Ryan Martin Charting The Uncharted Waters Of Athletic Performance And Human Potential

What Ryan Martin Charting The Uncharted Waters Of Athletic Performance And Human Potential Actually Is

I came across this framework about two years ago when a strength and conditioning colleague of mine mentioned it. It's essentially a performance mapping methodology that tracks athlete output across multiple physical and physiological variables simultaneously. The core idea is that traditional testing — vertical jump, sprint times, 1RM squats — gives you a snapshot, but it doesn't tell you the story of how an athlete is actually developing under load over a full training cycle. Ryan Martin Charting The Uncharted Waters Of Athletic Performance And Human Potential builds on the concept that human performance isn't linear. You can have two athletes with identical benchmark numbers who respond completely differently to the same training stimulus. The charting method attempts to identify those divergence points before they become injuries or plateaus.

The Core Methodology

At its simplest, the system requires you to log three categories of data every session: external load, internal load, and subjective recovery markers. External load covers metrics like total distance, number of accelerations above certain thresholds, and volume lifted. Internal load includes heart rate variability readings, resting heart rate trends, and sometimes blood lactate if you have access to that. Subjective markers are the RPE scales and sleep quality ratings the athletes give themselves each morning. The charting itself happens on a weekly basis. I use a simple spreadsheet with a moving 7-day average for each metric, then flag anything that deviates more than one standard deviation from that rolling average. When three or more metrics diverge in the same direction over consecutive weeks, that's where you start investigating. That's been my experience with at least 90% of the soft-tissue issues I've seen prevent an athlete from competing. I work with a collegiate track program and ran into a situation last fall where our sprinter had all her external load metrics looking fine — sprint volume was on target, acceleration counts were normal — but her heart rate variability was trending downward for three consecutive weeks and her morning resting heart rate was eight beats higher than her personal baseline. We pulled her back from a planned load increase, gave her two extra recovery days, and rechecked. Her HRV came back to normal within 48 hours. Without the charting system, I probably would have just increased the workload and hoped for the best.

Setting It Up Practically

You don't need expensive software to do this. The approach works with a basic data capture system and a willingness to be consistent with logging. Here's what I'd recommend as a starting point. First, pick your metrics. Start with three external load indicators, two internal load indicators, and one subjective recovery measure. Don't go beyond that in the beginning because you'll spend more time collecting data than actually using it. GPS vest for the sprinters, heart rate monitor for everyone, and a simple text-based morning check-in from the athletes covering sleep quality, muscle soreness, and motivation on a scale of one to ten. Second, establish your baseline. You need at least two weeks of collected data before you can meaningfully interpret deviations. During that baseline period, don't change anything about the training. Just let the data accumulate so you know what normal looks like for each athlete. This is where a lot of programs skip ahead too fast and end up misinterpreting their own data.

Third, review weekly. Set aside thirty minutes on a Friday afternoon to pull the numbers, calculate the rolling averages and standard deviations, and flag any outliers. If an athlete has a red flag, talk to them the same week. Don't wait. The whole point of catching these trends early is that you have time to adjust before the athlete hits a wall.

Common Mistakes I See

The biggest issue is treating the charting like it's predictive. It isn't. What it does is identify patterns that correlate with performance drops or injury risks. Correlation is not causation, and some of those flagged trends will turn out to be false alarms. I've had cases where an athlete's metrics looked concerning on paper but they performed exceptionally well the following week. The chart tells you to pay attention, not to pull the athlete from competition. Another mistake is not accounting for sport-specific demands. A distance runner and a shot putter are going to look very different on the same chart. Distance runners will show high external load volume with relatively stable internal load because their aerobic engine handles the stress efficiently. Shot putters will have low overall load but spikes in neuromuscular fatigue that show up in the HRV data. If you compare them directly instead of against their own baselines, you'll misread both. There's also the problem of athlete buy-in. If you're forcing a group of twenty wrestlers to fill out morning check-ins and they're doing it as a box-checking exercise, the data is garbage. I've seen this happen. The subjective markers end up being all fours and fives across the entire roster because nobody wanted to look weak or spend extra time on paperwork. The workaround was removing the subjective component from the team culture and having athletes log directly to a private app instead of in front of their peers. Motivation scores went up and the data quality improved noticeably within two weeks.

When This Approach Falls Apart

This system doesn't work well for individual sports where each athlete has a highly unique training block. I tried applying a standardized charting template to a swimmer who trained twice daily with massive volume variance between weeks, and the rolling averages never stabilized enough to be useful. The data was there, but the signal-to-noise ratio was too low to draw actionable conclusions. In that case, I went back to monitoring just two metrics — resting heart rate in the morning and perceived soreness on a simple scale — and ignored everything else. Sometimes less data is actually more informative. The other limitation is resource availability. If you don't have access to heart rate monitoring or GPS tracking, the framework gets significantly weaker. You can still chart external load through workout logs and internal load through subjective measures, but you lose the ability to distinguish between an athlete who is underloading versus one who is overloading. Those two states can look identical on subjective measures alone, which is a dangerous blind spot. I haven't found a single downloadable tool or software package that does this properly. Most commercial solutions want you to pay monthly subscriptions for features you won't use, or they oversimplify the data in ways that defeat the purpose. The spreadsheets I use are mine, built over several seasons of trial and error, and I don't share them publicly. If you want to implement this, you're going to build your own system or adapt one from whatever data sources your program already has access to. The methodology matters more than the tool.