Why One Athlete's Time Isn't the Group's Time

The Problem With a Single Stopwatch

I've spent plenty of time standing at the edge of the track with a stopwatch around my neck, and for a long time, that one number — the group's time on a 400m interval — was the only truth I wrote down on the sheet. The problem is that number lies, or at least it doesn't tell the whole truth.

When six athletes head out together to run a 400m and the coach hits the button once as the first one crosses and again when the last one crosses, what you get is a range, not a data point. The athlete who finished first might have run 72 seconds. The one who finished last, 81. If the coach writes down "76 seconds" as the interval time because that was the visual average, both athletes end up with a number that doesn't represent what their body actually did.

This isn't an administrative detail. It's the difference between training with data and training with a comfortable approximation.

What Exactly Gets Lost

1. Intra-set variability stays hidden

In an interval workout — say, 8x400m — what interests me most as a coach isn't the time of the first rep, but how that mark behaves across all eight. An athlete who runs 72, 73, 74, 75, 77, 79, 82, 85 is showing a very different fatigue curve than one who runs 75, 75, 76, 75, 76, 74, 75, 76. The first one is falling apart; the second is managing the effort with real reserve left. If all I have is the group average per rep, that individual curve disappears completely.

Neuromuscular and metabolic fatigue isn't linear, and it isn't the same across athletes, even within a group of similar level. Biomarkers of metabolic stress and muscle damage — lactate, CK, cortisol — vary according to each runner's individual profile in response to the same external load [PMID:41922043]. Two athletes can run the exact same time on rep 1 and have completely different internal physiological responses by rep 6. Without individual timing, that divergence is invisible until one of them gets injured or hits a plateau.

2. The "average time" hides the athlete who most needs adjusting

When the stopwatch only registers one number per interval, the athlete who structurally finishes last — whether due to level, accumulated fatigue, or coming back from injury — gets buried inside the average. You don't know if they're 2 seconds back or 8. You don't know if that gap is shrinking or growing rep after rep. And that's precisely the data point that tells you whether the training plan is helping them or sinking them.

This matters especially with masters athletes, where the cardiovascular response to sustained effort differs in measurable ways from younger athletes, even when training the same stimulus [PMID:40667749]. A mixed-age group running the same interval is not having the same internal physiological experience, even though the group stopwatch suggests otherwise.

3. The individual effort-recovery relationship gets lost

Recovery time between reps also gets measured differently when you time each athlete separately. Two runners might complete the 400m in the same time, but one needs 90 seconds for their heart rate to drop back into the reset zone while the other needs 150. If the coach restarts the set based on "the group is ready" — meaning, based on whoever recovered first, or some arbitrary average — some athletes get forced to start under-recovered while others wait too long, which also disrupts the intended metabolic stimulus.

The Case of GPS Version 1.8: What Changes When You Measure Differently

It's worth thinking through this with a technical example. The earliest GPS watches for runners — think of early software versions, something like a "v1.8" — solved the distance problem but not the individual-signal-within-a-group problem. They recorded pace, cumulative distance, total time. That was already a leap forward from the handheld stopwatch. But it was still an isolated recording: one athlete's watch tells that athlete's story, with no group context, no comparison across reps, no full narrative of how the collective session actually unfolded.

The coach's phone app stopwatch has the opposite problem: it captures the group, but doesn't differentiate the individual within the set unless the coach manually hits a separate button per runner — something unrealistic with more than three athletes on the track.

The real solution isn't choosing between "individual watch" or "group stopwatch." It's having a system that records both layers at once: each athlete's exact time on each rep, cross-referenced against the context of the full set — who went out with whom, what recovery each person got, how the group evolved rep after rep. That's what we're building with PMPRunCal: a place where individual data doesn't get swallowed by the collective data, but coexists with it.

Why This Isn't an Obsession With Numbers

I understand the objection: "you're measuring too much, running doesn't need this much technology." I understand it because I thought the same thing myself for years, armed with a notebook and a handheld stopwatch. But the question isn't whether measuring more is inherently better — it's whether the training decision you make tomorrow is based on what actually happened today, or on a visual approximation you made standing trackside.

This matters doubly in contexts where training is targeting specific metabolic adaptations. For example, we know that the type of high-intensity stimulus — cycling vs. running, say — produces differentiated effects on body composition and even gut microbiota [PMID:38233990]. If you don't know precisely what individual intensity each athlete reached during a set, you can't link that stimulus to the physiological response you're after, because the "stimulus" you recorded was actually a fictional average.

The same holds for glucose metabolism during fasting protocols combined with exercise: the AMPK/SIRT1/BDNF pathway responds to the real intensity of the effort, not the nominal intensity planned on the whiteboard [PMID:40765038]. And carbohydrate timing relative to evening high-intensity exercise also depends on how hard each athlete actually ran, not how hard they were supposed to run [PMID:40259503]. In every one of these cases, precise individual data is the foundation without which nutritional and metabolic science has nothing to stand on.

What to Do in Practice, Today, Without Waiting for Perfect Technology

Not every coach has immediate access to a full timing system. While that gets built out, there are concrete steps that make a real difference in the quality of data you collect:

• Time the first and last finisher of every interval, always — not just a visual average. That range alone tells you more than a single number.
• Ask each athlete to log their own time with whatever watch they have, even a basic one, and have them send it to you after the session. Cross-reference it with your group stopwatch.
• Record the actual recovery, not the planned one. If you called for "90 seconds rest" but the group took 2 minutes to restart, that's the number that matters for interpreting the next rep.
• Look for the curve, not the point. A single interval time says nothing without the reps before and after it alongside it.
• Ask yourself who's hiding in your average. If you've got a group of 6 and one group time, do you actually know which of the six is improving and which is compensating for a lack of fitness with sheer effort?

My Perspective as a Coach

After years of writing down times in notebooks and later in spreadsheets, what was hardest for me to learn wasn't how to use better technology — it was learning to distrust my own visual averages. A coach who times a large group well develops a trained eye for estimating, and that eye is useful, but it doesn't replace the data. Intuition tells you something is going well or poorly; individual data tells you exactly what, and in whom.

Building PMPRunCal comes out of that accumulated frustration — wanting to know, rep by rep, what happened to each athlete within the set, without losing the full picture of the group. It's not about having more numbers. It's about every athlete, when they review their training, seeing their own story — not an average that never fully represented them.

References

• Siebers M, Bizjak DA. Exercise biomarkers. Adv Clin Chem. 2026. [PMID:41922043]
• Frandsen J, Aaroe M. Cardiac Effects of Prolonged Endurance Exercise in Young and Older Athletes. Scand J Med Sci Sports. 2025. [PMID:40667749]
• Couvert A, Goumy L. Effects of a Cycling versus Running HIIT Program on Fat Mass Loss and Gut Microbiota Composition in Men with Overweight/Obesity. Med Sci Sports Exerc. 2024. [PMID:38233990]
• Wang X, Shi J. Different Fasting Methods Combined With Running Exercise Regulate Glucose Metabolism via AMPK/SIRT1/BDNF Pathway in Mice. Compr Physiol. 2025. [PMID:40765038]
• Mattsson S, Edin F. Impact of carbohydrate timing on glucose metabolism and substrate oxidation following high-intensity evening aerobic exercise in athletes: a randomized controlled study. J Int Soc Sports Nutr. 2025. [PMID:40259503]