BamPav Ltd
Sports photography where one frame tells the story of three rounds.
BamPav is my photography brand and registered company. Combat sports (boxing, MMA) and football events — narrative-driven coverage that captures the moment a fight turns, the emotion in the corner, and the weight of a decision in a single frame. Clients include Frontier Boxing.
- Professional event coverage: boxing shows, MMA cards, football — full event documentation from weigh-in to post-fight
- Sony A7III system — full-frame mirrorless with fast AF, high-ISO performance for arena lighting conditions
- Lightroom Classic workflow: RAW ingestion, culling, colour grading, client export — consistent quality at volume
- 20,000+ frames across events — a library that documents UK combat sports over multiple years
- 143 manually selected 5-star DNGs in current curated export — the best of the archive, not every frame
Custom imaging tools
BAM Band Removal Engine: I built the tool because the problem was real and nothing off-the-shelf fixed it.
LED/PWM banding in arena sports photography is a genuine technical problem — rolling shutter cameras in LED-lit venues produce horizontal banding artefacts that are invisible to the naked eye but obvious in still images. I wrote a PyQt6 desktop application to correct it.
- Masked Gaussian profile correction — uses painted reference regions to isolate banding from legitimate lighting gradients
- FFT notch analysis — frequency-domain detection of periodic banding, with notch removal and smooth reconstruction
- Venue profile system — calibrate once per arena, save the profile, apply to every frame from that shoot
- Batch processing — correct an entire event's output folder overnight, not one image at a time
- Before/after split compare, heatmap overlay, banding strength gauge — visual QC built in
BamPav 2.0 — in development
AI photography curation SaaS: turning a 20,000-frame problem into a scalable product.
The manual process of culling, rating, and selecting sports photography at volume is solvable with AI — but only if the model understands what makes a sports frame worth keeping. BamPav 2.0 is being built on real training data from real events, not stock photography.
- 5-star culling engine: AI-assisted selection trained on BamPav's own rated archive — what makes a sports frame worth keeping
- Portfolio selection: surfaces the strongest narrative arc from an event shoot, not just the technically best frames
- Style matching: applies a consistent visual style across a batch without manual colour grading of every frame
- Pi Photo Shuttle: Raspberry Pi-based wireless photo transfer hardware — removes the tether between camera and laptop during a shoot
- Target: productised SaaS accessible to other sports photographers, not just an internal tool