Pitch Tipping Analysis
Game intelligence from pitcher tendency detection — find the tells in your opponents and eliminate them in your own staff before someone else does.
High-fidelity flat vector product mockup in enterprise SaaS marketing style: a video-analysis application window — a large left pane showing a paused video frame as a dark charcoal (#16212e) panel with a simple stick-figure pitcher skeleton overlay and small joint markers, a right pane with a timing chart of several overlapping delivery traces and a sequence heat-strip of small squares, exactly one diverging trace highlighted in royal blue (#004cce), playback controls beneath the video pane. Soft pale gray (#f3f6fa) page background; white (#ffffff) panels with thin (#e3e9f1) hairline borders and very subtle drop shadows; slate (#5d6b7b) secondary marks. All text as greeked placeholder bars — no legible words, letters, or numbers. Crisp straight-on geometry, generous spacing, modern and corporate, no people, no faces, no photographs What it does
Every pitcher leaks information. A glove that sets a half-inch higher before the changeup. A tempo that quickens with runners on. A sequence pattern that becomes predictable on 1-1. This build is a detection system for those leaks — pointed at opponents to find an edge, and pointed at your own staff to take the edge away.
The system works from the material clubs already have: broadcast and high-speed video, ball-tracking data, and pitch-by-pitch logs. It analyzes a pitcher along two distinct axes. The first is physical tells — measurable differences in setup, glove position, hand set, tempo, and delivery timing that correlate with pitch type. The second is sequence predictability — how much information about the next pitch is contained in the count, the previous pitches, the game state, and the catcher’s patterns, regardless of anything the body gives away. Both produce ranked, quantified findings: not “he might be tipping,” but which cue, how reliably, in which situations, with the video clips to prove it.
Findings arrive as a reviewable report — claim, magnitude, confidence, evidence — because a tell you can’t verify on video is a tell you can’t use, and one you can’t defend to a coaching staff.
Who it’s for
Advance scouting and run-prevention groups on the offensive side; pitching coaches and pitching-strategy departments on the defensive side, where the assignment is self-scouting: find our own leaks before an opponent’s video room does.
The quantitative methods
Computer-vision pose and keypoint extraction from video; time-series alignment and classification of delivery mechanics by pitch type; mutual-information and predictability metrics over sequence data; change-point detection to catch tells that appear only after fatigue or a mechanical adjustment; and statistical controls to separate a real cue from a small-sample coincidence — the failure mode that plagues manual tipping work.
Example outcomes
Representative outcomes: a verified opponent tell delivered with the situations where it holds; a self-scout that catches a reliever’s new leak within a week instead of after a bad month; sequence-predictability report cards for a whole staff; and a standing discipline — every leak found by math and video, not rumor.
From sample to engagement
A PoC here is deliberately narrow: one staff, one season of video and tracking data, findings you can verify frame by frame. The MVP grows it into a standing program — automated ingestion of new outings, alerting when a pitcher’s predictability moves, and a review workflow that keeps the coaching staff in control of what reaches the clubhouse.