Wednesday, July 22, 2015

Strava Uses Cycling Data as the Ultimate Performance Enhancer


My most recent mountain biking adventure ended up with a trip to the hospital for [thankfully] minor injuries. As I patiently twiddled my thumbs, waiting for medical attention in an emergency room lobby, my cycling-obsessed friend began jumping for joy.

“Have you seen my Strava stats for this ride?!?!”

For those unfamiliar, Strava lets you experience what we call social fitness, allowing athletes and fitness enthusiasts of all levels connect and compete with each other (and themselves) via free mobile and online apps. Strava lets you track your rides and runs via your iPhone, Android or dedicated GPS device and helps you analyze and quantify your performance, comparing times and biometric data. Users are able to interact through comments and give accolades on the activities of other athletes, should they choose to make them public.

Various aspects of logged activity include:

  • Route
  • Elevation (net and unidirectional)
  • Speed (average, min/max)
  • Timing (total elapsed and moving time)
  • Power/energy
  • Heart rate


strava cycling analysis

I am both slightly cycling-obsessed and a data geek, so the fact that my friend was so enthusiastic over a bunch of numbers and statistics relating to a bike ride was exciting, regardless of the throbbing wound on my leg.

Data matters. If you can measure your performance you can improve it, and Strava allows just that for the fitness community.

How Strava works

Let’s start with KOM and QOM’s. For the uninitiated, KOM is short for “king of the mountain” (and “queen” for the QOM. Because ladies are fierce, too). It’s a term borrowed from the Tour de France that means you posted the top time on one of Strava’s millions of global “segments”— ferocious hills or innocuous straightaways that Strava users have parceled out. KOM/QOMs are like shiny prizes in a massive multiplayer online game, and obtaining them will put you at the top of the public leaderboards. Strava has also managed to engineer esteem through data segmentation—allowing users to drill down into data and filter by characteristics such as age, gender and weight, comparing their efforts to other users most similar to them.

But just as important as the competition against other athletes is the ability to compete against oneself. The platform collects an individual’s ride information each time a specific route is taken, comparing efforts and awarding PR’s (or personal records) when applicable.


Strava’s army of more than a million users are turning their local tarmacs into daily time trials, but the idea of cyclists recording data is nothing new. In his 1978 book The Rider, Dutch writer and cyclist Tim Krabbé describes recording stopwatch times in a notebook:

“Always the same route, just under 40 kilometers, so I could compare my times….until I gradually started wondering whether I was getting good.”

It has also been historically noted that on climbs like the Tour de France’s Alpe d’Huez, there were record-keeping methods that people were using, such as nearby hotels recording times.

What Strava did was turn those private notations into a measured, database-matched, global community with the ability to turn any ride into a race without having to show up at a 6 a.m. starting line.

Bigger Picture

On a much larger scale, data analysis stretches far beyond this sport-devotee app and plays a major role in the world of procycling. Performance data and the insights within them have been used to indicate signs of doping, helped standardize nutrition, training, and recovery techniques to achieve optimal cycling performance, and even drive clothing, equipment and product innovations.

Just like athletes and coaches are using historical data as their newest performance enhancer, marketers should similarly utilize data and associated analysis approaches as a way to gain information to better support their initiatives and drive decisions.

Whether you’re an athlete, entrepreneur, marketer, or farmer, you are to some extent a decision-maker and must make a data-driven approach part of your culture.