Jacob Lund - stock.adobe.com
Data the difference at Olympics for Allyson Felix
Frustrated after winning silver in the 200 meters at the 2004 and 2008 Olympics, the sprinter used analytics to identify weaknesses and alter her training before winning gold in 2012.
In the Olympics, the difference between gold and silver is sometimes … data.
Allyson Felix is the most decorated American track athlete and most decorated female track athlete internationally in the history of the Olympics.
Over five Olympiads, she has won 11 medals, including seven golds, three silvers and one bronze. In 2021 in Tokyo, at the age of 35 and after giving birth to a daughter in 2018, she won bronze in the 400 meters and gold in the 4x400 relay.
But after the 2008 Games in Beijing, she was struggling.
She had just won silver in the 200 meters -- her specialty -- for the second time, having fallen 0.13 seconds short of gold in 2004 in Athens and 0.19 short of gold in 2008.
She had to do something in order to make up those mere hundredths of a second. She had to do something different in order for her to break through and win gold in 2012.
Data a differentiator
Analytics was the difference as she prepared for the 2012 Olympics in London. Like many athletes, data analysis is what Felix and her team turned to when seeking a competitive edge.
In London, she ran 21.88 in the final, .05 seconds faster than she ran in Beijing and .30 seconds faster than she ran in Athens. And she won gold, beating Jamaica's Shelly-Ann Fraser-Pryce by .21 seconds.
In addition, she won gold medals in the 4x100 relay and 4x400 relay in London.
"The second silver medal was perhaps the most defining moment of my career because it forced me to look at every aspect of my training and say, 'How can I get better?'" Felix said on Nov. 9 during Tableau Conference 2021, a virtual user conference hosted by analytics vendor Tableau. "I used data to look at my training, my conditioning."
By examining data, Felix and her team realized they weren't doing enough work on the first part of the race, her explosion out of the starting blocks and ramp up to full speed.
So speed in the early stages of the race became her focus as she prepared for London.
Allyson Felix11-time Olympic medalist
That meant more sprinting in practice, and more strength work such as pushing a sled while running and weightlifting.
"All of that speed work was what was missing for me," Felix said. "I needed to be more efficient out of the blocks and faster at 10 meters, 20 meters. I could really pinpoint where I was losing the race, and it was all up front, it was all about power."
Slowly, as the 2012 Olympics approached, Felix's data-driven approach helped her improve and ultimately win a gold medal.
"When I went back in 2012, I really felt like a different athlete," Felix said. "I was able to work on all the areas I was weak in, and I truly believe it was all able to come together because I could see what was missing and what work needed to be done."
Advancing with age
Now at age 35, analytics continues to be a critical means of staying competitive, and both the technology and the information she uses have evolved since she first started using data to inform her training for the 2012 Olympics.
Felix no longer specializes in the 200, which, given the short format of the race, relies heavily on reaction time at the start.
Instead, she focuses on the 400, in which speed over time can make up for lost hundredths of a second out of the starting blocks. And her time in Tokyo, while not enough to win gold, set a Masters world record -- she holds the top three times for women 35 and over, and is the only woman to run the 400 in under 50 seconds after turning 35.
Perhaps most importantly, Felix uses data to examine her biomechanics.
She looks at her start, attempting to cut milliseconds between the time the starting gun pops and the time she rises out of the blocks. She looks at her form throughout races -- stride length, arm motion, head position. She looks at her lean at the end, that instant when thrusting her torso forward could be the difference between winning and second in a race that comes down to inches.
Data also informs what Felix does in practice.
She and her team log everything she does during every training session and use analytics to discover insights about what to do during workouts, and how to schedule her workouts. That data helps lead to peak performance at the time of the biggest meets, such as the Olympics and World Championships.
She also uses data to examine every aspect of her life off the track -- what she eats and when she eats it, her sleep patterns, her travel schedule -- and how things like sleep, nutrition and travel affect performance.
And now that Felix has years of data to look back on, she's able to use historical data to compare year-over-year information to see where she is now in relation to where she was previously at the same point in a training cycle. Using that data, she and her team can make tweaks, ramping up in one area or easing up in another.
"It comes down to hundredths of a second," Felix said. "I've lost multiple races by a very small amount. When you're dealing with sprints, one wrong angle can cost you the race. I'm older now, so it's much more about the quality of work I'm doing -- how can I use data to be more efficient?"
One of the analytics programs Felix uses superimposes a model on top of a recording of her running to show where she is throughout a race in comparison to her best performances and in comparison to world-record pace. That enables her to pinpoint where in a race she's lagging behind and where she's at her best, and attempt to figure out why.
She can examine her mechanics during those moments when she's lagging behind or making up ground, seeing whether her stride may have shortened at a certain point or where she may have drifted in her lane and cost herself fractions of a second.
"Having all the video, the breakdowns, the logs allows me to pinpoint exactly where I'm trying to go and how I can accomplish that," Felix said. "It's invaluable to have all this information and have a plan, have a roadmap. We're relying on data to get us there."