Systematic logging and retrospective evaluation of the reasoning chain behind each football operations decision — including rejected options and the criteria used at each fork — to distinguish process quality from outcome quality. A good decision that produces a bad outcome should not be penalized; a bad decision that produces a good outcome should not be rewarded. This is the "decision scientist" role from big tech (Meta/Google) applied to football.
For each major decision (signing, tactical change, lineup): (1) log the options considered, (2) log the criteria and data used, (3) log what was rejected and why, (4) log the expected outcome. Retrospectively evaluate: was the process sound even if the outcome was bad? The compounding effect of consistently good processes produces structural optionality for future decisions.
Without systematic decision process logging, organizations evaluate on outcomes, not process quality. A signing that works out masks a flawed process that will fail next time. A signing that fails despite a sound process gets punished, discouraging the correct approach. The compounding effect of consistently good processes produces structural optionality, but only if the organization evaluates process separately from outcome.
When EPV of the best available action is known, comparing it to the EPV of the player's chosen action reveals decision quality independent of execution. A player who consistently chooses actions within 5% of the optimal available action has elite vision — even if their execution sometimes fails. A player who chooses actions 30% below optimal is making poor decisions regardless of their completion rate. This "decision gap" metric is the closest available proxy for football intelligence.