Performance stays exercise-specific
Load and repetitions are interpreted within the same movement instead of being compared across mechanically different exercises.
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A lifting log built around useful questions
Know what your training data actually means.
Lorenzo's Lifting Ledger connects exercise performance to a transparent model of muscular involvement. It records the work you did, shows the assumptions behind every interpretation, and avoids impressive-looking statistics that cannot guide a training decision.
- 138catalogued exercises
- 40detailed muscle entities
- 13clear UI muscle groups
How it works
A record, a model, and a clear boundary between them.
The app does not turn every input into a single score. It preserves the underlying performance data, then applies documented models only where they add a defensible interpretation.
- 01
Log the set
Record the exercise, load, repetitions, set type and proximity to failure. Warm-ups remain distinct from working sets.
- 02
Resolve the exercise
Workout history and the global catalogue use the same exercise identity, avoiding silent duplication and broken trends.
- 03
Map relevant muscles
An authored exercise-to-muscle layer identifies worthwhile hypertrophic relevance for a sufficiently hard set performed as intended.
- 04
Read the trend
Session history and personal statistics emphasize consistency, hard sets and exercise-specific progression rather than decorative totals.
What makes the approach different
More data is not automatically more insight.
Many lifting dashboards can total whatever is easy to multiply. This project starts with the decision the metric is meant to support, then asks whether the calculation survives differences in effort, exercise mechanics, technique and equipment.
Every metric should help a person evaluate training, monitor progression, understand relevant exposure, manage fatigue, or make a better programming decision.
Tonnage is not called volume
Weight × repetitions measures external mass moved repeatedly. It is not treated as hypertrophic stimulus, workload quality or progression.
Muscle detail has two levels
Forty anatomical entities support modelling while thirteen larger groups keep session history readable. Users can switch between both views.
Uncertainty remains visible
Published evidence, calculated estimates, authored models and product decisions are labelled as different kinds of knowledge.
What the app pays attention to
Fewer metrics, each with a job.
SignalPurposeBoundary
Working setsA practical starting point for relevant training exposure.A set still depends on effort, execution and exercise context.
Load + repetitionsTracks performance within an exercise over time.Load and repetitions are not combined into cross-exercise tonnage.
RIRPreserves proximity-to-failure context.Self-reported and interpreted with appropriate caution.
Muscle relevanceConnects a hard set to muscles worth considering.An authored relevance model, not measured force or growth.
Estimated 1RM rangeOffers exercise-specific performance context.An equation-based estimate, never a measured maximum.
Scientific foundations
The reasoning is part of the product.
These documents expose the evidence standard, data model, authored assumptions, limitations and product decisions behind the interface.
How studies are judged
Evidence is assessed claim by claim, with attention to directness, trained populations, comparators, adherence and uncertainty.
Study-selection protocolMuscle-group taxonomy
Forty muscle entities balance anatomical resolution with what can be modelled and defended for hypertrophy applications.
Muscle taxonomyMovement-pattern coefficients
Independent contribution ratings organize exercises biomechanically without pretending to be measured joint torque or stimulus.
Movement to muscle function
A 40 × 40 matrix records anatomical capability as a functional prior, while leaving exercise context to downstream layers.
Functional-anatomy modelExercise to muscle composition
Ordinary matrix composition provides a traceable bridge from exercises through patterns to muscles without becoming a growth score.
Composition methodHypertrophic relevance
An authored 138 × 40 layer filters anatomical possibility through exercise context for one sufficiently hard, competently performed set.
Relevance methodCurrent mapping limitations
The project documents missing context—including force-sharing, muscle length and resistance profiles—rather than hiding it.
Current limitationsWhy tonnage is excluded
External mass × repetitions is rejected as a hypertrophy, progression, workload or personal-statistics metric.
Tonnage decisionDesign rules
Transparent calculations, defensible metrics, owner-scoped data and visible limitations govern what reaches the interface.
Design rulesEstimated one-rep max
Brzycki and Epley estimates form an exercise-specific range, with invalid domains and practical limitations made explicit.
Brzycki
weight × 36 ÷ (37 − reps)Epleyweight × (1 + reps ÷ 30)Literature
Document
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Training log
Session history
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Results include sessions containing exercises whose names match your search.
My data
Understand what is changing in your training.
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Training overview
Weighted sets
Muscle exposure
Modelled from working sets and exercise-specific muscle relevance.
Each working set (reported RIR 0–3) contributes between 0 and 1 weighted set to a muscle or group based on the exercise's authored hypertrophic-relevance mapping. Warm-ups and 4+ RIR sets are excluded by this product rule; that does not mean 4+ RIR produces literally zero hypertrophy. This estimates worthwhile muscular involvement, not growth, force, or “effective sets.”
Over time
What trained it
Exercise progression
Each qualifying set preserves Brzycki and Epley estimates from completed reps plus both estimates using reps + reported RIR. This four-value model spread is not a confidence interval or a measured max. 4+ RIR sets are excluded. Relative mode divides each value independently by body weight on its workout date.
Last 4 weeks vs previous 4 weeks
Recent change
Percentage change uses the previous 4 weeks as the baseline. “New this period” is shown when that baseline is zero. Increases are green and decreases are red, with a signed text label so color is never the only cue. Changes are descriptive and are not recommendations about how much a muscle should be trained.
How these numbers are calculated
Sessions counts dated workout records in the selected period. Working sets includes non-warm-up sets with reported RIR 0–3; warm-ups and 4+ RIR sets remain in history but are excluded. Exercises trained counts exact catalogue exercise names with a working set.
Modelled muscle exposure adds the authored exercise–muscle relevance coefficient for every working set. A UI group's value uses the highest child-muscle coefficient for each set, preventing groups with more anatomical entities from being inflated.
Exercise progression selects one representative RIR 0–3 working set per equipment identity and session using completed performance only: higher lower observed Brzycki/Epley estimate, then higher upper estimate, load, and reps. It then plots four inspectable values: observed Brzycki and Epley, and both formulas using completed reps + reported RIR.
Relative estimated 1RM divides all four calculated values independently by measured or interpolated body weight on the workout date. It is shown in × BW, is unavailable outside body-weight coverage, and never changes stored absolute estimates.
Account data
Settings
Body Weight Data
Import measured scale weights in kilograms. Missing days between measurements are calculated by linear interpolation and are never stored as observations.
- Measurements
- 0
- Measured coverage
- No data
- Last imported
- Never imported
Delete body-weight data
Removes only your body-weight measurements and their import provenance.
My Stuff
My Presets
Reusable exercise pools for starting future workouts. Exercises are kept alphabetically, not in a prescribed order.
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No presets yet
Create a reusable pool from the exercise catalogue or one of your previous sessions.
Scientific foundations
Literature
A transparent record of the evidence, authored models, assumptions, and limits behind the app.
Our standard
Useful claims require more than a citation
The app separates published evidence, calculated estimates, authored product models, and product decisions. A value is not presented as scientific fact merely because it is numerical.
Evaluation protocol
01
How studies are judged
Outcome evidence is assessed claim by claim. The protocol prioritizes direct measurement, trained populations, appropriate comparators, adherence, uncertainty, and whether the study actually isolates the variable being discussed.
- Evidence is weighted rather than counted as votes.
- Non-significance is not treated as proof of equivalence.
- Study quality is not collapsed into a magic numerical score.
Authored taxonomy
02
Muscle-group taxonomy
The hypertrophy model begins with 40 muscle entities chosen at the highest anatomical resolution that is both useful and defensible. Muscles are separated when joint function, joint-crossing anatomy, longitudinal evidence, or exercise modelling makes the distinction meaningful.
- Distinct hypertrophic responses justify added granularity.
- Regional growth alone does not automatically create a new muscle entity.
- The taxonomy remains revisable as stronger evidence emerges.
Authored model
03
Movement-pattern coefficients
The movement matrix organizes exercises across biomechanical dimensions. Its 0–1 coefficients are expert-authored contribution ratings—not percentages, probabilities, measured joint torques, or direct estimates of muscular stimulus.
- Each movement pattern is an independent dimension.
- Row totals have no scientific meaning.
- Small coefficient differences must not be presented as exact.
Functional-anatomy model
04
Movement pattern to muscle function
This 40 × 40 matrix records which canonical muscles are anatomically capable of materially producing each movement pattern. Its coefficients are functional priors, not force shares or estimates of hypertrophic stimulus.
- Coefficients are independent and must not be row-normalized.
- Joint angle and exercise context can change force-sharing between synergists.
- Exercise-specific modelling remains a separate downstream layer.
Derived functional model
05
Exercise to muscle composition
The exercise × muscle matrix is the transparent product of the exercise-to-pattern and pattern-to-muscle matrices. It traces every exercise to every canonical muscle without pretending the result is a measured hypertrophy score.
- Each cell is a sum of movement-path products.
- Raw scores are never normalized or capped and may exceed one.
- The model preserves both upstream version identities.
Exercise-specific model
06
Hypertrophic relevance by exercise
This authored 138 × 40 layer filters anatomical possibility through exercise context. It assigns worthwhile hypertrophic relevance under the assumption of one sufficiently hard set, intended technique, and a normal/full useful range of motion.
- Values range from 0 to 1 in five documented relevance levels.
- Coefficients are independent, not percentages, and are never normalized.
- Effort, set count, progression, execution, and fatigue remain separate downstream variables.
Model limitations
07
Current mapping limitations
The present exercise → movement pattern → muscle function pipeline is a useful first-pass anatomical model, but it is not yet calibrated as a quantitative model of hypertrophic stimulus.
- Joint actions do not determine force-sharing between synergists.
- Muscle length, resistance profiles, and biarticular interference remain incomplete.
- Longitudinal exercise-specific hypertrophy evidence is not yet a calibration layer.
Product decision
08
Why tonnage is excluded
External mass multiplied by repetitions is not used as a hypertrophy, workload, progression, or personal-statistics metric. It ignores effort, biomechanics, resistance profiles, technique, muscle contribution, and differences between exercises.
- Load and repetitions remain useful as separate performance data.
- Hard working sets are a more relevant starting point for training exposure.
- Decorative metrics are omitted when they cannot guide a decision.
Product standard
09
Design rules
The app's product standard requires transparent calculations, defensible metrics, owner-scoped data, and interfaces that expose the meaning and limitations of modelled results.
- No magic math or unexplained scores.
- Scientific uncertainty must remain visible.
- Fewer meaningful metrics are preferred to decorative statistics.
Calculated estimate
10
Estimated one-rep max
The app reports a range from the Brzycki and Epley equations using the logged load and repetitions. This is an estimate of exercise-specific performance, not a direct measurement of maximal strength.
Brzycki weight × 36 ÷ (37 − reps)
Epley weight × (1 + reps ÷ 30)
The range is omitted when the inputs are incomplete or outside Brzycki’s 1–36 repetition domain. Equipment, technique, fatigue, and individual response can all affect its accuracy.
Literature
Document
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