Original episode & show notes | Raw transcript
This document provides a comprehensive analysis of the concepts presented in the Empirical Cycling Podcast episode, “Quantifying Training Volume.” The discussion between host Kolie Moore and guest Marinus Petersen centers on a fundamental question in endurance coaching: What is the best way to measure and prescribe training volume?
The conversation deconstructs three primary metrics—Hours, Kilojoules (kJ), and Training Stress Score (TSS)—exploring their physiological underpinnings, practical applications, and inherent limitations. This guide will dissect their arguments, clarify the underlying science, and synthesize their “convergently evolved” coaching philosophies for an intelligent student audience.
The core of the podcast is a debate over how to best quantify the training that stimulates endurance adaptations, particularly for work done below the first lactate threshold (LT1).
Marinus champions the kilojoule as the most fundamental measure of training stimulus for sub-threshold endurance work.
The Core Argument: Endurance adaptations are a direct response to a disturbance in cellular homeostasis, primarily driven by energy turnover (the use and regeneration of ATP). Kilojoules are a direct, objective measure of the total work performed by the muscles. Therefore, the total work done is more directly proportional to the adaptive signal than the time spent doing that work.
Physiological Basis: Processes that drive endurance adaptation, such as the activation of the AMPK and CaMK signaling pathways, are sensitive to the magnitude of cellular energy turnover. More work done means more energy turnover, a greater disturbance of homeostasis, and thus a stronger signal for adaptation.
Pros:
Objectivity: It is a direct measure of physical work, with no interpretation needed.
Direct Physiological Link: It is closely tied to the actual energy flux within the muscle cells, which is the root of the adaptive signal.
Flexibility: It provides a framework for understanding trade-offs. For example, if a group ride is slower than usual, an athlete can achieve a similar training stimulus by extending the ride to match the target kilojoule count, promoting the idea that “longer and easier” can be more beneficial by accumulating work with less fatigue.
Cons & Misinterpretations:
The “Harder is Better” Fallacy: A naive interpretation could lead athletes to believe they should always ride harder to accumulate kilojoules faster. This is counterproductive, as riding above LT1 generates disproportionately more fatigue, which compromises subsequent training.
Limited Scope: The direct relationship between kilojoules and adaptation/fatigue breaks down for high-intensity work. As Marinus states, “a thousand kilojoules of work over threshold is not even close to equal to a thousand kilojoules of work below LT1.”
Practical Complexity: Planning a ride based on a kilojoule target is less intuitive for most athletes than planning based on duration.
Kolie prefers using total training hours as his primary metric for quantifying volume, especially from a practical coaching perspective.
The Core Argument: For a given athlete, the intensity of their endurance rides tends to be relatively consistent. Therefore, increasing the hours spent training reliably increases the total work done (kilojoules). More importantly, focusing on hours has a crucial psychological and behavioral benefit: it discourages athletes from riding their endurance sessions too hard.
Practical Basis: It is the simplest metric for an athlete to plan and execute. The focus shifts from hitting a specific power or work target to simply spending time on the bike at an appropriate, sustainable intensity. Kolie finds that total hours has the strongest correlation with an athlete’s fatigue resistance.
Pros:
Simplicity: Easy for both coach and athlete to understand, prescribe, and track.
Promotes Correct Intensity: By focusing on duration, it implicitly encourages a lower, more sustainable intensity, which is critical for managing fatigue and allowing for high-quality intensive sessions. This helps solve the common problem of athletes riding their “easy” days too hard.
Accounts for All Pedaling: It values all time spent training, recognizing that even low-intensity pedaling contributes to the overall training load and fatigue resistance over time.
Cons & Misinterpretations:
Intensity Ambiguity: It doesn’t differentiate between a very easy hour and a moderately hard hour. This can be problematic if an athlete’s “endurance pace” is not well-calibrated.
The “Fuzzy Border” Issue: It raises the question of a minimum effective intensity. Does an hour at 50 watts “count” the same as an hour at 200 watts? Kolie addresses this by separating “recovery rides” from “endurance rides,” acknowledging the former has minimal adaptive stimulus.
Indirect Link to Physiology: The connection to cellular adaptation is less direct than with kilojoules. Its effectiveness relies on the assumption that intensity remains within an appropriate range.
Both speakers agree that TSS, while widely used, is a flawed and often misleading metric.
How it Works: TSS attempts to quantify the overall training load of a session by combining its duration and intensity. It is calculated using Normalized Power, duration, Intensity Factor (the ratio of Normalized Power to FTP), and is scaled such that one hour at FTP equals 100 TSS.
The Core Argument (Against TSS): It is an invented, “wizardly” algorithm based on a set of assumptions that do not hold true for all athletes. It creates a false equivalence between physiologically distinct types of training sessions and is centered on a single, often misunderstood metric (FTP).
Flaws:
Ignores LT1: TSS is centered entirely on FTP. However, an athlete’s LT1 can range from 50% to over 85% of their FTP. Two athletes with the same FTP could experience vastly different levels of fatigue from a ride at 70% of FTP, but their TSS would be identical.
False Equivalence: It suggests that 100 TSS from a long, slow ride is equivalent to 100 TSS from a short, high-intensity interval session. Physiologically, these are completely different stimuli with different fatigue and adaptation profiles.
Unreliable Performance Management: The associated Performance Management Chart (PMC) and its metrics like Training Stress Balance (TSB) are often poor predictors of an athlete’s actual freshness or fatigue state.
Promotes Wrong Behavior: An athlete trying to maintain a certain TSS with limited time may be tempted to replace low-intensity volume with high-intensity work, leading to burnout.
Metric
Pros
Cons
Kilojoules (kJ)
Objective, direct link to physiological stimulus (energy turnover).
Can be misinterpreted as “harder is better,” less intuitive, only applies well to sub-LT1 work.
Hours
Simple, practical, encourages appropriate (lower) endurance intensity.
Ambiguous about intensity, less direct physiological link.
TSS
Attempts to create a single, all-encompassing metric for training load.
Algorithmic, ignores LT1, creates false equivalences, promotes poor training decisions.
A deep understanding of the following concepts is essential to appreciate the nuance of the podcast’s discussion.
Definition: LT1 (First Lactate Threshold) is the exercise intensity above which blood lactate levels begin to rise consistently above baseline. It represents a metabolic transition point where the body begins to rely more heavily on carbohydrates for fuel and experiences a slight increase in autonomic stress.
Why It Matters: The speakers argue that LT1, not a percentage of FTP, is the true ceiling for sustainable endurance training. Training below LT1 allows for the accumulation of large volumes of work (kilojoules) with minimal fatigue, driving potent aerobic adaptations without compromising high-intensity sessions. Training above LT1 introduces a significant increase in fatigue.
Practical Estimation: Since lab testing is impractical for most, they suggest field methods to estimate LT1:
The Talk Test: The highest intensity at which you can hold a full, comfortable conversation without taking deep breaths mid-sentence.
Nasal Breathing: The highest intensity you can maintain while breathing exclusively through your nose.
RPE (Rate of Perceived Exertion): It should feel genuinely “easy” or “all day” pace.
Heart Rate: Your LT1 heart rate is generally more stable day-to-day than the corresponding power output.
The Concept: The nervous system recruits muscle fibers in an orderly fashion, from smallest (slow-twitch, Type I) to largest (fast-twitch, Type II), as the demand for force increases.
The Duration Effect: During very long endurance rides (e.g., 5+ hours), even at a low intensity, the initially recruited slow-twitch fibers begin to fatigue. To maintain the same power output, the body must start recruiting larger, fast-twitch fibers.
The Adaptation: This process is crucial because it allows for the endurance training of fast-twitch fibers, making them more fatigue-resistant and efficient at using fat for fuel—properties typically associated with slow-twitch fibers. This is a key reason why long, slow rides are irreplaceable for developing deep fatigue resistance.
Despite their different preferred metrics, Kolie and Marinus arrive at nearly identical coaching recommendations. Their discussion highlights that the metric is just a tool; the underlying principles are what matter.
RPE is Monarch: Both coaches agree that an athlete’s subjective perception of effort (RPE) is the ultimate guide. No metric can replace listening to your body.
Keep Easy Days Easy: The most common mistake among amateur athletes is riding their endurance sessions too hard (i.e., above LT1). This creates systemic fatigue that blunts the effectiveness of crucial high-intensity workouts.
Long and Easy Beats Short and Medium: For developing foundational endurance and fatigue resistance, accumulating more total work at a lower intensity is superior to doing less time at a medium “tempo” intensity.
Metrics are Tools, Not Masters: Whether using hours, kilojoules, or TSS, a coach must understand the metric’s limitations and use it as part of a holistic view that includes athlete feedback, performance data, and life stress.
Individualization is Everything: An athlete’s response to training volume, intensity, and recovery is highly individual. Tapering for a race, ramping up volume, and managing fatigue requires a personalized approach, not a one-size-fits-all formula derived from a chart.
In conclusion, the debate between quantifying training with hours versus kilojoules is more academic than practical. Both frameworks, when applied correctly, lead to the same outcome: a training structure that prioritizes a large volume of low-intensity work to build a deep aerobic base, while carefully managing fatigue to allow for targeted, high-quality intensity. The best metric is the one that best facilitates this outcome for a given coach and athlete.