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Concept Client Project

Running Club Analytics

Building the club version — individual engine is live with 170+ runs of real data. Partner needed for scale.

Strava-connected club analytics platform — member rankings, group pace optimization, injury prevention monitoring, and GPS-based meetup recommendations. Proof of concept running with real data.

Live Demo
Astro SSR React 19 TypeScript Supabase Strava API PostGIS Tailwind CSS v4

The Problem

Running clubs face three challenges that Strava’s built-in features don’t solve:

1. No group-level injury prevention. Each member trains at different intensities, but there’s no system to monitor the club’s collective training load or flag members approaching overtraining before it becomes an injury.

2. Suboptimal group runs. Club runs default to the fastest runner’s pace, alienating beginners. No tool suggests a pace that genuinely benefits every member based on their current fitness level.

3. Meeting logistics waste time. “Where should we meet?” is a weekly debate. GPS data from members’ past runs could identify optimal meeting points — but no one aggregates it.

The Proposed Solution

A Strava-connected platform that turns individual training data into team intelligence:

  • Member rankings & monthly awards — Distance, consistency, most improved, top effort
  • ACWR-based group training plans — “This Saturday, 6km at 5’30”/km keeps 80% of members in their optimal zone”
  • GPS-powered meetup recommendations — Cluster analysis of members’ run locations to suggest fair meeting points
  • Injury prevention dashboard — Monitor each member’s acute:chronic workload ratio, flag risks before they become injuries
  • Team personality & culture — Aggregate individual running personalities into a club profile

Architecture

Strava OAuth (per member)
        ↓
Activity Sync (Webhooks + Cache)
        ↓                    ┌──────────────────────┐
   Supabase DB  ────────→    │   Analytics Engine    │
  (PostgreSQL)               │  ACWR · Riegel · GPS  │
                             │  Clustering · Trends   │
                             └──────────┬───────────┘
                                        ↓
                    ┌───────────────────────────────────┐
                    │        Club Dashboard (SSR)       │
                    │  Rankings · Group Plans · Maps    │
                    │  Injury Alerts · Monthly Reports  │
                    └───────────────────────────────────┘

Core stack: Astro SSR + React 19 islands, Supabase (PostgreSQL + Auth + RLS), Strava Webhooks for real-time sync, PostGIS for spatial queries, Vercel Edge deployment.

Working Proof of Concept

The individual analytics engine is live in production with real Strava data (170+ runs, 1,200+ km):

  • Running Dashboard — Personal records, training frequency heatmap, distance & location filters, Today’s Plan with injury boundary visualization
  • Running Intelligence — 12-section deep analytics: ACWR training load, race predictions (Riegel formula), pace trends, running personality radar, year-over-year comparison, route familiarity, milestone countdowns

Every computation shown in these pages — ACWR, race prediction, training boundary visualization, 7-scenario planning — is the exact engine that would power the club version. No mock data. No prototypes. Real analytics on real running data.

Why This Exists as a Concept

This is a B2B SaaS opportunity, not a side project. Scaling from 1 to N users requires:

  • Real club partnerships to validate product-market fit
  • Multi-user Strava OAuth and privacy compliance
  • Webhook infrastructure for sustainable API usage at scale (Strava rate limits: 1,000 requests/day)

The individual version proves the analytics work. The club version needs a partner.

Let’s Build It

If you run a running club, work at a sports tech company, or lead a corporate wellness program — the engine is built, the architecture is designed, and I’m ready to execute.

Get in touch

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