Short answer: AI has made football strategy more data-driven by finding patterns in player movement, opponent tendencies, and optimal plays. Coaches now use models to inform tactics, substitutions, and set-piece planning in ways that weren’t possible with intuition alone.

Key terms

• Machine learning — algorithms that learn patterns from data.
• Event/Tracking data — event: discrete actions (passes, shots); tracking: continuous player/location coordinates.
• Expected goals (xG) — a model estimating chance quality for each shot.
• Decision-support — tools that give coaches actionable recommendations.

How it works

• Collects large event and tracking datasets from matches.
• Trains models to predict outcomes (goals, pass success, opponent moves).
• Clusters patterns (e.g., common attacking shapes) to reveal tendencies.
• Optimizes tactics (pressing triggers, formation changes, substitution timing).
• Integrates real‑time feeds for in-match adjustments.

Simple example

• An AI model spots the opponent concedes more from crosses after the 70th minute and suggests switching to wide attacks late in matches.

Pitfalls or nuances

• Data quality and representativeness matter; models can overfit to specific teams.
• Human judgment remains crucial—AI suggests, coaches decide.

Next questions to explore

• How do teams integrate AI into coaching workflows?
• What data privacy or ethical issues arise from player tracking?

Further reading / references

• “The Expected Goals Philosophy” — Michael Caley et al. (book).
• Search query: “football tracking data machine learning research” (use for academic papers, e.g., MIT Sloan Sports Analytics Conference).