FA-85146 / Fantasy sports scoring / Open access

Overtime losses score like regulation losses · case 01

Goalies lose the overtime-loss point.

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

ROOT CAUSE

The decision table has no entry for OTL.

VERIFIED REPAIR

Award 1 point for the OTL decision code.

Unsuccessful approach: Keying the table by OT does not match the OTL decision code the feed uses.

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'] - ga
    pts = saves * 2 - ga * 20
    pts += {'W': 40}.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: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 45, 'team_ga': 5, 'toi_sec': 3599}],
   {'points': -10, 'saves': 40}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 39, 'team_ga': 0, 'toi_sec': 3599}],
   {'points': 88, 'saves': 39}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 41, 'team_ga': 3, 'toi_sec': 3540}],
   {'points': 26, 'saves': 38}),
  ('normal control 1',
   [{'decision': 'W', 'empty_net_ga': 2, 'shots_faced': 15, 'team_ga': 3, 'toi_sec': 3600}],
   {'points': 48, 'saves': 14}),
  ('normal control 2',
   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 1, 'toi_sec': 2750}],
   {'points': 28, 'saves': 14}),
  ('normal control 3',
   [{'decision': None, 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 1, 'toi_sec': 3599}],
   {'points': 28, 'saves': 14}),
  ('normal control 4',
   [{'decision': 'W', 'empty_net_ga': 1, 'shots_faced': 37, 'team_ga': 3, 'toi_sec': 2400}],
   {'points': 70, 'saves': 35})],
 [('regression: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 3, 'team_ga': 3, 'toi_sec': 3900}],
   {'points': -50, 'saves': 0}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 27, 'team_ga': 2, 'toi_sec': 3540}],
   {'points': 20, 'saves': 25}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 2, 'shots_faced': 20, 'team_ga': 4, 'toi_sec': 3599}],
   {'points': 6, 'saves': 18}),
  ('normal control 1',
   [{'decision': None, 'empty_net_ga': 0, 'shots_faced': 28, 'team_ga': 3, 'toi_sec': 2400}],
   {'points': -10, 'saves': 25}),
  ('normal control 2',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 27, 'team_ga': 3, 'toi_sec': 3540}],
   {'points': -12, 'saves': 24}),
  ('normal control 3',
   [{'decision': None, 'empty_net_ga': 1, 'shots_faced': 15, 'team_ga': 2, 'toi_sec': 2400}],
   {'points': 8, 'saves': 14}),
  ('normal control 4',
   [{'decision': None, 'empty_net_ga': 2, 'shots_faced': 31, 'team_ga': 4, 'toi_sec': 2400}],
   {'points': 18, 'saves': 29})],
 [('regression: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 15, 'team_ga': 5, 'toi_sec': 1961}],
   {'points': -70, 'saves': 10}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 26, 'team_ga': 1, 'toi_sec': 3600}],
   {'points': 40, 'saves': 25}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 2, 'team_ga': 1, 'toi_sec': 2400}],
   {'points': 14, 'saves': 2}),
  ('normal control 1',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 0, 'toi_sec': 3900}],
   {'points': 110, 'saves': 40}),
  ('normal control 2',
   [{'decision': 'W', 'empty_net_ga': 1, 'shots_faced': 32, 'team_ga': 6, 'toi_sec': 3599}],
   {'points': -6, 'saves': 27}),
  ('normal control 3',
   [{'decision': 'L', 'empty_net_ga': 2, 'shots_faced': 27, 'team_ga': 3, 'toi_sec': 3599}],
   {'points': 32, 'saves': 26}),
  ('normal control 4',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 34, 'team_ga': 3, 'toi_sec': 3599}],
   {'points': 2, 'saves': 31})],
 [('regression: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 5, 'toi_sec': 2400}],
   {'points': -20, 'saves': 35}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 18, 'team_ga': 6, 'toi_sec': 2400}],
   {'points': -64, 'saves': 13}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 31, 'team_ga': 2, 'toi_sec': 3540}],
   {'points': 50, 'saves': 30}),
  ('normal control 1',
   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 27, 'team_ga': 6, 'toi_sec': 762}],
   {'points': -56, 'saves': 22}),
  ('normal control 2',
   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 33, 'team_ga': 3, 'toi_sec': 3599}],
   {'points': 22, 'saves': 31}),
  ('normal control 3',
   [{'decision': None, 'empty_net_ga': 0, 'shots_faced': 18, 'team_ga': 0, 'toi_sec': 3392}],
   {'points': 36, 'saves': 18}),
  ('normal control 4',
   [{'decision': None, 'empty_net_ga': 2, 'shots_faced': 2, 'team_ga': 2, 'toi_sec': 3900}],
   {'points': 34, 'saves': 2})],
 [('regression: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 36, 'team_ga': 3, 'toi_sec': 3715}],
   {'points': 16, 'saves': 33}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 8, 'team_ga': 5, 'toi_sec': 2400}],
   {'points': -84, 'saves': 3}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 19, 'team_ga': 6, 'toi_sec': 2400}],
   {'points': -62, 'saves': 14}),
  ('normal control 1',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 2, 'toi_sec': 3540}],
   {'points': 36, 'saves': 38}),
  ('normal control 2',
   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 12, 'team_ga': 3, 'toi_sec': 2400}],
   {'points': -20, 'saves': 10}),
  ('normal control 3',
   [{'decision': 'W', 'empty_net_ga': 0, 'shots_faced': 7, 'team_ga': 0, 'toi_sec': 3600}],
   {'points': 84, 'saves': 7}),
  ('normal control 4',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 23, 'team_ga': 5, 'toi_sec': 3540}],
   {'points': -64, 'saves': 18})]]
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: overtime loss decision	`{'points': -20, 'saves': 40}`	`{'points': -10, 'saves': 40}`	Failed
partial repair probe: overtime loss decision	`{'points': 78, 'saves': 39}`	`{'points': 88, 'saves': 39}`	Failed
second regression	`{'points': 16, 'saves': 38}`	`{'points': 26, 'saves': 38}`	Failed
normal control 1	`{'points': 48, 'saves': 14}`	`{'points': 48, 'saves': 14}`	Passed
normal control 2	`{'points': 28, 'saves': 14}`	`{'points': 28, 'saves': 14}`	Passed
normal control 3	`{'points': 28, 'saves': 14}`	`{'points': 28, 'saves': 14}`	Passed
normal control 4	`{'points': 70, 'saves': 35}`	`{'points': 70, 'saves': 35}`	Passed

SHA-256 / f201ca1bd3385db3e22cfa86116a5ad16796e852bba08e806346cdbded4a5e5a

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
    pts = saves * 2 - ga * 20
    pts += {'W': 40, 'OT': 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: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 45, 'team_ga': 5, 'toi_sec': 3599}],
   {'points': -10, 'saves': 40}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 39, 'team_ga': 0, 'toi_sec': 3599}],
   {'points': 88, 'saves': 39}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 41, 'team_ga': 3, 'toi_sec': 3540}],
   {'points': 26, 'saves': 38}),
  ('normal control 1',
   [{'decision': 'W', 'empty_net_ga': 2, 'shots_faced': 15, 'team_ga': 3, 'toi_sec': 3600}],
   {'points': 48, 'saves': 14}),
  ('normal control 2',
   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 1, 'toi_sec': 2750}],
   {'points': 28, 'saves': 14}),
  ('normal control 3',
   [{'decision': None, 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 1, 'toi_sec': 3599}],
   {'points': 28, 'saves': 14}),
  ('normal control 4',
   [{'decision': 'W', 'empty_net_ga': 1, 'shots_faced': 37, 'team_ga': 3, 'toi_sec': 2400}],
   {'points': 70, 'saves': 35})],
 [('regression: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 3, 'team_ga': 3, 'toi_sec': 3900}],
   {'points': -50, 'saves': 0}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 27, 'team_ga': 2, 'toi_sec': 3540}],
   {'points': 20, 'saves': 25}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 2, 'shots_faced': 20, 'team_ga': 4, 'toi_sec': 3599}],
   {'points': 6, 'saves': 18}),
  ('normal control 1',
   [{'decision': None, 'empty_net_ga': 0, 'shots_faced': 28, 'team_ga': 3, 'toi_sec': 2400}],
   {'points': -10, 'saves': 25}),
  ('normal control 2',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 27, 'team_ga': 3, 'toi_sec': 3540}],
   {'points': -12, 'saves': 24}),
  ('normal control 3',
   [{'decision': None, 'empty_net_ga': 1, 'shots_faced': 15, 'team_ga': 2, 'toi_sec': 2400}],
   {'points': 8, 'saves': 14}),
  ('normal control 4',
   [{'decision': None, 'empty_net_ga': 2, 'shots_faced': 31, 'team_ga': 4, 'toi_sec': 2400}],
   {'points': 18, 'saves': 29})],
 [('regression: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 15, 'team_ga': 5, 'toi_sec': 1961}],
   {'points': -70, 'saves': 10}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 26, 'team_ga': 1, 'toi_sec': 3600}],
   {'points': 40, 'saves': 25}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 2, 'team_ga': 1, 'toi_sec': 2400}],
   {'points': 14, 'saves': 2}),
  ('normal control 1',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 0, 'toi_sec': 3900}],
   {'points': 110, 'saves': 40}),
  ('normal control 2',
   [{'decision': 'W', 'empty_net_ga': 1, 'shots_faced': 32, 'team_ga': 6, 'toi_sec': 3599}],
   {'points': -6, 'saves': 27}),
  ('normal control 3',
   [{'decision': 'L', 'empty_net_ga': 2, 'shots_faced': 27, 'team_ga': 3, 'toi_sec': 3599}],
   {'points': 32, 'saves': 26}),
  ('normal control 4',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 34, 'team_ga': 3, 'toi_sec': 3599}],
   {'points': 2, 'saves': 31})],
 [('regression: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 5, 'toi_sec': 2400}],
   {'points': -20, 'saves': 35}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 18, 'team_ga': 6, 'toi_sec': 2400}],
   {'points': -64, 'saves': 13}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 31, 'team_ga': 2, 'toi_sec': 3540}],
   {'points': 50, 'saves': 30}),
  ('normal control 1',
   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 27, 'team_ga': 6, 'toi_sec': 762}],
   {'points': -56, 'saves': 22}),
  ('normal control 2',
   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 33, 'team_ga': 3, 'toi_sec': 3599}],
   {'points': 22, 'saves': 31}),
  ('normal control 3',
   [{'decision': None, 'empty_net_ga': 0, 'shots_faced': 18, 'team_ga': 0, 'toi_sec': 3392}],
   {'points': 36, 'saves': 18}),
  ('normal control 4',
   [{'decision': None, 'empty_net_ga': 2, 'shots_faced': 2, 'team_ga': 2, 'toi_sec': 3900}],
   {'points': 34, 'saves': 2})],
 [('regression: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 36, 'team_ga': 3, 'toi_sec': 3715}],
   {'points': 16, 'saves': 33}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 8, 'team_ga': 5, 'toi_sec': 2400}],
   {'points': -84, 'saves': 3}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 19, 'team_ga': 6, 'toi_sec': 2400}],
   {'points': -62, 'saves': 14}),
  ('normal control 1',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 2, 'toi_sec': 3540}],
   {'points': 36, 'saves': 38}),
  ('normal control 2',
   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 12, 'team_ga': 3, 'toi_sec': 2400}],
   {'points': -20, 'saves': 10}),
  ('normal control 3',
   [{'decision': 'W', 'empty_net_ga': 0, 'shots_faced': 7, 'team_ga': 0, 'toi_sec': 3600}],
   {'points': 84, 'saves': 7}),
  ('normal control 4',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 23, 'team_ga': 5, 'toi_sec': 3540}],
   {'points': -64, 'saves': 18})]]
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: overtime loss decision	`{'points': -20, 'saves': 40}`	`{'points': -10, 'saves': 40}`	Failed
partial repair probe: overtime loss decision	`{'points': 78, 'saves': 39}`	`{'points': 88, 'saves': 39}`	Failed
second regression	`{'points': 16, 'saves': 38}`	`{'points': 26, 'saves': 38}`	Failed
normal control 1	`{'points': 48, 'saves': 14}`	`{'points': 48, 'saves': 14}`	Passed
normal control 2	`{'points': 28, 'saves': 14}`	`{'points': 28, 'saves': 14}`	Passed
normal control 3	`{'points': 28, 'saves': 14}`	`{'points': 28, 'saves': 14}`	Passed
normal control 4	`{'points': 70, 'saves': 35}`	`{'points': 70, 'saves': 35}`	Passed

SHA-256 / 80105606d1ab8d0f10a62e4983f82a2efbe7f24ce61a3947fe14e9145406beae

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: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 45, 'team_ga': 5, 'toi_sec': 3599}],
   {'points': -10, 'saves': 40}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 39, 'team_ga': 0, 'toi_sec': 3599}],
   {'points': 88, 'saves': 39}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 41, 'team_ga': 3, 'toi_sec': 3540}],
   {'points': 26, 'saves': 38}),
  ('normal control 1',
   [{'decision': 'W', 'empty_net_ga': 2, 'shots_faced': 15, 'team_ga': 3, 'toi_sec': 3600}],
   {'points': 48, 'saves': 14}),
  ('normal control 2',
   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 1, 'toi_sec': 2750}],
   {'points': 28, 'saves': 14}),
  ('normal control 3',
   [{'decision': None, 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 1, 'toi_sec': 3599}],
   {'points': 28, 'saves': 14}),
  ('normal control 4',
   [{'decision': 'W', 'empty_net_ga': 1, 'shots_faced': 37, 'team_ga': 3, 'toi_sec': 2400}],
   {'points': 70, 'saves': 35})],
 [('regression: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 3, 'team_ga': 3, 'toi_sec': 3900}],
   {'points': -50, 'saves': 0}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 27, 'team_ga': 2, 'toi_sec': 3540}],
   {'points': 20, 'saves': 25}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 2, 'shots_faced': 20, 'team_ga': 4, 'toi_sec': 3599}],
   {'points': 6, 'saves': 18}),
  ('normal control 1',
   [{'decision': None, 'empty_net_ga': 0, 'shots_faced': 28, 'team_ga': 3, 'toi_sec': 2400}],
   {'points': -10, 'saves': 25}),
  ('normal control 2',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 27, 'team_ga': 3, 'toi_sec': 3540}],
   {'points': -12, 'saves': 24}),
  ('normal control 3',
   [{'decision': None, 'empty_net_ga': 1, 'shots_faced': 15, 'team_ga': 2, 'toi_sec': 2400}],
   {'points': 8, 'saves': 14}),
  ('normal control 4',
   [{'decision': None, 'empty_net_ga': 2, 'shots_faced': 31, 'team_ga': 4, 'toi_sec': 2400}],
   {'points': 18, 'saves': 29})],
 [('regression: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 15, 'team_ga': 5, 'toi_sec': 1961}],
   {'points': -70, 'saves': 10}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 26, 'team_ga': 1, 'toi_sec': 3600}],
   {'points': 40, 'saves': 25}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 2, 'team_ga': 1, 'toi_sec': 2400}],
   {'points': 14, 'saves': 2}),
  ('normal control 1',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 0, 'toi_sec': 3900}],
   {'points': 110, 'saves': 40}),
  ('normal control 2',
   [{'decision': 'W', 'empty_net_ga': 1, 'shots_faced': 32, 'team_ga': 6, 'toi_sec': 3599}],
   {'points': -6, 'saves': 27}),
  ('normal control 3',
   [{'decision': 'L', 'empty_net_ga': 2, 'shots_faced': 27, 'team_ga': 3, 'toi_sec': 3599}],
   {'points': 32, 'saves': 26}),
  ('normal control 4',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 34, 'team_ga': 3, 'toi_sec': 3599}],
   {'points': 2, 'saves': 31})],
 [('regression: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 5, 'toi_sec': 2400}],
   {'points': -20, 'saves': 35}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 18, 'team_ga': 6, 'toi_sec': 2400}],
   {'points': -64, 'saves': 13}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 31, 'team_ga': 2, 'toi_sec': 3540}],
   {'points': 50, 'saves': 30}),
  ('normal control 1',
   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 27, 'team_ga': 6, 'toi_sec': 762}],
   {'points': -56, 'saves': 22}),
  ('normal control 2',
   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 33, 'team_ga': 3, 'toi_sec': 3599}],
   {'points': 22, 'saves': 31}),
  ('normal control 3',
   [{'decision': None, 'empty_net_ga': 0, 'shots_faced': 18, 'team_ga': 0, 'toi_sec': 3392}],
   {'points': 36, 'saves': 18}),
  ('normal control 4',
   [{'decision': None, 'empty_net_ga': 2, 'shots_faced': 2, 'team_ga': 2, 'toi_sec': 3900}],
   {'points': 34, 'saves': 2})],
 [('regression: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 36, 'team_ga': 3, 'toi_sec': 3715}],
   {'points': 16, 'saves': 33}),
  ('partial repair probe: overtime loss decision',
   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 8, 'team_ga': 5, 'toi_sec': 2400}],
   {'points': -84, 'saves': 3}),
  ('second regression',
   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 19, 'team_ga': 6, 'toi_sec': 2400}],
   {'points': -62, 'saves': 14}),
  ('normal control 1',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 2, 'toi_sec': 3540}],
   {'points': 36, 'saves': 38}),
  ('normal control 2',
   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 12, 'team_ga': 3, 'toi_sec': 2400}],
   {'points': -20, 'saves': 10}),
  ('normal control 3',
   [{'decision': 'W', 'empty_net_ga': 0, 'shots_faced': 7, 'team_ga': 0, 'toi_sec': 3600}],
   {'points': 84, 'saves': 7}),
  ('normal control 4',
   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 23, 'team_ga': 5, 'toi_sec': 3540}],
   {'points': -64, 'saves': 18})]]
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: overtime loss decision	`{'points': -10, 'saves': 40}`	`{'points': -10, 'saves': 40}`	Passed
partial repair probe: overtime loss decision	`{'points': 88, 'saves': 39}`	`{'points': 88, 'saves': 39}`	Passed
second regression	`{'points': 26, 'saves': 38}`	`{'points': 26, 'saves': 38}`	Passed
normal control 1	`{'points': 48, 'saves': 14}`	`{'points': 48, 'saves': 14}`	Passed
normal control 2	`{'points': 28, 'saves': 14}`	`{'points': 28, 'saves': 14}`	Passed
normal control 3	`{'points': 28, 'saves': 14}`	`{'points': 28, 'saves': 14}`	Passed
normal control 4	`{'points': 70, 'saves': 35}`	`{'points': 70, 'saves': 35}`	Passed

SHA-256 / c32dc5c81071c070c74ca16aaa739fc0b60dd01d047185209b5b4c9fbbe151ee

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 2026-09-29T14:50:37.742691+00:00.

Case digest / a2e89b28617fe60d8b486765b9641a947f9813539006e53757db62a8de80f971