Strava analytics explained: what your data is really telling you

Moving time versus elapsed time

Every Strava activity has two clocks. Elapsed time is start to finish. Moving time strips out the periods the app judged you were stopped — at traffic lights, at a gate, at the top of a climb. Pace is calculated from moving time, which is why a city run can show a respectable average pace while taking 15 minutes longer than expected.

For training purposes moving time is the right default. But be aware of the opposite problem: on a twisty trail or in dense woods, GPS drift can register slow movement as fast, inflating moving time and flattering your pace. If a session's pace looks impossible, check elapsed time before you believe it.

Average pace and grade-adjusted pace

Average pace is one number for the whole session, which makes it a poor comparison tool. A 5:00/km run on the flat and a 5:20/km run with 200 m of climbing may represent very similar effort. Grade-adjusted pace (GAP) converts your pace to the flat-ground equivalent, so the second run might show as 4:58/km adjusted.

GAP is not perfect — it under-rewards steep descents and cannot model technical ground — but it is the single most useful pace number for runners comparing sessions across a hilly year. Track GAP for trends; use raw pace when you are specifically rehearsing a flat race.

Relative effort and suffer score

Strava's relative effort (formerly suffer score) is a heart-rate-zone-weighted estimate of how hard a session was. It is genuinely useful for one purpose: comparing a session against your own sessions. It is not comparable between athletes, and it collapses entirely if your max heart rate is set wrongly — a common problem, since the default estimate is based on age and is frequently 10 beats off either way.

Set your max heart rate from an actual hard effort, not a formula, then check whether the numbers line up with how the session felt. If a session that felt easy scores higher than a race, something in the zones is wrong.

Heart-rate zones and cardiac drift

Zones tell you the intensity distribution of a session: how much time you spent easy, in tempo, at threshold. For most club runners the useful insight is not any single session but the weekly balance — if 80 per cent of your time is not genuinely easy, you are in the grey zone where adaptations stall.

Cardiac drift is the second signal. If your heart rate climbs steadily at constant pace, especially in heat or on a long run, your aerobic system is working harder for the same output. A drift of more than about five per cent over an hour usually points to heat, dehydration or fatigue rather than fitness loss.

Cadence, elevation and the numbers people over-read

Cadence is a useful diagnostic and a terrible target. Low cadence with long ground contact often accompanies overstriding, but the "180 steps per minute" rule was derived from elite racing and is not a universal standard. Elevation gain is similarly noisy: barometric altimeters are accurate within a few metres, GPS-derived elevation is not, which is why the same hill can read 80 m one day and 120 m the next.

Weekly volume, consistency and training load

Two numbers explain most performance change over a season. Volume is how much you do; consistency is how evenly it is spread. A runner doing 40 km a week for twelve weeks will outperform a runner alternating 80 km weeks with blank ones, on the same total.

Training load combines the two with intensity. The most practical version is the acute:chronic workload ratio: last 7 days against your 28-day weekly average. It is not a diagnostic, but a ratio above 1.5 is a reliable prompt to look at the calendar and ask whether that jump was planned.

What Strava does not show you

Strava is built as a social feed, which shapes what it surfaces. Four things are missing for anyone training seriously:

• Comparison against a cohort. You can see a friend's run, but not whether your weekly volume is typical for a club runner of your age and race times.
• Weather-adjusted pace. A windy Tuesday and a still Saturday are presented as identical data points, so a good session can look bad and vice versa.
• Long-range aggregation. Year-over-year trends, month-on-month elevation, and 12-week rolling averages require manual digging.
• Interpretation. Strava gives you the numbers. It does not tell you which one to act on this week.

A ten-minute monthly review

If you only do one analytics task a month, do this:

1. Open the last four weeks and note total distance and total elevation, not just the biggest week.
2. Check the easy-to-hard split by time in zone. Most of it should be genuinely easy.
3. Look at your longest session — is it long enough for the race you are targeting?
4. Compare GAP on your benchmark route or interval session with the same session one month ago.
5. Re-run a race time prediction and see whether it moved.

That is the whole cycle: volume, intensity, endurance, speed, prediction. If all five are moving in the right direction, the plan is working regardless of how any single run felt.