David Geddes
CEO at International Sports Technology Association,
Strategy Head at CogSoft®
AI is running the game — ads, drafts, training, refs and fandom. The next edge isn’t more data-driven analytics; it’s knowledge-powered AI that understands the moment and helps teams or fans make smarter calls.
Philosophers debate the lines between data, information and knowledge, but few loom larger than Immanuel Kant.
His ideas still punch hard: experience is where knowledge begins, but it’s not a recording of reality. The mind is an editor — shaping what we see, hear and feel into meaning. Kant’s Theory of Knowledge provides a basis for the missing layer in high-stakes AI-driven decision systems.
AI’s problem in sport
Today’s pattern-matching AI can’t always explain why a play turned or what to do when conditions shift. Live sport runs on context — an ankle tweak, foul trouble, a whistle, wind shift or the matchup. Humans still read these cues, weigh intent and evidence and anticipate what’s next. The next AI wave involves explicitly engineering that context. Enter the graph.
Whether AI uses statistical approximations or exact software representations, it’s converging on the same backbone: graphs. Graphs model entities and relationships so systems can reason over connected context — not isolated data — to produce more accurate, explainable and usable decisions. They’re efficient, intuitive to humans and increasingly useful in graph databases, analytics, engines — especially knowledge graphs.
AI can explain, test scenarios and recommend
next moves with trustworthy results.
Applications of knowledge graphs
A knowledge graph doesn’t store stats, but things that matter — and how they connect: facts, roles, rules and the “why” behind decisions. In sports, that could mean scouting notes, coaching theories, injury protocols, officiating standards and fan behaviour.
AI can explain, test scenarios and recommend next moves with trustworthy results. The point isn’t to replace people; it’s to provide interactive support in real time – context lives in a knowledge graph.
The next AI investment wave won’t depend on data, but on how organisations translate context into decisions, and explain them under pressure. Treat a knowledge graph as a core asset, and you don’t just optimise performance; you build a defensible advantage in how the organisation thinks, acts and engages. The next billion-dollar sports brands will be built on executable knowledge graphs.
