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Executive Summary

Tennis coaches and serious players currently have access to ball-tracking tools like SwingVision and Playsight, but these miss the bigger picture of how players actually move around the court. A coach reviewing match video still has to manually spot footwork problems, poor court positioning, and movement inefficiencies that separate advanced players from intermediates. The 14 million US and EU players taking regular lessons represent a $2.8 billion market assuming $200 annual spend on coaching technology, and this group already records hundreds of thousands of match and practice videos that go largely unanalyzed beyond what a coach can catch by eye. The smallest credible MVP would be a mobile app that uses pose estimation AI (MediaPipe or similar) to track player movement from standard smartphone video and generates a simple report on court coverage, split-step timing, and recovery patterns. This software-only approach requires no special cameras and could piggyback on the video players are already capturing. An initial version focused on singles play for 12-18 year old tournament juniors would narrow the technical scope while targeting the segment most likely to pay ($50-150 per analysis or $30/month subscription). The timing looks strong because 60-70% of coaches now regularly review student video remotely, creating built-in distribution for tools that make their review process faster or more insightful. Pose estimation AI has matured enough to run on-device at 30fps, and junior tournaments now generate over 100,000 match videos monthly that parents and coaches record but rarely analyze systematically. The market score of 82/100 and revenue potential of 84/100 reflect genuine demand in a segment accustomed to paying premium prices for incremental improvement. The first validation move should be manually analyzing 20-30 junior match videos to document specific movement patterns that coaches care about but currently miss, then showing these insights to 10 coaches to test willingness to pay $30-50 per report. The GTM path likely runs through tennis academies and high-level coaches who work with multiple juniors, offering them a coach dashboard before building a direct-to-consumer play. The biggest risk is that movement insights, while interesting, may not be actionable enough to justify recurring payment when coaches can already see obvious footwork problems, so early conversations must validate that quantified metrics (court coverage maps, reaction time distributions) actually change coaching behavior and player outcomes.

Executive Summary

Why Now

Pose estimation and multi-object tracking AI models (like MediaPipe and YOLO) became production-ready in 2022-2023, enabling accurate player skeleton tracking on mobile-recorded video without specialized cameras. The COVID-19 pandemic accelerated remote coaching adoption, and coaches now routinely receive video from students via phone recording. Junior tennis participation in the US grew 15-20% from 2020-2023 as parents invested in individual sports, expanding the prosumer coaching market. Existing ball-tracking solutions require $150+ annual subscriptions or dedicated hardware, creating price sensitivity among club coaches managing 10-30 students who each generate 2-4 match videos monthly.

Pain Points

Coaches spend hours manually reviewing match video to identify player positioning errors, footwork patterns, and tactical movement because existing tools only track ball trajectory and speed

Serious amateur and junior players lack affordable access to biomechanics and movement analysis that professional teams use, forcing them to rely on subjective coach feedback or expensive in-person analysis sessions

Tournament video analysis focuses on shot outcomes rather than the decision-making and court positioning that led to winners or errors, making it hard to diagnose recurring tactical mistakes

MVP Preview

Auto-court detection & player tracking -- Upload match video (MP4/MOV), CV model detects court boundaries and tracks both players frame-by-frame, outputs timestamped position coordinates and heatmaps showing court coverage patterns so coaches see positioning weaknesses without manual annotation

Movement efficiency scoring -- Analyzes player velocity, direction changes, and distance to ball contact points across rallies, generates efficiency score (0-100) comparing actual path to optimal path, highlights 3-5 positioning errors per set with video clips so players identify footwork waste

Split-step timing detection -- CV model identifies split-step moments (frame-level foot plant detection), measures timing relative to opponent contact (-200ms to +200ms ideal window), flags late/early splits with side-by-side comparison clips so coaches drill reaction timing with objective data

Validation Questions

Hardware dependency -- Requires stable tripod or fixed-angle recording for accurate court and player tracking; handheld parent video may produce unreliable results, limiting usable footage to 30-40% of recordings unless onboarding is carefully managed

Buyer-user split complexity -- Coaches (buyers) must see ROI in student retention or lesson efficiency, while players (users) need simple enough UX to review insights independently; misalignment could stall adoption if students do not engage with reports

Incumbent bundle pressure -- PlaySight and Swing Vision may add movement tracking to existing ball-tracking subscriptions at marginal cost, leveraging installed base and brand trust to commoditize the feature before a new entrant reaches scale

Tennis video analysis beyond ball tracking for player movement insights

Executive Summary

Tennis video analysis beyond ball tracking for player movement insights

Executive Summary

Executive Summary

Why Now

Pain Points

MVP Preview

Validation Questions

Market Validation

Market Opportunity

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