docs(fifa17): record discard economy validation evidence and impact

Adds scripts/fifa17-discard-impact.py (owned-instance economic impact, computed from the shipped implementation's matrix -- informational, never a reason to alter a value) and records the measured results. Owned club, 1993 instances: legacy 1,820,700 -> recovered 19,128,031 = 10.51x. Players 10.53x, manager 1.88x, consumables 0.19x (the ladder overpaid them ~5x), staff 0.24x, club items 900 -> 0.
2026-08-21 23:59:35 +00:00
parent a96d06dbc0
commit 0a7c4e129c
1 changed files with 76 additions and 0 deletions
@@ -0,0 +1,76 @@
+#!/usr/bin/env python3
+"""Economic impact of the recovered discard table, over OWNED instances.
+
+Informational only: it exists to make the promotion decision explicit, never to
+justify altering a value. Values come from the matrix emitted by
+`cargo run -p openfut-adapter-fifa17 --example discard_matrix`, i.e. the shipped
+implementation.
+
+Usage:
+    python3 scripts/fifa17-discard-impact.py --matrix /tmp/discard-matrix.csv
+"""
+import argparse
+import collections
+import csv
+import sqlite3
+
+DB = "/home/alex/openfut-sold-staging/staging-core.db"
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--matrix", required=True)
+    ap.add_argument("--db", default=DB)
+    a = ap.parse_args()
+
+    matrix = {}
+    with open(a.matrix) as fh:
+        for row in csv.DictReader(fh):
+            matrix[row["definition"]] = row
+
+    con = sqlite3.connect("file:%s?mode=ro" % a.db, uri=True)
+    owned = con.execute("SELECT card_id, content_kind FROM owned_cards").fetchall()
+    con.close()
+
+    by_kind = collections.defaultdict(lambda: [0, 0, 0])  # n, legacy, recovered
+    deltas = []
+    missing = 0
+    for card_id, kind in owned:
+        row = matrix.get(card_id)
+        if row is None:
+            missing += 1
+            continue
+        legacy = int(row["legacy"])
+        rec = int(row["recovered"]) if row["recovered"] != "-" else legacy
+        b = by_kind[kind]
+        b[0] += 1
+        b[1] += legacy
+        b[2] += rec
+        deltas.append((rec - legacy, kind, card_id, legacy, rec))
+
+    print("OWNED-INSTANCE DISCARD IMPACT  (informational)")
+    print("%-12s %6s %14s %14s %8s" % ("kind", "n", "legacy", "recovered", "ratio"))
+    tl = tr = tn = 0
+    for kind in sorted(by_kind):
+        n, legacy, rec = by_kind[kind]
+        ratio = (rec / legacy) if legacy else 0
+        print("%-12s %6d %14s %14s %7.2fx" % (kind, n, f"{legacy:,}", f"{rec:,}", ratio))
+        tl += legacy
+        tr += rec
+        tn += n
+    print("%-12s %6d %14s %14s %7.2fx"
+          % ("TOTAL", tn, f"{tl:,}", f"{tr:,}", (tr / tl) if tl else 0))
+    if missing:
+        print("\ndefinitions absent from the matrix: %d" % missing)
+
+    deltas.sort()
+    print("\nlargest DECREASES")
+    for d, kind, cid, legacy, rec in deltas[:5]:
+        print("  %-10s %-18s %6d -> %-7d  (%+d)" % (kind, cid, legacy, rec, d))
+    print("largest INCREASES")
+    for d, kind, cid, legacy, rec in deltas[-5:][::-1]:
+        print("  %-10s %-18s %6d -> %-7d  (%+d)" % (kind, cid, legacy, rec, d))
+
+
+if __name__ == "__main__":
+    main()