Most cycle apps describe their predictions with a single unexplained number, or no number at all. We think you deserve the same standard a scientist would demand: the metric defined, the dataset named, the comparison shown, and the weaknesses admitted. This page is that standard, and we update it whenever the engine's published numbers change.
1. The headline numbers
| Metric | Result |
|---|---|
| Median error (next period) | 1 day |
| Mean error | 1.82 days |
| Within 2 days | 75.6% |
| Within 1 day | 56.8% |
| Mean error, 3 days out | 1.55 days |
| Calendar method, mean error (same data) | 2.08 days |
2. How we measured it
We replayed Ferrabelle's real production prediction engine against a public research dataset: the Fehring/Marquette menstrual cycle study (159 women, 1,665 cycles, published by Marquette University). For each woman we predicted every period as of several days before it actually arrived, using only the data that existed at that moment. This method is called leave-future-out walk-forward validation. 120 women and 3,282 predictions qualified for scoring. There is no way for the engine to "peek" at the answer.
Two things make this number honest. First, the engine was never trained or tuned on this dataset: it saw each cycle cold, the same way it sees yours. Second, the evaluation is deterministic and repeatable: the harness lives in our codebase and reruns on demand, so this page can always be regenerated from scratch.
3. Where the engine's assumptions come from
Before you have logged anything, Ferrabelle starts from assumptions grounded in published population research covering over 600,000 cycles (including Bull et al. 2019 in npj Digital Medicine and the Apple Women's Health Study), conditioned on age. From your first logged cycle onward, the engine personalizes to you, and your own data always outweighs the population as it accumulates. If you connect a wearable, its nightly temperature and heart-rate signals feed ovulation detection where your device provides them; validated wearable-anchored accuracy numbers are coming (see Section 4).
4. Where we are weaker (told straight)
- Long and irregular cycles are harder. On the same public dataset, cycles in the regular range predicted at 1.79 days mean error; long cycles (over 35 days) at about 2.8 days. If your cycles are irregular, expect wider, honest confidence windows rather than falsely precise dates.
- The public dataset has no wearable temperature data, so the numbers above reflect the engine without its strongest signal. Wearable temperature is where ovulation detection sharpens most; we will publish wearable-anchored numbers when we have a hormone-confirmed validation set.
- Hormonal birth control changes what prediction means. If you are on hormonal birth control, ovulation-based prediction does not apply the same way. The app's hormonal birth control mode turns predictions and phases off, so you log bleeds and how you feel instead of seeing dates that would not mean anything.
5. How predictions improve over time
Your predictions start from published population research and personalize from your first logged cycle. Once a week, Ferrabelle recalibrates to your own history, right on your phone. Nothing is pooled across users: the Android app has no server to send your data to.
6. Privacy receipts (each one checkable)
- No advertising or analytics code of our own on this site or in the app. Watch your phone's data usage for the app, or check the app's privacy meter. (While the AI assistant is on, Google's on-phone AI library can send Google usage and error statistics.)
- With the AI assistant and pressure tracking off, which is the default, the app makes no internet connections at all. We measured the release build with the assistant off: zero bytes left the phone.
- Predictions come from a transparent statistical engine that runs on your phone. No AI is involved in them. The optional AI assistant only explains and answers questions when you turn it on, and it never sees raw Health Connect values.
- You can check the engine on your own phone: Settings → Check prediction accuracy replays 300 test cases and confirms your phone gives the same answers as the engine measured on this page.
- Export is free (Settings → Your data → Export your data), and Delete all data on the same screen erases everything on the phone.
7. What these numbers are not
Ferrabelle is not a medical device and not a contraceptive, and its predictions must never be used to prevent pregnancy or to diagnose or treat any condition. Prediction error is a population statistic: your cycles are your own, and any app can be wrong about them. If your cycle changes unexpectedly or worries you, talk to a clinician.
Questions about our methodology: ferrabelle@proton.me.