Fitness & Sports

Training Load Calculator

Session-RPE load, monotony, strain and acute:chronic ratio — with the statistical fragility the wearables leave out.

Training Load Calculator

Results recalculate instantly on every keystroke. Nothing you type is transmitted.

This week, day by day
AU
AU
AU
AU
AU
AU
AU
Your recent baseline
AU/wk
Weekly training load
Session RPE multiplied by minutes, summed across seven days.
Monotony
Strain
Acute:chronic workload ratio
The standard deviation problem
The shape of your week
Hardest and easiest days
What the ACWR evidence actually says
How to lower monotony without training less
How to score a session honestly
What these numbers cannot see
Where to go next

What this result does not account for

  • Arbitrary units: a load figure is meaningful only against your own history, never against another athlete's.
  • Cannot distinguish 60 minutes at RPE 5 from 30 minutes at RPE 10, though both score 300 AU.
  • Session RPE is an internal load measure and does not represent mechanical load on tissue, which is what causes most injuries.
  • Monotony is undefined when every day is identical, and its verdict against 2.0 depends on an unstated standard deviation convention.
  • The ACWR sweet spot is contested and should not be treated as a prediction of injury risk.
Zero-Server Execution Updated 11 Aug 2026 Reviewed by Dr. Ayesha Rahman IEEE-754 Double Precision

In short: Seven daily loads of 420, 350, 480, 300, 450, 0 and 400 give a weekly load of 2,400 AU and a monotony of 2.28 — above Foster's 2.0 threshold, so strain reads 5,461. But compute the same week with a sample standard deviation instead of a population one and monotony falls to 2.11. Any week between 2.00 and 2.16 changes verdict on that choice alone, and no published paper says which one to use.

Formula

session load = RPE × duration in minutes
weekly load = Σ daily loads
monotony = mean daily load ÷ SD of daily load
strain = weekly load × monotony
ACWR = this week's load ÷ mean weekly load over the last four weeks

Foster's session-RPE method. Units are arbitrary (AU) and meaningful only against your own history. Monotony is reported on the population standard deviation (dividing the squared deviations by n), which is what Foster's worked examples reproduce; the sample standard deviation (dividing by n−1) is shown alongside it because it is always lower by a factor of √(7/6) = 1.0801 on a seven-day week, and that 7.4% gap straddles the 2.0 threshold for any week between 2.00 and 2.16.

Worked Example

  1. Score each session 1 to 10 for overall effort, about 30 minutes after finishing it.
  2. Multiply each score by the session duration in minutes to get that session's load in AU.
  3. Sum multiple sessions on the same day into one daily figure, and enter 0 for rest days.
  4. Enter all seven days — the rest days matter most, because they create the variation monotony measures.
  5. Enter your average weekly load over the previous four weeks for the acute:chronic ratio.
  6. Read monotony against 2.0, then read the standard deviation note to see how much tolerance that verdict carries.
  7. Treat the ACWR as a description of change, not a prediction of injury.

A week of 420, 350, 480, 300, 450, 0 and 400 AU totals 2,400 AU with a daily mean of 342.9. The population standard deviation is 150.7, so monotony is 2.28 and strain is 5,461. The sample standard deviation is 162.8, which would give a monotony of 2.11 instead. Against a 2,000 AU four-week average the ACWR is 1.20.

Strengths & Limits Of This Model

Where this engine is strong

  • Publishes the standard deviation ambiguity and computes both figures rather than picking one silently
  • Detects the zero-variance week and names it instead of printing infinity
  • Treats rest days as real zeros, which is where most implementations go wrong
  • Carries the published ACWR critique as a headline output, not a footnote
  • Shows how to lower monotony while holding weekly load constant

Where it stops

  • Depends entirely on honest and consistently calibrated RPE scoring
  • Blind to training type, mechanical loading and life stress
  • Fixed seven-day window with no exponentially weighted option
  • Thresholds are observational conventions, not physiological constants

Risk & accuracy notice. These metrics describe training patterns; they do not predict injury or illness for an individual, and the evidence behind the acute:chronic sweet spot in particular is contested. Do not use a favourable number as reassurance to train through pain, illness or unusual fatigue, and do not treat an unfavourable one as a diagnosis. Persistent fatigue, performance decline or recurrent illness warrant qualified medical and coaching assessment rather than an arithmetic adjustment.

Practical Use Cases

Catching a week that is flat rather than hard

Two athletes can log an identical weekly total while training completely differently: one alternates genuinely hard days with genuine rest, the other grinds the same moderate session daily. The totals cannot tell them apart, and monotony is the metric that can. Foster's original observation was that high monotony combined with high load coincided with illness and overtraining, so the flatness is the finding, independent of the total.

Auditing a build phase before it becomes a problem

During a heavy block, weekly load rises and the temptation is to judge the block by that number alone. Strain multiplies load by monotony, so it rises much faster when a big week is also an undifferentiated one. Tracking strain week to week across a build makes the compounding visible early, while there is still time to insert variation rather than an unplanned break.

Understanding what your wearable's readiness score is actually doing

Most platforms compute some version of these numbers and present the acute:chronic ratio with a colour-coded sweet spot. Reproducing the arithmetic yourself shows both how simple it is and how much interpretation is being layered on top. The critique output here gives the evidence for and against the sweet spot, which is not what the interface implies.

Planning a deload that keeps the shape of the week

A deload is usually implemented by cutting every session proportionally, which lowers load but leaves monotony untouched — the week is just as flat, only smaller. Modelling the reduction here shows what actually happens to monotony and strain, and makes the case for deloading by removing whole sessions rather than by shaving every one of them.

Methodology & Editorial Standards

Implements Foster's session-RPE family: session load = RPE × minutes, weekly load as the seven-day sum, monotony as the daily mean divided by the daily standard deviation, and strain as weekly load multiplied by monotony. The acute:chronic workload ratio compares the current week against a four-week mean supplied by the user, so the user controls whether that baseline is coupled or uncoupled. Monotony is reported on the POPULATION standard deviation, which reproduces Foster's worked examples, and the sample-based figure is displayed alongside it. This is not a stylistic choice: the two differ by exactly √(7/6) = 1.0801 on a seven-day week, so the sample figure is always 7.4% lower, and any week whose population monotony falls between 2.00 and 2.160 receives a different verdict against Foster's 2.0 threshold depending on a convention the source literature does not specify. The engine detects when the user's own week straddles that boundary and says so explicitly. A week of seven identical loads gives a standard deviation of exactly zero, making monotony undefined rather than large. That case is detected and named rather than printed as infinity or silently substituted, because an entirely unvaried week is the precise pattern the metric was designed to flag. The ACWR critique (Impellizzeri et al. 2020 on mathematical coupling, arbitrary windows and the distinction between training load and mechanical load; the elite soccer and pentathlon cohort in which the most days lost to injury fell inside the supposed sweet spot) is carried as a primary output rather than a disclaimer, because the metric is widely presented by commercial platforms as settled.

Computation runs in IEEE-754 double precision at full internal precision; rounding to two decimal places occurs strictly at the display layer, so no cumulative drift enters the result. All monetary outputs use accounting presentation — grouped thousands, two decimals, negatives in parentheses — so figures can be transcribed directly into a model or working paper. Division-by-zero and out-of-domain inputs return an em-dash rather than a misleading number.

This engine was reconciled against an independent reference implementation and hand-verified for the worked example above before release. Our full five-stage review process is published on the About Us page.

Dr. Ayesha Rahman Clinical & Life Sciences Lead · ApexConverter

Exercise physiology and sports-science metrics. Last reviewed: 11 August 2026.

Disclaimer. This calculator is provided for informational and modelling purposes only and does not constitute financial, tax, legal, medical, or engineering advice. Verify all figures with a qualified professional before acting on them.


Training Load Calculator — 20 Expert FAQs

20 analyst-written answers to the questions practitioners actually ask — optimised for voice and answer-engine retrieval.

What is session RPE and how do I score it?

Session RPE is a single rating from 1 to 10 for how hard the whole session felt, given about 30 minutes after it finishes. Multiplied by the duration in minutes it produces a training load in arbitrary units. The delay matters: rating during the session captures the hardest moment rather than the whole, and rating the next day captures soreness instead of effort. Rate the session as a whole, including warm-up and cool-down in the duration.

What is a good monotony score?

Below about 1.5 is generally considered healthy variation, and above 2.0 is the threshold Foster associated with problems when it coincided with high load. But treat 2.0 as a soft boundary for two reasons. First, it is a threshold derived from observational data in specific athlete populations, not a physiological constant. Second, the number itself depends on which standard deviation you compute, and the two conventions differ by 7.4% — enough to move a week from one side of 2.0 to the other.

Why does my monotony differ from the figure my software reports?

Most likely the standard deviation convention. Dividing by n and dividing by n−1 give results that differ by a factor of 1.0801 on a seven-day week, so the sample-based monotony is always 7.4% lower. Neither is wrong, and the source papers do not specify. Other candidates are whether rest days are included as zeros (they should be, and excluding them substantially deflates monotony) and whether the window is a calendar week or a rolling seven days.

Should rest days be entered as zero or left out?

Entered as zero, always. A rest day is a real data point and it is the single most influential one in the whole calculation, because a zero sits further from the mean than any training day can, which raises the standard deviation and lowers monotony. Omitting rest days is the most common way to get a badly wrong monotony figure, and it errs in the dangerous direction: it makes an unvaried week look more varied than it was.

What happens if every day of my week is identical?

The standard deviation is exactly zero and monotony is undefined rather than merely high, because the formula divides by it. This page detects that and says so instead of printing infinity or silently substituting a number. Treat it as the most severe possible reading: a week with no variation at all is the exact pattern the metric was built to catch. Adding one genuinely easy day or one full rest day makes every figure computable again.

What is the acute:chronic workload ratio meant to tell me?

It compares this week's load against your recent average, so it describes how much your training changed. The claim attached to it — that staying between 0.8 and 1.3 lowers injury risk — is much weaker than its ubiquity suggests. It is a description of change. Whether that change predicts injury is a separate question the evidence does not settle in its favour.

Is the ACWR sweet spot real?

The honest answer is that it is not well supported. Impellizzeri and colleagues showed the ratio is mathematically coupled, that the 7-day and 28-day windows have no physiological justification, and that a randomly generated chronic load was as associated with injury as the real one — concluding the framework should be dismissed. A study of elite soccer players and pentathletes found the most days lost to injury occurred inside the supposed sweet spot. What survives is modest: large spikes are plausibly risky and a very low chronic base is its own risk factor.

Should I use a coupled or uncoupled ACWR?

Uncoupled is the more defensible choice, meaning the current week is excluded from the chronic average it is compared against. When the acute load sits inside its own denominator the two are correlated by construction, which inflates the apparent relationship. This page takes your four-week average as an input rather than computing it, so you control which convention you feed it — but if you are assembling that average yourself, leave the current week out of it.

Can I compare my training load to another athlete's?

No, and this is the most common misuse. RPE is a subjective rating, calibrated differently by every person who gives one, so the units really are arbitrary. A 2,400 AU week means something only against your own previous weeks. The metrics that travel slightly better are monotony and ACWR, because they are ratios of an athlete's own numbers, but even those assume the underlying RPE scoring was internally consistent.

How do I lower monotony without cutting training?

Move load rather than remove it. Take load off your easiest training day and add it to your hardest: the weekly total is unchanged, the standard deviation rises, and monotony falls. A genuine rest day is the most effective single change available, because a zero is the furthest any day can sit from the mean. This is why hard-easy scheduling shows up in the arithmetic and not just in coaching folklore.

Does this work for strength training as well as endurance?

Yes. Session RPE was validated across a wide range of sports and it needs nothing but a rating and a duration, which is its main practical advantage over heart-rate-based load measures that struggle with intermittent resistance work. The caveat is that a 300 AU resistance session and a 300 AU run impose quite different mechanical stresses, and the arbitrary unit cannot see that difference.

Is a high strain score dangerous?

Strain is weekly load multiplied by monotony, so it rises fastest when a big week is also an undifferentiated one, which is the combination Foster associated with illness and overtraining. Early work reported that a large share of illness and injury episodes followed spikes in individual strain in the preceding days. That is an observed association in particular athlete groups rather than a causal law, so use strain as a prompt to look at the shape of your week, not as a diagnosis.

Is this training load calculator free to use?

Yes. It is free, requires no account, and has no usage limits. ApexConverter is funded by contextual advertising, never by selling user data.

Is my data sent to a server?

No. The engine runs as Vanilla JavaScript inside your browser under our Zero-Server Client-Side Execution model. Your figures are computed locally and are never transmitted, logged, or stored.

How accurate is this calculator?

It applies the standard closed-form formula in IEEE-754 double precision, rounding only at the display layer. The engine is reconciled against an independent reference implementation before release.

Does it work on mobile?

Yes. The interface is mobile-first with numeric keypad hints and is tested down to a 320-pixel viewport with no horizontal scrolling.

Can I use it offline?

Largely, yes. Because computation is client-side, the page continues to calculate without a network connection once it has loaded.

Which currency does it use?

Amounts display in US$ accounting format, but the underlying mathematics is currency-agnostic. The result is identical in any currency, so simply read the figures in your own.

Why does a result show an em-dash?

An em-dash indicates the calculation is not defined for the inputs given — typically a division by zero or a value outside the valid domain. We show a dash rather than a misleading number.

How do I report an error?

Email apexconverter.praxiscalc@gmail.com with the tool URL, your exact inputs, the output received and the output you expected. Verified mathematical errors are patched within 72 hours.

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