FA-85126 / Fantasy sports scoring / Open access
Four double-digit categories earn no bonus · case 01
A rare quadruple-double scores no bonus at all.
ROOT CAUSE
The triple-double tier matches only exactly three categories.
VERIFIED REPAIR
Award the triple-double bonus for three or more categories.
Unsuccessful approach: Widening the double-double branch instead pays a quadruple-double the smaller bonus.
Case contract
Score a basketball line in tenths: points 1.0, rebounds (offensive + defensive) 1.2, assists 1.5, steals 3, blocks 3, turnovers -1, threes made 0.5 extra. Count double-digit categories among points, rebounds, assists, steals and blocks; three or more earns only the triple-double bonus, exactly two earns the double-double bonus.
Why this case matters
Double-double and triple-double bonuses are exclusive tiers in points leagues and are frequently double-awarded.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(line, bonus):
line = dict(line, reb=line['oreb'] + line['dreb'])
w = {'pts': 10, 'reb': 12, 'ast': 15, 'stl': 30, 'blk': 30, 'tov': -10, 'fg3m': 5}
score = sum(w[k] * line[k] for k in w)
doubles = sum(1 for k in ('pts', 'reb', 'ast', 'stl', 'blk') if line[k] >= 10)
if doubles == 3:
score += bonus['td']
elif doubles == 2:
score += bonus['dd']
return score
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 11, 'dreb': 10, 'fg3m': 1, 'oreb': 5, 'pts': 35, 'stl': 10, 'tov': 0},
{'dd': 15, 'td': 30}],
1345),
('partial repair probe: quadruple-double tier',
[{'ast': 10, 'blk': 10, 'dreb': 10, 'fg3m': 2, 'oreb': 4, 'pts': 11, 'stl': 3, 'tov': 6},
{'dd': 15, 'td': 30}],
798),
('second regression',
[{'ast': 10, 'blk': 11, 'dreb': 9, 'fg3m': 4, 'oreb': 3, 'pts': 21, 'stl': 2, 'tov': 4},
{'dd': 15, 'td': 30}],
904),
('normal control 1',
[{'ast': 10, 'blk': 2, 'dreb': 0, 'fg3m': 0, 'oreb': 6, 'pts': 10, 'stl': 0, 'tov': 1},
{'dd': 15, 'td': 30}],
387),
('normal control 2',
[{'ast': 11, 'blk': 1, 'dreb': 4, 'fg3m': 2, 'oreb': 2, 'pts': 10, 'stl': 3, 'tov': 5},
{'dd': 15, 'td': 30}],
432),
('normal control 3',
[{'ast': 10, 'blk': 2, 'dreb': 9, 'fg3m': 3, 'oreb': 2, 'pts': 1, 'stl': 2, 'tov': 5},
{'dd': 15, 'td': 30}],
392),
('normal control 4',
[{'ast': 11, 'blk': 2, 'dreb': 7, 'fg3m': 5, 'oreb': 1, 'pts': 10, 'stl': 0, 'tov': 2},
{'dd': 15, 'td': 30}],
441)],
[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 0, 'dreb': 10, 'fg3m': 3, 'oreb': 5, 'pts': 10, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
755),
('partial repair probe: quadruple-double tier',
[{'ast': 11, 'blk': 10, 'dreb': 4, 'fg3m': 6, 'oreb': 0, 'pts': 10, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
953),
('second regression',
[{'ast': 11, 'blk': 10, 'dreb': 9, 'fg3m': 1, 'oreb': 4, 'pts': 11, 'stl': 3, 'tov': 4},
{'dd': 15, 'td': 30}],
816),
('normal control 1',
[{'ast': 10, 'blk': 11, 'dreb': 10, 'fg3m': 5, 'oreb': 1, 'pts': 9, 'stl': 0, 'tov': 6},
{'dd': 15, 'td': 30}],
697),
('normal control 2',
[{'ast': 9, 'blk': 1, 'dreb': 9, 'fg3m': 4, 'oreb': 5, 'pts': 9, 'stl': 2, 'tov': 0},
{'dd': 15, 'td': 30}],
503),
('normal control 3',
[{'ast': 6, 'blk': 2, 'dreb': 10, 'fg3m': 4, 'oreb': 5, 'pts': 39, 'stl': 1, 'tov': 3},
{'dd': 15, 'td': 30}],
755),
('normal control 4',
[{'ast': 9, 'blk': 10, 'dreb': 4, 'fg3m': 0, 'oreb': 3, 'pts': 10, 'stl': 2, 'tov': 4},
{'dd': 15, 'td': 30}],
654)],
[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 10, 'dreb': 9, 'fg3m': 2, 'oreb': 2, 'pts': 10, 'stl': 1, 'tov': 0},
{'dd': 15, 'td': 30}],
752),
('partial repair probe: quadruple-double tier',
[{'ast': 11, 'blk': 2, 'dreb': 5, 'fg3m': 5, 'oreb': 5, 'pts': 11, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
790),
('second regression',
[{'ast': 11, 'blk': 11, 'dreb': 4, 'fg3m': 6, 'oreb': 1, 'pts': 29, 'stl': 10, 'tov': 5},
{'dd': 15, 'td': 30}],
1155),
('normal control 1',
[{'ast': 11, 'blk': 0, 'dreb': 4, 'fg3m': 6, 'oreb': 3, 'pts': 9, 'stl': 0, 'tov': 5},
{'dd': 15, 'td': 30}],
319),
('normal control 2',
[{'ast': 6, 'blk': 0, 'dreb': 5, 'fg3m': 3, 'oreb': 2, 'pts': 10, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
584),
('normal control 3',
[{'ast': 11, 'blk': 10, 'dreb': 5, 'fg3m': 0, 'oreb': 0, 'pts': 9, 'stl': 2, 'tov': 3},
{'dd': 15, 'td': 30}],
660),
('normal control 4',
[{'ast': 11, 'blk': 1, 'dreb': 8, 'fg3m': 3, 'oreb': 3, 'pts': 34, 'stl': 0, 'tov': 3},
{'dd': 15, 'td': 30}],
682)],
[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 2, 'dreb': 10, 'fg3m': 1, 'oreb': 3, 'pts': 10, 'stl': 10, 'tov': 0},
{'dd': 15, 'td': 30}],
801),
('partial repair probe: quadruple-double tier',
[{'ast': 10, 'blk': 2, 'dreb': 9, 'fg3m': 4, 'oreb': 6, 'pts': 11, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
830),
('second regression',
[{'ast': 10, 'blk': 11, 'dreb': 10, 'fg3m': 1, 'oreb': 2, 'pts': 9, 'stl': 10, 'tov': 5},
{'dd': 15, 'td': 30}],
999),
('normal control 1',
[{'ast': 10, 'blk': 1, 'dreb': 4, 'fg3m': 2, 'oreb': 5, 'pts': 10, 'stl': 1, 'tov': 1},
{'dd': 15, 'td': 30}],
433),
('normal control 2',
[{'ast': 9, 'blk': 10, 'dreb': 10, 'fg3m': 1, 'oreb': 0, 'pts': 11, 'stl': 2, 'tov': 1},
{'dd': 15, 'td': 30}],
750),
('normal control 3',
[{'ast': 10, 'blk': 1, 'dreb': 8, 'fg3m': 3, 'oreb': 4, 'pts': 26, 'stl': 2, 'tov': 2},
{'dd': 15, 'td': 30}],
669),
('normal control 4',
[{'ast': 10, 'blk': 0, 'dreb': 5, 'fg3m': 3, 'oreb': 4, 'pts': 10, 'stl': 10, 'tov': 5},
{'dd': 15, 'td': 30}],
653)],
[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 10, 'dreb': 10, 'fg3m': 0, 'oreb': 5, 'pts': 10, 'stl': 1, 'tov': 0},
{'dd': 15, 'td': 30}],
790),
('partial repair probe: quadruple-double tier',
[{'ast': 10, 'blk': 11, 'dreb': 8, 'fg3m': 3, 'oreb': 6, 'pts': 11, 'stl': 2, 'tov': 6},
{'dd': 15, 'td': 30}],
803),
('second regression',
[{'ast': 11, 'blk': 10, 'dreb': 10, 'fg3m': 5, 'oreb': 0, 'pts': 24, 'stl': 1, 'tov': 6},
{'dd': 15, 'td': 30}],
850),
('normal control 1',
[{'ast': 9, 'blk': 2, 'dreb': 5, 'fg3m': 2, 'oreb': 4, 'pts': 11, 'stl': 1, 'tov': 4},
{'dd': 15, 'td': 30}],
413),
('normal control 2',
[{'ast': 10, 'blk': 2, 'dreb': 9, 'fg3m': 2, 'oreb': 4, 'pts': 10, 'stl': 0, 'tov': 2},
{'dd': 15, 'td': 30}],
486),
('normal control 3',
[{'ast': 10, 'blk': 1, 'dreb': 8, 'fg3m': 5, 'oreb': 3, 'pts': 10, 'stl': 0, 'tov': 4},
{'dd': 15, 'td': 30}],
427),
('normal control 4',
[{'ast': 11, 'blk': 0, 'dreb': 10, 'fg3m': 5, 'oreb': 4, 'pts': 10, 'stl': 2, 'tov': 5},
{'dd': 15, 'td': 30}],
498)]]
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: quadruple-double tier | 1315 | 1345 | Failed |
| partial repair probe: quadruple-double tier | 768 | 798 | Failed |
| second regression | 874 | 904 | Failed |
| normal control 1 | 387 | 387 | Passed |
| normal control 2 | 432 | 432 | Passed |
| normal control 3 | 392 | 392 | Passed |
| normal control 4 | 441 | 441 | Passed |
SHA-256 / 373dbc16210d68abf192fbe7796cba3de921bd7153c78dceed1d8f5322a8cffd
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(line, bonus):
line = dict(line, reb=line['oreb'] + line['dreb'])
w = {'pts': 10, 'reb': 12, 'ast': 15, 'stl': 30, 'blk': 30, 'tov': -10, 'fg3m': 5}
score = sum(w[k] * line[k] for k in w)
doubles = sum(1 for k in ('pts', 'reb', 'ast', 'stl', 'blk') if line[k] >= 10)
if doubles == 3:
score += bonus['td']
elif doubles >= 2:
score += bonus['dd']
return score
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 11, 'dreb': 10, 'fg3m': 1, 'oreb': 5, 'pts': 35, 'stl': 10, 'tov': 0},
{'dd': 15, 'td': 30}],
1345),
('partial repair probe: quadruple-double tier',
[{'ast': 10, 'blk': 10, 'dreb': 10, 'fg3m': 2, 'oreb': 4, 'pts': 11, 'stl': 3, 'tov': 6},
{'dd': 15, 'td': 30}],
798),
('second regression',
[{'ast': 10, 'blk': 11, 'dreb': 9, 'fg3m': 4, 'oreb': 3, 'pts': 21, 'stl': 2, 'tov': 4},
{'dd': 15, 'td': 30}],
904),
('normal control 1',
[{'ast': 10, 'blk': 2, 'dreb': 0, 'fg3m': 0, 'oreb': 6, 'pts': 10, 'stl': 0, 'tov': 1},
{'dd': 15, 'td': 30}],
387),
('normal control 2',
[{'ast': 11, 'blk': 1, 'dreb': 4, 'fg3m': 2, 'oreb': 2, 'pts': 10, 'stl': 3, 'tov': 5},
{'dd': 15, 'td': 30}],
432),
('normal control 3',
[{'ast': 10, 'blk': 2, 'dreb': 9, 'fg3m': 3, 'oreb': 2, 'pts': 1, 'stl': 2, 'tov': 5},
{'dd': 15, 'td': 30}],
392),
('normal control 4',
[{'ast': 11, 'blk': 2, 'dreb': 7, 'fg3m': 5, 'oreb': 1, 'pts': 10, 'stl': 0, 'tov': 2},
{'dd': 15, 'td': 30}],
441)],
[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 0, 'dreb': 10, 'fg3m': 3, 'oreb': 5, 'pts': 10, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
755),
('partial repair probe: quadruple-double tier',
[{'ast': 11, 'blk': 10, 'dreb': 4, 'fg3m': 6, 'oreb': 0, 'pts': 10, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
953),
('second regression',
[{'ast': 11, 'blk': 10, 'dreb': 9, 'fg3m': 1, 'oreb': 4, 'pts': 11, 'stl': 3, 'tov': 4},
{'dd': 15, 'td': 30}],
816),
('normal control 1',
[{'ast': 10, 'blk': 11, 'dreb': 10, 'fg3m': 5, 'oreb': 1, 'pts': 9, 'stl': 0, 'tov': 6},
{'dd': 15, 'td': 30}],
697),
('normal control 2',
[{'ast': 9, 'blk': 1, 'dreb': 9, 'fg3m': 4, 'oreb': 5, 'pts': 9, 'stl': 2, 'tov': 0},
{'dd': 15, 'td': 30}],
503),
('normal control 3',
[{'ast': 6, 'blk': 2, 'dreb': 10, 'fg3m': 4, 'oreb': 5, 'pts': 39, 'stl': 1, 'tov': 3},
{'dd': 15, 'td': 30}],
755),
('normal control 4',
[{'ast': 9, 'blk': 10, 'dreb': 4, 'fg3m': 0, 'oreb': 3, 'pts': 10, 'stl': 2, 'tov': 4},
{'dd': 15, 'td': 30}],
654)],
[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 10, 'dreb': 9, 'fg3m': 2, 'oreb': 2, 'pts': 10, 'stl': 1, 'tov': 0},
{'dd': 15, 'td': 30}],
752),
('partial repair probe: quadruple-double tier',
[{'ast': 11, 'blk': 2, 'dreb': 5, 'fg3m': 5, 'oreb': 5, 'pts': 11, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
790),
('second regression',
[{'ast': 11, 'blk': 11, 'dreb': 4, 'fg3m': 6, 'oreb': 1, 'pts': 29, 'stl': 10, 'tov': 5},
{'dd': 15, 'td': 30}],
1155),
('normal control 1',
[{'ast': 11, 'blk': 0, 'dreb': 4, 'fg3m': 6, 'oreb': 3, 'pts': 9, 'stl': 0, 'tov': 5},
{'dd': 15, 'td': 30}],
319),
('normal control 2',
[{'ast': 6, 'blk': 0, 'dreb': 5, 'fg3m': 3, 'oreb': 2, 'pts': 10, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
584),
('normal control 3',
[{'ast': 11, 'blk': 10, 'dreb': 5, 'fg3m': 0, 'oreb': 0, 'pts': 9, 'stl': 2, 'tov': 3},
{'dd': 15, 'td': 30}],
660),
('normal control 4',
[{'ast': 11, 'blk': 1, 'dreb': 8, 'fg3m': 3, 'oreb': 3, 'pts': 34, 'stl': 0, 'tov': 3},
{'dd': 15, 'td': 30}],
682)],
[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 2, 'dreb': 10, 'fg3m': 1, 'oreb': 3, 'pts': 10, 'stl': 10, 'tov': 0},
{'dd': 15, 'td': 30}],
801),
('partial repair probe: quadruple-double tier',
[{'ast': 10, 'blk': 2, 'dreb': 9, 'fg3m': 4, 'oreb': 6, 'pts': 11, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
830),
('second regression',
[{'ast': 10, 'blk': 11, 'dreb': 10, 'fg3m': 1, 'oreb': 2, 'pts': 9, 'stl': 10, 'tov': 5},
{'dd': 15, 'td': 30}],
999),
('normal control 1',
[{'ast': 10, 'blk': 1, 'dreb': 4, 'fg3m': 2, 'oreb': 5, 'pts': 10, 'stl': 1, 'tov': 1},
{'dd': 15, 'td': 30}],
433),
('normal control 2',
[{'ast': 9, 'blk': 10, 'dreb': 10, 'fg3m': 1, 'oreb': 0, 'pts': 11, 'stl': 2, 'tov': 1},
{'dd': 15, 'td': 30}],
750),
('normal control 3',
[{'ast': 10, 'blk': 1, 'dreb': 8, 'fg3m': 3, 'oreb': 4, 'pts': 26, 'stl': 2, 'tov': 2},
{'dd': 15, 'td': 30}],
669),
('normal control 4',
[{'ast': 10, 'blk': 0, 'dreb': 5, 'fg3m': 3, 'oreb': 4, 'pts': 10, 'stl': 10, 'tov': 5},
{'dd': 15, 'td': 30}],
653)],
[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 10, 'dreb': 10, 'fg3m': 0, 'oreb': 5, 'pts': 10, 'stl': 1, 'tov': 0},
{'dd': 15, 'td': 30}],
790),
('partial repair probe: quadruple-double tier',
[{'ast': 10, 'blk': 11, 'dreb': 8, 'fg3m': 3, 'oreb': 6, 'pts': 11, 'stl': 2, 'tov': 6},
{'dd': 15, 'td': 30}],
803),
('second regression',
[{'ast': 11, 'blk': 10, 'dreb': 10, 'fg3m': 5, 'oreb': 0, 'pts': 24, 'stl': 1, 'tov': 6},
{'dd': 15, 'td': 30}],
850),
('normal control 1',
[{'ast': 9, 'blk': 2, 'dreb': 5, 'fg3m': 2, 'oreb': 4, 'pts': 11, 'stl': 1, 'tov': 4},
{'dd': 15, 'td': 30}],
413),
('normal control 2',
[{'ast': 10, 'blk': 2, 'dreb': 9, 'fg3m': 2, 'oreb': 4, 'pts': 10, 'stl': 0, 'tov': 2},
{'dd': 15, 'td': 30}],
486),
('normal control 3',
[{'ast': 10, 'blk': 1, 'dreb': 8, 'fg3m': 5, 'oreb': 3, 'pts': 10, 'stl': 0, 'tov': 4},
{'dd': 15, 'td': 30}],
427),
('normal control 4',
[{'ast': 11, 'blk': 0, 'dreb': 10, 'fg3m': 5, 'oreb': 4, 'pts': 10, 'stl': 2, 'tov': 5},
{'dd': 15, 'td': 30}],
498)]]
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: quadruple-double tier | 1330 | 1345 | Failed |
| partial repair probe: quadruple-double tier | 783 | 798 | Failed |
| second regression | 889 | 904 | Failed |
| normal control 1 | 387 | 387 | Passed |
| normal control 2 | 432 | 432 | Passed |
| normal control 3 | 392 | 392 | Passed |
| normal control 4 | 441 | 441 | Passed |
SHA-256 / 776ed47285cbeb63d5de8a5f02440a76f349423348b7a157fe92517d32059a39
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(line, bonus):
line = dict(line, reb=line['oreb'] + line['dreb'])
w = {'pts': 10, 'reb': 12, 'ast': 15, 'stl': 30, 'blk': 30, 'tov': -10, 'fg3m': 5}
score = sum(w[k] * line[k] for k in w)
doubles = sum(1 for k in ('pts', 'reb', 'ast', 'stl', 'blk') if line[k] >= 10)
if doubles >= 3:
score += bonus['td']
elif doubles == 2:
score += bonus['dd']
return score
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 11, 'dreb': 10, 'fg3m': 1, 'oreb': 5, 'pts': 35, 'stl': 10, 'tov': 0},
{'dd': 15, 'td': 30}],
1345),
('partial repair probe: quadruple-double tier',
[{'ast': 10, 'blk': 10, 'dreb': 10, 'fg3m': 2, 'oreb': 4, 'pts': 11, 'stl': 3, 'tov': 6},
{'dd': 15, 'td': 30}],
798),
('second regression',
[{'ast': 10, 'blk': 11, 'dreb': 9, 'fg3m': 4, 'oreb': 3, 'pts': 21, 'stl': 2, 'tov': 4},
{'dd': 15, 'td': 30}],
904),
('normal control 1',
[{'ast': 10, 'blk': 2, 'dreb': 0, 'fg3m': 0, 'oreb': 6, 'pts': 10, 'stl': 0, 'tov': 1},
{'dd': 15, 'td': 30}],
387),
('normal control 2',
[{'ast': 11, 'blk': 1, 'dreb': 4, 'fg3m': 2, 'oreb': 2, 'pts': 10, 'stl': 3, 'tov': 5},
{'dd': 15, 'td': 30}],
432),
('normal control 3',
[{'ast': 10, 'blk': 2, 'dreb': 9, 'fg3m': 3, 'oreb': 2, 'pts': 1, 'stl': 2, 'tov': 5},
{'dd': 15, 'td': 30}],
392),
('normal control 4',
[{'ast': 11, 'blk': 2, 'dreb': 7, 'fg3m': 5, 'oreb': 1, 'pts': 10, 'stl': 0, 'tov': 2},
{'dd': 15, 'td': 30}],
441)],
[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 0, 'dreb': 10, 'fg3m': 3, 'oreb': 5, 'pts': 10, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
755),
('partial repair probe: quadruple-double tier',
[{'ast': 11, 'blk': 10, 'dreb': 4, 'fg3m': 6, 'oreb': 0, 'pts': 10, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
953),
('second regression',
[{'ast': 11, 'blk': 10, 'dreb': 9, 'fg3m': 1, 'oreb': 4, 'pts': 11, 'stl': 3, 'tov': 4},
{'dd': 15, 'td': 30}],
816),
('normal control 1',
[{'ast': 10, 'blk': 11, 'dreb': 10, 'fg3m': 5, 'oreb': 1, 'pts': 9, 'stl': 0, 'tov': 6},
{'dd': 15, 'td': 30}],
697),
('normal control 2',
[{'ast': 9, 'blk': 1, 'dreb': 9, 'fg3m': 4, 'oreb': 5, 'pts': 9, 'stl': 2, 'tov': 0},
{'dd': 15, 'td': 30}],
503),
('normal control 3',
[{'ast': 6, 'blk': 2, 'dreb': 10, 'fg3m': 4, 'oreb': 5, 'pts': 39, 'stl': 1, 'tov': 3},
{'dd': 15, 'td': 30}],
755),
('normal control 4',
[{'ast': 9, 'blk': 10, 'dreb': 4, 'fg3m': 0, 'oreb': 3, 'pts': 10, 'stl': 2, 'tov': 4},
{'dd': 15, 'td': 30}],
654)],
[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 10, 'dreb': 9, 'fg3m': 2, 'oreb': 2, 'pts': 10, 'stl': 1, 'tov': 0},
{'dd': 15, 'td': 30}],
752),
('partial repair probe: quadruple-double tier',
[{'ast': 11, 'blk': 2, 'dreb': 5, 'fg3m': 5, 'oreb': 5, 'pts': 11, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
790),
('second regression',
[{'ast': 11, 'blk': 11, 'dreb': 4, 'fg3m': 6, 'oreb': 1, 'pts': 29, 'stl': 10, 'tov': 5},
{'dd': 15, 'td': 30}],
1155),
('normal control 1',
[{'ast': 11, 'blk': 0, 'dreb': 4, 'fg3m': 6, 'oreb': 3, 'pts': 9, 'stl': 0, 'tov': 5},
{'dd': 15, 'td': 30}],
319),
('normal control 2',
[{'ast': 6, 'blk': 0, 'dreb': 5, 'fg3m': 3, 'oreb': 2, 'pts': 10, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
584),
('normal control 3',
[{'ast': 11, 'blk': 10, 'dreb': 5, 'fg3m': 0, 'oreb': 0, 'pts': 9, 'stl': 2, 'tov': 3},
{'dd': 15, 'td': 30}],
660),
('normal control 4',
[{'ast': 11, 'blk': 1, 'dreb': 8, 'fg3m': 3, 'oreb': 3, 'pts': 34, 'stl': 0, 'tov': 3},
{'dd': 15, 'td': 30}],
682)],
[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 2, 'dreb': 10, 'fg3m': 1, 'oreb': 3, 'pts': 10, 'stl': 10, 'tov': 0},
{'dd': 15, 'td': 30}],
801),
('partial repair probe: quadruple-double tier',
[{'ast': 10, 'blk': 2, 'dreb': 9, 'fg3m': 4, 'oreb': 6, 'pts': 11, 'stl': 10, 'tov': 2},
{'dd': 15, 'td': 30}],
830),
('second regression',
[{'ast': 10, 'blk': 11, 'dreb': 10, 'fg3m': 1, 'oreb': 2, 'pts': 9, 'stl': 10, 'tov': 5},
{'dd': 15, 'td': 30}],
999),
('normal control 1',
[{'ast': 10, 'blk': 1, 'dreb': 4, 'fg3m': 2, 'oreb': 5, 'pts': 10, 'stl': 1, 'tov': 1},
{'dd': 15, 'td': 30}],
433),
('normal control 2',
[{'ast': 9, 'blk': 10, 'dreb': 10, 'fg3m': 1, 'oreb': 0, 'pts': 11, 'stl': 2, 'tov': 1},
{'dd': 15, 'td': 30}],
750),
('normal control 3',
[{'ast': 10, 'blk': 1, 'dreb': 8, 'fg3m': 3, 'oreb': 4, 'pts': 26, 'stl': 2, 'tov': 2},
{'dd': 15, 'td': 30}],
669),
('normal control 4',
[{'ast': 10, 'blk': 0, 'dreb': 5, 'fg3m': 3, 'oreb': 4, 'pts': 10, 'stl': 10, 'tov': 5},
{'dd': 15, 'td': 30}],
653)],
[('regression: quadruple-double tier',
[{'ast': 10, 'blk': 10, 'dreb': 10, 'fg3m': 0, 'oreb': 5, 'pts': 10, 'stl': 1, 'tov': 0},
{'dd': 15, 'td': 30}],
790),
('partial repair probe: quadruple-double tier',
[{'ast': 10, 'blk': 11, 'dreb': 8, 'fg3m': 3, 'oreb': 6, 'pts': 11, 'stl': 2, 'tov': 6},
{'dd': 15, 'td': 30}],
803),
('second regression',
[{'ast': 11, 'blk': 10, 'dreb': 10, 'fg3m': 5, 'oreb': 0, 'pts': 24, 'stl': 1, 'tov': 6},
{'dd': 15, 'td': 30}],
850),
('normal control 1',
[{'ast': 9, 'blk': 2, 'dreb': 5, 'fg3m': 2, 'oreb': 4, 'pts': 11, 'stl': 1, 'tov': 4},
{'dd': 15, 'td': 30}],
413),
('normal control 2',
[{'ast': 10, 'blk': 2, 'dreb': 9, 'fg3m': 2, 'oreb': 4, 'pts': 10, 'stl': 0, 'tov': 2},
{'dd': 15, 'td': 30}],
486),
('normal control 3',
[{'ast': 10, 'blk': 1, 'dreb': 8, 'fg3m': 5, 'oreb': 3, 'pts': 10, 'stl': 0, 'tov': 4},
{'dd': 15, 'td': 30}],
427),
('normal control 4',
[{'ast': 11, 'blk': 0, 'dreb': 10, 'fg3m': 5, 'oreb': 4, 'pts': 10, 'stl': 2, 'tov': 5},
{'dd': 15, 'td': 30}],
498)]]
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: quadruple-double tier | 1345 | 1345 | Passed |
| partial repair probe: quadruple-double tier | 798 | 798 | Passed |
| second regression | 904 | 904 | Passed |
| normal control 1 | 387 | 387 | Passed |
| normal control 2 | 432 | 432 | Passed |
| normal control 3 | 392 | 392 | Passed |
| normal control 4 | 441 | 441 | Passed |
SHA-256 / 657c3f00aa7692cdb8f2c1bfb1d1272c9729d2b8656278642338480bef6f971e
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 2026-09-29T14:50:37.483186+00:00.
Case digest / d0f4b7113fb78849c20d4407f93e9365638696f90d04fa21a83b5d675b980635