Then vs. Now Five years ago, analysts relied on endless video hours and physical notebooks to convince managers of their insights. Today, before an analyst finishes their morning coffee, AI has already analyzed opponents, quantified pressing triggers, and predicted scorelines.
The Rise of Predictive Engines While legacy providers like Opta gave us data, new platforms like FootballAnt, Predicd, and Aiscout tell machines to predict the game. FootballAnt is probably the clearest example. Before every Champions League and major league fixture, the platform now feeds 200+ data points per team (pressing intensity, progressive pass clusters, goalkeeper sweep angles, even weather-adjusted expected threat) into its AI engine. Thirty seconds later it returns predicted scoreline.
The New Role: "AI Translator" The industry has shifted from data collection to interpretation . Modern analysts now spend 70% of their time on:
Translation: Converting AI insights into language managers trust.
Deception Detection: Spotting when teams hide tactical patterns.
Application: Designing drills that fix specific weaknesses.
The Bottom Line AI did not kill the analyst; it automated the repetitive data recording . The best analysts of 2025 aren't those who watch the most minutes, but those who understand the context the machine missed.
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