Executive Summary

Coaches, broadcasters, player development staff, beat writers and advanced fans all struggle to tell the story of what actually happened to a given batter across an at-bat or game: existing visual tools show isolated pitches or aggregate heatmaps, but nobody packages every pitch a batter swung at into a clean, time-ordered, shareable sequence annotated with pitch type, exit result and swing decision. That gap forces teams and media to spend hours manually assembling plots or to rely on inconsistent screenshots from different vendors; the target customers include ~30 MLB clubs, hundreds of professional and collegiate programs, and thousands of content creators and local broadcasters who need quick, trusted visuals. The product would be a cloud service that ingests standardized inputs (Statcast/TrackMan/Rapsodo and calibrated consumer sensors), normalizes pitch/strike/swing labels, and outputs precise pitch-by-pitch visualizations plus short automated narrative captions and exportable assets. Delivery would include a SaaS dashboard for teams and media, an API and embeddable widgets for real-time broadcast overlays, and tiered pricing (from $99/month for creators to bespoke enterprise licensing for pro teams and networks). This is attractive now because pitch-level tracking has become widely available and cheaper: using a 10% addressable slice of a $3.5B global sports-analytics market yields a $350M opportunity, and demand for visual storytelling from broadcasters and social platforms is growing while consumer-sensor accuracy improves. To stand out you must solve two technical and commercial challenges well: build superior normalization and visualization that reduce noise from heterogeneous sensors, and secure early distribution partnerships (a single MLB club or regional network could validate a proof point). The strengths are defensible product differentiation (time-ordered, annotated pitch sequences) and clear buyer use cases; the risks are licensing complexity with major data providers, the need for rigorous validation against TrackMan/Statcast, and a medium-level competitive field that will push you to move quickly on integration and UX.

Market Opportunity

Precise pitch-by-pitch plotting — visualize every pitch a batter swung at targets a $350M = $3.5B global sports-analytics market x 10% share addressable for pitch-level baseball analytics total addressable market with medium saturation and a year-over-year growth rate of 15% YoY adoption growth for advanced analytics tools among professional/amateur teams.

Key trends driving demand: Statcast & tracking proliferation -- More pitch-level data from MLB/third-party sensors creates standardized inputs and lowers acquisition cost for analytics products.; Consumer-sensor accuracy -- Improved phone cameras and low-cost radars means hobbyists and smaller facilities can produce usable pitch data, expanding the customer base.; Demand for visual storytelling -- Broadcasters, coaches, and social channels want clean, shareable visualizations of pitch sequences — a product that packages plots + narrative wins adoption.; AI-powered data cleaning -- Advances in ML allow automatic normalization across devices and removal of noise, enabling consistent per-pitch plotting without heavy manual curation..

Key competitors include Baseball Savant (MLB Statcast), TrackMan Baseball, Rapsodo, FanGraphs & Community Tools (Brooks, Statcast APIs, DIY).

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Precise pitch-by-pitch plotting — visualize every pitch a batter swung at

Executive Summary

Market Validation

Market Opportunity

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