I attended a meeting a couple of years ago where we spent two days discussing how to apply science and technology to lead to improved athletic performance. This meeting highlighted the vast array of tools that are currently available to collect and analyze data. But the meeting also stressed the challenges in applying this data to a training program in a practical way. There was way more information presented at this meeting than I can summarize in this article, so here are some of the take-home messages:
1. We can measure A LOT of data, but is it useful?
The challenge lies in translating data into performance by making sense of the data we can use and filtering out unnecessary data we can’t use.
Data doesn’t mean anything unless you know what it means! This is where the coach helps the athlete; the coach does the interpretation and filtering. This isn’t to imply that interpretation is easy. The amount of data we collect is overwhelming and can be difficult to analyze. Now there are complex analysis tools that often require computers and advanced algorithms. Still, analysis tools are still decades behind our ability to collect data.
We also need to be careful when interpreting the data, especially for athletes. For example, normal clinical values don’t compare to elite athletes. Two examples: 1) rugby players’ muscle damage markers after a game are similar to 3rd degree burn victims, and 2) markers associated with cardiac damage are elevated for 72 hours after a marathon. If a physician looked at the data and didn’t know that the patient was an athlete, they may conclude they are dealing with a burn victim or a patient who just suffered a heart attack, when in reality, they are dealing with an athlete who just finished a competitive event.
2. True progress is quantified only if we collect data over time.
Technology is constantly being developed to understand how the athlete is responding to stress (training). It is important to incorporate labs tests along with field-testing to repeat measurements on a particular marker (40-yard time, VO2 max, resting heart rate, etc.). These markers serve as useful data for the coach AND targets for the athlete to work on (goals). The coach’s job is to apply stress to the athlete and hope that over time “that” particular marker improves. And, the best way to determine if you are improving is to compare measurements of various markers over time.
Athlete Performs ->
Coach Observes ->
Performance is analyzed ->
Coach plans ->
Coach Executes ->
start over with “Athlete Performs”…
3. Know the learning style of the athlete and teach (coach) to their learning style.
Everyone learns differently. It is the coach’s job to educate the athlete appropriately. The more the athlete knows and understands the training and the technology, the better chance for success. This includes educating the athlete about tools and gadgets that enhance data collection and convincing the athlete to use these tools in training.
With the amount of information and technology available, the only competitive advantage you can gain is to learn faster than your opponent. The willingness to learn and adapt to these tools may be the difference between getting a Kona spot and being off the podium.
4. Adherence is a major obstacle to data/technology.
This is true for athletes as well as coaches. A coach cannot make precise measurements if the athlete doesn’t use available technology. On the other hand, an athlete can make lots of measurements, but it is wasted if the coach will not use the data to maximize the training.
Until recently, coaches relied solely on perception.
Coach: “That guy is working hard”
Trainer: “How do you know?”
Coach: “Look, he is sweating and breathing hard.”
Now coaches work with numbers/data/outcome measurements and compete with other coaches to show that “their” coaching method is the best. This is a win-win for the coach and athlete. Coaches are able to quantify their tr aining strategy, and athletes are able to benefit from the coaches desire to improve the athlete’s numbers.
5. Athletes need feedback to continue participating in providing data.
Without feedback, why would the athlete go to the trouble of using a power meter, wearing an accelerometer, or filling out questionnaires if they never see the benefit? My job as a coach is to not only collect and analyze the data, but also to explain the data to the athlete so they can begin to, as Bill Gates said in the Wall Street Journal, “set a clear goal and find a measure that will drive progress toward that goal.”
With all of the improvements in performance knowledge, it is crazy not to take advantage of science and technology. The data-driven, coached athlete gains a HUGE advantage. The athlete doesn’t need to spend their time learning about the technology or interpreting the data. They put their trust in the coach and put their time into the training.