Structuring an analytics department's scope, headcount, and tool choices based on the club's specific constraints — budget, salary cap structure, data access, player provenance breadth, and competition level — rather than copying a template from a top club. A League One club with 2 analysts needs different infrastructure than a Champions League club with 15. The "when in doubt, database" principle: always prioritize building queryable data infrastructure because it compounds in value.
Assess: (1) What decisions does analytics need to inform? (2) Who are the decision-makers and what's their analytics literacy? (3) What data is available? (4) Is analytics embedding into existing processes or co-designing new ones? Build tools that make users self-sufficient ("tools not answers") rather than creating analyst bottlenecks.
Analytics departments that deliver answers create bottlenecks and distrust. Departments that deliver self-service tools let decision-makers explore data themselves and draw their own conclusions. The psychological mechanism: people trust conclusions they reached themselves more than conclusions handed to them, even if the underlying data is identical.
Analytics department size is not correlated with impact. A 2-person team embedded in tactical meetings, solving the coach's actual pain points, will have more influence on match outcomes than a 10-person team building sophisticated models in isolation. The constraint on analytics impact in football is almost never technical capability — it's organizational integration and problem selection.