Statistical Terms
Statistical terms in training are the measures used to smooth, compare and classify data series: rolling mean, standard deviation, z-score and baseline.
Definition
“Statistical terms” here means the set of measures obseed uses to turn individual readings into reliable statements. A single heart rate or HRV value says little; it only becomes information when compared with the usual range.
The six most important terms at a glance:
| Term | Meaning | Where it appears in obseed |
|---|---|---|
| Rolling mean | Average of the last n values, all weighted equally | 30-second smoothing for Normalized Power, 7-day HRV |
| Exponentially weighted moving average (EWMA) | Average in which recent values count more | Chronic Training Load (42 days), Acute Training Load (7 days) |
| Standard deviation (SD) | Measure of spread around the mean | Normal range of resting heart rate and HRV |
| z-score | Distance of a value from the mean in standard deviations | Classifying a daily value as normal or unusual |
| Coefficient of variation (CV) | Standard deviation divided by mean, as a percentage | Comparing spread across people and metrics |
| Baseline | Individual reference range, usually mean ± 1 SD over 28–60 days | Basis of every deviation alert |
Why it matters
Physiological values fluctuate from day to day, even without a cause. Interpreting every swing means reacting to noise. Never reacting means missing the cases where the body is actually sending a signal.
Statistics draws the line between the two. It answers whether an HRV of 48 ms in someone with a mean of 62 ms and a standard deviation of 8 ms is still normal, or far enough outside to justify changing the training plan.
How obseed measures it
obseed computes the baseline for resting heart rate and HRV as a rolling mean and standard deviation over recent weeks and compares each new morning value against it:
z = (value − mean) / standard deviation
CV = standard deviation / mean × 100For training load obseed uses the exponentially weighted moving average of daily TSS. The smoothing factor α = 1 − e^(−1/τ) with time constant τ in days sets how quickly old load loses weight: τ = 42 for CTL, τ = 7 for ATL.
| z-score | Classification in obseed |
|---|---|
| between −1 and +1 | Normal range, no action |
| between −1.5 and −1 | Watch, lean towards reducing intensity |
| below −1.5 | Unusual, adjust training |
| above +1.5 for resting heart rate | Unusual, sign of infection or overreaching |
Example
An athlete has the following HRV baseline over 28 days: mean 62 ms, standard deviation 8 ms. The coefficient of variation is 8 / 62 × 100 = 12.9 %.
On the morning after a hard block she measures 48 ms. The z-score is (48 − 62) / 8 = −1.75. The value lies below the −1.5 limit and is flagged as unusual. The planned intervals are swapped for an easy base run.
Two days later HRV is 58 ms, z = −0.5, back in the normal range. Her CTL that week: with 95 points the day before and 120 TSS today, α = 1 − e^(−1/42) ≈ 0.0235 gives a new CTL of 95 + 0.0235 × (120 − 95) ≈ 95.6. A single hard day barely moves chronic load; that is exactly the purpose of exponential smoothing.
Categories
- Datenanalyse
- Technische Begriffe