AI sports analytics lets coaches and analysts ask questions of tracking, event, video and wellness data in plain language and get answers backed by the numbers and the footage, so the week goes on analysis rather than exports.
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A performance department is rarely short of data. It has GPS and accelerometer load from Catapult or STATSports, event data from Opta or StatsBomb, tracking where the league provides it, video tagged in Hudl Sportscode or similar, and daily wellness questionnaires. Each sits in its own tool, keyed differently, and joining them for a single question like “how did our pressing intensity hold up in the last fifteen minutes of away games after a short turnaround?” is a day’s work.
We build the join and the question layer. Data is aligned by match, player and timestamp in a store the club controls. An analyst assistant turns a staff question into a query, returns the figures with the method shown, and links each finding to the clips it came from, so the coach watches the evidence instead of trusting a number.
Computer vision helps where data is missing: tagging set pieces or pressing triggers in your own footage, or estimating positions from a fixed tactical camera at levels without official tracking. Those outputs are marked as estimates and checked by analysts.
Analysts set the test: a set of questions they have answered by hand, with the correct figures. The assistant must reproduce them before anyone relies on it, and every answer afterwards carries the query and data it used, so a wrong number can be traced in minutes.
Wellness, injury and biometric data is special-category data under GDPR Article 9. It stays separated from general performance data, readable only by the roles that need it, and is never used to train a model. Player data shared with or licensed from providers is used only within the terms of those agreements.
in plain language
which sources, which matches
load · events · tracking
clips behind each figure
method shown
health data, by role only
Computer vision turns video into tracking and event data, machine learning finds patterns in that data, and language models now let staff ask questions of it in plain language. In practice the most useful step for many clubs is joining the data they already have, from load, events, tracking and video, so one question can draw on all of it, with each answer linked to the evidence.
Yes, within limits. Models can detect players and the ball, find and tag moments such as set pieces or pressing sequences, and estimate positions from a suitable camera angle. Accuracy depends heavily on camera position and footage quality, so we test on your own footage first and mark anything estimated as such.
It helps to have an analyst who owns the questions and checks the answers, but you do not need a data-science team to run it. The system is built so analysts and coaches can query it directly, and every answer shows its method so it can be checked without writing code.
What we built, what stage it is at, and a link to the live thing wherever there is one.
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Bring five questions your analysts answered by hand last month; we will show how the assistant would answer them, with its working.
30-minute technical call · no deck