Data analytics is a buzzword in modern business. For those not familiar with the term, Investopedia defines it as “the science of analyzing raw data in order to make conclusions about that information.” In other words, people gather a bunch of data elements and study them to spot trends or metrics that can be used to help improve a business’s efficiency.
Today’s riders have access to many different kinds of raw data thanks to advances in technology. Bike computers, fitness watches, and other electronic gadgetry collect all kinds of raw data. All this raw data can be useful to riders if they know how to analyze it and use it to their benefit. Here are three ways riders can use data analytics to become better at mountain biking.
Use data analytics to help dial in a new bike
When I bought my new trail bike this year, I had a hard time setting it up to where I felt completely comfortable on it. I had only ridden XC bikes before, so it was a big change. I kept fiddling with the suspension adjustments and tire pressures as I searched for the ideal setup. By diligently charting the adjustments I made, and comparing them to my ride times and speeds, I found the settings that worked best for me.
I made my adjustments in stages, riding the same trail three times at the same suspension settings and tire pressures to get a good feel for what the bike was doing before making further adjustments. One ride isn’t a good indicator of a bike’s performance, especially if there is a substantial change in trail or weather conditions between rides. Now that winter is here, I am learning how colder temperatures affect my bike’s performance, and the adjustments I need to make to compensate for the weather.
As I played around with the tire pressures on my bike, tracking the different air pressures I used made me realize that my fastest rides weren’t always the most fun rides. Running higher air pressures made me quicker, but it also made the rides harsher. I settled on air pressures that were a healthy compromise between speed and comfort.
Use data analytics to expose your weaknesses
I know I have some weak points in my riding. For example, I ride best when the temperature and humidity is close to the environment on the inside of my office or gym. Add heat and humidity, and my ride times plummet. Comparing the weather data with ride data shows me that I need to improve my conditioning in order to get faster during the summer months. When I ride more often in these conditions, the data shows that my ride times improve as my body adjusts to the weather.
There are other ways riders can use data analytics to spot weaknesses. If you have a bike computer that can break down rides into trail segments, look at the data elements for each segment and identify which trail sections are your weakest ones. It could be a section of trail with tight switchbacks, or a steep climb. Once you’ve identified the weak point, do some session work on it until you get faster. Some weaknesses are easy to spot, but others can require data analytics to identify.
Use data analytics to celebrate past accomplishments and set future goals
Analyzing time, distance, and speed is a great way for riders to measure their progress over time. I can easily see how I’ve developed as a rider over the years by looking back at my ride times on a particular trail. On one of our local trails, my ride times dropped by over 10 minutes during the past 5 years. Seeing that level of progress gives me a feeling of accomplishment.
The data is also useful to me as I think about the goals I would like to set for myself in the coming year. My current goal is to lay down times and speeds on my favorite trail that are comparable to the ones I set on my XC bikes. Will it be possible? Who knows, but it will be fun to try.
Data analytics can be useful to riders, but it doesn’t tell the whole story
These days, riders can tap into a nearly limitless array of raw data to analyze, but numbers alone don’t always give a complete picture. This past year, I was able to successfully ride a new trail system in a different state, and conquer two sections of my favorite local trail that had intimidated me for years. There are no data points that capture the joy I felt from each of those accomplishments.
Data can tell you how good you might be, but it doesn’t show how much fun you had on a ride, or how memorable a particular ride was. Additionally, it’s easy to start obsessing over the data and lose your enjoyment of the sport. So, if you decide to employ data analytics to help you improve as a rider, do so in moderation and keep in mind that your ultimate goal is to have fun.
Do you use data analytics as a rider? If so, please share how you use it in the comments section below.