session-analysis

To analyze a single Strava training session for per-km splits and HR drift, you provide a date or Strava activity ID to generate a structured JSON report. This output includes narrative summaries, pace shape, and zone distribution.

Yes, you can automatically detect intervals, warmups, and cooldowns from Strava running data by leveraging lap and stream-based work blocks. The analysis identifies these segments to evaluate effort distribution within the session.

HR drift analysis tracks heart rate changes relative to pace over the duration of a Strava activity. It requires optional threshold parameters to compute HR zones and pace bands, outputting the results within a structured JSON schema.

You compare an athlete's actual pacing against a planned workout by running a session analysis that includes a plan comparison. This evaluates the athlete's readiness and effort distribution relative to the intended training targets.

Session analysis supports both running and cycling activities. It leverages raw activity data, per-km splits, and stream-based work blocks to inspect pacing and effort distribution across multi-sport contexts.

You need a Python environment with access to the Strava DB and optional threshold parameters to compute HR zones and pace bands. This setup enables the generation of consistent JSON deep-dive reports for fitness coaching analysis.