Institutional Ownership Calculator
The formula
What the institutional ownership tells you
Tape-meat body composition for institutional ownership: the US Navy circumference method — the field standard gyms and physicals actually use — with fat and lean mass in kilograms.
Calculation: US Navy: BF% = 86.010·log₁₀(waist−neck) − 70.041·log₁₀(height) + 36.76 (men); +1.08·…−84.52 women. Enter your inputs and every substitution is shown below the result.
Reading the result
Track the trend monthly under the same conditions; ±2 points of tape error is normal.
Limitations and best practice
Hydration and recent meals shift centimetres — measure fasted, morning, same spot.
This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Frequently asked questions
How accurate is tape-meat body fat?
Which formula is best for me?
Source & methodology
US Navy Body Composition Program method.
Institutional Ownership: the number, the method and the caveats
You are here because you need a specific number, and you want to see how it was produced before you rely on it. Institutional Ownership is the kind of number people argue about from memory. This page ends that argument with arithmetic you can watch happen: enter your figures, get Body fat in real time, and see every substitution printed below.
Every investing metric in Investing & Markets is a bet about the future priced in today’s language. Institutional Ownership translates that bet: US Navy: BF% = 86.010·log₁₀(waist−neck) − 70.041·log₁₀(height) + 36.76 (men); +1.08·…−84.52 women. The translation is mechanical and reliable; the judgement is deciding whether the inputs you feed it are honest forecasts or wishful numbers.
The useful way to hold Institutional Ownership is as a ratio with a story attached: US Navy: BF% = 86.010·log₁₀(waist−neck) − 70.041·log₁₀(height) + 36.76 (men); +1.08·…−84.52 women says how the parts combine, and the 31.0% figure above is simply that sentence written in numbers. Everything that follows unpacks one clause of the sentence at a time.
What makes a calculation page trustworthy is that its copy is derived from its own arithmetic: here body fat = 31.0%, recomputed every time a field changes, and the sensitivity section ranks the inputs that move the result — Waist / hip circumference first, at ≈21.1 % of the headline for a ten-percent error either way in that input. Work down the page and by the end you could re-derive the result in a spreadsheet from memory.
Inside the Institutional Ownership calculation: formula and every input
The engine implements a single stated model: US Navy: BF% = 86.010·log₁₀(waist−neck) − 70.041·log₁₀(height) + 36.76 (men); +1.08·…−84.52 women. There is no hidden coefficient and no login-gated precision — the formula printed is the formula executed, which is also what makes the result citable.
Weight (kg) (example value 120) is a measured number in the unit named by the label; the formula does not convert for you. Stay in one unit system top to bottom; conversion rounding is where small errors enter.
Height (cm) (example value 177) is a measured number in the unit named by the label; the formula does not convert for you. Small measurement errors grow here: a length that is squared or cubed in the model magnifies a one-percent slip into a three-percent answer.
Neck / waist circumference (cm) (example value 35) is a measured number in the unit named by the label; the formula does not convert for you. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
Waist / hip circumference (cm) (example value 93) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
Sex (example value Male) is a selector, not a measurement: the engine follows a different route for each option. This input feeds the formula directly; a defensible figure here is worth more than false precision anywhere else.
What each “Sex” choice actually does: Male → 31.0% · Female → 2.0% — each value above is the model’s own output for that option, so the choice can be made on evidence rather than on what a category feels like it means.
Institutional Ownership on the example inputs: the complete substitution
Abstract formulas are hard to trust, so here is the complete substitution using the default inputs, written the way an examiner would want it. Starting figures: Weight (kg), Height (cm), Neck / waist circumference (cm), Waist / hip circumference (cm). The endpoint of that chain is 31.0% of body fat, sitting in the “Above the healthy band — the trend matters more than today’s point” band.
- Circumference difference waist−neck = 58.0 cm
- Navy formula (log₁₀ terms) → 31.0 %
- Fat mass = 120.0 × 31.0 % = 37.2 kg
Fat mass — 37.2 kg. This is the number experienced users quote when the headline alone would mislead.
Lean mass — 82.8 kg. If your inputs change, this is usually the first companion figure to move.
BMI — 38.3. If your inputs change, this is usually the first companion figure to move.
Keep this worked example open while you enter your own figures: matching the shape of the substitution is the fastest way to spot a unit or convention mistake.
How far Institutional Ownership can reasonably move
Single-point answers invite false confidence, so the same engine was run across a band around the input that matters most here. Each row is a full recalculation of Institutional Ownership, not an interpolation.
| Waist / hip circumference setting | Body fat |
|---|---|
| 80 % of the example value | 16.5% |
| 90 % of the example value | 24.5% |
| example inputs | 31.0% |
| 110 % of the example value | 36.5% |
| 120 % of the example value | 41.4% |
That spread — 16.5% at the low end, 41.4% at the high — is what a careful read of Institutional Ownership looks like: a band you can state out loud, with the reason for each edge. Reproduce it in the form above by editing Waist / hip circumference alone; the substitution steps will follow your figures exactly.
Sensitivity: which inputs change Institutional Ownership the most
Generic advice says “it depends on your inputs”; this section says exactly how much. Each field was perturbed by ±10 % in isolation and the body fat recomputed, so the ranking below is measured rather than asserted. For Institutional Ownership the ranking is measured rather than guessed, and it belongs to these defaults: shift the starting point and the order can shift with it.
- Waist / hip circumference: ten percent up gives 36.5%, ten percent down gives 24.5% — ≈21.1 % of the body fat either way
- Height: ten percent up gives 28.1%, ten percent down gives 34.2% — ≈10.3 % of the body fat either way
- Nudge Neck / waist circumference by ±10 % and Institutional Ownership lands between 28.7% and 33.2% (sensitivity ≈7.5 %)
Waist / hip circumference is the dominant lever here: ten percent either way moves the result by about 21.1 %, from 24.5% to 36.5%. Spend your verification effort there first, and treat Height as the second priority (≈10.3 % per ten percent). Everything below that can stay approximate, which is a licence to stop chasing data that would not change the decision.
Interpreting the Institutional Ownership verdict
A number on its own is trivia; a number against a range is intelligence. The default case lands in the “Above the healthy band — the trend matters more than today’s point” band, and bands are deliberately wide because real situations overlap. The sensitivity list shows how far a modest input error moves you along that ladder: for Waist / hip circumference, ±10 % covers 24.5% to 36.5%. The practical reading rules: ask what would have to change to move you one band. If the answer is “a small, plausible input difference”, the band itself is not the finding — the uncertainty is.
Set Institutional Ownership beside its neighbours and it usually plays a specific role: this figure establishes the size of something, and the neighbouring tools establish whether that size is acceptable. Skipping the first step makes the second one arbitrary.
Why Institutional Ownership results go wrong — and how to prevent it
Hydration and recent meals shift centimetres — measure fasted, morning, same spot. This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Reusing an Institutional Ownership from last season. In Investing & Markets the inputs are the thing that drifts; the formula does not.
This model has its own particular trap: Waist / hip circumference carries ≈21.1 % of the response per ten percent of error, so effort spent elsewhere is largely decorative.
Another is comparing across contexts: an Institutional Ownership computed with one set of conventions should never be lined up against a figure computed with another. Check the inputs behind both before ranking them.
Institutional Ownership verification checklist
- Verify each input against its original source document, not memory.
- State, in one sentence, which convention each rate, unit or period in the form is using.
- Re-verify Waist / hip circumference against a source document — ten percent of error there moves the headline by ≈21.1 %.
- Compare the result with one independent method or published benchmark.
- Recompute once with the dominant input deliberately set low, and see whether the decision changes.
- Repeat the ±10 % test on the inputs that matter and see whether the decision — not just the number — still holds.
Step by step: getting a trustworthy Institutional Ownership out of this tool
The tool does the arithmetic; the routine below is what makes the arithmetic trustworthy. Five steps, a minute total — and afterwards you will know not just what the number is, but which assumption produced it.
Step 1 — one document, one pass. Open the statement, label or record that holds weight (kg), height (cm), neck / waist circumference (cm), waist / hip circumference (cm), sex and transcribe straight into the form. Transcription from a single source removes the mismatch errors that appear when figures are assembled from three different places. If the verdict changes with a ten-percent nudge, report the nudge alongside the number.
Step 2 — start from the case you can defend. Enter pessimistic-but-honest figures first; the optimistic run is a ceiling, not a plan. A conclusion that holds on conservative inputs holds everywhere. Suspiciously tidy answers deserve the same scrutiny as untidy ones.
Step 3 — play with the inputs on purpose. Ten percent up, ten percent down, one field at a time. Within a few minutes you will know this calculation better than most people who quote its results. Repeating a calculation with the same numbers proves only that the arithmetic is reliable — which is worth knowing.
Step 4 — run the scenario trio. Expected, pessimistic, optimistic. The gap between pessimistic and expected is the number to plan against; the optimistic result is useful only as a ceiling. Write the figure down before moving on; memory flatters whichever number you saw last.
Step 5 — schedule the next run. In Investing & Markets the inputs drift quietly. Put a reminder a quarter out, re-enter the same fields, and read the change rather than the level. A result worth acting on survives having its two shakiest inputs halved.
The real-world conversations Institutional Ownership settles
Numbers earn their keep in conversations, and Institutional Ownership is no exception. In Investing & Markets discussions around Stocks & Equities, this figure decides whether an option stays on the table: it converts an opinionated debate into an arithmetic one, and arithmetic is the only referee both sides accept. The point of transparency is that a reader can disagree with a specific line rather than with you.
The companion outputs — Fat mass and Lean mass — exist for the second stage of that screen: the headline sizes the prize, while the companions describe the trade-off you are accepting to get it. Most surprises in numbers are surprises about inputs, not about formulas.
Re-running this page costs nothing and catches the slow drifts that never announce themselves. In Investing & Markets the expensive mistakes are rarely sudden; they are small changes in Weight (kg) that nobody recalculated for a year. Compare the calculation to the rough guess you arrived with, and ask why they differ.
The questions that come up most about Institutional Ownership
What affects Institutional Ownership the most?
Institutional Ownership is driven above all by Waist / hip circumference. Perturbing only that field by ten percent either way produced 36.5% against the 31.0% baseline — a ≈21.1 % response, while Height manages ≈10.3 %.
Which matters more: height or waist / hip circumference?
By the numbers, Waist / hip circumference: a ten-percent change in it moves the result ≈21.1 %, versus ≈10.3 % for Height. Spend your measuring effort in that order — precision on the weaker lever buys almost nothing.
How much does the “Sex” choice change the answer?
Across every option this tool offers, the body fat ranges from 2.0% (Female) to 31.0% (Male) with all other inputs held at their example values. The per-option table is printed in the inputs section — this is a decision that genuinely moves the number, not a cosmetic dropdown.
What is a typical Institutional Ownership?
With the example inputs on this page the result is 31.0%, labelled “Above the healthy band — the trend matters more than today’s point”. “Typical” varies with situation, which is why every result here ships with its verdict band and its sensitivity list: they turn one figure into a range you can locate yourself inside.
How do I calculate Institutional Ownership by hand?
Three steps: write the formula (US Navy: BF% = 86.010·log₁₀(waist−neck) − 70.041·log₁₀(height) + 36.76 (men); +1.08·…−84.52 women), insert your values, and simplify. The worked example section shows exactly that chain for the default inputs; replicating it once on paper teaches you more about Institutional Ownership than any number of calculator runs.
What happens to Institutional Ownership if waist / hip circumference changes by 10 %?
On these inputs a ±10 % change in waist / hip circumference takes Institutional Ownership to 36.5% against the 31.0% example — around 21.1 % of the headline. The same test on every other field is in the sensitivity section.
Is the Institutional Ownership result of 31.0% good or bad?
The tool answers “what”, not “whether”: 31.0% carries the verdict “Above the healthy band — the trend matters more than today’s point” for the default case, which is a starting interpretation rather than a verdict on your situation. Re-run with your own figures and compare the bands.
What is a good Institutional Ownership?
Markets give away free benchmarks: anything that beats the relevant index after costs earns the label "good", and this tool’s verdict band translates Institutional Ownership against experience ranges. Before that, check that the inputs are realistic — a great Institutional Ownership built on optimistic inputs is just a nicer-looking error.
How often should I check Institutional Ownership?
Whenever an input changes by more than a few percent, and otherwise on the rhythm of your decisions — before a purchase, a review, a renewal. Institutional Ownership is a snapshot, not a subscription; stale snapshots are the most common silent error in Investing & Markets planning.
How accurate is tape-meat body fat?
±3 points versus DEXA in lean subjects, better in groups; the trend is reliable even when the level is soft.
Which formula is best for me?
Consistency beats formula choice — the same method, same conditions, every time.
Do I need an account to use this Institutional Ownership calculator?
No, and no data leaves the tab. The engine runs in the page itself, which is why the worked example, the bands and the sensitivity ranking all respond instantly to your keystrokes.
Glossary for Institutional Ownership
- Fat mass
- 37.2 kg in the default Institutional Ownership calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Lean mass
- 82.8 kg for the Institutional Ownership worked case; it sits next to the main result because it carries context the headline leaves out.
- BMI
- 38.3 in the default Institutional Ownership calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Body fat
- The primary output of this tool: 31.0% for the default inputs, computed exactly from US Navy: BF% = 86.010·log₁₀(waist−neck) − 70.041·log₁₀(height) + 36.76 (men); +1.08·…−84.52 women.
- Worked example
- Every substitution in order, from the example inputs to 31.0% — the audit trail for the headline figure.
- Verdict band
- A named zone on the range of plausible results; the example inputs land in “Above the healthy band — the trend matters more than today’s point”, and your inputs may not.
- Sensitivity (±10 %)
- How far the Institutional Ownership moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Method, sources and what this tool cannot tell you
The model is published arithmetic implemented exactly as written: US Navy: BF% = 86.010·log₁₀(waist−neck) − 70.041·log₁₀(height) + 36.76 (men); +1.08·…−84.52 women. Source of record: US Navy Body Composition Program method. Any other implementation of this model should return the same figure for the same inputs. If one does not, the difference is in its assumptions — which this page prints and that one probably does not.
This page is an estimation aid, not professional advice. The design rule is simple: two people entering identical figures must get identical answers. The design constraint was set by usefulness rather than by comfort: an estimate you can interrogate beats an answer you have to trust.
Turning this Institutional Ownership into a decision
Save the number together with its inputs and today’s date; an Institutional Ownership figure without context is unusable the moment an input changes.
Whoever you are (people who need one trustworthy number before their next decision), the next step is the same: replace the example inputs with your own, note the date, and re-run whenever a figure moves. In Investing & Markets, a dated figure is a trend, and an undated one is a rumour.