scouting · 4 minHow algorithms help clubs find talentClubs no longer just watch videos: they cross thousands of actions per match to find players others miss.arbitraje · 3 minSemi-automated offside: the AI that tracks 22 playersCameras, computer vision and a sensor in the ball help decide whether there was an offside. The decision is still human.tacticas · 4 minxG: the model that taught computers to value a shotExpected goals are the probability, estimated by a model, that a shot becomes a goal.salud · 3 minAI and injury prevention: what it can really doGPS vests and load models flag when a player is at higher risk. The decision stays with the medical staff.herramientas · 4 minAI tools an amateur club can already useCameras that follow the ball on their own, open data and assistants for planning training: technology is no longer only for the big clubs.scouting · 3 minWhat AI does with your player video (and how to help it)Many platforms use AI to tag actions and find players. A clear video helps it find you better.scouting · 4 minThe limits of AI in scouting: what an algorithm does not seeAI finds and compares players at scale, but it also inherits the biases of its data.scouting · 3 minAI can correct (or repeat) the relative age effectIn youth football, players born early in the year tend to stand out. An AI model can inherit that bias or help correct it.tacticas · 4 minHow AI detects and measures pressingWith tracking data, a model can measure how much a team presses and when.tacticas · 3 minHow AI measures space: from zone 14 to control maps“Pitch control” models estimate which team dominates each area of the pitch at every moment.tacticas · 4 minHow AI reads a match: from video to pass mapsTracking systems turn the game into data. This is what can and cannot be known.arbitraje · 4 minVAR and AI: what the machine does and what the person decidesVAR is a human team with video. AI appears in tools such as semi-automated offside.arbitraje · 3 minGoal line: computer vision that tracks the ballCamera-based systems calculate in milliseconds whether the ball crossed the line. The AI “sees” what the eye cannot.arbitraje · 4 minReferees assisted by AI? What exists and what is missingTechnology already helps with offside and the goal line. Judgement calls are still human.salud · 3 minAI and training load: warning without promisingWith GPS vests and watches, some models flag changes in load. Predicting injuries is much harder.salud · 3 minWatches, apps and AI: do they measure recovery?Devices estimate sleep and recovery with algorithms. They work better for spotting trends than for giving certainties.salud · 4 minAI and youth football: which minors' data can be used and which cannotAI tools ask for data about young players. It is worth knowing what to ask before handing it over.herramientas · 3 minAI apps for youth coaches: what they promise and what they deliverVideo tagging, planning assistants, scouting platforms: what to look at before using one.herramientas · 3 minYour team's video + AI: how to start without spendingA phone, a tripod and a tagging tool are enough to let AI help you review a match.herramientas · 4 minUsing an AI assistant to plan training (and its limits)An AI assistant can give you ideas for a session, but you are still the coach.futuro · 3 minAI and sports content: summaries, translations and personalisationAI already generates summaries and translations. The hard part is making them correct.futuro · 3 minSmart stadiums: AI in the fan experienceTickets, access, queues and safety: technology is changing how we live a match day.futuro · 4 minThe scout of the future: person plus algorithmAI will help find talent in more places. Responsibility will remain human.