AI for running performance data

Your running watch knows you better than your coach does — if you actually read it.

What we’re actually working with

Running data spans pace, HR, HRV, training load, vertical, cadence, and race times — often across years and devices.

Why doing this without a method fails

Apps celebrate PRs. They rarely surface 'why did your easy pace HR drift up this block?'

How the method handles running

Layer 01

Research

Have sourced AI summarise the actual evidence on polarized training, threshold work, lactate, and zone-2 — at your level.

Layer 02

Ledger

Export multiple seasons of training. Let AI find the blocks that produced your best races and what they had in common.

Layer 03

Protocol

Design a 12-week build using the patterns your own data supports, not generic plans.

Three prompts you can use today

Paste any of these into the AI chat tool you already use. No setup.

Best-block analysis

Here are 3 years of weekly mileage, HR, and race times. Find my best 12-week race build and describe its mileage, intensity, and recovery pattern.

Aerobic decoupling

Across 12 weeks of long runs, calculate aerobic decoupling each week and tell me whether my aerobic base is improving.

Race plan

Given my last 6 months of running data and a target half-marathon time, design a realistic 10-week plan that fits my actual capacity, not a textbook.

Common questions

Will this replace my coach?+

It makes your coach more useful — they can spend time on you, not your spreadsheet.

Can it read Strava exports?+

Yes. The course shows exactly which fields to feed in.

Does it work for trail / ultra?+

Yes. Distance- and terrain-aware prompts in the course.

More on running

Everything we’ve published that touches this topic — refreshed automatically as new entries ship.

From the blog

Case studies

Start with 10 free days.

The free 10-day email challenge teaches the same method on whatever data you already collect. No credit card.