The art & science of coaching & programming.

Coaching and programming have always seemed to me as equal parts art and science. Creative expression shaking hands with hard numbers, logic, and reasoning. Despite what we may be led to believe, coaching and programming deal in hypotheses, not certainty. It is a game of our best educated guesses as coaches and programmers for the proper approaches, a cauldron of trial and error, and an art of constant refinement as we gain more knowledge through the process. This knowledge makes our educated guesses more… educated.

There is seemingly infinite optionality when it comes to coaching and programming, and hundreds to thousands of small decisions that culminate in what becomes the final approach. When the results come in, you can never know for certain that your approach was absolutely optimal; rather, a positive result can merely hint that you are on a good path.

This uncertainty can sometimes be uncomfortable, but I also think it is part of what makes coaching so interesting.

Every program is an experiment

If you write training programs, you are constantly running small experiments. You might prescribe a certain training volume, choose a particular progression, adjust an athlete’s intensity, change the order of movements, or decide that an athlete needs more or less of a certain style of training. This is done based on the information available to us, and then we get to see what happens. The result gives us more information, which helps influence the next decision we make.

Luckily for us, the data available to help make these decisions is everywhere.

If you expected an athlete to finish a workout in ten minutes and they finished in fifteen, that is data. If you expected a training session to leave an athlete tired, but capable of training well the next day, only for them to be completely wrecked for the next forty-eight hours, that is data. If an athlete is progressing in their lifts but also seems increasingly unmotivated to come to the gym, that is data, too. 

Where the data comes from

For competitive CrossFit athletes, competitions can provide massive amounts of useful information. What did the event schedule look like? How were the tests ordered? How tightly grouped were scores across the leaderboard? Where did your athlete gain or lose points? Did a weakness that seemed significant in training actually cost meaningful places in competition, or did an area of fitness an athlete considered a strength turn out to be less valuable against the field than expected? All of this information can help guide what comes next.

On top of this, there is the increasingly accessible amount of objective data through technology like fitness wearables & trackers. Heart rate, resting heart rate, sleep duration, HRV, VO2 estimates, training load, pace, power output, and plenty of other metrics are becoming easier for athletes and coaches to access. These numbers can be incredibly useful, especially when tracked over longer periods.

The objective numbers are only one part of the available information, and have to be held in context of the more subjective data points, such as:

• How does the athlete feel? 
• How is their mood? 
• Do they look excited to train, or does every session seem to require more mental effort than the last? 
• Is their body holding up? 
• Are little aches and pains accumulating? 
• Are they recovering between sessions, or slowly beating themselves down? 
• What does their life look like outside of the gym? 
• Are they sleeping enough? 
• Is work particularly stressful? 
• Are they traveling? 
• Are relationships, family responsibilities, or other parts of life demanding more of their energy than normal?

The answers to all of these questions can be valuable data, too.

Too much information is still a challenge

The challenge for a coach or programmer is not simply finding information. In many cases, there is almost too much information available. The challenge is deciding how to analyze and interpret it, then deciding which data points should actually influence the approach. 

This is where continuing to ask questions becomes so important. Questions such as: 

• Why did that workout go differently than expected? 
• Why is this athlete suddenly struggling at loads that were moving easily three weeks ago? 
• Why did this progression work incredibly well for one athlete but seemingly do nothing for another? 
• Why did performance improve when volume came down? 
• Why did performance improve when volume went up?

The answers to these questions can become the guiding lights for the best coaches and programmers.

Sometimes, the data can lead you to lean further into your current approach with an athlete, and other times, it can lead you to lean away. Go heavier. Go lighter. Add volume. Remove volume. Push the intensity. Pull the intensity back. Give an athlete more freedom. Give an athlete more structure. There may not always be an obvious answer, and I think accepting that is an important part of becoming a better coach and a better programmer.

Avoid jumping to conclusions

One workout is rarely enough information to draw a meaningful conclusion. One bad training week does not necessarily mean the program needs to change, just as one great training week does not necessarily mean you have discovered the perfect approach. Like any good scientist, a coach needs repeated data points before becoming overly confident in a conclusion, and success can actually make this particularly tricky.

If an athlete PRs a lift, wins a competition, or has the best season of their career, it is very easy to look backwards and assume everything that preceded the result must have been correct. But a positive result does not necessarily mean the approach was optimal. Maybe the athlete succeeded because of the program. Maybe they succeeded despite parts of it. Maybe a different approach could have produced an even better result. We simply cannot know for certain.

Failure deserves the same consideration in the opposite direction. A poor result does not automatically mean that every decision leading up to it was wrong. Sometimes a sound approach produces a disappointing outcome. Sometimes it takes more time for a sound approach to bear the fruit of great results in the gym or in competition; other times, bad results expose something in the training process that needs to change. The point remains: there is information to be gained if you are willing to look for it.

When the data does not give you a clear answer

This is where I think the artistic side of coaching and programming becomes particularly important. There will inevitably be times when the available data is incomplete, conflicting, or inconclusive. At some point, a decision must still be made. Experience, intuition, creativity, and even inspiration from outside sources can help fill those gaps.

A workout you see another coach write might spark an idea. A conversation with an athlete might completely change the direction you thought their training needed to go. Something from another sport may make you reconsider how you develop a particular quality. Sometimes an idea simply comes to you, and you have a logical reason to believe it might work, and the only way to find out is to try it.

Then, once again, you have to watch what happens and interpret from there, and the cycle starts over.

Embracing the cycle 

That cycle never really ends. Create, apply, observe, question, refine, and create again.

The nature of this process is why I believe the best coaches and programmers remain curious. The more experience you gain, the more information you have to make decisions, but ideally, that experience does not lead you to suddenly think you have everything figured out. Instead, it gives you a larger collection of experiences and data points to pull from the next time you are required to make an educated guess.

There will always be trial and error, and there will always be approaches that need to be scrapped, redesigned, or refined, just as an artist may rework a sculpture to reach their finished product or a scientist might rerun an experiment to obtain more conclusive results. 

There will always be successes worth studying just as much as there are failures worth the same. This is exactly what makes both coaching and programming endeavors that are never truly perfected. Great coaching and programming are both information-informed and human-directed. 

Science gives us information; art helps us decide what to do with it; and experience refines both. From there, we continue making our next best educated guess and see what happens.