how much pressure is being applied across the foot. .

My busiest time is during the summer months because during the winter months the team travels. As I join them at a camp in Colorado this winter to review sensor-in-snow boots and video tech, I’ll already be trying to fix some issues for the Milano Cortina Winter Olympics in 2026. I have to. collect that data now, as you need two to three years of data to make sure these results are reliable.

My favorite part of the job is creating a new analysis and fixing a problem that hasn’t been resolved yet.

It’s exciting to discover something that you know right away is going to make a difference.

We did a study focused on what makes the difference five days before a race. We looked at indicators of well-being such as sleep, multiple gym sessions, or heavy snow training. Our findings have been implemented. Races are won to the 100th of a second. It all makes the difference.

The worst part of my job is seeing athletes who have struggled in rehabilitation, returned to the snow, and then suffered another injury. It’s really hard to see because you know what they’ve been through.

If you are a data analyst and want to get into sports, there may be more opportunities in non-profit Olympic sports or sports with less resources than professional sports.

Talk to coaches and administrators to find out if you can help solve a problem for them. If you succeed, you will open doors for yourself.

The world of sports is quite small, so if you take the time to attend conferences, like the MIT Sloan Sports Analytics conference in Boston, and meet people, you will find that these are the people you will see at. several times on the road.

The future of sport will be a data analyst built into every team. This is when we can really make a difference.

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