Reading Your Own Training Data: Mining Gold from the Junk

Coach’s Opening: The Athlete Who Lost Sleep Over a Watch

In fifteen years of coaching athletes, I’ve seen all kinds of people stumble over the same hurdle. Recently, a student named Xiao Chen, preparing for his first 113 half-distance triathlon, had been training quite diligently. But when he came to see me, his face looked worse than if he’d just run 30 kilometers. Why? That morning, his watch had flashed “Training Status: Unproductive” along with a downward-pointing red arrow. He didn’t sleep well that night, his scheduled intervals the next day were sloppy, and then the watch gave him an even worse score the following day—a vicious cycle.

I looked at his data from the past three months and told him: “You’re not in a bad state. You got scared by a single day’s noise, and then you turned it into reality with your own hands.”

That’s the topic I want to discuss today. Almost every triathlete now holds a mountain of data: power, heart rate, pace, HRV, sleep score, training load, recovery time… The problem has never been “not enough data,” but rather that most people don’t know which numbers are signals and which are noise, let alone how to dig out the true gold that can guide their training from a pile of seemingly contradictory numbers.

Data itself is meaningless; it’s the interpreter who gives it value. In this article, I’ll walk you through the actual approach I use with athletes: from concepts and scientific foundations, to specific practical methods, common misinterpretations, and how athletes at different levels should get started. The goal is simple—to turn that expensive device on your wrist from a source of anxiety into a true coaching assistant.

Conceptual Foundation: Signal, Noise, and the Questions You Should Really Be Asking

What is Signal? What is Noise?

Borrowing from statistics and engineering: Signal is the part of the data that is real, repeatable, and reflects true changes in the body; noise is the random fluctuation caused by measurement error or unrelated factors. Your task isn’t to eliminate noise (you can’t), but to learn to recognize the signal within the noise.

Here’s a relatable example. You measure your HRV (heart rate variability) this morning, and the number is 8% lower than yesterday. What does that mean?

• It could be signal: You did a hard interval session yesterday, and your body is under real training stress.
• It could also be noise: You had a couple of extra beers last night, your measurement posture was different, the room temperature changed, you used the bathroom right before measuring, or even just a slightly different sensor fit.

Looking at that single day, you simply cannot tell which of the two that 8% represents. This is why “single-day data” should almost never be the sole basis for a training decision.

Physiological Data Is Inherently “Noisy”

This isn’t a flaw in your device; the human body is simply a system full of variability. Take HRV, for example. Research literature generally indicates that an individual’s daily HRV naturally fluctuates to a considerable degree, which is why sports science recommends looking at rolling averages and the coefficient of variation, rather than staring at single-day absolute values. One study monitoring daily HRV in adolescent swimmers over 11 weeks observed that after 3 to 5 consecutive days of high training volume, HRV showed a consistent decrease of approximately 4.5%, inversely related to large swings in training load—in other words, it takes several days of directional change to constitute a credible signal.

I often use this analogy with my athletes: A single day of data is like catching a glimpse of a person from a train window—you can’t be sure who they are. Trend data is like seeing the same person on the same platform for a week straight—only then can you confidently say, “This person commutes here every day.”

The Question Isn’t “Is Today’s Number Good?”

Many people’s first thought when opening their watch is: “Is today’s score good?” That’s the wrong question. When coaches look at data, these are the questions running through their minds:

1. Which direction is this number trending? (past 7 days, past 28 days)
2. Can this change be causally linked to my recent training and life?
3. If this is a real signal, what adjustment does it require from me?
4. Do multiple data sources corroborate each other? Or is only one screaming?

These four questions are the gold-panning sieve that turns garbage into gold.

Scientific Foundation: The Signal-to-Noise Ratio of the Three Data Types You Stare At Most

Different data types have wildly different “credibility.” Some numbers are stable with low noise, and you can trust them more; others are noisy and only make sense after heavy smoothing. Let’s establish this map first:

Data Type	Noise Level	Time Scale Needed	Coach’s Trust Level	Common Interference Factors
Power (on the bike)	Low	Readable within a single session	High	Power meter not zeroed, low battery, abnormal cadence
Pace (running)	Medium	Readable in a single session, but needs terrain context	Medium-High	GPS drift, hills, wind, temperature
Heart Rate	Medium	Needs interpretation with current context	Medium	Heart rate drift, dehydration, caffeine, optical sensor error
HRV / Resting Heart Rate	High	7+ day rolling average	High for trends, low for single days	Sleep, alcohol, measurement posture, illness, menstrual cycle
Sleep Score	High	Weekly trend	Medium-Low	Algorithm black box, device fit
Vendor “Training Status”	Very High	Reference only	Low	Algorithm assumptions, insufficient data, mixed sports

Why Power Is Relatively Trustworthy

Power is “the work you’re doing on the pedals right now.” It’s a direct mechanical measurement, unlike heart rate, which is the body’s “response” to load (and can drift due to dehydration, temperature, caffeine, or sleep). This is why, for bike training, we prefer power as the primary metric and heart rate as a secondary reference.

Take the classic FTP (Functional Threshold Power) estimation as an example: the widely adopted industry practice is to take the average power from a 20-minute all-out test and multiply it by 95% as the FTP estimate. This 5% reduction compensates for the fatigue difference between a 20-minute effort and a full 60-minute effort. However, it’s important to note that this is just an estimate—one study comparing Critical Power (CP) testing with the 20-minute FTP test found that CP tends to be higher than FTP; while highly correlated, they shouldn’t be used interchangeably. Furthermore, the “20-minute power × 0.95” formula isn’t accurate for a significant portion of athletes. This is the key point: even the most trustworthy data is a model with assumptions, not absolute truth.

Why HRV Is Noisy, Yet Still Worth Watching

HRV reflects the balance of your autonomic nervous system (sympathetic vs. parasympathetic). It’s extremely sensitive to stress—which is both a strength and a weakness. The strength is that it can flag overtraining or accumulating life stress early. The weakness is that it’s so sensitive that a single drink or a bad night’s sleep can make the daily number jump. So the correct usage is always: look at the direction of the 7-day rolling average relative to your personal baseline, not at whether today is higher or lower than yesterday.

Practical Methods: How a Coach Turns Data into Decisions

Enough theory; here’s what you can actually use. Below is the interpretation process and tools I actually use with my athletes.

Method One: Establish a “Personal Baseline” and Use It as Your Reference Point

Without a baseline, any number is an orphan. Everyone’s normal range is different—someone’s resting heart rate might be 42 bpm, another’s 58 bpm, and both can be perfectly healthy. The first thing you need to do is spend 2 to 4 weeks collecting data under normal training and lifestyle conditions to calculate your own baseline range.

Using HRV as an example, a personalized interpretation approach looks roughly like this:

Today’s Value vs. 7-Day Baseline	Interpretation	Recommended Action
Within normal fluctuation range	Everything as usual	Execute the plan as scheduled
Clearly low, but only for 1 day	Possibly noise	Train as usual, but pay attention to how you feel
Low for 2–3 consecutive days	Starting to look like a signal	Reduce intensity, increase recovery, observe
Consistently low, accompanied by rising resting heart rate and subjective fatigue	Strong signal	Proactively reduce volume or schedule a recovery day
Sudden sharp drop + sore throat/elevated temperature	Possible illness	Stop high-intensity work, rest

Note the key word in the last two rows: “accompanied by.” If a single data point is crying out, you don’t necessarily need to listen. But when multiple independent sources are all pointing in the same direction simultaneously, that’s a golden signal—you must act.

Method Two: Always Look at Trend Charts, Not Today’s Number

Plot your key metrics (HRV, resting heart rate, weight, training load, subjective fatigue) as line charts and look at the past 28 days. You’ll suddenly realize that the single-day highs and lows that caused you anxiety yesterday are, on a 28-day scale, just normal jagged fluctuations. The real trend line is actually moving steadily up or down.

Here’s a super simple mental framework I teach—the “3-7-28 Rule” (my own mnemonic for athletes, not an official term):

• 3 days: Look at acute response. Right after a big session, data getting worse is normal—that’s fatigue, not a bad thing.
• 7 days: Look at short-term trends. This is the primary timescale for deciding “should I adjust this week’s training?”
• 28 days (about 4 weeks): Look at the overall direction of the training block. Whether your fitness is actually improving—this is where you see it.

Method Three: Use the “Training Load vs. Recovery” Framework to Understand Fatigue

Many platforms give you an “Acute Load / Chronic Load Ratio” (commonly known as ATL vs. CTL, or Training Stress Balance, TSB). The core concept of this framework is actually quite intuitive:

• Chronic Load (Fitness): Your average training volume over roughly the past 6 weeks. It represents the “foundation you’ve built.”
• Acute Load (Fatigue): Your training volume over roughly the past week. It represents “how tired you are right now.”
• The Difference (Form): Fitness minus fatigue roughly corresponds to your current competitive form.

The logic of tapering for a race is a direct application of this framework: deliberately reducing acute load (letting fatigue subside) before race day while trying not to drop chronic load too much (preserving fitness), allowing your form to surface. This is why I often remind athletes: don’t panic when your watch says “fitness declining” during taper week—that’s by design. What you want is form, not fatigue.

A Sample One-Week Plan That Puts Data to Work

Below is a week I gave to an advanced age-group athlete (training about 10 hours per week) during the build phase. The key is that every critical decision point is annotated with “what data to look at”:

Day	Workout	Primary Data to Watch	Decision Logic
Monday	Complete rest	HRV, sleep	Monday’s data is the reference point for the week’s starting state
Tuesday	Bike: Threshold 2×20 min @ FTP 95–100%	Power (primary), Heart Rate (secondary)	Whether power targets are met indicates threshold progress
Wednesday	Run: Easy aerobic 60 min	Heart rate, pace	Heart rate must stay in the aerobic zone; don’t sneak into moderate intensity
Thursday	Swim: Technique + short sprints	Subjective feel, stroke rate	Swim data is noisy; focus on feel and technique
Friday	Bike: VO2max 5×4 min	Power, cadence	If HRV dropped significantly the day before, reduce the number of intervals
Saturday	Run: Long run 90–120 min	Pace, heart rate drift	Observe heart rate drift in the latter half to assess endurance
Sunday	Bike: Long aerobic 3 hours	Power, normalized power	Accumulate aerobic volume, stay in Zone 2

Pay special attention to Friday’s row: “If HRV dropped significantly the day before, reduce the number of intervals.” This is the moment data truly intervenes in decision-making—it’s not about letting data dictate everything, but letting data fine-tune your original plan.

Method Four: Verify Progress with “Same-Workout Retests,” Not by Comparing Different Workouts

Many people want to know “am I actually getting stronger?” but they compare two completely different workouts—of course you can’t tell. The correct approach is to design a fixed benchmark workout, repeat it under as similar conditions as possible at regular intervals, and compare key metrics. It’s like a control group in a lab—only by holding variables constant can you separate the “improvement” signal from the noise.

Here’s a comparison of three benchmark workouts I commonly give athletes. Pick one and track it long-term:

Benchmark Workout	Fixed Conditions	Key Metric to Track	What Improvement Looks Like
Bike: Zone 2 at fixed power for 60 min	Same power, indoor trainer, same temperature	Average heart rate, heart rate drift	Lower heart rate and less drift at the same power
Run: 5 km at fixed pace	Same route, similar temperature	Average heart rate, RPE	Lower heart rate and RPE at the same pace
Swim: Fixed set 10×100 meters	Same pool, same rest interval	Average time per 100m, stroke count	Fewer strokes at the same time, more efficient

The most fascinating part of this method is: it makes something as abstract as “aerobic efficiency” visible. When you discover that three months ago, riding Zone 2 at 180 watts put your heart rate at 145 bpm, and now the same 180 watts only requires 138 bpm, that’s ironclad proof of progress—no vendor score is more honest than that.

Common Data Misinterpretations and Corrections

This section is the cream of the crop. I’ve compiled the most common misinterpretations I’ve seen in years of coaching—every single one I’ve personally witnessed ruin someone’s training or derail their race preparation.

Misinterpretation One: Treating Single-Day Noise as a Signal and Overreacting

This is Xiao Chen’s story from the opening. The watch said “Unproductive,” and he made himself unproductive.

Correction: Establish a rule for yourself—“Any single-day data point can only make me ‘take notice’; it cannot make me ‘change my plan.’ To change the plan, I need at least 2–3 consecutive days pointing in the same direction, or multiple independent data points corroborating each other.” Treat this as an iron law, and you’ll save yourself a tremendous amount of wasted effort.

Misinterpretation Two: Confusing “Correlation” with “Causation”

One athlete noticed that on days when his sleep score was high, his training performance was particularly good. So he started obsessively chasing a high sleep score, even getting so anxious about it that he slept worse. The problem is: it’s entirely possible that “he was in good form” simultaneously caused both “sleeping well” and “training well”—the sleep score is a result, not a cause. Trying to manipulate an outcome metric often proves futile.

Correction: Distinguish between inputs you can directly control (training volume, intensity, bedtime, diet) and outputs your body gives you as feedback (HRV, sleep score, performance). You manipulate the inputs and observe the outputs, rather than chasing the output numbers themselves.

Misinterpretation Three: Comparing Data from Different Conditions

“Coach, today I ran the same route at the same pace, but my heart rate was 10 bpm higher. Am I regressing?”—Then I ask: What was the temperature today? The athlete says 34°C. Last time it was 24°C.

This is incredibly common in Taiwan’s summer. High heat and humidity cause heart rate to rise significantly (the body shunts extra blood to the skin for cooling)—this is a completely normal physiological response, not regression. With Taiwan’s summer temperatures routinely exceeding 32°C and humidity off the charts, if you compare summer heart rate to winter, you’ll almost inevitably “look like you’re regressing.”

Correction: When comparing data, you must control the variables. Power is relatively unaffected by temperature outside of running (though the same power feels harder in heat), making it a more reliable anchor in summer. For running, compare only within the same temperature range and same route. Taiwanese athletes especially need to internalize this: discount your heart rate data in summer. Pace dropping and heart rate rising is a seasonal norm. Focus on whether the overall trend across the summer is improving, rather than comparing directly to the cooler spring.

Misinterpretation Four: Blindly Trusting Vendor Black-Box Scores

The “Training Status,” “Recovery Time,” and “Fitness Age” from various watch brands are all proprietary algorithms. You don’t know their assumptions or how they calculate. They might be reasonably accurate for someone doing a single sport with regular training, but for triathlon’s multi-discipline mixed training, they often misjudge—for example, underestimating the load of a swim session because it can’t capture power, or misclassifying a long Zone 2 ride as ineffective training.

Correction: Treat vendor scores as a “second opinion for reference.” Never let them override your own raw data (power, heart rate, pace, subjective feel). Learn to read the raw data, and you won’t be led around by a score you don’t understand.

Misinterpretation Five: Only Looking at Numbers, Ignoring “Subjective Feel”—Your Most Powerful Sensor

This is the most counterintuitive point, but the one I most want to emphasize. The validated Rating of Perceived Exertion (RPE) is actually an extremely reliable tool in sports science. Your body is the most expensive, most integrated sensor in the world. It tells you things your watch can’t—the heaviness in your legs, mental fatigue, whether you feel like moving.

Correction: After every training session, take 10 seconds to record a subjective fatigue score on a 1–10 scale. When your subjective feel and device data agree, your confidence soars. When they conflict (e.g., data says you’re fine but you feel terrible), that’s precisely the moment to stop and think—and usually, that’s when you should trust your body.

Misinterpretation Six: Chasing False Precision

Some people agonize over “Is my FTP 248 watts or 251 watts?” Honestly, that 3-watt difference is far smaller than the error introduced by whether you slept well, hydrated enough, or zeroed your power meter. Physiological data gives you a range, not a value precise to two decimal places.

Correction: Think in “ranges,” not “single points.” Your threshold is “approximately the 245–255 watt range.” Your aerobic pace is “the 5:30–5:45 per km range.” This way, you’ll be far less rattled by daily fluctuations.

Actionable Advice for Athletes at Different Levels

How deep you should go with data depends directly on your level. Using the wrong level either backfires or manufactures anxiety.

First-Timers / Newbies Who Just Finished a Race

The last thing you need right now is to be overwhelmed by data. At this stage, training benefits come primarily from “moving consistently” and “progressing gradually.” Any reasonable plan will make you improve; the marginal value of data is very low.

• Track only 3 things: weekly training hours, subjective fatigue for each session (1–10), and whether you’re getting enough sleep.
• Don’t touch HRV or power yet—or if you have them, just record them without making decisions based on them.
• Build feel: Learning to distinguish “easy,” “a bit breathless,” and “about to crack” is more important than any number.
• Remember: The biggest risk for a beginner is doing too much, too fast, and getting injured—not training unscientifically.

Advanced / Age-Groupers Chasing PBs

This is where data starts to deliver real value, because your progress has slowed and you need more precision to determine if training is working.

• Introduce power (bike) and threshold pace (run) as objective anchors for intensity. Stop doing intervals purely by feel.
• Start looking at 7-day and 28-day trends, using the “3-7-28 Rule” to judge whether a training block is working.
• Establish a personal baseline, making HRV and resting heart rate references for deloading and recovery (still trend-based).
• Test every 4–6 weeks (FTP or threshold pace)—this is the hardest metric for verifying training effectiveness.
• Learn to interpret data within Taiwan’s seasonal context: train by power/RPE in summer; don’t compare pace directly to winter.

Elite / Chasing a Kona Slot or National Ranking

At this level, data is a tool for fine-tuning. You and your coach will do more complex analyses together.

• Multi-data cross-validation becomes the norm: no single metric dominates decisions.
• Watch for subtle signals: heart rate decoupling at the same power, efficiency decline in the latter half of long sessions, slow shifts in HRV baseline.
• Manage taper state meticulously, using the load framework to precisely drain fatigue before race day.
• But even at this level, I’ll still say: the very best athletes are often the ones who best understand “when to turn off the watch and trust the body.” Data serves your judgment; it never replaces it.

A “Data Health Check” Checklist for Everyone

Here’s a self-check you can run immediately. Go through this before you look at your data next time:

1. Am I looking at a single day or a trend? (If it’s a single day, calm down first)
2. Is there an explainable cause for this change? (Big session yesterday? Alcohol? Temperature? Illness?)
3. Is there a second data source to corroborate? (If only one is screaming, just observe)
4. What does my subjective feel say? (Does it agree or conflict with the data?)
5. Is this number within its trustworthy timescale? (Don’t judge HRV on a single day; power is fine for a single session)
6. If this is a real signal, what’s the smallest, most conservative adjustment? (Don’t rush to overhaul the plan)

Run through these six questions, and you’ll find that 80% of “data anxiety” doesn’t hold up. The remaining 20% is the gold truly worth acting on.

FAQ

In years of coaching, I’ve been asked the same questions countless times. Here are the most frequent ones, answered once and for all to help you avoid the pitfalls.

Q1: Should I measure HRV every morning?

You can, but you need to use it correctly. The value of measuring daily isn’t in “today’s number,” but in accumulating a stable personal baseline and rolling average. If measuring makes you anxious, or a single day’s number ruins your mood, then it’s better not to measure—a monitoring tool that ruins your sleep ends up harming the very thing it’s meant to protect. When measuring, conditions must be fixed: same time (ideally right after waking, before getting out of bed, before coffee), same posture, same device. If conditions aren’t fixed, the noise in the data will be large enough to distort the trend.

Q2: My power meter and heart rate monitor disagree. Which should I trust?

First ask “what are you doing.” On the bike, power is primary and heart rate is secondary, because power is your actual current output, while heart rate is a delayed and drifting response. But if you notice one day that “at the same power, heart rate is abnormally high,” that itself is a valuable signal—it could be dehydration, lack of sleep, high temperature, or accumulated fatigue. This phenomenon of “normal power but decoupled heart rate” is particularly worth noting in the latter half of long sessions. It often tells you earlier than any vendor score that “your body is actually struggling today.”

Q3: My test (FTP / threshold) number dropped. Am I regressing?

Don’t jump to conclusions. Check the test conditions first. A single test result is influenced by too many immediate factors: sleep, fatigue from previous days, temperature, mental state during the test, even what you ate. Doing an FTP test in Taiwan’s summer and getting a number a few percent lower than in cooler seasons is extremely common. What you should really look at is the long-term trend across multiple tests under similar conditions. A single drop should at most make you “take notice,” never make you “invalidate an entire training block.” This is entirely consistent with the iron law from earlier—trends are the signal.

Q4: My sleep tracker says I’m not getting enough deep sleep. What should I do?

First, lower your trust in that number. Consumer-grade devices have limited accuracy for sleep staging (deep, light, REM)—they estimate it from indirect signals like heart rate and movement, not from brain waves. Instead of obsessing over “deep sleep percentage,” focus on what you can directly control and is more reliable: total sleep duration, regularity of bedtime, and most importantly—your daytime energy and training feel. If you’re sleeping a solid 7–8 hours, have energy during the day, and train well, then if your watch says you lack deep sleep, you can just laugh it off.

Q5: How much effort should I actually spend looking at data?

My principle is: Data should never take more than one-tenth of the time you spend training. If you spend half an hour analyzing your watch every day but only train for an hour, you’ve got it backwards. Data is meant to help you “train smarter,” not to make you “analyze more anxiously.” A healthy ratio is: 1 minute after training to record your subjective score, 10 minutes once a week to review trend charts, and half an hour at the end of each training block for a full review. The rest of the time, go train and go recover.

An Extended Case Study: Two Athletes with Identical Numbers, Completely Different Stories

Here’s a real example to deepen your understanding. I coached two athletes simultaneously. One week, both had HRV about 10% below baseline, and both had slightly elevated resting heart rates. If you only looked at the numbers, they were “identical.” Should I give them the same advice?

Absolutely not.

• Athlete A: Had just finished an extremely hard mountain bike training camp three days prior. Subjective fatigue had been 8–9 out of 10 for three consecutive days, but he was mentally sharp, had a normal appetite, and got sufficient sleep. This is classic functional overreaching—he’s absorbing planned, high training stress. The drop in data is “expected fatigue.” As long as adequate recovery follows, he’ll supercompensate and get stronger. My advice to him: follow the plan into the recovery week, don’t worry.

• Athlete B: Training volume was actually low, but this week work had exploded with overtime, sleep was fragmented, and he was starting to feel a slight tickle in his throat. The same HRV drop, in his case, was a warning sign—life stress plus a possible pre-infection state. My advice was the complete opposite: stop all high-intensity work immediately, sleep more, hydrate, and observe. Better to miss two days of training than to let a minor cold turn into a two-week ordeal.

The same numbers, because the ‘story’ behind them was different, led to opposite decisions. This is why I keep emphasizing: data must always be interpreted within the context of your life and training. A number divorced from context is just a number. A coach who asks “why” can pan for gold; one who only recites numbers is just a scoreboard.

Conclusion: Be the Master of Data, Not Its Slave

Back to Xiao Chen from the opening. The first thing I taught him wasn’t to buy a more expensive watch, but to turn off the “Training Status” score on his watch’s main screen and replace it with only the raw data he would learn to read himself. Then we built a baseline together, learned to look at 28-day trends, and recorded a subjective fatigue score every day. Three months later, he not only finished his 113 race successfully, but his PB was better than expected. More importantly, he told me: “Coach, I don’t panic when I look at data anymore. I know which ones to pay attention to and which are garbage.”

That’s the ability I most want to give every reader. The value of data isn’t in how much you collect, but in whether you can accurately identify the true signal within the noise and make the right adjustment at the right time. Your watch will give you a mountain of ore tailings. Whether you can pan out the gold depends on the interpretation sieve in your head.

Training is like this, and to some extent, so is life. Learn to distinguish signal from noise, and you won’t be dragged around by every temporary fluctuation. You’ll be able to hold your direction and steady your mindset through long training cycles. May every piece of data in your hands become a force that pushes you forward, not a shackle that binds you. See you at the race.

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This article is for educational purposes and does not replace individual assessment by a physician, physical therapist, or nutritionist.

References

• Daily Resting Heart Rate Variability in Adolescent Swimmers during 11 Weeks of Training: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7143004/
• Monitoring Training Adaptation and Recovery Status in Athletes Using Heart Rate Variability via Mobile Devices: A Narrative Review: https://www.mdpi.com/1424-8220/26/1/3
• Relationship Between the Critical Power Test and a 20-min Functional Threshold Power Test in Cycling (Frontiers in Physiology): https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2020.613151/full
• The FTP Test: Physiology and New Protocols (TrainingPeaks): https://www.trainingpeaks.com/blog/the-physiology-of-ftp-and-new-testing-protocols/

Related Reading

• How to Read Training Software Without Getting Lost: Three Things to Adjust from a Pile of Charts
• The Science of Wearables: How Accurate Is the Data on Your Wrist?
• The Future of Sports Tech: AI, Sensors, and Personalization—Opportunities and Pitfalls Through a Coach’s Eyes
• The Science of Wearable Accuracy: Optical Heart Rate, GPS, and Power Errors—A Coach’s Guide to Reading Your Data