FA-85041 / Fantasy sports scoring / Open access

Ties decided on the displayed tenths · case 01

Entries scoring 149.95 and 149.93 are paid as if tied.

Verified by executionVariant 1 · 7 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Tie grouping compares scores truncated to tenths rather than exact hundredths.

VERIFIED REPAIR

Group only entries whose hundredths scores are equal.

Unsuccessful approach: A 0.1 tolerance window still merges distinct scores.

Case contract

Daily contest entries are [entry id, score in hundredths]; payouts[r] is the prize in cents for finishing position r (0-based), positions past the list pay 0. Entries with equal scores share the positions they jointly occupy: their prizes are summed and split equally, whole cents only, with leftover cents given one each to the tied entries in ascending id order. Return id -> cents for entries receiving more than 0.

Why this case matters

Tie splitting in paid contests moves real money; cent remainders and the pooled positions must be exact.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(entries, payouts):
    ordered = sorted(entries, key=lambda e: (-e[1], e[0]))
    out = {}
    i = 0
    while i < len(ordered):
        j = i
        while j < len(ordered) and ordered[j][1] // 10 == ordered[i][1] // 10:
            j += 1
        pool = sum(payouts[i:j])
        group = sorted(e[0] for e in ordered[i:j])
        share, extra = divmod(pool, len(group))
        for k, eid in enumerate(group):
            amt = share + (1 if k < extra else 0)
            if amt > 0:
                out[eid] = amt
        i = j
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: tie detection precision',
   [[['e06', 1497], ['e21', 1500], ['e12', 1445], ['e14', 1495]], [1000, 301, 250, 1]],
   {'e06': 301, 'e12': 1, 'e14': 250, 'e21': 1000}),
  ('partial repair probe: tie detection precision',
   [[['e28', 1445], ['e26', 1497], ['e10', 1500], ['e09', 1500], ['e02', 1497]], [501, 501, 500, 500]],
   {'e02': 500, 'e09': 501, 'e10': 501, 'e26': 500}),
  ('second regression',
   [[['e02', 1493], ['e20', 1445], ['e21', 1490], ['e29', 1445], ['e14', 1497], ['e27', 1490], ['e28', 1495]],
    [1000, 501, 77, 77]],
   {'e02': 77, 'e14': 1000, 'e21': 39, 'e27': 38, 'e28': 501}),
  ('normal control 1', [[['e05', 1493], ['e24', 1445], ['e23', 1445]], [301, 199, 77, 77, 2]],
   {'e05': 301, 'e23': 138, 'e24': 138}),
  ('normal control 2', [[['e10', 1500], ['e01', 1445], ['e11', 1490]], [1000, 501, 77, 2]],
   {'e01': 77, 'e10': 1000, 'e11': 501}),
  ('normal control 3', [[['e06', 1420], ['e05', 1420], ['e11', 1420]], [77, 77]],
   {'e05': 52, 'e06': 51, 'e11': 51}),
  ('normal control 4', [[['e14', 1445], ['e16', 1500], ['e05', 1490]], [1000, 199, 100, 77]],
   {'e05': 199, 'e14': 100, 'e16': 1000})],
 [('regression: tie detection precision', [[['e16', 1495], ['e29', 1445], ['e26', 1497]], [1000, 250, 1]],
   {'e16': 250, 'e26': 1000, 'e29': 1}),
  ('partial repair probe: tie detection precision',
   [[['e07', 1323], ['e27', 1333], ['e19', 1278], ['e22', 1328], ['e18', 1326], ['e13', 1323], ['e26', 1333],
     ['e16', 1333]],
    [1000, 301, 199, 77]],
   {'e16': 500, 'e22': 77, 'e26': 500, 'e27': 500}),
  ('second regression',
   [[['e14', 1333], ['e04', 1278], ['e22', 1326], ['e05', 1333], ['e18', 1330], ['e27', 1330], ['e07', 1278]],
    [250, 77]],
   {'e05': 164, 'e14': 163}),
  ('normal control 1', [[['e04', 1420], ['e19', 1420], ['e29', 1410]], [1000, 250, 1]],
   {'e04': 625, 'e19': 625, 'e29': 1}),
  ('normal control 2', [[['e05', 1493], ['e28', 1493], ['e19', 1445]], [301, 199]], {'e05': 250, 'e28': 250}),
  ('normal control 3', [[['e08', 1500], ['e03', 1445], ['e22', 1497]], [301, 301]], {'e08': 301, 'e22': 301}),
  ('normal control 4', [[['e10', 1445], ['e23', 1500], ['e26', 1500]], [1000, 301, 199, 100]],
   {'e10': 199, 'e23': 651, 'e26': 650})],
 [('regression: tie detection precision', [[['e21', 1497], ['e14', 1493], ['e03', 1500]], [1000, 77]],
   {'e03': 1000, 'e21': 77}),
  ('partial repair probe: tie detection precision',
   [[['e25', 1278], ['e28', 1323], ['e04', 1328]], [501, 250, 199, 2]],
   {'e04': 501, 'e25': 199, 'e28': 250}),
  ('second regression',
   [[['e21', 1323], ['e08', 1326], ['e27', 1323], ['e26', 1333], ['e24', 1278], ['e02', 1326]],
    [501, 301, 250, 2, 1]],
   {'e02': 276, 'e08': 275, 'e21': 2, 'e26': 501, 'e27': 1}),
  ('normal control 1',
   [[['e21', 1500], ['e22', 1500], ['e15', 1500], ['e08', 1490], ['e23', 1445], ['e01', 1490]], [250, 77]],
   {'e15': 109, 'e21': 109, 'e22': 109}),
  ('normal control 2',
   [[['e07', 1365], ['e11', 1413], ['e15', 1413], ['e01', 1413]], [1000, 199, 100, 77, 1]],
   {'e01': 433, 'e07': 77, 'e11': 433, 'e15': 433}),
  ('normal control 3', [[['e07', 1415], ['e09', 1415], ['e05', 1415]], [1000, 500]],
   {'e05': 500, 'e07': 500, 'e09': 500}),
  ('normal control 4', [[['e15', 1500], ['e10', 1490], ['e18', 1490]], [500, 500, 301, 2, 2]],
   {'e10': 401, 'e15': 500, 'e18': 400})],
 [('regression: tie detection precision',
   [[['e17', 1323], ['e28', 1330], ['e12', 1326], ['e09', 1330], ['e19', 1330], ['e20', 1326], ['e15', 1328],
     ['e26', 1278]],
    [501, 500, 100, 100]],
   {'e09': 367, 'e15': 100, 'e19': 367, 'e28': 367}),
  ('partial repair probe: tie detection precision',
   [[['e29', 1413], ['e01', 1420], ['e28', 1365], ['e16', 1410], ['e26', 1420], ['e25', 1420]],
    [500, 301, 199, 199]],
   {'e01': 334, 'e25': 333, 'e26': 333, 'e29': 199}),
  ('second regression',
   [[['e16', 1415], ['e17', 1415], ['e14', 1413], ['e09', 1420], ['e01', 1420], ['e07', 1420], ['e12', 1420],
     ['e23', 1417]],
    [1000, 1000, 250]],
   {'e01': 563, 'e07': 563, 'e09': 562, 'e12': 562}),
  ('normal control 1',
   [[['e09', 1420], ['e03', 1365], ['e23', 1410], ['e21', 1410], ['e05', 1420], ['e17', 1365], ['e25', 1420],
     ['e13', 1410]],
    [2, 1]],
   {'e05': 1, 'e09': 1, 'e25': 1}),
  ('normal control 2',
   [[['e12', 1278], ['e27', 1323], ['e23', 1333], ['e01', 1323], ['e26', 1278]], [301, 199, 77]],
   {'e01': 138, 'e23': 301, 'e27': 138}),
  ('normal control 3', [[['e17', 1278], ['e01', 1333], ['e06', 1333]], [1000, 250, 250, 199, 1]],
   {'e01': 625, 'e06': 625, 'e17': 250}),
  ('normal control 4', [[['e21', 1410], ['e29', 1420], ['e16', 1420], ['e27', 1410]], [1, 1]],
   {'e16': 1, 'e29': 1})],
 [('regression: tie detection precision',
   [[['e12', 1445], ['e15', 1493], ['e25', 1493], ['e27', 1445], ['e22', 1497], ['e04', 1445]], [501, 250]],
   {'e15': 125, 'e22': 501, 'e25': 125}),
  ('partial repair probe: tie detection precision',
   [[['e07', 1500], ['e13', 1497], ['e27', 1495], ['e24', 1495], ['e21', 1500], ['e22', 1490]], [77, 2]],
   {'e07': 40, 'e21': 39}),
  ('second regression',
   [[['e20', 1417], ['e06', 1365], ['e19', 1410], ['e15', 1415], ['e05', 1420], ['e28', 1365], ['e29', 1410]],
    [501, 500, 500, 1]],
   {'e05': 501, 'e15': 500, 'e19': 1, 'e20': 500}),
  ('normal control 1', [[['e02', 1420], ['e28', 1410], ['e17', 1410]], [199, 2, 2]],
   {'e02': 199, 'e17': 2, 'e28': 2}),
  ('normal control 2', [[['e18', 1330], ['e15', 1330], ['e06', 1330]], [501, 500, 301, 301]],
   {'e06': 434, 'e15': 434, 'e18': 434}),
  ('normal control 3',
   [[['e06', 1500], ['e14', 1500], ['e13', 1500], ['e24', 1500], ['e10', 1500], ['e15', 1500], ['e11', 1500]],
    [1000, 250, 199, 1]],
   {'e06': 208, 'e10': 207, 'e11': 207, 'e13': 207, 'e14': 207, 'e15': 207, 'e24': 207}),
  ('normal control 4',
   [[['e12', 1500], ['e11', 1490], ['e03', 1445], ['e25', 1500], ['e13', 1500]], [501, 301, 199, 100, 77]],
   {'e03': 77, 'e11': 100, 'e12': 334, 'e13': 334, 'e25': 333})]]
for label, args, expected in fixtures[N-1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
regression: tie detection precision	`{'e06': 276, 'e12': 1, 'e14': 275, 'e21': 1000}`	`{'e06': 301, 'e12': 1, 'e14': 250, 'e21': 1000}`	Failed
partial repair probe: tie detection precision	`{'e02': 500, 'e09': 501, 'e10': 501, 'e26': 500}`	`{'e02': 500, 'e09': 501, 'e10': 501, 'e26': 500}`	Passed
second regression	`{'e02': 331, 'e14': 331, 'e21': 331, 'e27': 331, 'e28': 331}`	`{'e02': 77, 'e14': 1000, 'e21': 39, 'e27': 38, 'e28': 501}`	Failed
normal control 1	`{'e05': 301, 'e23': 138, 'e24': 138}`	`{'e05': 301, 'e23': 138, 'e24': 138}`	Passed
normal control 2	`{'e01': 77, 'e10': 1000, 'e11': 501}`	`{'e01': 77, 'e10': 1000, 'e11': 501}`	Passed
normal control 3	`{'e05': 52, 'e06': 51, 'e11': 51}`	`{'e05': 52, 'e06': 51, 'e11': 51}`	Passed
normal control 4	`{'e05': 199, 'e14': 100, 'e16': 1000}`	`{'e05': 199, 'e14': 100, 'e16': 1000}`	Passed

SHA-256 / e22fbe2a0bc6328ef50f48b4f0aaee631af0861ddeacd04a8b41144a25f7bf6d

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(entries, payouts):
    ordered = sorted(entries, key=lambda e: (-e[1], e[0]))
    out = {}
    i = 0
    while i < len(ordered):
        j = i
        while j < len(ordered) and abs(ordered[j][1] - ordered[i][1]) < 10:
            j += 1
        pool = sum(payouts[i:j])
        group = sorted(e[0] for e in ordered[i:j])
        share, extra = divmod(pool, len(group))
        for k, eid in enumerate(group):
            amt = share + (1 if k < extra else 0)
            if amt > 0:
                out[eid] = amt
        i = j
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: tie detection precision',
   [[['e06', 1497], ['e21', 1500], ['e12', 1445], ['e14', 1495]], [1000, 301, 250, 1]],
   {'e06': 301, 'e12': 1, 'e14': 250, 'e21': 1000}),
  ('partial repair probe: tie detection precision',
   [[['e28', 1445], ['e26', 1497], ['e10', 1500], ['e09', 1500], ['e02', 1497]], [501, 501, 500, 500]],
   {'e02': 500, 'e09': 501, 'e10': 501, 'e26': 500}),
  ('second regression',
   [[['e02', 1493], ['e20', 1445], ['e21', 1490], ['e29', 1445], ['e14', 1497], ['e27', 1490], ['e28', 1495]],
    [1000, 501, 77, 77]],
   {'e02': 77, 'e14': 1000, 'e21': 39, 'e27': 38, 'e28': 501}),
  ('normal control 1', [[['e05', 1493], ['e24', 1445], ['e23', 1445]], [301, 199, 77, 77, 2]],
   {'e05': 301, 'e23': 138, 'e24': 138}),
  ('normal control 2', [[['e10', 1500], ['e01', 1445], ['e11', 1490]], [1000, 501, 77, 2]],
   {'e01': 77, 'e10': 1000, 'e11': 501}),
  ('normal control 3', [[['e06', 1420], ['e05', 1420], ['e11', 1420]], [77, 77]],
   {'e05': 52, 'e06': 51, 'e11': 51}),
  ('normal control 4', [[['e14', 1445], ['e16', 1500], ['e05', 1490]], [1000, 199, 100, 77]],
   {'e05': 199, 'e14': 100, 'e16': 1000})],
 [('regression: tie detection precision', [[['e16', 1495], ['e29', 1445], ['e26', 1497]], [1000, 250, 1]],
   {'e16': 250, 'e26': 1000, 'e29': 1}),
  ('partial repair probe: tie detection precision',
   [[['e07', 1323], ['e27', 1333], ['e19', 1278], ['e22', 1328], ['e18', 1326], ['e13', 1323], ['e26', 1333],
     ['e16', 1333]],
    [1000, 301, 199, 77]],
   {'e16': 500, 'e22': 77, 'e26': 500, 'e27': 500}),
  ('second regression',
   [[['e14', 1333], ['e04', 1278], ['e22', 1326], ['e05', 1333], ['e18', 1330], ['e27', 1330], ['e07', 1278]],
    [250, 77]],
   {'e05': 164, 'e14': 163}),
  ('normal control 1', [[['e04', 1420], ['e19', 1420], ['e29', 1410]], [1000, 250, 1]],
   {'e04': 625, 'e19': 625, 'e29': 1}),
  ('normal control 2', [[['e05', 1493], ['e28', 1493], ['e19', 1445]], [301, 199]], {'e05': 250, 'e28': 250}),
  ('normal control 3', [[['e08', 1500], ['e03', 1445], ['e22', 1497]], [301, 301]], {'e08': 301, 'e22': 301}),
  ('normal control 4', [[['e10', 1445], ['e23', 1500], ['e26', 1500]], [1000, 301, 199, 100]],
   {'e10': 199, 'e23': 651, 'e26': 650})],
 [('regression: tie detection precision', [[['e21', 1497], ['e14', 1493], ['e03', 1500]], [1000, 77]],
   {'e03': 1000, 'e21': 77}),
  ('partial repair probe: tie detection precision',
   [[['e25', 1278], ['e28', 1323], ['e04', 1328]], [501, 250, 199, 2]],
   {'e04': 501, 'e25': 199, 'e28': 250}),
  ('second regression',
   [[['e21', 1323], ['e08', 1326], ['e27', 1323], ['e26', 1333], ['e24', 1278], ['e02', 1326]],
    [501, 301, 250, 2, 1]],
   {'e02': 276, 'e08': 275, 'e21': 2, 'e26': 501, 'e27': 1}),
  ('normal control 1',
   [[['e21', 1500], ['e22', 1500], ['e15', 1500], ['e08', 1490], ['e23', 1445], ['e01', 1490]], [250, 77]],
   {'e15': 109, 'e21': 109, 'e22': 109}),
  ('normal control 2',
   [[['e07', 1365], ['e11', 1413], ['e15', 1413], ['e01', 1413]], [1000, 199, 100, 77, 1]],
   {'e01': 433, 'e07': 77, 'e11': 433, 'e15': 433}),
  ('normal control 3', [[['e07', 1415], ['e09', 1415], ['e05', 1415]], [1000, 500]],
   {'e05': 500, 'e07': 500, 'e09': 500}),
  ('normal control 4', [[['e15', 1500], ['e10', 1490], ['e18', 1490]], [500, 500, 301, 2, 2]],
   {'e10': 401, 'e15': 500, 'e18': 400})],
 [('regression: tie detection precision',
   [[['e17', 1323], ['e28', 1330], ['e12', 1326], ['e09', 1330], ['e19', 1330], ['e20', 1326], ['e15', 1328],
     ['e26', 1278]],
    [501, 500, 100, 100]],
   {'e09': 367, 'e15': 100, 'e19': 367, 'e28': 367}),
  ('partial repair probe: tie detection precision',
   [[['e29', 1413], ['e01', 1420], ['e28', 1365], ['e16', 1410], ['e26', 1420], ['e25', 1420]],
    [500, 301, 199, 199]],
   {'e01': 334, 'e25': 333, 'e26': 333, 'e29': 199}),
  ('second regression',
   [[['e16', 1415], ['e17', 1415], ['e14', 1413], ['e09', 1420], ['e01', 1420], ['e07', 1420], ['e12', 1420],
     ['e23', 1417]],
    [1000, 1000, 250]],
   {'e01': 563, 'e07': 563, 'e09': 562, 'e12': 562}),
  ('normal control 1',
   [[['e09', 1420], ['e03', 1365], ['e23', 1410], ['e21', 1410], ['e05', 1420], ['e17', 1365], ['e25', 1420],
     ['e13', 1410]],
    [2, 1]],
   {'e05': 1, 'e09': 1, 'e25': 1}),
  ('normal control 2',
   [[['e12', 1278], ['e27', 1323], ['e23', 1333], ['e01', 1323], ['e26', 1278]], [301, 199, 77]],
   {'e01': 138, 'e23': 301, 'e27': 138}),
  ('normal control 3', [[['e17', 1278], ['e01', 1333], ['e06', 1333]], [1000, 250, 250, 199, 1]],
   {'e01': 625, 'e06': 625, 'e17': 250}),
  ('normal control 4', [[['e21', 1410], ['e29', 1420], ['e16', 1420], ['e27', 1410]], [1, 1]],
   {'e16': 1, 'e29': 1})],
 [('regression: tie detection precision',
   [[['e12', 1445], ['e15', 1493], ['e25', 1493], ['e27', 1445], ['e22', 1497], ['e04', 1445]], [501, 250]],
   {'e15': 125, 'e22': 501, 'e25': 125}),
  ('partial repair probe: tie detection precision',
   [[['e07', 1500], ['e13', 1497], ['e27', 1495], ['e24', 1495], ['e21', 1500], ['e22', 1490]], [77, 2]],
   {'e07': 40, 'e21': 39}),
  ('second regression',
   [[['e20', 1417], ['e06', 1365], ['e19', 1410], ['e15', 1415], ['e05', 1420], ['e28', 1365], ['e29', 1410]],
    [501, 500, 500, 1]],
   {'e05': 501, 'e15': 500, 'e19': 1, 'e20': 500}),
  ('normal control 1', [[['e02', 1420], ['e28', 1410], ['e17', 1410]], [199, 2, 2]],
   {'e02': 199, 'e17': 2, 'e28': 2}),
  ('normal control 2', [[['e18', 1330], ['e15', 1330], ['e06', 1330]], [501, 500, 301, 301]],
   {'e06': 434, 'e15': 434, 'e18': 434}),
  ('normal control 3',
   [[['e06', 1500], ['e14', 1500], ['e13', 1500], ['e24', 1500], ['e10', 1500], ['e15', 1500], ['e11', 1500]],
    [1000, 250, 199, 1]],
   {'e06': 208, 'e10': 207, 'e11': 207, 'e13': 207, 'e14': 207, 'e15': 207, 'e24': 207}),
  ('normal control 4',
   [[['e12', 1500], ['e11', 1490], ['e03', 1445], ['e25', 1500], ['e13', 1500]], [501, 301, 199, 100, 77]],
   {'e03': 77, 'e11': 100, 'e12': 334, 'e13': 334, 'e25': 333})]]
for label, args, expected in fixtures[N-1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
regression: tie detection precision	`{'e06': 517, 'e12': 1, 'e14': 517, 'e21': 517}`	`{'e06': 301, 'e12': 1, 'e14': 250, 'e21': 1000}`	Failed
partial repair probe: tie detection precision	`{'e02': 501, 'e09': 501, 'e10': 500, 'e26': 500}`	`{'e02': 500, 'e09': 501, 'e10': 501, 'e26': 500}`	Failed
second regression	`{'e02': 331, 'e14': 331, 'e21': 331, 'e27': 331, 'e28': 331}`	`{'e02': 77, 'e14': 1000, 'e21': 39, 'e27': 38, 'e28': 501}`	Failed
normal control 1	`{'e05': 301, 'e23': 138, 'e24': 138}`	`{'e05': 301, 'e23': 138, 'e24': 138}`	Passed
normal control 2	`{'e01': 77, 'e10': 1000, 'e11': 501}`	`{'e01': 77, 'e10': 1000, 'e11': 501}`	Passed
normal control 3	`{'e05': 52, 'e06': 51, 'e11': 51}`	`{'e05': 52, 'e06': 51, 'e11': 51}`	Passed
normal control 4	`{'e05': 199, 'e14': 100, 'e16': 1000}`	`{'e05': 199, 'e14': 100, 'e16': 1000}`	Passed

SHA-256 / 0d1ee8989ce8c4f55eefe5324022aacbef9d98c78e4d7d70fb285a6aed1fe3a2

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(entries, payouts):
    ordered = sorted(entries, key=lambda e: (-e[1], e[0]))
    out = {}
    i = 0
    while i < len(ordered):
        j = i
        while j < len(ordered) and ordered[j][1] == ordered[i][1]:
            j += 1
        pool = sum(payouts[i:j])
        group = sorted(e[0] for e in ordered[i:j])
        share, extra = divmod(pool, len(group))
        for k, eid in enumerate(group):
            amt = share + (1 if k < extra else 0)
            if amt > 0:
                out[eid] = amt
        i = j
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: tie detection precision',
   [[['e06', 1497], ['e21', 1500], ['e12', 1445], ['e14', 1495]], [1000, 301, 250, 1]],
   {'e06': 301, 'e12': 1, 'e14': 250, 'e21': 1000}),
  ('partial repair probe: tie detection precision',
   [[['e28', 1445], ['e26', 1497], ['e10', 1500], ['e09', 1500], ['e02', 1497]], [501, 501, 500, 500]],
   {'e02': 500, 'e09': 501, 'e10': 501, 'e26': 500}),
  ('second regression',
   [[['e02', 1493], ['e20', 1445], ['e21', 1490], ['e29', 1445], ['e14', 1497], ['e27', 1490], ['e28', 1495]],
    [1000, 501, 77, 77]],
   {'e02': 77, 'e14': 1000, 'e21': 39, 'e27': 38, 'e28': 501}),
  ('normal control 1', [[['e05', 1493], ['e24', 1445], ['e23', 1445]], [301, 199, 77, 77, 2]],
   {'e05': 301, 'e23': 138, 'e24': 138}),
  ('normal control 2', [[['e10', 1500], ['e01', 1445], ['e11', 1490]], [1000, 501, 77, 2]],
   {'e01': 77, 'e10': 1000, 'e11': 501}),
  ('normal control 3', [[['e06', 1420], ['e05', 1420], ['e11', 1420]], [77, 77]],
   {'e05': 52, 'e06': 51, 'e11': 51}),
  ('normal control 4', [[['e14', 1445], ['e16', 1500], ['e05', 1490]], [1000, 199, 100, 77]],
   {'e05': 199, 'e14': 100, 'e16': 1000})],
 [('regression: tie detection precision', [[['e16', 1495], ['e29', 1445], ['e26', 1497]], [1000, 250, 1]],
   {'e16': 250, 'e26': 1000, 'e29': 1}),
  ('partial repair probe: tie detection precision',
   [[['e07', 1323], ['e27', 1333], ['e19', 1278], ['e22', 1328], ['e18', 1326], ['e13', 1323], ['e26', 1333],
     ['e16', 1333]],
    [1000, 301, 199, 77]],
   {'e16': 500, 'e22': 77, 'e26': 500, 'e27': 500}),
  ('second regression',
   [[['e14', 1333], ['e04', 1278], ['e22', 1326], ['e05', 1333], ['e18', 1330], ['e27', 1330], ['e07', 1278]],
    [250, 77]],
   {'e05': 164, 'e14': 163}),
  ('normal control 1', [[['e04', 1420], ['e19', 1420], ['e29', 1410]], [1000, 250, 1]],
   {'e04': 625, 'e19': 625, 'e29': 1}),
  ('normal control 2', [[['e05', 1493], ['e28', 1493], ['e19', 1445]], [301, 199]], {'e05': 250, 'e28': 250}),
  ('normal control 3', [[['e08', 1500], ['e03', 1445], ['e22', 1497]], [301, 301]], {'e08': 301, 'e22': 301}),
  ('normal control 4', [[['e10', 1445], ['e23', 1500], ['e26', 1500]], [1000, 301, 199, 100]],
   {'e10': 199, 'e23': 651, 'e26': 650})],
 [('regression: tie detection precision', [[['e21', 1497], ['e14', 1493], ['e03', 1500]], [1000, 77]],
   {'e03': 1000, 'e21': 77}),
  ('partial repair probe: tie detection precision',
   [[['e25', 1278], ['e28', 1323], ['e04', 1328]], [501, 250, 199, 2]],
   {'e04': 501, 'e25': 199, 'e28': 250}),
  ('second regression',
   [[['e21', 1323], ['e08', 1326], ['e27', 1323], ['e26', 1333], ['e24', 1278], ['e02', 1326]],
    [501, 301, 250, 2, 1]],
   {'e02': 276, 'e08': 275, 'e21': 2, 'e26': 501, 'e27': 1}),
  ('normal control 1',
   [[['e21', 1500], ['e22', 1500], ['e15', 1500], ['e08', 1490], ['e23', 1445], ['e01', 1490]], [250, 77]],
   {'e15': 109, 'e21': 109, 'e22': 109}),
  ('normal control 2',
   [[['e07', 1365], ['e11', 1413], ['e15', 1413], ['e01', 1413]], [1000, 199, 100, 77, 1]],
   {'e01': 433, 'e07': 77, 'e11': 433, 'e15': 433}),
  ('normal control 3', [[['e07', 1415], ['e09', 1415], ['e05', 1415]], [1000, 500]],
   {'e05': 500, 'e07': 500, 'e09': 500}),
  ('normal control 4', [[['e15', 1500], ['e10', 1490], ['e18', 1490]], [500, 500, 301, 2, 2]],
   {'e10': 401, 'e15': 500, 'e18': 400})],
 [('regression: tie detection precision',
   [[['e17', 1323], ['e28', 1330], ['e12', 1326], ['e09', 1330], ['e19', 1330], ['e20', 1326], ['e15', 1328],
     ['e26', 1278]],
    [501, 500, 100, 100]],
   {'e09': 367, 'e15': 100, 'e19': 367, 'e28': 367}),
  ('partial repair probe: tie detection precision',
   [[['e29', 1413], ['e01', 1420], ['e28', 1365], ['e16', 1410], ['e26', 1420], ['e25', 1420]],
    [500, 301, 199, 199]],
   {'e01': 334, 'e25': 333, 'e26': 333, 'e29': 199}),
  ('second regression',
   [[['e16', 1415], ['e17', 1415], ['e14', 1413], ['e09', 1420], ['e01', 1420], ['e07', 1420], ['e12', 1420],
     ['e23', 1417]],
    [1000, 1000, 250]],
   {'e01': 563, 'e07': 563, 'e09': 562, 'e12': 562}),
  ('normal control 1',
   [[['e09', 1420], ['e03', 1365], ['e23', 1410], ['e21', 1410], ['e05', 1420], ['e17', 1365], ['e25', 1420],
     ['e13', 1410]],
    [2, 1]],
   {'e05': 1, 'e09': 1, 'e25': 1}),
  ('normal control 2',
   [[['e12', 1278], ['e27', 1323], ['e23', 1333], ['e01', 1323], ['e26', 1278]], [301, 199, 77]],
   {'e01': 138, 'e23': 301, 'e27': 138}),
  ('normal control 3', [[['e17', 1278], ['e01', 1333], ['e06', 1333]], [1000, 250, 250, 199, 1]],
   {'e01': 625, 'e06': 625, 'e17': 250}),
  ('normal control 4', [[['e21', 1410], ['e29', 1420], ['e16', 1420], ['e27', 1410]], [1, 1]],
   {'e16': 1, 'e29': 1})],
 [('regression: tie detection precision',
   [[['e12', 1445], ['e15', 1493], ['e25', 1493], ['e27', 1445], ['e22', 1497], ['e04', 1445]], [501, 250]],
   {'e15': 125, 'e22': 501, 'e25': 125}),
  ('partial repair probe: tie detection precision',
   [[['e07', 1500], ['e13', 1497], ['e27', 1495], ['e24', 1495], ['e21', 1500], ['e22', 1490]], [77, 2]],
   {'e07': 40, 'e21': 39}),
  ('second regression',
   [[['e20', 1417], ['e06', 1365], ['e19', 1410], ['e15', 1415], ['e05', 1420], ['e28', 1365], ['e29', 1410]],
    [501, 500, 500, 1]],
   {'e05': 501, 'e15': 500, 'e19': 1, 'e20': 500}),
  ('normal control 1', [[['e02', 1420], ['e28', 1410], ['e17', 1410]], [199, 2, 2]],
   {'e02': 199, 'e17': 2, 'e28': 2}),
  ('normal control 2', [[['e18', 1330], ['e15', 1330], ['e06', 1330]], [501, 500, 301, 301]],
   {'e06': 434, 'e15': 434, 'e18': 434}),
  ('normal control 3',
   [[['e06', 1500], ['e14', 1500], ['e13', 1500], ['e24', 1500], ['e10', 1500], ['e15', 1500], ['e11', 1500]],
    [1000, 250, 199, 1]],
   {'e06': 208, 'e10': 207, 'e11': 207, 'e13': 207, 'e14': 207, 'e15': 207, 'e24': 207}),
  ('normal control 4',
   [[['e12', 1500], ['e11', 1490], ['e03', 1445], ['e25', 1500], ['e13', 1500]], [501, 301, 199, 100, 77]],
   {'e03': 77, 'e11': 100, 'e12': 334, 'e13': 334, 'e25': 333})]]
for label, args, expected in fixtures[N-1]:
    check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
regression: tie detection precision	`{'e06': 301, 'e12': 1, 'e14': 250, 'e21': 1000}`	`{'e06': 301, 'e12': 1, 'e14': 250, 'e21': 1000}`	Passed
partial repair probe: tie detection precision	`{'e02': 500, 'e09': 501, 'e10': 501, 'e26': 500}`	`{'e02': 500, 'e09': 501, 'e10': 501, 'e26': 500}`	Passed
second regression	`{'e02': 77, 'e14': 1000, 'e21': 39, 'e27': 38, 'e28': 501}`	`{'e02': 77, 'e14': 1000, 'e21': 39, 'e27': 38, 'e28': 501}`	Passed
normal control 1	`{'e05': 301, 'e23': 138, 'e24': 138}`	`{'e05': 301, 'e23': 138, 'e24': 138}`	Passed
normal control 2	`{'e01': 77, 'e10': 1000, 'e11': 501}`	`{'e01': 77, 'e10': 1000, 'e11': 501}`	Passed
normal control 3	`{'e05': 52, 'e06': 51, 'e11': 51}`	`{'e05': 52, 'e06': 51, 'e11': 51}`	Passed
normal control 4	`{'e05': 199, 'e14': 100, 'e16': 1000}`	`{'e05': 199, 'e14': 100, 'e16': 1000}`	Passed

SHA-256 / 033c8c436096bd38ac160262449d93d396953a033fe6b1a82826dbdc9ad3b54f

Verification & scope

A deterministic toy scoring contract stipulated for this example; it is not the rulebook of any real fantasy platform. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:50:36.656980+00:00.

Case digest / 36f860230767c807d872c8891dd02934d9253417b7e206c67d6edd38a7d5b2d4