EV/EBIT Calculator
The formula
What the ev/ebit tells you
Whether ev/ebit is worth playing at all: the expected value of one attempt, the break-even win rate, and the edge you hold over it.
Calculation: EV = p×gain − (1−p)×loss; break-even win rate = loss ÷ (gain + loss). Enter your inputs and every substitution is shown below the result.
Reading the result
A positive EV with ruinous variance is still a trap — the per-100-attempts line is the budget reality check.
Limitations and best practice
Probability estimates are where people fool themselves; anchor win % to observed frequency, not hope.
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
Is positive EV enough?
How many samples to trust the number?
Source & methodology
Expected value; foundational decision theory.
EV/EBIT explained: formula, worked example and honest limits
This page was written for one job: turning your own inputs into a defensible figure, with the working shown. There are plenty of pages about EV/EBIT; almost none of them show the working. This one does: the formula, every input, a fully computed example, and a sensitivity analysis calculated live from this engine’s own defaults.
It helps to separate the mechanics from the interpretation. The mechanics are fixed: EV = p×gain − (1−p)×loss; break-even win rate = loss ÷ (gain + loss). Anyone, anywhere, entering the same inputs will get the same $86.60. The interpretation is where experience counts: whether $86.60 is good, normal or alarming depends on comparisons covered below, and that is the part most articles about EV/EBIT never explain.
There is a habit that separates people who get useful answers from Stocks & Equities numbers: they decide what question the figure answers before they compute it. EV/EBIT answers “Expected value per attempt?” — and nothing else, which is precisely why the result is trustworthy.
What makes a calculation page trustworthy is that its copy is derived from its own arithmetic: here expected value per attempt = $86.60, recomputed every time a field changes, and the sensitivity section ranks the inputs that move the result — Win probability first, at ≈15.7 % of the headline for a ten-percent error either way in that input — which is the thread the rest of this guide pulls on.
Every input in EV/EBIT, and what it does to the answer
One formula, published in full: EV = p×gain − (1−p)×loss; break-even win rate = loss ÷ (gain + loss). Anything you can read, you can reproduce in a spreadsheet, and that reproducibility is the whole point of printing it rather than hiding it behind a button.
Win probability (%) (example value 40) is a plain measured quantity — the unit shown in the label is the unit the formula expects. Enter it exactly as your document states it — quoted annual, monthly and effective rates are three different numbers.
Gain per win ($) (example value 290) is a quantity rather than a category — precision here is cheap and worth having. If you are unsure what belongs in this field, look at the printed steps: the substitution shows exactly how the engine uses it.
Loss per defeat ($) (example value 49) is a quantity rather than a category — precision here is cheap and worth having. This input feeds the formula directly; a defensible figure here is worth more than false precision anywhere else.
EV/EBIT with real numbers, step by step
Abstract formulas are hard to trust, so here is the complete substitution using the default inputs, written the way an examiner would want it. Starting figures: Win probability (%), Gain per win ($), Loss per defeat ($). The endpoint of that chain is $86.60 of expected value per attempt, sitting in the “Positive but thin — costs and variance can erase the edge” band.
- EV = 0.40 × $290.00 − 0.60 × $49.00 = $86.60
- Break-even p = 49 ÷ (290 + 49) = 14.45 %
Break-even win rate — 14.45%. This is the number experienced users quote when the headline alone would mislead.
Edge over break-even — 25.55 pts. This is the number experienced users quote when the headline alone would mislead.
EV per 100 attempts — $8,660. Read next to the headline, this is the figure that shows the shape of the trade-off.
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.
The EV/EBIT corridor: your result across a realistic band
Read this table as the practical error bar on the headline figure. It answers the question people actually have after seeing a single number: how wrong could this be if the main input is off by ten or twenty percent?
| Win probability setting | Expected value per attempt |
|---|---|
| 80 % of the example value | $59.48 |
| 90 % of the example value | $73.04 |
| example inputs | $86.60 |
| 110 % of the example value | $100.16 |
| 120 % of the example value | $113.72 |
Across 5 settings of Win probability, the result spans $59.48 to $113.72. A decision that only works at one row of this table is a decision waiting for an input to move. Reproduce it in the form above by editing Win probability alone; the substitution steps will follow your figures exactly.
The levers behind EV/EBIT, ranked by effect
Nobody should have to guess which input matters. The engine was re-run once per field below — ten percent up, ten percent down, everything else frozen — and the results ranked by how far they moved the expected value per attempt. For EV/EBIT the ranking is measured rather than guessed, and it belongs to these defaults: shift the starting point and the order can shift with it.
- Win probability +10 % → EV/EBIT = $100.16 · −10 % → $73.04 (the expected value per attempt shifts ≈15.7 %)
- Nudge Gain per win by ±10 % and EV/EBIT lands between $75.00 and $98.20 (sensitivity ≈13.4 %)
- Loss per defeat +10 % → EV/EBIT = $83.66 · −10 % → $89.54 (the expected value per attempt shifts ≈3.4 %)
Win probability is the dominant lever here: ten percent either way moves the result by about 15.7 %, from $73.04 to $100.16. Spend your verification effort there first, and treat Gain per win as the second priority (≈13.4 % per ten percent). Everything below that can stay approximate, which is a licence to stop chasing data that would not change the decision.
Is your EV/EBIT good — reading the result bands
The value above only becomes useful the moment you know what it is being measured against. The verdict attached to the default calculation — “Positive but thin — costs and variance can erase the edge” — is one band on a ladder your own figure may sit somewhere else on. The sensitivity list shows how far a modest input error moves you along that ladder: for Win probability, ±10 % covers $73.04 to $100.16. 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.
Compared with charting sites, this page explains the formula rather than hiding it — the goal is a number you can defend in a meeting, not just display.
Why EV/EBIT results go wrong — and how to prevent it
Probability estimates are where people fool themselves; anchor win % to observed frequency, not hope. 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.
Treating the example inputs as a recommendation. They are a demonstration of the mechanism — a decision-grade EV/EBIT begins the moment your own figures replace them.
This model has its own particular trap: Win probability carries ≈15.7 % of the response per ten percent of error, so effort spent elsewhere is largely decorative.
A final trap is presentation: reporting EV/EBIT to more decimal places than the inputs support. The tool prints what the arithmetic gives; you should quote only what your least precise field justifies.
Six things to confirm before acting on EV/EBIT
- Re-run the calculation with a ±10 % stress on the two most sensitive inputs.
- Confirm the example values are plausible for your own situation before replacing them.
- Re-verify Win probability against a source document — ten percent of error there moves the headline by ≈15.7 %.
- Recompute once with the dominant input deliberately set low, and see whether the decision changes.
- Check that every field the formula needs actually arrived from a document, not from recollection.
- Repeat the ±10 % test on the inputs that matter and see whether the decision — not just the number — still holds.
A routine for decision-grade EV/EBIT
Using a calculator well sounds trivial, but the difference between a decision-grade figure and a throwaway one lives entirely in the routine. The routine for EV/EBIT takes a minute to learn and applies to every input above.
Step 1 — start from evidence. The inputs behind EV/EBIT are win probability (%), gain per win ($), loss per defeat ($). Write them down with their source next to each one; if you cannot name where a figure came from, that is the input most likely to be wrong. Half of a good calculation is knowing which input you would hate to be wrong about.
Step 2 — compute the version you would not be embarrassed by. If a stranger audited your inputs, which set would you hand over? Enter that set, read the result, and treat everything else as upside. Precision is a promise you make about your data; make sure your inputs can keep it.
Step 3 — measure what deserves measuring. The ranked levers above tell you where a careful figure pays off. Everything else can stay approximate, which is a licence to stop hunting for data you do not need. Comparisons are cheap: run the identical calculation for the alternative and look at the gap.
Step 4 — ask what would have to be true. Take the result you want and work backwards through the inputs until you find the assumption that produces it; then decide honestly whether that assumption is defensible. A calculation you cannot explain to somebody else is a calculation you cannot defend.
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. Most surprises in numbers are surprises about inputs, not about formulas.
Who actually uses EV/EBIT — and when
Most Stocks & Equities disagreements are arithmetic disagreements in disguise. When Expected value per attempt of $86.60 sits on the table with its working shown, the conversation stops being about who sounds more confident and starts being about which input is wrong. Suspiciously tidy answers deserve the same scrutiny as untidy ones.
The companion outputs — Break-even win rate and Edge over break-even — 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. A calculation you cannot explain to somebody else is a calculation you cannot defend.
Nothing here is stored or transmitted, so the habit of checking is free: open the page, re-enter current figures, compare with the number you saved last time. Half of a good calculation is knowing which input you would hate to be wrong about.
EV/EBIT — frequently asked questions
What affects EV/EBIT the most?
The short answer: Win probability. The longer answer is the sensitivity table on this page, where every numeric input was probed by ten percent and the expected value per attempt recalculated — Win probability moved it furthest, by roughly 15.7 % ($73.04 to $100.16).
Which matters more: gain per win or win probability?
For EV/EBIT, Win probability wins the head-to-head (≈15.7 % against ≈13.4 % per ten-percent change). Knowing which input dominates is the difference between careful work and merely slow work.
What is a typical EV/EBIT?
With the example inputs on this page the result is $86.60, labelled “Positive but thin — costs and variance can erase the edge”. “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 EV/EBIT by hand?
Three steps: write the formula (EV = p×gain − (1−p)×loss; break-even win rate = loss ÷ (gain + loss)), 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 EV/EBIT than any number of calculator runs.
What happens to EV/EBIT if win probability changes by 10 %?
The engine was re-run with that field moved by ten percent while everything else stayed fixed: the expected value per attempt goes across $73.04 to $100.16, a swing of roughly 15.7 % around the $86.60 baseline. That ratio is the practical definition of how much this input matters.
Is the EV/EBIT result of $86.60 good or bad?
Neither, on its own — the label “Positive but thin — costs and variance can erase the edge” is the band this figure falls in for the example inputs, and your own inputs will place you elsewhere on the same ladder. Judge the number against your history and your alternatives, and remember that a ten-percent error in Win probability alone moves it by ≈15.7 %.
Can I predict future returns with EV/EBIT?
No tool predicts; EV/EBIT computes implications. Use it to ask "if these inputs hold, what follows" — then stress the assumptions.
Is EV/EBIT accurate?
The arithmetic is exact: EV = p×gain − (1−p)×loss; break-even win rate = loss ÷ (gain + loss) is evaluated with full floating-point precision and every substitution is printed in the worked example. What limits accuracy is input quality and the model’s assumptions, so treat the $86.60 result as the correct answer to the question you typed — and check the assumptions section before acting on it.
Is positive EV enough?
It is necessary but not sufficient — bankroll sizing (Kelly criterion) exists precisely because variance can kill a positive-EV player before the edge pays.
How many samples to trust the number?
Rule of thumb: hundreds, not dozens — the standard error of a 40 % estimate needs ~600 trials to shrink under 2 points.
Does this EV/EBIT tool store my numbers?
No. The entire calculation runs in your browser session; nothing entered above is transmitted or saved, which is the correct behaviour for sensitive Investing & Markets figures and the reason you can re-run this page as often as you like.
Key terms on this page
- Break-even win rate
- 14.45% for the EV/EBIT worked case; it sits next to the main result because it carries context the headline leaves out.
- Edge over break-even
- 25.55 pts in the default EV/EBIT calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- EV per 100 attempts
- $8,660 on the example inputs — a companion figure printed beside the headline of EV/EBIT so that no result is read in isolation.
- Expected value per attempt
- The primary output of this tool: $86.60 for the default inputs, computed exactly from EV = p×gain − (1−p)×loss; break-even win rate = loss ÷ (gain + loss).
- Worked example
- The calculation written out in full so that nothing on this page has to be taken on trust.
- Verdict band
- The interpretation attached to a result (“Positive but thin — costs and variance can erase the edge” on these inputs), which turns a bare number into a position on a scale.
- Sensitivity (±10 %)
- How far the EV/EBIT moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Methodology, sources and limits
The model is published arithmetic implemented exactly as written: EV = p×gain − (1−p)×loss; break-even win rate = loss ÷ (gain + loss). Source of record: Expected value; foundational decision theory. Where a professional version of this calculation differs, it differs in inputs and jurisdiction-specific coefficients, not in the algebra — which is why re-checking your inputs beats hunting for a second calculator.
Nothing here replaces a qualified professional, and nothing here is approximate either — the arithmetic is exact, and the honesty is in the inputs you supply. Nothing is smoothed, weighted or adjusted behind the scenes; where judgement enters, this page says so and names the input responsible.
Turning this EV/EBIT into a decision
Re-run the calculation with your own figures, stress the two most sensitive inputs by ±10 %, and only then act on the result.
The audience here is people who need one trustworthy number before their next decision. Everything you need to defend the result — formula, substitution, sensitivity, source — is printed above, so the only remaining variable is the honesty of your inputs.