Build an AI Skating Jump Analysis App

1. 1

   Prove the no-hardware value proposition

   The whole pitch is that a skater films a jump on a phone and the app returns height, rotation speed, airtime, and landing quality, with no sensor to wear and no multi-camera rig to rent. This is the documented shift displacing wearable sensors on cost and convenience (an MIT-spinout app in figure skating, partnered with U.S. Figure Skating, is the named context, not a template). Build around the fact that the phone the athlete already owns is the only hardware.

2. 2

   Build and validate the computer-vision pipeline

   Develop pose estimation and motion analysis that reliably extract jump metrics from varied phone footage, lighting, and angles. The hard part is accuracy: validate the model against ground-truth measurements so coaches trust the numbers, because a metric a coach cannot rely on is worse than none. Underinvesting in validation is the fastest way to lose credibility in a coaching tool.

3. 3

   Pick one discipline and win it first

   The space is crowded with near-identical apps, so do not launch a generic skating analyzer. Choose one discipline (figure skating, artistic roller, or inline) and one athlete tier, and become the best tool for that exact user with the metrics and workflow they care about. Depth in one niche beats breadth across a market already full of look-alike products.

4. 4

   Design the coach and athlete workflow

   Metrics alone are a feature, not a business. Build the workflow coaches and athletes actually use: session history, progress tracking over time, side-by-side comparison, and easy sharing between coach and skater. The apps that retain users solve the coaching loop, not just the measurement, so the product must fit how training actually happens week to week.

5. 5

   Price on subscription and club tiers

   Monetize the way the category does: individual monthly subscriptions plus club or team membership tiers that let a coach manage many athletes. Federation or governing-body partnerships (as the named context app secured) can provide credibility and distribution. Recurring subscription revenue, not one-time sales, is the model, so retention and the coach relationship drive the economics.

6. 6

   Compete on accuracy, niche, or workflow, honestly

   Because competitors offer the same core metrics, your edge must be real: measurably better accuracy, a discipline nobody serves well, a superior coaching workflow, or a federation relationship. Be honest internally that this is a crowded field of similar products; the winners differentiate on something defensible and earn trust metric by metric, rather than assuming a first-mover advantage that does not exist here.

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Best for: ML and product builders who can win a specific skating niche in a crowded field

Not for: Not for anyone expecting an uncontested market; this is a crowded field of near-identical jump-analysis apps, and undifferentiated entrants fail. Not for teams that will skip rigorous accuracy validation, because a coaching tool coaches do not trust has no path to retention.