Sports Video Intelligence · Hangzhou, China

From match footage to
tactical insight.

LynxAct combines vision-language models, multi-object tracking, and LLM reasoning to automate sports video analysis — from annotation pipelines to coach-ready tactical reports.

View on GitHub See it in action
98.7%
zero-shot VLM accuracy
on activity recognition probe
±1 m
pitch calibration
reprojection accuracy
4-tool
function-calling loop
in the AI Coach Agent

Products

One pipeline, three layers

We build open tooling for every layer of sports video understanding — so clubs, analysts, and researchers can adopt exactly the depth they need.

Tactical Annotation

VLM-assisted pre-annotation with human expert review. Annotators verify machine proposals instead of labeling from scratch — cutting manual tagging time by an order of magnitude while keeping expert judgment in the loop.

VLM pre-labeling · Expert verification · Event taxonomy

AI Coach Agent

An LLM agent that turns match data into natural-language tactical reports. A four-tool function-calling loop queries events, surfaces patterns, and drafts analysis a coach can act on — backed by a curated library of 62 technique cards.

LLM reasoning · Function calling · Tactical reports

Player Tracking

An open pipeline that extracts 2D pitch coordinates from broadcast footage — detection, multi-object tracking, and pitch calibration. Validated on professional match footage with sub-meter reprojection accuracy.

YOLO · ByteTrack · Homography calibration

Technology

Measured, not promised

Every capability on this page is backed by a reproducible probe on real data. Here is what we have verified so far.

98.7%Zero-shot VLM accuracy on a 150-image activity-recognition probe (Stanford40), 8 of 10 classes perfect
0.62–1.25mPitch-calibration reprojection error on professional broadcast footage, 5-anchor homography
95.7%Of frames with ≥8 tracked players — 12.84 players per frame on average, YOLOv8 + ByteTrack
62Technique cards in the Coach Agent knowledge base, covering dribbling, passing, and finishing
Match footage
Detection & tracking
Pitch calibration
Tactical events
LLM analysis
Coach report

In action

Working software, not slideware

Multi-source fusion pre-annotation workflow: fusion vote, one-click correction, and adjudication
Pre-annotation studio. Three prediction sources are fused into a single proposal; the expert confirms or corrects with one click.
AI Coach Agent output: automatically identified top moments from a match
Coach Agent output. The agent scans match data and surfaces the moments that matter, with reasoning attached.

Open source

Open source, on the way

Our core pipelines are being prepared for public release under Apache-2.0 — documentation, license cleanup, and reproducible probes included. Watch this space.

lynxact-coach Coming soon

AI Coach Agent for football tactical analysis — a four-tool function-calling loop over match data, producing natural-language coaching reports.

lynxmove-oss Coming soon

Multi-source fusion pre-annotation toolkit for sports video — VLM proposals, player-prior signals, and expert review in one web UI.

Company

Built by practitioners

Hangzhou Lingxu Technology Co., Ltd.

杭州凌序科技有限公司

LynxAct is developed by Hangzhou Lingxu Technology, a software company founded in 2026 and headquartered in Hangzhou, China. We build AI tooling for sports analysis — annotation pipelines, tracking infrastructure, and LLM-powered reporting.

Founded
2026
Headquarters
Hangzhou, China
Focus
Sports AI · Video intelligence
Stage
Bootstrapped, pre-seed

Founder

Zhongjie Li

Zhongjie holds an M.Sc. in Physical Activity for Health from the University of Edinburgh and has worked across sports data analysis and R-based research pipelines. He started LynxAct to bring modern vision-language and LLM tooling to a field still run on manual tagging.

Reach us at founder@lxlynx.com