FA-85136 / Fantasy sports scoring / Open access
Saves reduced by empty-net goals the goalie never faced · case 01
Save totals drop by one for every empty-net goal.
ROOT CAUSE
Saves subtract the team goals against from shots the goalie faced.
VERIFIED REPAIR
Subtract only goals the goalie allowed from shots he faced.
Unsuccessful approach: Subtracting empty-net goals on top of goalie goals double-counts the same mistake.
Case contract
Score a hockey goalie in tenths. Goals against charged to the goalie exclude empty-net goals scored on his team. shots_faced counts only shots the goalie faced, so saves = shots_faced - goalie goals against. Save 0.2, goal against -2, win 4, overtime loss 1, regulation loss 0. A shutout (+3) needs zero goalie goals against and at least 3600 seconds on ice; the decision does not matter.
Why this case matters
Goalie scoring depends on attribution of empty-net goals and on playing-time conditions for shutouts.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(g):
ga = g['team_ga'] - g['empty_net_ga']
saves = g['shots_faced'] - g['team_ga']
pts = saves * 2 - ga * 20
pts += {'W': 40, 'OTL': 10}.get(g['decision'], 0)
if ga == 0 and g['toi_sec'] >= 3600:
pts += 30
return {'saves': saves, 'points': pts}
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: save derivation',
[{'decision': 'W', 'empty_net_ga': 2, 'shots_faced': 26, 'team_ga': 4, 'toi_sec': 1645}],
{'points': 48, 'saves': 24}),
('partial repair probe: save derivation',
[{'decision': 'L', 'empty_net_ga': 2, 'shots_faced': 30, 'team_ga': 2, 'toi_sec': 3600}],
{'points': 90, 'saves': 30}),
('second regression',
[{'decision': 'OTL', 'empty_net_ga': 2, 'shots_faced': 12, 'team_ga': 5, 'toi_sec': 3900}],
{'points': -32, 'saves': 9}),
('normal control 1',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 30, 'team_ga': 5, 'toi_sec': 3599}],
{'points': -40, 'saves': 25}),
('normal control 2',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 7, 'team_ga': 3, 'toi_sec': 3600}],
{'points': -52, 'saves': 4}),
('normal control 3',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 20, 'team_ga': 5, 'toi_sec': 3600}],
{'points': -60, 'saves': 15}),
('normal control 4',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 14, 'team_ga': 0, 'toi_sec': 3540}],
{'points': 28, 'saves': 14})],
[('regression: save derivation',
[{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 33, 'team_ga': 2, 'toi_sec': 2400}],
{'points': 44, 'saves': 32}),
('partial repair probe: save derivation',
[{'decision': 'OTL', 'empty_net_ga': 2, 'shots_faced': 9, 'team_ga': 7, 'toi_sec': 3599}],
{'points': -82, 'saves': 4}),
('second regression',
[{'decision': None, 'empty_net_ga': 2, 'shots_faced': 31, 'team_ga': 4, 'toi_sec': 3900}],
{'points': 18, 'saves': 29}),
('normal control 1',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 30, 'team_ga': 5, 'toi_sec': 3599}],
{'points': -50, 'saves': 25}),
('normal control 2',
[{'decision': 'W', 'empty_net_ga': 0, 'shots_faced': 25, 'team_ga': 1, 'toi_sec': 3900}],
{'points': 68, 'saves': 24}),
('normal control 3',
[{'decision': 'W', 'empty_net_ga': 0, 'shots_faced': 25, 'team_ga': 3, 'toi_sec': 3599}],
{'points': 24, 'saves': 22}),
('normal control 4',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 16, 'team_ga': 5, 'toi_sec': 3599}],
{'points': -78, 'saves': 11})],
[('regression: save derivation',
[{'decision': 'L', 'empty_net_ga': 2, 'shots_faced': 39, 'team_ga': 3, 'toi_sec': 3600}],
{'points': 56, 'saves': 38}),
('partial repair probe: save derivation',
[{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 6, 'toi_sec': 2400}],
{'points': -82, 'saves': 9}),
('second regression',
[{'decision': None, 'empty_net_ga': 2, 'shots_faced': 21, 'team_ga': 7, 'toi_sec': 2154}],
{'points': -68, 'saves': 16}),
('normal control 1',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 32, 'team_ga': 0, 'toi_sec': 2400}],
{'points': 64, 'saves': 32}),
('normal control 2',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 22, 'team_ga': 1, 'toi_sec': 3900}],
{'points': 32, 'saves': 21}),
('normal control 3',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 29, 'team_ga': 5, 'toi_sec': 3540}],
{'points': -42, 'saves': 24}),
('normal control 4',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 35, 'team_ga': 2, 'toi_sec': 866}],
{'points': 26, 'saves': 33})],
[('regression: save derivation',
[{'decision': None, 'empty_net_ga': 1, 'shots_faced': 26, 'team_ga': 1, 'toi_sec': 3600}],
{'points': 82, 'saves': 26}),
('partial repair probe: save derivation',
[{'decision': None, 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 1, 'toi_sec': 3599}],
{'points': 28, 'saves': 14}),
('second regression',
[{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 5, 'team_ga': 1, 'toi_sec': 3600}],
{'points': 40, 'saves': 5}),
('normal control 1',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 28, 'team_ga': 0, 'toi_sec': 3900}],
{'points': 96, 'saves': 28}),
('normal control 2',
[{'decision': 'W', 'empty_net_ga': 0, 'shots_faced': 30, 'team_ga': 2, 'toi_sec': 3900}],
{'points': 56, 'saves': 28}),
('normal control 3',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 24, 'team_ga': 1, 'toi_sec': 2400}],
{'points': 26, 'saves': 23}),
('normal control 4',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 35, 'team_ga': 2, 'toi_sec': 3900}],
{'points': 26, 'saves': 33})],
[('regression: save derivation',
[{'decision': 'OTL', 'empty_net_ga': 2, 'shots_faced': 20, 'team_ga': 2, 'toi_sec': 3600}],
{'points': 80, 'saves': 20}),
('partial repair probe: save derivation',
[{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 7, 'team_ga': 4, 'toi_sec': 3731}],
{'points': -42, 'saves': 4}),
('second regression',
[{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 24, 'team_ga': 1, 'toi_sec': 3540}],
{'points': 48, 'saves': 24}),
('normal control 1',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 5, 'team_ga': 2, 'toi_sec': 2400}],
{'points': -34, 'saves': 3}),
('normal control 2',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 18, 'team_ga': 3, 'toi_sec': 807}],
{'points': -20, 'saves': 15}),
('normal control 3',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 5, 'team_ga': 3, 'toi_sec': 1133}],
{'points': -56, 'saves': 2}),
('normal control 4',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 18, 'team_ga': 5, 'toi_sec': 2400}],
{'points': -64, 'saves': 13})]]
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: save derivation | {'points': 44, 'saves': 22} | {'points': 48, 'saves': 24} | Failed |
| partial repair probe: save derivation | {'points': 86, 'saves': 28} | {'points': 90, 'saves': 30} | Failed |
| second regression | {'points': -36, 'saves': 7} | {'points': -32, 'saves': 9} | Failed |
| normal control 1 | {'points': -40, 'saves': 25} | {'points': -40, 'saves': 25} | Passed |
| normal control 2 | {'points': -52, 'saves': 4} | {'points': -52, 'saves': 4} | Passed |
| normal control 3 | {'points': -60, 'saves': 15} | {'points': -60, 'saves': 15} | Passed |
| normal control 4 | {'points': 28, 'saves': 14} | {'points': 28, 'saves': 14} | Passed |
SHA-256 / 498ef71aa8630bbc727cee970c7635a77fc52da02f89cbd2790e0d83c89da7a7
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(g):
ga = g['team_ga'] - g['empty_net_ga']
saves = g['shots_faced'] - ga - g['empty_net_ga']
pts = saves * 2 - ga * 20
pts += {'W': 40, 'OTL': 10}.get(g['decision'], 0)
if ga == 0 and g['toi_sec'] >= 3600:
pts += 30
return {'saves': saves, 'points': pts}
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: save derivation',
[{'decision': 'W', 'empty_net_ga': 2, 'shots_faced': 26, 'team_ga': 4, 'toi_sec': 1645}],
{'points': 48, 'saves': 24}),
('partial repair probe: save derivation',
[{'decision': 'L', 'empty_net_ga': 2, 'shots_faced': 30, 'team_ga': 2, 'toi_sec': 3600}],
{'points': 90, 'saves': 30}),
('second regression',
[{'decision': 'OTL', 'empty_net_ga': 2, 'shots_faced': 12, 'team_ga': 5, 'toi_sec': 3900}],
{'points': -32, 'saves': 9}),
('normal control 1',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 30, 'team_ga': 5, 'toi_sec': 3599}],
{'points': -40, 'saves': 25}),
('normal control 2',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 7, 'team_ga': 3, 'toi_sec': 3600}],
{'points': -52, 'saves': 4}),
('normal control 3',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 20, 'team_ga': 5, 'toi_sec': 3600}],
{'points': -60, 'saves': 15}),
('normal control 4',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 14, 'team_ga': 0, 'toi_sec': 3540}],
{'points': 28, 'saves': 14})],
[('regression: save derivation',
[{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 33, 'team_ga': 2, 'toi_sec': 2400}],
{'points': 44, 'saves': 32}),
('partial repair probe: save derivation',
[{'decision': 'OTL', 'empty_net_ga': 2, 'shots_faced': 9, 'team_ga': 7, 'toi_sec': 3599}],
{'points': -82, 'saves': 4}),
('second regression',
[{'decision': None, 'empty_net_ga': 2, 'shots_faced': 31, 'team_ga': 4, 'toi_sec': 3900}],
{'points': 18, 'saves': 29}),
('normal control 1',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 30, 'team_ga': 5, 'toi_sec': 3599}],
{'points': -50, 'saves': 25}),
('normal control 2',
[{'decision': 'W', 'empty_net_ga': 0, 'shots_faced': 25, 'team_ga': 1, 'toi_sec': 3900}],
{'points': 68, 'saves': 24}),
('normal control 3',
[{'decision': 'W', 'empty_net_ga': 0, 'shots_faced': 25, 'team_ga': 3, 'toi_sec': 3599}],
{'points': 24, 'saves': 22}),
('normal control 4',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 16, 'team_ga': 5, 'toi_sec': 3599}],
{'points': -78, 'saves': 11})],
[('regression: save derivation',
[{'decision': 'L', 'empty_net_ga': 2, 'shots_faced': 39, 'team_ga': 3, 'toi_sec': 3600}],
{'points': 56, 'saves': 38}),
('partial repair probe: save derivation',
[{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 6, 'toi_sec': 2400}],
{'points': -82, 'saves': 9}),
('second regression',
[{'decision': None, 'empty_net_ga': 2, 'shots_faced': 21, 'team_ga': 7, 'toi_sec': 2154}],
{'points': -68, 'saves': 16}),
('normal control 1',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 32, 'team_ga': 0, 'toi_sec': 2400}],
{'points': 64, 'saves': 32}),
('normal control 2',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 22, 'team_ga': 1, 'toi_sec': 3900}],
{'points': 32, 'saves': 21}),
('normal control 3',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 29, 'team_ga': 5, 'toi_sec': 3540}],
{'points': -42, 'saves': 24}),
('normal control 4',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 35, 'team_ga': 2, 'toi_sec': 866}],
{'points': 26, 'saves': 33})],
[('regression: save derivation',
[{'decision': None, 'empty_net_ga': 1, 'shots_faced': 26, 'team_ga': 1, 'toi_sec': 3600}],
{'points': 82, 'saves': 26}),
('partial repair probe: save derivation',
[{'decision': None, 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 1, 'toi_sec': 3599}],
{'points': 28, 'saves': 14}),
('second regression',
[{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 5, 'team_ga': 1, 'toi_sec': 3600}],
{'points': 40, 'saves': 5}),
('normal control 1',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 28, 'team_ga': 0, 'toi_sec': 3900}],
{'points': 96, 'saves': 28}),
('normal control 2',
[{'decision': 'W', 'empty_net_ga': 0, 'shots_faced': 30, 'team_ga': 2, 'toi_sec': 3900}],
{'points': 56, 'saves': 28}),
('normal control 3',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 24, 'team_ga': 1, 'toi_sec': 2400}],
{'points': 26, 'saves': 23}),
('normal control 4',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 35, 'team_ga': 2, 'toi_sec': 3900}],
{'points': 26, 'saves': 33})],
[('regression: save derivation',
[{'decision': 'OTL', 'empty_net_ga': 2, 'shots_faced': 20, 'team_ga': 2, 'toi_sec': 3600}],
{'points': 80, 'saves': 20}),
('partial repair probe: save derivation',
[{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 7, 'team_ga': 4, 'toi_sec': 3731}],
{'points': -42, 'saves': 4}),
('second regression',
[{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 24, 'team_ga': 1, 'toi_sec': 3540}],
{'points': 48, 'saves': 24}),
('normal control 1',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 5, 'team_ga': 2, 'toi_sec': 2400}],
{'points': -34, 'saves': 3}),
('normal control 2',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 18, 'team_ga': 3, 'toi_sec': 807}],
{'points': -20, 'saves': 15}),
('normal control 3',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 5, 'team_ga': 3, 'toi_sec': 1133}],
{'points': -56, 'saves': 2}),
('normal control 4',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 18, 'team_ga': 5, 'toi_sec': 2400}],
{'points': -64, 'saves': 13})]]
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: save derivation | {'points': 44, 'saves': 22} | {'points': 48, 'saves': 24} | Failed |
| partial repair probe: save derivation | {'points': 86, 'saves': 28} | {'points': 90, 'saves': 30} | Failed |
| second regression | {'points': -36, 'saves': 7} | {'points': -32, 'saves': 9} | Failed |
| normal control 1 | {'points': -40, 'saves': 25} | {'points': -40, 'saves': 25} | Passed |
| normal control 2 | {'points': -52, 'saves': 4} | {'points': -52, 'saves': 4} | Passed |
| normal control 3 | {'points': -60, 'saves': 15} | {'points': -60, 'saves': 15} | Passed |
| normal control 4 | {'points': 28, 'saves': 14} | {'points': 28, 'saves': 14} | Passed |
SHA-256 / 29e4cf2028c898f02379f0e79b74b7127d70355365ab9c2cd1ca9e49bdc74f57
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(g):
ga = g['team_ga'] - g['empty_net_ga']
saves = g['shots_faced'] - ga
pts = saves * 2 - ga * 20
pts += {'W': 40, 'OTL': 10}.get(g['decision'], 0)
if ga == 0 and g['toi_sec'] >= 3600:
pts += 30
return {'saves': saves, 'points': pts}
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: save derivation',
[{'decision': 'W', 'empty_net_ga': 2, 'shots_faced': 26, 'team_ga': 4, 'toi_sec': 1645}],
{'points': 48, 'saves': 24}),
('partial repair probe: save derivation',
[{'decision': 'L', 'empty_net_ga': 2, 'shots_faced': 30, 'team_ga': 2, 'toi_sec': 3600}],
{'points': 90, 'saves': 30}),
('second regression',
[{'decision': 'OTL', 'empty_net_ga': 2, 'shots_faced': 12, 'team_ga': 5, 'toi_sec': 3900}],
{'points': -32, 'saves': 9}),
('normal control 1',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 30, 'team_ga': 5, 'toi_sec': 3599}],
{'points': -40, 'saves': 25}),
('normal control 2',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 7, 'team_ga': 3, 'toi_sec': 3600}],
{'points': -52, 'saves': 4}),
('normal control 3',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 20, 'team_ga': 5, 'toi_sec': 3600}],
{'points': -60, 'saves': 15}),
('normal control 4',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 14, 'team_ga': 0, 'toi_sec': 3540}],
{'points': 28, 'saves': 14})],
[('regression: save derivation',
[{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 33, 'team_ga': 2, 'toi_sec': 2400}],
{'points': 44, 'saves': 32}),
('partial repair probe: save derivation',
[{'decision': 'OTL', 'empty_net_ga': 2, 'shots_faced': 9, 'team_ga': 7, 'toi_sec': 3599}],
{'points': -82, 'saves': 4}),
('second regression',
[{'decision': None, 'empty_net_ga': 2, 'shots_faced': 31, 'team_ga': 4, 'toi_sec': 3900}],
{'points': 18, 'saves': 29}),
('normal control 1',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 30, 'team_ga': 5, 'toi_sec': 3599}],
{'points': -50, 'saves': 25}),
('normal control 2',
[{'decision': 'W', 'empty_net_ga': 0, 'shots_faced': 25, 'team_ga': 1, 'toi_sec': 3900}],
{'points': 68, 'saves': 24}),
('normal control 3',
[{'decision': 'W', 'empty_net_ga': 0, 'shots_faced': 25, 'team_ga': 3, 'toi_sec': 3599}],
{'points': 24, 'saves': 22}),
('normal control 4',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 16, 'team_ga': 5, 'toi_sec': 3599}],
{'points': -78, 'saves': 11})],
[('regression: save derivation',
[{'decision': 'L', 'empty_net_ga': 2, 'shots_faced': 39, 'team_ga': 3, 'toi_sec': 3600}],
{'points': 56, 'saves': 38}),
('partial repair probe: save derivation',
[{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 6, 'toi_sec': 2400}],
{'points': -82, 'saves': 9}),
('second regression',
[{'decision': None, 'empty_net_ga': 2, 'shots_faced': 21, 'team_ga': 7, 'toi_sec': 2154}],
{'points': -68, 'saves': 16}),
('normal control 1',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 32, 'team_ga': 0, 'toi_sec': 2400}],
{'points': 64, 'saves': 32}),
('normal control 2',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 22, 'team_ga': 1, 'toi_sec': 3900}],
{'points': 32, 'saves': 21}),
('normal control 3',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 29, 'team_ga': 5, 'toi_sec': 3540}],
{'points': -42, 'saves': 24}),
('normal control 4',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 35, 'team_ga': 2, 'toi_sec': 866}],
{'points': 26, 'saves': 33})],
[('regression: save derivation',
[{'decision': None, 'empty_net_ga': 1, 'shots_faced': 26, 'team_ga': 1, 'toi_sec': 3600}],
{'points': 82, 'saves': 26}),
('partial repair probe: save derivation',
[{'decision': None, 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 1, 'toi_sec': 3599}],
{'points': 28, 'saves': 14}),
('second regression',
[{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 5, 'team_ga': 1, 'toi_sec': 3600}],
{'points': 40, 'saves': 5}),
('normal control 1',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 28, 'team_ga': 0, 'toi_sec': 3900}],
{'points': 96, 'saves': 28}),
('normal control 2',
[{'decision': 'W', 'empty_net_ga': 0, 'shots_faced': 30, 'team_ga': 2, 'toi_sec': 3900}],
{'points': 56, 'saves': 28}),
('normal control 3',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 24, 'team_ga': 1, 'toi_sec': 2400}],
{'points': 26, 'saves': 23}),
('normal control 4',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 35, 'team_ga': 2, 'toi_sec': 3900}],
{'points': 26, 'saves': 33})],
[('regression: save derivation',
[{'decision': 'OTL', 'empty_net_ga': 2, 'shots_faced': 20, 'team_ga': 2, 'toi_sec': 3600}],
{'points': 80, 'saves': 20}),
('partial repair probe: save derivation',
[{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 7, 'team_ga': 4, 'toi_sec': 3731}],
{'points': -42, 'saves': 4}),
('second regression',
[{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 24, 'team_ga': 1, 'toi_sec': 3540}],
{'points': 48, 'saves': 24}),
('normal control 1',
[{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 5, 'team_ga': 2, 'toi_sec': 2400}],
{'points': -34, 'saves': 3}),
('normal control 2',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 18, 'team_ga': 3, 'toi_sec': 807}],
{'points': -20, 'saves': 15}),
('normal control 3',
[{'decision': None, 'empty_net_ga': 0, 'shots_faced': 5, 'team_ga': 3, 'toi_sec': 1133}],
{'points': -56, 'saves': 2}),
('normal control 4',
[{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 18, 'team_ga': 5, 'toi_sec': 2400}],
{'points': -64, 'saves': 13})]]
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: save derivation | {'points': 48, 'saves': 24} | {'points': 48, 'saves': 24} | Passed |
| partial repair probe: save derivation | {'points': 90, 'saves': 30} | {'points': 90, 'saves': 30} | Passed |
| second regression | {'points': -32, 'saves': 9} | {'points': -32, 'saves': 9} | Passed |
| normal control 1 | {'points': -40, 'saves': 25} | {'points': -40, 'saves': 25} | Passed |
| normal control 2 | {'points': -52, 'saves': 4} | {'points': -52, 'saves': 4} | Passed |
| normal control 3 | {'points': -60, 'saves': 15} | {'points': -60, 'saves': 15} | Passed |
| normal control 4 | {'points': 28, 'saves': 14} | {'points': 28, 'saves': 14} | Passed |
SHA-256 / c95b6b995b769a84f9b0007e8698e6a4762d75785a0993f915c78b7b7bc07741
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 2026-09-29T14:50:37.663963+00:00.
Case digest / b860b16633324bf4a883c0df04d53c37c25be0c1082cf889d9738a98873d0894