I have an athlete who is also a journalist, and she’s working on a piece about something we’re calling “DataSoberTober.” For the month of October, she’s exploring what happens when she takes a break from some of the data that typically accompanies her training. Less checking numbers, less analyzing every workout, and less allowing a device to determine whether she had a good day or a bad one.
As a coach who uses data extensively, I find the premise interesting. I rely on power, pace, heart rate, and other training metrics to help athletes understand their fitness, manage training load, and make decisions about progression and recovery. There’s tremendous value in having access to this information, especially when we can look at patterns over time.
But I also think we’ve reached a point in endurance sports where athletes are increasingly looking to data for something it was never intended to provide: proof. Proof they’re fit, improving, recovered, ready to race, or simply doing enough. When the numbers don’t provide that reassurance, athletes start questioning things they might otherwise have felt perfectly confident about.
Her project raises a question I think is worth considering: Are we using our training data to learn, or are we using it to feel reassured?
When the numbers rewrite the experience
Consider an athlete who finishes a workout feeling strong. They executed the session as prescribed, managed their effort appropriately, and walked away feeling good about what they accomplished. Then they upload their workout and discover that their heart rate was higher than expected, their pace was slower than last month, or their watch suggests their training readiness isn’t where it should be.
Suddenly, what felt like a good workout becomes a disappointing one. Instead of considering what they accomplished, the athlete starts searching for what went wrong.
I’ve seen this happen often enough that I think it’s become an important part of the coaching conversation. We have access to so much information that it’s increasingly easy to give the numbers more authority than our own experience, even when those numbers lack the context necessary to explain what happened.
A higher heart rate during an easy run might reflect accumulated fatigue, but it might also reflect heat, dehydration, stress, poor sleep, or the demands of that particular route. A slower pace at the same perceived effort doesn’t automatically mean an athlete is losing fitness, just as a lower power output doesn’t necessarily mean the training isn’t working.
These are observations that deserve context, particularly when they differ from what we expected. The number tells us something about what happened, but it doesn’t necessarily tell us why it happened or what we should do next.
This is where I think we need to get better at treating data as clues rather than conclusions. When we’re looking for proof, an unexpected number can quickly become a judgment about our fitness or progress. When we’re looking for clues, we’re more inclined to get curious about the circumstances and consider what the information might be telling us. That distinction changes how we interpret our training and how we make decisions.
Are we using training data to learn or to feel reassured?
I think part of our growing dependence on training metrics has less to do with performance and more to do with our discomfort with uncertainty. Endurance training asks us to invest an enormous amount of time and energy into outcomes we cannot fully predict. We might train consistently for months without seeing obvious improvements in every workout. Fitness develops over time, and the process rarely gives us the immediate confirmation we’d like.
Naturally, we want to know that the work is paying off. We look at our fitness scores, HRV, predicted race times, recovery metrics, and comparisons to previous workouts. When the numbers move in the direction we want, we feel confident. When they don’t, we start questioning our training.
For some athletes, this creates a cycle of constantly looking for more information to feel better about what they’re doing. The irony is that the more information they collect, the less confident they sometimes become in their own ability to evaluate their training. Instead of using data to develop understanding, they begin relying on it for reassurance. There’s a real difference between those two things.
If we’re using data to learn, an unexpected number becomes an opportunity to ask better questions. We consider the conditions, the purpose of the workout, our recent training, and how we felt. If we’re using data for reassurance, we’re more likely to interpret that same number as a judgment about whether we’re making progress.
Over time, this influences how we train, how we respond to setbacks, and how much confidence we have in our ability to make decisions without external confirmation.
Learning to recognize effort without a watch
One of the things I find most interesting about DataSoberTober is the opportunity it creates for an athlete to become more attentive to the experience of training.
Can you recognize an easy effort without checking your pace? Can you distinguish between aerobic endurance and threshold intensity without constantly watching your power meter? Can you identify fatigue before your watch tells you that your recovery is compromised? Can you finish a workout, evaluate how well you executed it, and feel satisfied without immediately looking for a score? These are skills that deserve as much attention as learning how to interpret the numbers themselves.
We spend a great deal of time teaching athletes about training zones, but understanding a zone numerically isn’t quite the same as recognizing what that intensity feels like. An athlete might know that their Zone 2 power falls within a particular range, yet struggle to recognize when the effort has become too demanding without looking at the screen. They might know their threshold pace but have difficulty identifying how a sustainable effort should feel during a longer interval.
This is where rating of perceived exertion, or RPE, becomes such a valuable part of endurance training. RPE gives athletes a way to evaluate intensity using their own experience of effort, including breathing, muscular fatigue, concentration, and the ability to sustain an effort.
As athletes gain experience, they can become more proficient at recognizing these signals and understanding how they relate to the objective data they’re collecting. They begin to recognize what different intensities feel like, how those sensations change as fatigue accumulates, and when their perception of effort doesn’t align with the numbers.
I find this especially valuable because the relationship between pace, power, heart rate, and perceived effort isn’t fixed. It changes with training, fatigue, environmental conditions, and countless other factors. Learning to recognize those changes gives athletes another source of information when deciding how to approach a workout.
Why athletes need both data and perceived effort
I don’t believe training by feel is superior to training with data. I think athletes need to become proficient at both, and the real value comes from understanding how objective and subjective information complement each other.
Objective metrics provide information that perception alone can miss. An athlete might feel comfortable during the early stages of a workout while producing an effort that’s too demanding to sustain. Power and pace can help prevent that mistake, particularly when motivation or excitement makes it difficult to judge intensity accurately.
At the same time, perceived effort provides context that the numbers can’t always capture. Two athletes might complete identical workouts at similar intensities, yet experience them very differently. One finishes feeling strong and capable of doing more, while the other struggles through the final interval and spends the remainder of the day feeling exhausted. From a numerical standpoint, the workouts might look nearly identical. From a coaching standpoint, they tell very different stories.
Even within the same athlete, the relationship between external workload and internal effort can change depending on sleep, stress, nutrition, environmental conditions, and accumulated fatigue. This is why I value detailed training comments from my athletes.
I can look at a workout file and see the pace, power, heart rate, and duration. I can evaluate whether the athlete met the prescribed targets and how the session fits into the larger training plan. But I also want to know how the effort felt, whether they struggled to maintain concentration, how they recovered afterward, and whether anything about the session was unusual.
That information helps me interpret the numbers and make better decisions about what comes next. The goal is to develop athletes who can consider the data, how they feel, and what’s happening rather than relying exclusively on any one source of information.
Not every workout needs to prove you’re improving
There’s another part of this conversation that I think deserves attention, and it’s our tendency to evaluate almost everything we do in training according to whether it demonstrates improvement. Was the pace faster? Was the heart rate lower? Did the power increase? Did the fitness score move in the right direction?
These comparisons can be useful, but they can also create the expectation that every workout should provide some measurable evidence that we’re getting better. When that evidence isn’t there, we begin to question the value of the work.
The purpose of every workout isn’t to demonstrate that we’re fitter than we were last week. Some sessions are intended to build endurance, while others develop durability, improve technical skills, or provide a manageable training stimulus that we can recover from and repeat consistently.
Many of the adaptations we’re working toward take weeks, months, and sometimes years to develop. A workout can be entirely successful without producing an impressive number or providing immediate evidence of progress.
I think athletes would benefit from spending more time evaluating their training according to its intended purpose. Did you execute the session appropriately? Did you manage the effort? Did you respond well to the training? Are you able to come back and continue the work?
Those questions often tell us more about the value of a session than whether we produced a personal best in training. They also help shift our attention toward the process of becoming a better athlete rather than constantly seeking confirmation that we’ve already become one.
You don’t need an entire month without data
While my athlete has committed to DataSoberTober, I don’t think anyone needs to give up training data for an entire month to benefit from this exercise. Even a session here and there can be useful, particularly when the intention is to become more aware of how effort feels and how often we look to our devices for reassurance.
Try an easy run without checking your pace. Settle into what feels sustainable and see how closely your perception matches the numbers afterward. Complete a bike workout using RPE to guide the effort, then review the power and heart rate data when you’re finished. During a swim, pay attention to your breathing, rhythm, and technique without making every interval an evaluation of your speed.
You can still record the workout and review the information afterward. The difference is that you’re giving yourself an opportunity to experience the session before allowing the numbers to influence your interpretation of it.
Over time, this can improve your understanding of perceived exertion and help you recognize how effort changes with fatigue, conditions, and training demands. It also gives you an opportunity to compare what you thought was happening with what the data shows, which can be equally valuable when the two don’t align.
I also think there’s an enjoyment component to this that we tend to overlook. When you’re less preoccupied with whether every number is where it should be, there’s more room to pay attention to what you’re doing. You become more engaged in the session, more aware of your surroundings, and more capable of recognizing the small changes in effort and technique that might otherwise go unnoticed.
There’s something valuable about finishing a workout and knowing how it went before you look at the file. When you do review the numbers, you have your own observations to compare against them, and that process helps experience and information reinforce each other.
Developing judgment is part of becoming a better endurance athlete
One of my goals as a coach is to help athletes become increasingly capable of making good decisions for themselves. I want them to understand the purpose of their workouts, recognize appropriate intensity, identify when fatigue is accumulating, and know when an adjustment is warranted.
I also want them to become comfortable with the fact that not every unexpected number requires an explanation or intervention. Some workouts will feel harder than expected, some days the pace will be slower, and heart rate and recovery metrics will fluctuate. We can pay attention to these things without assigning every change a larger meaning.
This ability becomes especially important in racing, where we spend months preparing for a particular event and often develop very specific expectations about the pace, power, or heart rate we hope to sustain. But racing requires us to respond to conditions as they unfold. The weather changes, fatigue accumulates, and the effort we expected to feel at a certain pace or power output might be different from what we experience on the day.
An athlete who understands both the numbers and their own perception of effort has more information available to make appropriate decisions. If their watch fails, their power meter disconnects, or the numbers simply aren’t what they expected, they still have the skills to execute.
This is an important part of performance that we should be developing throughout the training process. The more experience athletes have interpreting their own effort alongside the data, the more capable they become of making decisions when things don’t unfold exactly as planned.
Learning to trust ourselves
I’m interested to see what my athlete takes away from DataSoberTober. I don’t expect her to finish the month and decide that training data is unnecessary. I expect she’ll return to using it with a better understanding of how it influences her experience, her decisions, and her confidence.
She may discover that her perception of effort is more accurate than she realized, or identify situations where her perception and the objective data don’t align. Both outcomes provide useful information, and both contribute to a more complete understanding of how she’s responding to training. More importantly, I hope she becomes more comfortable evaluating her training without immediately looking outside herself for confirmation.
We have access to more training information than ever before, and I consider that an enormous advantage. But the value of that information depends on what we do with it. Data can help us identify patterns, evaluate progress, and make better decisions, while our own observations help us understand the circumstances behind those numbers and how we’re responding to the work.
The more proficient we become at using both, the more engaged we become in the process and the more capable we are of making decisions with confidence.
That is how we learn to trust ourselves.
Keep reading
- Relearning Effort: Interoception, RPE, and the Hidden Skill of Internal Awareness
- Why Listening to Athletes Matters More Than Their Metrics
- Stop Chasing Exact Numbers: Why Power and Pace Ranges Work Better in Training
About Elizabeth Waterstraat
Elizabeth Waterstraat is the founder and head coach of Multisport Mastery, established in 2007. A USA Triathlon Level III Coach with 20 years of coaching experience and 26 years as an endurance athlete, Elizabeth works with athletes ranging from first-time triathletes to national champions and world championship qualifiers. Her coaching approach emphasizes individualized training, athlete education, consistency, and long-term development.
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