PitchingIQ.

The problem

High school and college pitchers train hard but fly blind — no idea why some days they're sharp and others flat. Elite systems like TrackMan and motion capture cost $15,000–$25,000 and only reveal what's mechanically wrong, never why it's happening from a pitcher's own habits and physiology.

The workload-to-velocity link is established in research, but only at the population level, ignoring recovery and sleep and missing non-game throwing volume. So no tool answers the individual question: does this pitcher's velocity drop when he sleeps poorly three nights before a start, or when recovery is low? The same biometric reading means different things for different pitchers.

The solution

PitchIQ is a personal pattern-discovery engine — not a coaching or training platform — that connects a pitcher's own wearable biometrics to their training and game performance and surfaces recurring patterns confirmed from that athlete's history alone, never population averages.

The differentiators are individual-only baselines, biometric-plus-performance correlation that competitors don't attempt, and repetition-confirmed patterns that surface only after a pattern recurs enough times in that athlete's record — the core method IP. It delivers $25K-system-level insight from data the athlete already collects, at $15/month, working three ways: on-demand pattern checks, an agent that continuously finds and confirms patterns, and a monthly self-comparison report.

How it works

The entire experience runs inside a Telegram chatbot. The athlete logs each day via structured prompts (rest/training/game), while WHOOP biometrics — HRV, recovery, resting heart rate, sleep efficiency, strain — fetch automatically via API. A Pattern Agent tests preselected metric pairs on a cadence using Spearman correlation plus a repetition-confirmation gate; only patterns passing that gate are pushed to the athlete for ✓/✗ verification, and verified patterns are stored permanently.

Three models each handle a task: Claude for pattern interpretation, on-demand answers, and report generation; Whisper for voice-note transcription; and OpenAI text-embedding-3-small for a RAG science layer that cross-checks each pattern against six peer-reviewed articles as supported, contradicted, or no evidence. A strict hallucination guardrail ensures the LLM narrates only stats-validated patterns and never invents correlations, hedging every insight to the data available. On 180 days of synthetic data the agent surfaced 3 of 4 embedded patterns, confirming a ~50-day-per-day-type minimum.

Who it's for

The primary customer is B2C: the individual pitcher. The persona is "Marcus," a 17-year-old high school right-hander throwing ~88 mph, chasing D1 attention before signing day, who already owns a WHOOP, logs willingly, and lives on his phone — with parents paying the $15/month subscription. College pitchers form a secondary segment motivated by roster security.

Phase 2 adds B2B2C institutional tiers for academies, travel organizations, and small college programs (~$4K/year). A firm trust principle governs all phases: institutions never see raw biometrics — coaches see only confirmed patterns and the readiness signal — because a pitcher who fears being benched over his data disengages, destroying the dataset that is the moat.

Why it matters

AI-powered athlete performance analytics sits at roughly a 20–25% CAGR, within a large and growing US high school athlete population (8.26M+ in 2024–25). Comparable biometric platforms are richly funded — WHOOP raised $575M at a $10.1B valuation, Oura reached ~$11B — yet all aim at pros, elite teams, or broad consumer health. None serve the individual amateur pitcher. Bottom-up, the US pitcher beachhead is a ~$28M TAM, extending toward $300–500M across adjacent individual sports.

A pre-seed startup with a working end-to-end prototype, PitchIQ's next step is a closed pilot with one real pitcher over a full season to prove the method on live data. The compounding, per-athlete correlation dataset — never sold — is the long-term defensibility that late entrants can't replicate.

At a glance

Project

PitchingIQ

Built by

Sachkouskaya

One-liner

PitchingIQ is an AI performance product for high school and college pitchers that connects wearable biometrics with pitching-specific training and game outcomes.