Connection Risk Calculator
The formula
What the connection risk tells you
The 5×5 matrix the risk industry actually uses, for connection risk: gross likelihood × impact, discounted by how well your controls actually work.
Calculation: gross = likelihood × impact (1–25); net = gross × (1 − controls). Enter your inputs and every substitution is shown below the result.
Reading the result
Scores rank attention; they do not price losses. Anything 15+ gets a treatment plan with a named owner.
Rules of thumb: Most organisations carry 3–5 reds; more usually means likelihood inflation, not danger.
Limitations and best practice
Control ratings drift optimistic — audit one control’s evidence and re-mark the rest against it.
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
Why discount by controls?
Do these scores compare across teams?
Source & methodology
ISO 31000-aligned qualitative risk matrices.
Connection Risk: what this tool answers
You are here because you need a specific number, and you want to see how it was produced before you rely on it. Searchers land on a Connection Risk page for three reasons — to plan, to compare options, and to check that somebody else’s arithmetic is honest. This tool serves all three, and everything after the calculator explains the model instead of hiding it.
What makes Connection Risk worth calculating is not precision but position: it tells you which side of a threshold you are on, and by how much. A rough figure that settles the direction beats an elaborate one that never commits.
Most confusion about Connection Risk comes from mixing up the input you control with the input you merely observe. The formula treats them identically, but your decisions should not: one deserves measurement, the other deserves a defensible estimate.
What makes a calculation page trustworthy is that its copy is derived from its own arithmetic: here net risk score = 9.1 / 25, recomputed every time a field changes, and the sensitivity section ranks the inputs that move the result — Likelihood (1 = rare … 5 = almost certain) first, at ≈10.0 % of the headline for a ten-percent error either way in that input. Read the sections in order and you will be able to reproduce every figure by hand.
The Connection Risk formula, explained
One formula, published in full: gross = likelihood × impact (1–25); net = gross × (1 − controls). 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.
Likelihood (1 = rare … 5 = almost certain) (example value 4) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. If you are unsure what belongs in this field, look at the printed steps: the substitution shows exactly how the engine uses it.
Impact (1 = negligible … 5 = severe) (example value 4) 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.
Control effectiveness (0–100 %) (example value 43) is a measured number in the unit named by the label; the formula does not convert for you. Enter it exactly as your document states it — quoted annual, monthly and effective rates are three different numbers.
Connection Risk 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: Likelihood (1 = rare … 5 = almost certain), Impact (1 = negligible … 5 = severe), Control effectiveness (0–100 %). Run to the end, the net risk score resolves to 9.1 / 25 — Amber — needs an owner and a date on the bands this tool uses.
- Gross = 4 × 4 = 16
- Net = 16 × (1 − 43 %) = 9.1
Gross score — 16 / 25. This is the number experienced users quote when the headline alone would mislead.
Control credit — 6.9 pts. Compare it against the main result of 9.1 / 25 before drawing conclusions.
Likelihood × impact band — High×High. 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 Connection Risk can reasonably move
The honest version of any estimate is a corridor, and this one is cheap to produce: Connection Risk was recomputed at 5 points along the dominant input, with every other field left untouched.
| Likelihood (1 = rare … 5 = almost certain) setting | Net risk score |
|---|---|
| 80 % of the example value | 7.3 / 25 |
| 90 % of the example value | 8.2 / 25 |
| example inputs | 9.1 / 25 |
| 110 % of the example value | 10.0 / 25 |
| 120 % of the example value | 10.9 / 25 |
Across 5 settings of Likelihood (1 = rare … 5 = almost certain), the result spans 7.3 / 25 to 10.9 / 25. 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 Likelihood (1 = rare … 5 = almost certain) alone; the substitution steps will follow your figures exactly.
Sensitivity: which inputs change Connection Risk the most
This list was computed for this specific engine just now: each numeric input was nudged ±10 % while every other field held its example value, and the net risk score was recalculated from scratch. It is the fastest honest answer to “what matters most in Connection Risk?” — and it is specific to these defaults, because sensitivity moves with the starting point.
- Likelihood (1 = rare … 5 = almost certain) +10 % → Connection Risk = 10.0 / 25 · −10 % → 8.2 / 25 (the net risk score shifts ≈10.0 %)
- Nudge Impact (1 = negligible … 5 = severe) by ±10 % and Connection Risk lands between 8.2 / 25 and 10.0 / 25 (sensitivity ≈10.0 %)
- Control effectiveness +10 % → Connection Risk = 8.4 / 25 · −10 % → 9.8 / 25 (the net risk score shifts ≈7.5 %)
Likelihood (1 = rare … 5 = almost certain) is the dominant lever here: ten percent either way moves the result by about 10.0 %, from 8.2 / 25 to 10.0 / 25. Spend your verification effort there first, and Impact (1 = negligible … 5 = severe) is effectively tied with it (≈10.0 % for the same nudge), so verify the pair together. Everything below that can stay approximate, which is a licence to stop chasing data that would not change the decision.
What a Connection Risk of 9.1 / 25 means in practice
Nobody acts on a bare figure — they act on a figure with a comparison attached. On the example inputs the band reads “Amber — needs an owner and a date”; your own figures place you on a different rung of the same ladder. The sensitivity list shows how far a modest input error moves you along that ladder: for Likelihood (1 = rare … 5 = almost certain), ±10 % covers 8.2 / 25 to 10.0 / 25. 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.
Where Connection Risk sits next to similar measures: this tool answers “how much is the Connection Risk”, while the related calculators answer “what do I do about it”. Reading the result alone tends to over-weight a single snapshot; pairing it with the companion tools above turns the number into a decision.
How people get Connection Risk wrong
Control ratings drift optimistic — audit one control’s evidence and re-mark the rest against it. 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.
Confusing a large number with an important one. Connection Risk can be dominated by one input whose absolute value looks unimpressive, which is why this page ranks influence separately from magnitude.
Attention is also misallocated between fields. Having measured this engine, we can say where it belongs: Likelihood (1 = rare … 5 = almost certain) first, everything else second.
The subtlest error is answering the wrong question precisely. Confirm that net risk score is actually the quantity your decision needs — the arithmetic is flawless either way, which is what makes the mistake invisible.
Six things to confirm before acting on Connection Risk
- State, in one sentence, which convention each rate, unit or period in the form is using.
- Compare the result with one independent method or published benchmark.
- Put Likelihood (1 = rare … 5 = almost certain) under a magnifier; the sensitivity table above is the reason why.
- Compare the output against one published benchmark or a colleague’s independent estimate.
- Re-run the calculation with a ±10 % stress on the two most sensitive inputs.
- Run the ±10 % stress on the top two levers and check that your conclusion survives both.
A routine for decision-grade Connection Risk
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 Connection Risk takes a minute to learn and applies to every input above.
Step 1 — collect, then type. Everything this tool asks for is listed above (likelihood (1 = rare … 5 = almost certain), impact (1 = negligible … 5 = severe), control effectiveness (0–100 %)). Pull each figure from something you can point at — paperwork, a dashboard, a label — before touching a field. Guesswork at the input stage cannot be repaired at the interpretation stage. If a result feels obviously right, that is the moment to check the inputs hardest.
Step 2 — enter the conservative case first. Resist the flattering scenario. Type the inputs you would defend in front of a sceptical reader, read the Net risk score (9.1 / 25 for the example values), and note the verdict band. When in doubt, prefer the boring input: the interesting one is usually the one you misread.
Step 3 — change one input at a time. Nudge a single field by ten percent, watch the headline and its companions move, then undo it. Two or three fields will dominate — the sensitivity table above already ranks them for this engine. Re-running a number costs seconds; retracting a decision costs considerably more.
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. Note the assumption you are least sure about; that is where the real risk lives.
Step 5 — schedule the next run. In Travel & Transportation the inputs drift quietly. Put a reminder a quarter out, re-enter the same fields, and read the change rather than the level. A calculation you cannot explain to somebody else is a calculation you cannot defend.
The real-world conversations Connection Risk settles
In Air Travel the figure computed here is usually the first one somebody asks to see. Having it — with the formula behind it — changes your position in the conversation from participant to reference. Write the assumptions in the same file as the result, and future arguments take minutes instead of meetings.
Professionals in Air Travel use this style of calculation as a screening filter before spending real money or effort: a quick Connection Risk check eliminates weak options in minutes, and the survivors earn the deeper, slower analysis. Compare the calculation to the rough guess you arrived with, and ask why they differ.
Treat Connection Risk like a smoke alarm rather than a painting — its value is in being tested periodically, not in being admired once. When two sources disagree, it is almost never the arithmetic that differs.
Connection Risk — frequently asked questions
What affects Connection Risk the most?
Measured on this engine, Likelihood (1 = rare … 5 = almost certain) is the dominant lever: moving it ±10 % shifts the net risk score across 8.2 / 25 to 10.0 / 25 — about 10.0 % of the headline, with Impact (1 = negligible … 5 = severe) next at ≈10.0 %. The ranked list in the sensitivity section is computed live, not asserted from a template.
Do impact (1 = negligible … 5 = severe) and likelihood (1 = rare … 5 = almost certain) matter equally?
Effectively. Measured on this engine they land at ≈10.0 % and ≈10.0 % for the same ten-percent nudge, so the honest advice is to get both right; chasing precision in only one of them buys almost nothing.
What is a typical Connection Risk?
On the worked example above Connection Risk comes out at 9.1 / 25. Treat that as one point on a distribution rather than a norm: the bands, the companion outputs and the sensitivity ranking tell you how wide the plausible range really is.
How do I calculate Connection Risk by hand?
Use the stated model — gross = likelihood × impact (1–25); net = gross × (1 − controls) — and substitute in the order the tool prints (it starts from Gross = 4 × 4 = 16). On the example inputs the hand route lands on 9.1 / 25, exactly what the calculator shows. A spreadsheet reproduces it in two or three cells; doing it once by hand is what makes the automated answer trustworthy.
What happens to Connection Risk if likelihood (1 = rare … 5 = almost certain) changes by 10 %?
On these inputs a ±10 % change in likelihood (1 = rare … 5 = almost certain) takes Connection Risk to 10.0 / 25 against the 9.1 / 25 example — around 10.0 % of the headline. The same test on every other field is in the sensitivity section.
Is the Connection Risk result of 9.1 / 25 good or bad?
Good and bad are comparisons, so the honest test is three questions: where does your own Connection Risk sit against the 9.1 / 25 reference above, how far would a plausible input error move it (≈10.0 % per ten percent on Likelihood (1 = rare … 5 = almost certain)), and what does the alternative option compute to?
How often should I check Connection Risk?
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. Connection Risk is a snapshot, not a subscription; stale snapshots are the most common silent error in Travel & Transportation planning.
Can Connection Risk be negative or empty?
Yes, and that is informative rather than broken: a negative Connection Risk usually means the inputs encode an outflow or a shortfall, and the verdict band above labels exactly that situation instead of hiding it.
Why discount by controls?
A 20-score hazard half-mitigated is a 10, and 10s behave like a different risk class.
Do these scores compare across teams?
Only if the likelihood and impact scales are calibrated to the same definitions — the matrix is shared, the calibration is organisational.
Do I need an account to use this Connection Risk 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.
The vocabulary of Connection Risk
- Gross score
- 16 / 25 in the default Connection Risk calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Control credit
- 6.9 pts in the default Connection Risk calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Likelihood × impact band
- High×High in the default Connection Risk calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Net risk score
- The primary output of this tool: 9.1 / 25 for the default inputs, computed exactly from gross = likelihood × impact (1–25); net = gross × (1 − controls).
- Worked example
- Every substitution in order, from the example inputs to 9.1 / 25 — the audit trail for the headline figure.
- Verdict band
- The interpretation attached to a result (“Amber — needs an owner and a date” on these inputs), which turns a bare number into a position on a scale.
- Sensitivity (±10 %)
- How far the Connection Risk moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
The provenance of this Connection Risk calculation
The model is published arithmetic implemented exactly as written: gross = likelihood × impact (1–25); net = gross × (1 − controls). Source of record: ISO 31000-aligned qualitative risk matrices. 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.
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 Connection Risk into a decision
Save the number together with its inputs and today’s date; a Connection Risk figure without context is unusable the moment an input changes.
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.