The following is an excerpt from our free eBook titled How to Leverage Data and Science to Improve Your Cycling. Sign up for our newsletter and grab it for free.


In 1991, my family moved across the country from Virginia to California’s Napa Valley. As I began the fourth grade in a new school and state, I faced a momentous choice.

Oakland A’s or San Francisco Giants? 

When I arrived in California, I didn’t know much about the A’s or Giants. With the help of baseball cards and a robust AM radio signal, I became a quick study in Mark McGwire, Jose Canseco, and PED’s (OK, I’m joking, I didn’t learn about PED’s for a few more decades). At any rate, I made my decision. I was throwing in my lot with the Bash Brothers.

Growing up an A’s fan, I lived through a period of baseball bliss in the early 2000s defined by working-class overachievement. As an organization, the A’s were famous penny pinchers, scouring the statistics of players around the league in search of undervalued potential [1].


Training can be confusing. In our free eBook, we’ll show you four ways to use your data and insights from science to ride better than ever.


Baseball and Data

Popularized in Michael Lewis’ 2004 book, Moneyball, the A’s were pioneers in evaluating players based on evidence, rather than mythical “ballplayer” aesthetic. While their closest rivals were chasing the biggest names in baseball, the A’s were getting down with data.

My most enduring memory of the “Moneyball” A’s came on August 12, 2001, when I sat with 47,000 fans in Oakland and watched Jason Giambi hit a walk-off home run against Mike Stanton of the Yankees. My fourth-grade commitment to green and gold was paying dividends. The A’s, and their data-driven approach to baseball were winning, a lot. 

Changed by Data and Science

Across the sporting divide, skinny endurance athletes were getting faster and faster on bikes as they began integrating more data and science into their training [2, 3]. Alright fine, maybe a bit of that cycling speed was earned using the science of pharmacology [4-6], but stay with me. 

The wide-spread adoption of the power meter in the early 2000s, alongside training and nutrition techniques born of scientific principles, signaled a permanent shift in how the best cyclists in the world got fast [3]. 

Data and science were driving peak performance from highway 880 in Oakland to the Champs-Élysées in Paris.

Why it should matter to you 

Phrases like “science based” and “data driven” have steadily risen in popularity since the early 2000’s [7]. Whatever the sport, there is increasing interest, and financial reward, for those who make decisions based on evidence [8]. 

In the age of social media, a clearer understanding of data and science is a bulwark against lousy advice and viral anecdotes that are more likely to sabotage your progress than contribute to a podium finish. 

So here’s my sales pitch:

Spend a bit of effort digging into your cycling data and broaden your understanding of exercise science. Your training and performance will improve.

As simple as that sounds, training gets confusing when you consider the endless combinations of intensity and duration. 

How long should you ride? What kind of intervals should you do? How much rest should you take? While answering these questions is beyond this ebook’s scope, I will cover a few fundamentals for how to better understand your data and get better at reading science. 

Let’s start with your data. 


Training can be confusing. In our free eBook, we’ll show you four ways to use your data and insights from science to ride better than ever.


Written by Nate Dunn M.S.

Nate Dunn has spent his entire career in education and coaching. As a former teacher and now founder/head coach at Data Driven Athlete, he is most excited about helping cyclists discover their potential as they experience more great days on the bike.