The Sports Videographer's Shortcut to Player Highlight Reels
Finding every clip of one player across hours of multi-camera sports footage used to take a full day. AI sorts it in the background while you do something else.
Every sports videographer knows the ask: "Can you make a highlight reel for player #22?"
And every sports videographer knows what that actually means: scan every file from every camera angle, pull every clip where that number is clearly visible, sort it, trim the dead air, and have it ready by tonight.
For a full tournament - 3 days, 8 cameras, 40 hours of footage - that's a full-day job. Per player.
Why sports footage is hard to sort
Most footage organisation tools are built around files, not faces. You can sort by date, by camera, by duration. You cannot sort by "every clip where this athlete appears."
The traditional answer is manual selects: skim each file at high speed, mark in-points, export. It works. It just destroys the rest of your production day.
Coaching tools like Hudl solve a different problem - they're for tactical analysis, clip tagging by play type, team performance review. They're not built to produce a personal highlight reel in an output folder.
Face recognition for sports footage
AI face recognition changes the maths. Instead of you scanning the footage, the AI does it - frame by frame, across every camera - and groups every appearance of each athlete into their own folder.
The catch with sports: faces move fast, obscuring is common (helmets, head-down motion, crowd overlap), and the same player looks different from a sideline camera versus an overhead drone shot.
ClipMine handles this with a high-accuracy model trained for dense, fast-moving footage. For sports footage with good face visibility - entrance shots, celebration moments, sideline interviews - accuracy is high.
For helmeted sports like American football or cycling, face recognition is less reliable. ClipMine works best for court sports (basketball, volleyball), martial arts and combat sports, athletics events, and any sport where faces are regularly in frame.
The workflow: upload during halftime, collect by end of day
- Upload raw footage from every camera into one ClipMine project - any format, any resolution
- Walk away. Processing runs on cloud GPUs in the background (typically 20–60 minutes for a full tournament day)
- Collect: One folder per athlete. Every clip they appear in, trimmed, ready for your NLE
- Edit the highlight reel from a pre-sorted library instead of a wall of unnamed files
For an athlete at a 3-day tournament, this replaces hours of scrubbing with a 20-minute review of what the AI produced.
The upsell this enables
Player highlight reels are high-margin deliverables. Parents, athletes, and coaches all want them - but most videographers don't have the time to produce them individually at scale.
When sorting the footage takes minutes instead of hours, individual reel packages become economically viable. You've already shot the footage. ClipMine does the selects pass. You do the edit.
Upload your next tournament.
Come back to a folder for every player that appeared on camera - ready to cut.
Try ClipMine free →No install. No credit card.