Short answer
AI has transformed football by turning large streams of match and tracking data into tactical insights and player performance metrics, helping coaches make more informed decisions on formations, substitutions, and training. It augments — but does not replace — human judgment.

Key terms

• Tracking data — positional/time data for each player and the ball.
• Event data — logged actions (passes, shots, tackles).
• Computer vision — AI that extracts data from video.
• Expected Goals (xG) — modelled probability a shot becomes a goal.
• Reinforcement learning — AI learning strategies by trial and error.

How it works

• Cameras and sensors collect player and ball positions.
• Computer-vision models convert video into structured tracking data.
• Statistical/ML models compute metrics (xG, pressing intensity, passing networks).
• Pattern-analysis and clustering reveal common tactical shapes and opponent tendencies.
• Simulation and reinforcement learning test strategy alternatives.

Simple example
An xG model rates a low-angle header as low probability, leading coaches to prioritize crossing patterns that increase higher-xG chances.

Pitfalls or nuances

• Data quality and bias (camera blindspots, small-sample noise).
• Overreliance on metrics can miss context: morale, fatigue, refereeing.
• Models can be opaque; causation vs correlation matters.

Next questions to explore

• How do specific teams integrate AI into match prep?
• What ethical/privacy issues arise from player tracking?

Further reading / references

• “The Numbers Game” — Sky Sports / analytics primers (search query: “football analytics book The Numbers Game”)
• “Expected goals and football analytics” — Opta / StatsBomb articles (search query: “expected goals xG explanation StatsBomb Opta”)