The Technology Behind Pitchside AI

Pitchside is powered by a custom machine learning model trained specifically on small-sided football footage. The model was built this way because grassroots football does not look like professional 11-a-side football.

Small-sided games have tighter spaces, shorter passing patterns, faster transitions, different camera angles and more crowded visual cues. A model trained only on professional broadcast footage would miss too much context. Pitchside is designed around the footage real amateur teams can actually capture.

Pitchside started with 5-a-side because it is one of the hardest and most useful grassroots formats to understand. The game is fast, compact and full of repeated actions: goals, saves, passes, tackles, assists and quick turnovers.

The model has been trained using local small-sided game footage, with many hours spent annotating clips frame by frame. This helps Pitchside learn the visual patterns of real grassroots football instead of relying on assumptions from elite-level match footage.

The same approach also supports 6-a-side and 7-a-side football, where the pitch is still smaller than full 11-a-side and the game remains player-moment heavy.

Pitchside uses computer vision to read match footage and identify football actions from video. The goal is to understand what happened in the game, not just store a recording.

The system is being built to detect key football events, assign those events to teams and players, and turn long recordings into useful match output. That includes statistics, highlights and leaderboards for players who want proof of performance.

The first release is designed to generate full match highlights and assign core football statistics to teams and individual players.

The planned first-release stats include goals, assists, saves, passes and tackles. Pitchside can identify players and assign those same statistics to individuals, which allows teams to create leaderboards and compete across each stat.

This makes Pitchside different from a basic football camera app. The goal is not only to record football matches, but to turn the footage into stats, highlights and player moments.

Pitchside works best when the match is recorded from the halfway line, above head height and facing toward one goal. The ideal setup is two phones: one pointing toward each goal. The app can then combine the data and highlights from both recordings.

A one-phone setup can also work if it captures the full pitch clearly. Ball-tracking tripods may also work well because they can help keep the main action in frame.

Most training footage was captured during British winter conditions: dark outside but floodlit. That is currently where Pitchside sees some of its best results. The system can still operate in sunlight, and performance should improve over time as more footage is processed.

Pitchside is still improving. Accuracy is not perfect yet, and the system should be presented honestly as a learning model that gets better with more footage.

The current upload process can also take longer than ideal. At the moment, footage may take up to 45 minutes to upload and process because the system waits until the game has finished recording before uploading.

A future improvement is to stream the upload during the recording period, which should reduce waiting time after the match.

Pitchside is built around a learning algorithm, so the product should improve as it processes more match footage. More recordings help the system understand different lighting conditions, player movements, pitch types and camera setups.

The long-term goal is to make football video analysis easier for grassroots players: record the game, upload the footage, receive stats, generate highlights and compete on player leaderboards without needing GPS vests or expensive camera hardware.

Most amateur players do not have analysts, camera operators or expensive football tracking systems. They have phones, teammates and matches worth remembering. Pitchside is being built for that reality.

The technology is designed to support football camera app searches, AI football analysis, football video analysis, Veo alternative comparisons, GPS vest alternative searches and football stats app users, but the product focus is simple: make grassroots match footage useful.