{"abstract":"Goalies lose the overtime-loss point.","category":"Fantasy sports scoring","checks":7,"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.","contract_signature":"g","evaluation_group":"w2-fantasy-sports-scoring-goalie-line","failed_approach":"Keying the table by OT does not match the OTL decision code the feed uses.","family":"w2-fantasy-sports-scoring-goalie-line-overtime-loss-decision","id":"FA-85146","implementations":{"attempt":{"sha256":"80105606d1ab8d0f10a62e4983f82a2efbe7f24ce61a3947fe14e9145406beae","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(g):\n    ga = g['team_ga'] - g['empty_net_ga']\n    saves = g['shots_faced'] - ga\n    pts = saves * 2 - ga * 20\n    pts += {'W': 40, 'OT': 10}.get(g['decision'], 0)\n    if ga == 0 and g['toi_sec'] >= 3600:\n        pts += 30\n    return {'saves': saves, 'points': pts}\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 45, 'team_ga': 5, 'toi_sec': 3599}],\n   {'points': -10, 'saves': 40}),\n  ('partial repair probe: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 39, 'team_ga': 0, 'toi_sec': 3599}],\n   {'points': 88, 'saves': 39}),\n  ('second regression',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 41, 'team_ga': 3, 'toi_sec': 3540}],\n   {'points': 26, 'saves': 38}),\n  ('normal control 1',\n   [{'decision': 'W', 'empty_net_ga': 2, 'shots_faced': 15, 'team_ga': 3, 'toi_sec': 3600}],\n   {'points': 48, 'saves': 14}),\n  ('normal control 2',\n   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 1, 'toi_sec': 2750}],\n   {'points': 28, 'saves': 14}),\n  ('normal control 3',\n   [{'decision': None, 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 1, 'toi_sec': 3599}],\n   {'points': 28, 'saves': 14}),\n  ('normal control 4',\n   [{'decision': 'W', 'empty_net_ga': 1, 'shots_faced': 37, 'team_ga': 3, 'toi_sec': 2400}],\n   {'points': 70, 'saves': 35})],\n [('regression: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 3, 'team_ga': 3, 'toi_sec': 3900}],\n   {'points': -50, 'saves': 0}),\n  ('partial repair probe: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 27, 'team_ga': 2, 'toi_sec': 3540}],\n   {'points': 20, 'saves': 25}),\n  ('second regression',\n   [{'decision': 'OTL', 'empty_net_ga': 2, 'shots_faced': 20, 'team_ga': 4, 'toi_sec': 3599}],\n   {'points': 6, 'saves': 18}),\n  ('normal control 1',\n   [{'decision': None, 'empty_net_ga': 0, 'shots_faced': 28, 'team_ga': 3, 'toi_sec': 2400}],\n   {'points': -10, 'saves': 25}),\n  ('normal control 2',\n   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 27, 'team_ga': 3, 'toi_sec': 3540}],\n   {'points': -12, 'saves': 24}),\n  ('normal control 3',\n   [{'decision': None, 'empty_net_ga': 1, 'shots_faced': 15, 'team_ga': 2, 'toi_sec': 2400}],\n   {'points': 8, 'saves': 14}),\n  ('normal control 4',\n   [{'decision': None, 'empty_net_ga': 2, 'shots_faced': 31, 'team_ga': 4, 'toi_sec': 2400}],\n   {'points': 18, 'saves': 29})],\n [('regression: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 15, 'team_ga': 5, 'toi_sec': 1961}],\n   {'points': -70, 'saves': 10}),\n  ('partial repair probe: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 26, 'team_ga': 1, 'toi_sec': 3600}],\n   {'points': 40, 'saves': 25}),\n  ('second regression',\n   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 2, 'team_ga': 1, 'toi_sec': 2400}],\n   {'points': 14, 'saves': 2}),\n  ('normal control 1',\n   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 0, 'toi_sec': 3900}],\n   {'points': 110, 'saves': 40}),\n  ('normal control 2',\n   [{'decision': 'W', 'empty_net_ga': 1, 'shots_faced': 32, 'team_ga': 6, 'toi_sec': 3599}],\n   {'points': -6, 'saves': 27}),\n  ('normal control 3',\n   [{'decision': 'L', 'empty_net_ga': 2, 'shots_faced': 27, 'team_ga': 3, 'toi_sec': 3599}],\n   {'points': 32, 'saves': 26}),\n  ('normal control 4',\n   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 34, 'team_ga': 3, 'toi_sec': 3599}],\n   {'points': 2, 'saves': 31})],\n [('regression: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 5, 'toi_sec': 2400}],\n   {'points': -20, 'saves': 35}),\n  ('partial repair probe: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 18, 'team_ga': 6, 'toi_sec': 2400}],\n   {'points': -64, 'saves': 13}),\n  ('second regression',\n   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 31, 'team_ga': 2, 'toi_sec': 3540}],\n   {'points': 50, 'saves': 30}),\n  ('normal control 1',\n   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 27, 'team_ga': 6, 'toi_sec': 762}],\n   {'points': -56, 'saves': 22}),\n  ('normal control 2',\n   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 33, 'team_ga': 3, 'toi_sec': 3599}],\n   {'points': 22, 'saves': 31}),\n  ('normal control 3',\n   [{'decision': None, 'empty_net_ga': 0, 'shots_faced': 18, 'team_ga': 0, 'toi_sec': 3392}],\n   {'points': 36, 'saves': 18}),\n  ('normal control 4',\n   [{'decision': None, 'empty_net_ga': 2, 'shots_faced': 2, 'team_ga': 2, 'toi_sec': 3900}],\n   {'points': 34, 'saves': 2})],\n [('regression: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 36, 'team_ga': 3, 'toi_sec': 3715}],\n   {'points': 16, 'saves': 33}),\n  ('partial repair probe: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 8, 'team_ga': 5, 'toi_sec': 2400}],\n   {'points': -84, 'saves': 3}),\n  ('second regression',\n   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 19, 'team_ga': 6, 'toi_sec': 2400}],\n   {'points': -62, 'saves': 14}),\n  ('normal control 1',\n   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 2, 'toi_sec': 3540}],\n   {'points': 36, 'saves': 38}),\n  ('normal control 2',\n   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 12, 'team_ga': 3, 'toi_sec': 2400}],\n   {'points': -20, 'saves': 10}),\n  ('normal control 3',\n   [{'decision': 'W', 'empty_net_ga': 0, 'shots_faced': 7, 'team_ga': 0, 'toi_sec': 3600}],\n   {'points': 84, 'saves': 7}),\n  ('normal control 4',\n   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 23, 'team_ga': 5, 'toi_sec': 3540}],\n   {'points': -64, 'saves': 18})]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"f201ca1bd3385db3e22cfa86116a5ad16796e852bba08e806346cdbded4a5e5a","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(g):\n    ga = g['team_ga'] - g['empty_net_ga']\n    saves = g['shots_faced'] - ga\n    pts = saves * 2 - ga * 20\n    pts += {'W': 40}.get(g['decision'], 0)\n    if ga == 0 and g['toi_sec'] >= 3600:\n        pts += 30\n    return {'saves': saves, 'points': pts}\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 45, 'team_ga': 5, 'toi_sec': 3599}],\n   {'points': -10, 'saves': 40}),\n  ('partial repair probe: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 39, 'team_ga': 0, 'toi_sec': 3599}],\n   {'points': 88, 'saves': 39}),\n  ('second regression',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 41, 'team_ga': 3, 'toi_sec': 3540}],\n   {'points': 26, 'saves': 38}),\n  ('normal control 1',\n   [{'decision': 'W', 'empty_net_ga': 2, 'shots_faced': 15, 'team_ga': 3, 'toi_sec': 3600}],\n   {'points': 48, 'saves': 14}),\n  ('normal control 2',\n   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 1, 'toi_sec': 2750}],\n   {'points': 28, 'saves': 14}),\n  ('normal control 3',\n   [{'decision': None, 'empty_net_ga': 1, 'shots_faced': 14, 'team_ga': 1, 'toi_sec': 3599}],\n   {'points': 28, 'saves': 14}),\n  ('normal control 4',\n   [{'decision': 'W', 'empty_net_ga': 1, 'shots_faced': 37, 'team_ga': 3, 'toi_sec': 2400}],\n   {'points': 70, 'saves': 35})],\n [('regression: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 3, 'team_ga': 3, 'toi_sec': 3900}],\n   {'points': -50, 'saves': 0}),\n  ('partial repair probe: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 27, 'team_ga': 2, 'toi_sec': 3540}],\n   {'points': 20, 'saves': 25}),\n  ('second regression',\n   [{'decision': 'OTL', 'empty_net_ga': 2, 'shots_faced': 20, 'team_ga': 4, 'toi_sec': 3599}],\n   {'points': 6, 'saves': 18}),\n  ('normal control 1',\n   [{'decision': None, 'empty_net_ga': 0, 'shots_faced': 28, 'team_ga': 3, 'toi_sec': 2400}],\n   {'points': -10, 'saves': 25}),\n  ('normal control 2',\n   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 27, 'team_ga': 3, 'toi_sec': 3540}],\n   {'points': -12, 'saves': 24}),\n  ('normal control 3',\n   [{'decision': None, 'empty_net_ga': 1, 'shots_faced': 15, 'team_ga': 2, 'toi_sec': 2400}],\n   {'points': 8, 'saves': 14}),\n  ('normal control 4',\n   [{'decision': None, 'empty_net_ga': 2, 'shots_faced': 31, 'team_ga': 4, 'toi_sec': 2400}],\n   {'points': 18, 'saves': 29})],\n [('regression: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 15, 'team_ga': 5, 'toi_sec': 1961}],\n   {'points': -70, 'saves': 10}),\n  ('partial repair probe: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 26, 'team_ga': 1, 'toi_sec': 3600}],\n   {'points': 40, 'saves': 25}),\n  ('second regression',\n   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 2, 'team_ga': 1, 'toi_sec': 2400}],\n   {'points': 14, 'saves': 2}),\n  ('normal control 1',\n   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 0, 'toi_sec': 3900}],\n   {'points': 110, 'saves': 40}),\n  ('normal control 2',\n   [{'decision': 'W', 'empty_net_ga': 1, 'shots_faced': 32, 'team_ga': 6, 'toi_sec': 3599}],\n   {'points': -6, 'saves': 27}),\n  ('normal control 3',\n   [{'decision': 'L', 'empty_net_ga': 2, 'shots_faced': 27, 'team_ga': 3, 'toi_sec': 3599}],\n   {'points': 32, 'saves': 26}),\n  ('normal control 4',\n   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 34, 'team_ga': 3, 'toi_sec': 3599}],\n   {'points': 2, 'saves': 31})],\n [('regression: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 5, 'toi_sec': 2400}],\n   {'points': -20, 'saves': 35}),\n  ('partial repair probe: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 18, 'team_ga': 6, 'toi_sec': 2400}],\n   {'points': -64, 'saves': 13}),\n  ('second regression',\n   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 31, 'team_ga': 2, 'toi_sec': 3540}],\n   {'points': 50, 'saves': 30}),\n  ('normal control 1',\n   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 27, 'team_ga': 6, 'toi_sec': 762}],\n   {'points': -56, 'saves': 22}),\n  ('normal control 2',\n   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 33, 'team_ga': 3, 'toi_sec': 3599}],\n   {'points': 22, 'saves': 31}),\n  ('normal control 3',\n   [{'decision': None, 'empty_net_ga': 0, 'shots_faced': 18, 'team_ga': 0, 'toi_sec': 3392}],\n   {'points': 36, 'saves': 18}),\n  ('normal control 4',\n   [{'decision': None, 'empty_net_ga': 2, 'shots_faced': 2, 'team_ga': 2, 'toi_sec': 3900}],\n   {'points': 34, 'saves': 2})],\n [('regression: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 36, 'team_ga': 3, 'toi_sec': 3715}],\n   {'points': 16, 'saves': 33}),\n  ('partial repair probe: overtime loss decision',\n   [{'decision': 'OTL', 'empty_net_ga': 0, 'shots_faced': 8, 'team_ga': 5, 'toi_sec': 2400}],\n   {'points': -84, 'saves': 3}),\n  ('second regression',\n   [{'decision': 'OTL', 'empty_net_ga': 1, 'shots_faced': 19, 'team_ga': 6, 'toi_sec': 2400}],\n   {'points': -62, 'saves': 14}),\n  ('normal control 1',\n   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 40, 'team_ga': 2, 'toi_sec': 3540}],\n   {'points': 36, 'saves': 38}),\n  ('normal control 2',\n   [{'decision': 'L', 'empty_net_ga': 1, 'shots_faced': 12, 'team_ga': 3, 'toi_sec': 2400}],\n   {'points': -20, 'saves': 10}),\n  ('normal control 3',\n   [{'decision': 'W', 'empty_net_ga': 0, 'shots_faced': 7, 'team_ga': 0, 'toi_sec': 3600}],\n   {'points': 84, 'saves': 7}),\n  ('normal control 4',\n   [{'decision': 'L', 'empty_net_ga': 0, 'shots_faced': 23, 'team_ga': 5, 'toi_sec': 3540}],\n   {'points': -64, 'saves': 18})]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"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.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-fantasy-sports-scoring-goalie-line-overtime-loss-decision","generated_at":"2026-09-29T14:50:37.742691+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Goalie scoring depends on attribution of empty-net goals and on playing-time conditions for shutouts.","root_cause":"The decision table has no entry for OTL.","sha256":"9ce871f9d68e72af110aadcc95fa5ac4a9fa81468ab2da0848eac9c64154bfd0","title":"Overtime losses score like regulation losses · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":39.316,"exit_code":1,"observations":[{"actual":{"points":-20,"saves":40},"check":"regression: overtime loss decision","expected":{"points":-10,"saves":40},"passed":false},{"actual":{"points":78,"saves":39},"check":"partial repair probe: overtime loss decision","expected":{"points":88,"saves":39},"passed":false},{"actual":{"points":16,"saves":38},"check":"second regression","expected":{"points":26,"saves":38},"passed":false},{"actual":{"points":48,"saves":14},"check":"normal control 1","expected":{"points":48,"saves":14},"passed":true},{"actual":{"points":28,"saves":14},"check":"normal control 2","expected":{"points":28,"saves":14},"passed":true},{"actual":{"points":28,"saves":14},"check":"normal control 3","expected":{"points":28,"saves":14},"passed":true},{"actual":{"points":70,"saves":35},"check":"normal control 4","expected":{"points":70,"saves":35},"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: overtime loss decision\", \"actual\": {\"saves\": 40, \"points\": -20}, \"expected\": {\"points\": -10, \"saves\": 40}, \"passed\": false}, {\"check\": \"partial repair probe: overtime loss decision\", \"actual\": {\"saves\": 39, \"points\": 78}, \"expected\": {\"points\": 88, \"saves\": 39}, \"passed\": false}, {\"check\": \"second regression\", \"actual\": {\"saves\": 38, \"points\": 16}, \"expected\": {\"points\": 26, \"saves\": 38}, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": {\"saves\": 14, \"points\": 48}, \"expected\": {\"points\": 48, \"saves\": 14}, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": {\"saves\": 14, \"points\": 28}, \"expected\": {\"points\": 28, \"saves\": 14}, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": {\"saves\": 14, \"points\": 28}, \"expected\": {\"points\": 28, \"saves\": 14}, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": {\"saves\": 35, \"points\": 70}, \"expected\": {\"points\": 70, \"saves\": 35}, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":39.519,"exit_code":1,"observations":[{"actual":{"points":-20,"saves":40},"check":"regression: overtime loss decision","expected":{"points":-10,"saves":40},"passed":false},{"actual":{"points":78,"saves":39},"check":"partial repair probe: overtime loss decision","expected":{"points":88,"saves":39},"passed":false},{"actual":{"points":16,"saves":38},"check":"second regression","expected":{"points":26,"saves":38},"passed":false},{"actual":{"points":48,"saves":14},"check":"normal control 1","expected":{"points":48,"saves":14},"passed":true},{"actual":{"points":28,"saves":14},"check":"normal control 2","expected":{"points":28,"saves":14},"passed":true},{"actual":{"points":28,"saves":14},"check":"normal control 3","expected":{"points":28,"saves":14},"passed":true},{"actual":{"points":70,"saves":35},"check":"normal control 4","expected":{"points":70,"saves":35},"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: overtime loss decision\", \"actual\": {\"saves\": 40, \"points\": -20}, \"expected\": {\"points\": -10, \"saves\": 40}, \"passed\": false}, {\"check\": \"partial repair probe: overtime loss decision\", \"actual\": {\"saves\": 39, \"points\": 78}, \"expected\": {\"points\": 88, \"saves\": 39}, \"passed\": false}, {\"check\": \"second regression\", \"actual\": {\"saves\": 38, \"points\": 16}, \"expected\": {\"points\": 26, \"saves\": 38}, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": {\"saves\": 14, \"points\": 48}, \"expected\": {\"points\": 48, \"saves\": 14}, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": {\"saves\": 14, \"points\": 28}, \"expected\": {\"points\": 28, \"saves\": 14}, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": {\"saves\": 14, \"points\": 28}, \"expected\": {\"points\": 28, \"saves\": 14}, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": {\"saves\": 35, \"points\": 70}, \"expected\": {\"points\": 70, \"saves\": 35}, \"passed\": true}], \"passed\": false}\n"}},"member_only":{"stages":["fixed"],"fields":["implementations.fixed","verification.fixed","harness","repair"],"note":"The verified repair, its recorded checks, the repair description, and the scoring harness are available to members."}}