Forecast accuracy calculator
How good is your forecast, really? Enter a few past quarters of what you forecast vs. what actually closed to get your accuracy (MAPE), your over/under bias, and a letter grade.
Get this scored automatically from your closed quarters — Start free · 14-day full-Pro trial, no card
Worked examples
Three forecasting habits, graded by the same math as the calculator. Note the sandbagger: a respectable-looking accuracy score, but every miss points the same way — that consistent under-call is exactly what the bias measure catches.
| Scenario | Forecast vs. actual | MAPE | Bias | Habit | Grade |
|---|---|---|---|---|---|
| The calibrated caller | $1.02M vs $1.00M · $1.08M vs $1.10M · $990.00K vs $1.00M | 1.6% | -0.3% | balanced | A |
| The sandbagger | $900.00K vs $1.00M · $950.00K vs $1.05M · $1.00M vs $1.08M | 9% | -9% | sandbags | B |
| The optimist | $1.20M vs $1.00M · $1.30M vs $1.10M · $1.15M vs $1.00M | 17.7% | +17.7% | over-forecasts | D |
MAPE vs. bias — you need both
MAPE is how big your miss is on average, ignoring direction. Bias keeps the sign: consistently forecasting high means you’re optimistic; consistently low means you sandbag. A forecast can be accurate on average yet carry a bias worth correcting — see how to measure sales forecast accuracy for the full method.
Grading here uses your typed numbers. To grade a forecast on your OWN history — automatically, with no data entry — backtest it: Pipemetry replays what it would have forecast at day 1, midpoint, and 80% of each past quarter and scores the MAPE against what actually closed.
Forecast accuracy FAQ
How do you measure sales forecast accuracy?
Two numbers, per period. MAPE (mean absolute percentage error) is the average size of your miss regardless of direction — |forecast − actual| ÷ actual. Bias is the same error WITH its sign kept: a positive average means you consistently over-forecast (optimistic), a negative one means you sandbag. Accuracy and bias are different problems and you need both.
What is a good forecast accuracy or MAPE?
As a rule of thumb for a full-quarter revenue forecast: MAPE at or under 5% is excellent (A), under 10% is good (B), under 15% is fair (C), under 25% needs work (D), and above 25% means the number is not yet reliable (F). Earlier-in-quarter calls are naturally less accurate than end-of-quarter ones.
What is forecast bias and why does it matter?
Bias is the direction of your error over time. A team that beats its number every quarter is sandbagging (negative bias); one that misses is over-forecasting (positive bias). Even a low-MAPE forecast can carry a bias worth correcting — Pipemetry surfaces per-caller bias so a consistent over/under can be adjusted out.
Is this the same as a real accuracy report?
No — this grades numbers you type in. Pipemetry computes MAPE and bias automatically from your own closed quarters, and can backtest what it WOULD have forecast on your history at day 1 / midpoint / 80% of each quarter, so you can trust a forward number before you rely on it.
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Track accuracy automatically
Pipemetry scores MAPE and bias per owner from your live pipeline, flags a chronic over- or under-caller, and proves a forward number by backtesting your own closed quarters — from your CRM or a spreadsheet. 14-day full-Pro trial, no card