COMPARISON METHODS
Choose a fair baseline for comparing easy runs
The most useful comparison is the one that matches your question. Choose the baseline before looking for the biggest improvement.
Define what you want to compare
“Did I run the loop faster?” requires a consistent route and clock. “Did a similar easy pace coincide with a different heart rate?” also needs suitable sensor coverage and comparable effort. Those are related questions, but they do not use exactly the same selection criteria.
Write a short rule before browsing your best results. For example: the same loop and direction, a similar duration, an easy-run intention and no major recording gap. This reduces the temptation to change the comparison whenever a more attractive result appears.
Pick from the actual candidates
Imagine you are reviewing a steady 40-minute park loop. The following candidates are illustrative.
| Candidate | What differs | Usefulness |
|---|---|---|
| A: same loop, 42 minutes | Small duration difference; complete recording | A plausible comparison to inspect |
| B: flat 20-minute run | Different route and much shorter duration | Answers a different question |
| C: same loop with intervals | Different workout purpose | Compare work portions separately |
| D: same loop, missing half the HR | Unequal measurement coverage | Distance/time comparison remains possible |
Candidate A is not guaranteed to be physiologically equivalent. It is simply a better starting point for the stated question. Record any remaining differences instead of pretending the matching process eliminated them.
Keep exclusions visible
Exclude an activity for a clear reason, such as an interrupted recording, not because its result is inconvenient. If the reason matters repeatedly, make it part of the selection rule. Keep a separate list of activities that could only answer a narrower question.
Do not average a long collection of unrelated runs merely to obtain a more stable-looking number. More observations can still represent different routes, purposes and recording conditions. A small, clearly defined set is easier to interpret than an unlabeled collection.
Use both the pair and the wider history
In Runome, filter the activity history where suitable and inspect the chosen runs together. Read the available charts after comparing the summaries. A single pair can reveal what differed on two days; the broader matching set helps you judge whether the observation repeats.
State the conclusion at the level the evidence supports. “This comparable loop was quicker at a similar recorded average heart rate” is more defensible than treating one outing as a complete fitness assessment. Continue with a numerical two-run example or checking sensor coverage before interpreting efficiency.