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As of May 2026Footnote Metrics3 min read2 references cited

The Business Value of roi_decisions Archive — Reinforcement Decisions as Organizational Memory

Pro soccer clubs' greatest weakness: 'organizational memory disappears with each reinforcement-staff generation change.' New staff don't know the reasoning behind past decisions and repeat the same mistakes. Footnote's roi_decisions table persists who, when, what, and why for every reinforcement decision. Outcome_value_yen recorded 6 months later enables 'human judgment vs algorithm proposal' precision comparison and feeds G9 ML calibration. This article covers structure, usage patterns, and organizational learning effects.

Why Decision Archive Generates Business Value

Reinforcement staff average 3-5 year tenure. Over 10 years, 2-3 generation changes. Knowledge lost without databases.

J League club GM/SD average tenure is 3-5 years. Within a decade, 2-3 staff generations turn over. New staff inherit operations without knowing predecessors' reasoning — 'sold player X 3 years ago, was wrong, should have HOLD' learning is lost in oral tradition.

Excel limitations

Many clubs use Excel for decision tracking, but (1) file scatter, (2) non-uniform format, (3) zero search, (4) manual outcome verification — none yield organizational learning. Footnote's relational design links roi_decisions to player_profiles / roi_snapshots, enabling SQL queries like 'past 3 years of SELL decisions for CBs with outcomes' in one line.

roi_decisions Structure

7 decision fields at insertion + 3 outcome fields added later. Who, when, what, why decided — and how it turned out, all preserved.

Decision-time fields

  • player_id: target
  • club_id: own club (RLS-isolated)
  • decided_by_user_id: decider's Footnote user ID
  • decided_at: timestamp
  • decision: SELL / EXTEND / LOAN_OUT / HOLD / PROMOTE_TO_YOUTH etc.
  • target_window: '2026 summer', 'next season', etc.
  • rationale: free text, ≤2000 chars

Outcome fields (added later)

  • outcome_recorded_at: when outcome was logged
  • outcome_text: e.g., 'next season contract extended, starter role'
  • outcome_value_yen: actual transaction value (optional but recommended)

Outcome Recording — The Verification Cycle

Recording outcomes 3-6 months after the decision is what fuels organizational learning. The 'Pending Outcomes' banner in Squad ROI keeps the cycle going.

Squad ROI dashboard displays 'Pending Outcomes: N' banner (3+ months old + outcome empty). /club/squad-roi/pending-outcomes lists + bulk-enters. Monthly 30-minute habit sustains organizational learning.

Writing better outcomes

  • 1-2 sentences on what happened: e.g., 'External offer accepted, sold for ¥60M'
  • Variance from prediction: 'Predicted ¥50M → actual ¥60M, +20% above'
  • Lessons learned: 'Contract-residual-1yr players: early sale economically rational'
  • Enter outcome_value_yen: required for G9 ML learning sample

Three Organizational Learning Patterns

Accumulated decisions yield value in quarterly reviews, new-staff onboarding, and ML calibration.

  1. Quarterly precision review: aggregate past 6-12 months' outcome-confirmed decisions for absolute-error metrics, decision-type accuracy, per-decider variance
  2. New-staff onboarding: 3 years of roi_decisions becomes the philosophy reference for incoming reinforcement staff
  3. G9 ML calibration: outcome_value_yen feeds club_roi_calibration EMA update, club-specific bias becomes learned, FMV optimizes

Anti-patterns to Avoid

Records alone don't create learning. Four traps that turn archive into dead data.

  1. Empty rationale or 'decided': future utility = zero — write 2-3 lines minimum, specifying data viewed + judgment process
  2. No outcome recording: ML calibration starved, accuracy never improves — habituate monthly outcome backlog check
  3. Post-hoc rationalization: editing rationale after results destroys learning — keep decision-time rationale immutable, separate 'lessons' field
  4. Single-person record-keeping: GM-only logging vanishes on departure — multiple owner/admin records + monthly review meetings

References

  1. [1] Argote L., Miron-Spektor E. (2011). “Organizational learning: From experience to knowledge” Organization Science.
  2. [2] Kahneman D., Lovallo D., Sibony O. (2011). “Before You Make That Big Decision” Harvard Business Review.

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Last updated: 2026-05-18 ・ Footnote Editorial