{"abstract":"Entries scoring 149.95 and 149.93 are paid as if tied.","category":"Fantasy sports scoring","checks":7,"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.","evaluation_group":"w2-fantasy-sports-scoring-contest-payout-ties","failed_approach":"A 0.1 tolerance window still merges distinct scores.","family":"w2-fantasy-sports-scoring-contest-payout-ties-tie-detection-precision","id":"FA-85041","implementations":{"attempt":{"sha256":"0d1ee8989ce8c4f55eefe5324022aacbef9d98c78e4d7d70fb285a6aed1fe3a2","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(entries, payouts):\n    ordered = sorted(entries, key=lambda e: (-e[1], e[0]))\n    out = {}\n    i = 0\n    while i < len(ordered):\n        j = i\n        while j < len(ordered) and abs(ordered[j][1] - ordered[i][1]) < 10:\n            j += 1\n        pool = sum(payouts[i:j])\n        group = sorted(e[0] for e in ordered[i:j])\n        share, extra = divmod(pool, len(group))\n        for k, eid in enumerate(group):\n            amt = share + (1 if k < extra else 0)\n            if amt > 0:\n                out[eid] = amt\n        i = j\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: tie detection precision',\n   [[['e06', 1497], ['e21', 1500], ['e12', 1445], ['e14', 1495]], [1000, 301, 250, 1]],\n   {'e06': 301, 'e12': 1, 'e14': 250, 'e21': 1000}),\n  ('partial repair probe: tie detection precision',\n   [[['e28', 1445], ['e26', 1497], ['e10', 1500], ['e09', 1500], ['e02', 1497]], [501, 501, 500, 500]],\n   {'e02': 500, 'e09': 501, 'e10': 501, 'e26': 500}),\n  ('second regression',\n   [[['e02', 1493], ['e20', 1445], ['e21', 1490], ['e29', 1445], ['e14', 1497], ['e27', 1490], ['e28', 1495]],\n    [1000, 501, 77, 77]],\n   {'e02': 77, 'e14': 1000, 'e21': 39, 'e27': 38, 'e28': 501}),\n  ('normal control 1', [[['e05', 1493], ['e24', 1445], ['e23', 1445]], [301, 199, 77, 77, 2]],\n   {'e05': 301, 'e23': 138, 'e24': 138}),\n  ('normal control 2', [[['e10', 1500], ['e01', 1445], ['e11', 1490]], [1000, 501, 77, 2]],\n   {'e01': 77, 'e10': 1000, 'e11': 501}),\n  ('normal control 3', [[['e06', 1420], ['e05', 1420], ['e11', 1420]], [77, 77]],\n   {'e05': 52, 'e06': 51, 'e11': 51}),\n  ('normal control 4', [[['e14', 1445], ['e16', 1500], ['e05', 1490]], [1000, 199, 100, 77]],\n   {'e05': 199, 'e14': 100, 'e16': 1000})],\n [('regression: tie detection precision', [[['e16', 1495], ['e29', 1445], ['e26', 1497]], [1000, 250, 1]],\n   {'e16': 250, 'e26': 1000, 'e29': 1}),\n  ('partial repair probe: tie detection precision',\n   [[['e07', 1323], ['e27', 1333], ['e19', 1278], ['e22', 1328], ['e18', 1326], ['e13', 1323], ['e26', 1333],\n     ['e16', 1333]],\n    [1000, 301, 199, 77]],\n   {'e16': 500, 'e22': 77, 'e26': 500, 'e27': 500}),\n  ('second regression',\n   [[['e14', 1333], ['e04', 1278], ['e22', 1326], ['e05', 1333], ['e18', 1330], ['e27', 1330], ['e07', 1278]],\n    [250, 77]],\n   {'e05': 164, 'e14': 163}),\n  ('normal control 1', [[['e04', 1420], ['e19', 1420], ['e29', 1410]], [1000, 250, 1]],\n   {'e04': 625, 'e19': 625, 'e29': 1}),\n  ('normal control 2', [[['e05', 1493], ['e28', 1493], ['e19', 1445]], [301, 199]], {'e05': 250, 'e28': 250}),\n  ('normal control 3', [[['e08', 1500], ['e03', 1445], ['e22', 1497]], [301, 301]], {'e08': 301, 'e22': 301}),\n  ('normal control 4', [[['e10', 1445], ['e23', 1500], ['e26', 1500]], [1000, 301, 199, 100]],\n   {'e10': 199, 'e23': 651, 'e26': 650})],\n [('regression: tie detection precision', [[['e21', 1497], ['e14', 1493], ['e03', 1500]], [1000, 77]],\n   {'e03': 1000, 'e21': 77}),\n  ('partial repair probe: tie detection precision',\n   [[['e25', 1278], ['e28', 1323], ['e04', 1328]], [501, 250, 199, 2]],\n   {'e04': 501, 'e25': 199, 'e28': 250}),\n  ('second regression',\n   [[['e21', 1323], ['e08', 1326], ['e27', 1323], ['e26', 1333], ['e24', 1278], ['e02', 1326]],\n    [501, 301, 250, 2, 1]],\n   {'e02': 276, 'e08': 275, 'e21': 2, 'e26': 501, 'e27': 1}),\n  ('normal control 1',\n   [[['e21', 1500], ['e22', 1500], ['e15', 1500], ['e08', 1490], ['e23', 1445], ['e01', 1490]], [250, 77]],\n   {'e15': 109, 'e21': 109, 'e22': 109}),\n  ('normal control 2',\n   [[['e07', 1365], ['e11', 1413], ['e15', 1413], ['e01', 1413]], [1000, 199, 100, 77, 1]],\n   {'e01': 433, 'e07': 77, 'e11': 433, 'e15': 433}),\n  ('normal control 3', [[['e07', 1415], ['e09', 1415], ['e05', 1415]], [1000, 500]],\n   {'e05': 500, 'e07': 500, 'e09': 500}),\n  ('normal control 4', [[['e15', 1500], ['e10', 1490], ['e18', 1490]], [500, 500, 301, 2, 2]],\n   {'e10': 401, 'e15': 500, 'e18': 400})],\n [('regression: tie detection precision',\n   [[['e17', 1323], ['e28', 1330], ['e12', 1326], ['e09', 1330], ['e19', 1330], ['e20', 1326], ['e15', 1328],\n     ['e26', 1278]],\n    [501, 500, 100, 100]],\n   {'e09': 367, 'e15': 100, 'e19': 367, 'e28': 367}),\n  ('partial repair probe: tie detection precision',\n   [[['e29', 1413], ['e01', 1420], ['e28', 1365], ['e16', 1410], ['e26', 1420], ['e25', 1420]],\n    [500, 301, 199, 199]],\n   {'e01': 334, 'e25': 333, 'e26': 333, 'e29': 199}),\n  ('second regression',\n   [[['e16', 1415], ['e17', 1415], ['e14', 1413], ['e09', 1420], ['e01', 1420], ['e07', 1420], ['e12', 1420],\n     ['e23', 1417]],\n    [1000, 1000, 250]],\n   {'e01': 563, 'e07': 563, 'e09': 562, 'e12': 562}),\n  ('normal control 1',\n   [[['e09', 1420], ['e03', 1365], ['e23', 1410], ['e21', 1410], ['e05', 1420], ['e17', 1365], ['e25', 1420],\n     ['e13', 1410]],\n    [2, 1]],\n   {'e05': 1, 'e09': 1, 'e25': 1}),\n  ('normal control 2',\n   [[['e12', 1278], ['e27', 1323], ['e23', 1333], ['e01', 1323], ['e26', 1278]], [301, 199, 77]],\n   {'e01': 138, 'e23': 301, 'e27': 138}),\n  ('normal control 3', [[['e17', 1278], ['e01', 1333], ['e06', 1333]], [1000, 250, 250, 199, 1]],\n   {'e01': 625, 'e06': 625, 'e17': 250}),\n  ('normal control 4', [[['e21', 1410], ['e29', 1420], ['e16', 1420], ['e27', 1410]], [1, 1]],\n   {'e16': 1, 'e29': 1})],\n [('regression: tie detection precision',\n   [[['e12', 1445], ['e15', 1493], ['e25', 1493], ['e27', 1445], ['e22', 1497], ['e04', 1445]], [501, 250]],\n   {'e15': 125, 'e22': 501, 'e25': 125}),\n  ('partial repair probe: tie detection precision',\n   [[['e07', 1500], ['e13', 1497], ['e27', 1495], ['e24', 1495], ['e21', 1500], ['e22', 1490]], [77, 2]],\n   {'e07': 40, 'e21': 39}),\n  ('second regression',\n   [[['e20', 1417], ['e06', 1365], ['e19', 1410], ['e15', 1415], ['e05', 1420], ['e28', 1365], ['e29', 1410]],\n    [501, 500, 500, 1]],\n   {'e05': 501, 'e15': 500, 'e19': 1, 'e20': 500}),\n  ('normal control 1', [[['e02', 1420], ['e28', 1410], ['e17', 1410]], [199, 2, 2]],\n   {'e02': 199, 'e17': 2, 'e28': 2}),\n  ('normal control 2', [[['e18', 1330], ['e15', 1330], ['e06', 1330]], [501, 500, 301, 301]],\n   {'e06': 434, 'e15': 434, 'e18': 434}),\n  ('normal control 3',\n   [[['e06', 1500], ['e14', 1500], ['e13', 1500], ['e24', 1500], ['e10', 1500], ['e15', 1500], ['e11', 1500]],\n    [1000, 250, 199, 1]],\n   {'e06': 208, 'e10': 207, 'e11': 207, 'e13': 207, 'e14': 207, 'e15': 207, 'e24': 207}),\n  ('normal control 4',\n   [[['e12', 1500], ['e11', 1490], ['e03', 1445], ['e25', 1500], ['e13', 1500]], [501, 301, 199, 100, 77]],\n   {'e03': 77, 'e11': 100, 'e12': 334, 'e13': 334, 'e25': 333})]]\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":"e22fbe2a0bc6328ef50f48b4f0aaee631af0861ddeacd04a8b41144a25f7bf6d","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(entries, payouts):\n    ordered = sorted(entries, key=lambda e: (-e[1], e[0]))\n    out = {}\n    i = 0\n    while i < len(ordered):\n        j = i\n        while j < len(ordered) and ordered[j][1] // 10 == ordered[i][1] // 10:\n            j += 1\n        pool = sum(payouts[i:j])\n        group = sorted(e[0] for e in ordered[i:j])\n        share, extra = divmod(pool, len(group))\n        for k, eid in enumerate(group):\n            amt = share + (1 if k < extra else 0)\n            if amt > 0:\n                out[eid] = amt\n        i = j\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: tie detection precision',\n   [[['e06', 1497], ['e21', 1500], ['e12', 1445], ['e14', 1495]], [1000, 301, 250, 1]],\n   {'e06': 301, 'e12': 1, 'e14': 250, 'e21': 1000}),\n  ('partial repair probe: tie detection precision',\n   [[['e28', 1445], ['e26', 1497], ['e10', 1500], ['e09', 1500], ['e02', 1497]], [501, 501, 500, 500]],\n   {'e02': 500, 'e09': 501, 'e10': 501, 'e26': 500}),\n  ('second regression',\n   [[['e02', 1493], ['e20', 1445], ['e21', 1490], ['e29', 1445], ['e14', 1497], ['e27', 1490], ['e28', 1495]],\n    [1000, 501, 77, 77]],\n   {'e02': 77, 'e14': 1000, 'e21': 39, 'e27': 38, 'e28': 501}),\n  ('normal control 1', [[['e05', 1493], ['e24', 1445], ['e23', 1445]], [301, 199, 77, 77, 2]],\n   {'e05': 301, 'e23': 138, 'e24': 138}),\n  ('normal control 2', [[['e10', 1500], ['e01', 1445], ['e11', 1490]], [1000, 501, 77, 2]],\n   {'e01': 77, 'e10': 1000, 'e11': 501}),\n  ('normal control 3', [[['e06', 1420], ['e05', 1420], ['e11', 1420]], [77, 77]],\n   {'e05': 52, 'e06': 51, 'e11': 51}),\n  ('normal control 4', [[['e14', 1445], ['e16', 1500], ['e05', 1490]], [1000, 199, 100, 77]],\n   {'e05': 199, 'e14': 100, 'e16': 1000})],\n [('regression: tie detection precision', [[['e16', 1495], ['e29', 1445], ['e26', 1497]], [1000, 250, 1]],\n   {'e16': 250, 'e26': 1000, 'e29': 1}),\n  ('partial repair probe: tie detection precision',\n   [[['e07', 1323], ['e27', 1333], ['e19', 1278], ['e22', 1328], ['e18', 1326], ['e13', 1323], ['e26', 1333],\n     ['e16', 1333]],\n    [1000, 301, 199, 77]],\n   {'e16': 500, 'e22': 77, 'e26': 500, 'e27': 500}),\n  ('second regression',\n   [[['e14', 1333], ['e04', 1278], ['e22', 1326], ['e05', 1333], ['e18', 1330], ['e27', 1330], ['e07', 1278]],\n    [250, 77]],\n   {'e05': 164, 'e14': 163}),\n  ('normal control 1', [[['e04', 1420], ['e19', 1420], ['e29', 1410]], [1000, 250, 1]],\n   {'e04': 625, 'e19': 625, 'e29': 1}),\n  ('normal control 2', [[['e05', 1493], ['e28', 1493], ['e19', 1445]], [301, 199]], {'e05': 250, 'e28': 250}),\n  ('normal control 3', [[['e08', 1500], ['e03', 1445], ['e22', 1497]], [301, 301]], {'e08': 301, 'e22': 301}),\n  ('normal control 4', [[['e10', 1445], ['e23', 1500], ['e26', 1500]], [1000, 301, 199, 100]],\n   {'e10': 199, 'e23': 651, 'e26': 650})],\n [('regression: tie detection precision', [[['e21', 1497], ['e14', 1493], ['e03', 1500]], [1000, 77]],\n   {'e03': 1000, 'e21': 77}),\n  ('partial repair probe: tie detection precision',\n   [[['e25', 1278], ['e28', 1323], ['e04', 1328]], [501, 250, 199, 2]],\n   {'e04': 501, 'e25': 199, 'e28': 250}),\n  ('second regression',\n   [[['e21', 1323], ['e08', 1326], ['e27', 1323], ['e26', 1333], ['e24', 1278], ['e02', 1326]],\n    [501, 301, 250, 2, 1]],\n   {'e02': 276, 'e08': 275, 'e21': 2, 'e26': 501, 'e27': 1}),\n  ('normal control 1',\n   [[['e21', 1500], ['e22', 1500], ['e15', 1500], ['e08', 1490], ['e23', 1445], ['e01', 1490]], [250, 77]],\n   {'e15': 109, 'e21': 109, 'e22': 109}),\n  ('normal control 2',\n   [[['e07', 1365], ['e11', 1413], ['e15', 1413], ['e01', 1413]], [1000, 199, 100, 77, 1]],\n   {'e01': 433, 'e07': 77, 'e11': 433, 'e15': 433}),\n  ('normal control 3', [[['e07', 1415], ['e09', 1415], ['e05', 1415]], [1000, 500]],\n   {'e05': 500, 'e07': 500, 'e09': 500}),\n  ('normal control 4', [[['e15', 1500], ['e10', 1490], ['e18', 1490]], [500, 500, 301, 2, 2]],\n   {'e10': 401, 'e15': 500, 'e18': 400})],\n [('regression: tie detection precision',\n   [[['e17', 1323], ['e28', 1330], ['e12', 1326], ['e09', 1330], ['e19', 1330], ['e20', 1326], ['e15', 1328],\n     ['e26', 1278]],\n    [501, 500, 100, 100]],\n   {'e09': 367, 'e15': 100, 'e19': 367, 'e28': 367}),\n  ('partial repair probe: tie detection precision',\n   [[['e29', 1413], ['e01', 1420], ['e28', 1365], ['e16', 1410], ['e26', 1420], ['e25', 1420]],\n    [500, 301, 199, 199]],\n   {'e01': 334, 'e25': 333, 'e26': 333, 'e29': 199}),\n  ('second regression',\n   [[['e16', 1415], ['e17', 1415], ['e14', 1413], ['e09', 1420], ['e01', 1420], ['e07', 1420], ['e12', 1420],\n     ['e23', 1417]],\n    [1000, 1000, 250]],\n   {'e01': 563, 'e07': 563, 'e09': 562, 'e12': 562}),\n  ('normal control 1',\n   [[['e09', 1420], ['e03', 1365], ['e23', 1410], ['e21', 1410], ['e05', 1420], ['e17', 1365], ['e25', 1420],\n     ['e13', 1410]],\n    [2, 1]],\n   {'e05': 1, 'e09': 1, 'e25': 1}),\n  ('normal control 2',\n   [[['e12', 1278], ['e27', 1323], ['e23', 1333], ['e01', 1323], ['e26', 1278]], [301, 199, 77]],\n   {'e01': 138, 'e23': 301, 'e27': 138}),\n  ('normal control 3', [[['e17', 1278], ['e01', 1333], ['e06', 1333]], [1000, 250, 250, 199, 1]],\n   {'e01': 625, 'e06': 625, 'e17': 250}),\n  ('normal control 4', [[['e21', 1410], ['e29', 1420], ['e16', 1420], ['e27', 1410]], [1, 1]],\n   {'e16': 1, 'e29': 1})],\n [('regression: tie detection precision',\n   [[['e12', 1445], ['e15', 1493], ['e25', 1493], ['e27', 1445], ['e22', 1497], ['e04', 1445]], [501, 250]],\n   {'e15': 125, 'e22': 501, 'e25': 125}),\n  ('partial repair probe: tie detection precision',\n   [[['e07', 1500], ['e13', 1497], ['e27', 1495], ['e24', 1495], ['e21', 1500], ['e22', 1490]], [77, 2]],\n   {'e07': 40, 'e21': 39}),\n  ('second regression',\n   [[['e20', 1417], ['e06', 1365], ['e19', 1410], ['e15', 1415], ['e05', 1420], ['e28', 1365], ['e29', 1410]],\n    [501, 500, 500, 1]],\n   {'e05': 501, 'e15': 500, 'e19': 1, 'e20': 500}),\n  ('normal control 1', [[['e02', 1420], ['e28', 1410], ['e17', 1410]], [199, 2, 2]],\n   {'e02': 199, 'e17': 2, 'e28': 2}),\n  ('normal control 2', [[['e18', 1330], ['e15', 1330], ['e06', 1330]], [501, 500, 301, 301]],\n   {'e06': 434, 'e15': 434, 'e18': 434}),\n  ('normal control 3',\n   [[['e06', 1500], ['e14', 1500], ['e13', 1500], ['e24', 1500], ['e10', 1500], ['e15', 1500], ['e11', 1500]],\n    [1000, 250, 199, 1]],\n   {'e06': 208, 'e10': 207, 'e11': 207, 'e13': 207, 'e14': 207, 'e15': 207, 'e24': 207}),\n  ('normal control 4',\n   [[['e12', 1500], ['e11', 1490], ['e03', 1445], ['e25', 1500], ['e13', 1500]], [501, 301, 199, 100, 77]],\n   {'e03': 77, 'e11': 100, 'e12': 334, 'e13': 334, 'e25': 333})]]\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"},"fixed":{"sha256":"033c8c436096bd38ac160262449d93d396953a033fe6b1a82826dbdc9ad3b54f","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(entries, payouts):\n    ordered = sorted(entries, key=lambda e: (-e[1], e[0]))\n    out = {}\n    i = 0\n    while i < len(ordered):\n        j = i\n        while j < len(ordered) and ordered[j][1] == ordered[i][1]:\n            j += 1\n        pool = sum(payouts[i:j])\n        group = sorted(e[0] for e in ordered[i:j])\n        share, extra = divmod(pool, len(group))\n        for k, eid in enumerate(group):\n            amt = share + (1 if k < extra else 0)\n            if amt > 0:\n                out[eid] = amt\n        i = j\n    return out\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: tie detection precision',\n   [[['e06', 1497], ['e21', 1500], ['e12', 1445], ['e14', 1495]], [1000, 301, 250, 1]],\n   {'e06': 301, 'e12': 1, 'e14': 250, 'e21': 1000}),\n  ('partial repair probe: tie detection precision',\n   [[['e28', 1445], ['e26', 1497], ['e10', 1500], ['e09', 1500], ['e02', 1497]], [501, 501, 500, 500]],\n   {'e02': 500, 'e09': 501, 'e10': 501, 'e26': 500}),\n  ('second regression',\n   [[['e02', 1493], ['e20', 1445], ['e21', 1490], ['e29', 1445], ['e14', 1497], ['e27', 1490], ['e28', 1495]],\n    [1000, 501, 77, 77]],\n   {'e02': 77, 'e14': 1000, 'e21': 39, 'e27': 38, 'e28': 501}),\n  ('normal control 1', [[['e05', 1493], ['e24', 1445], ['e23', 1445]], [301, 199, 77, 77, 2]],\n   {'e05': 301, 'e23': 138, 'e24': 138}),\n  ('normal control 2', [[['e10', 1500], ['e01', 1445], ['e11', 1490]], [1000, 501, 77, 2]],\n   {'e01': 77, 'e10': 1000, 'e11': 501}),\n  ('normal control 3', [[['e06', 1420], ['e05', 1420], ['e11', 1420]], [77, 77]],\n   {'e05': 52, 'e06': 51, 'e11': 51}),\n  ('normal control 4', [[['e14', 1445], ['e16', 1500], ['e05', 1490]], [1000, 199, 100, 77]],\n   {'e05': 199, 'e14': 100, 'e16': 1000})],\n [('regression: tie detection precision', [[['e16', 1495], ['e29', 1445], ['e26', 1497]], [1000, 250, 1]],\n   {'e16': 250, 'e26': 1000, 'e29': 1}),\n  ('partial repair probe: tie detection precision',\n   [[['e07', 1323], ['e27', 1333], ['e19', 1278], ['e22', 1328], ['e18', 1326], ['e13', 1323], ['e26', 1333],\n     ['e16', 1333]],\n    [1000, 301, 199, 77]],\n   {'e16': 500, 'e22': 77, 'e26': 500, 'e27': 500}),\n  ('second regression',\n   [[['e14', 1333], ['e04', 1278], ['e22', 1326], ['e05', 1333], ['e18', 1330], ['e27', 1330], ['e07', 1278]],\n    [250, 77]],\n   {'e05': 164, 'e14': 163}),\n  ('normal control 1', [[['e04', 1420], ['e19', 1420], ['e29', 1410]], [1000, 250, 1]],\n   {'e04': 625, 'e19': 625, 'e29': 1}),\n  ('normal control 2', [[['e05', 1493], ['e28', 1493], ['e19', 1445]], [301, 199]], {'e05': 250, 'e28': 250}),\n  ('normal control 3', [[['e08', 1500], ['e03', 1445], ['e22', 1497]], [301, 301]], {'e08': 301, 'e22': 301}),\n  ('normal control 4', [[['e10', 1445], ['e23', 1500], ['e26', 1500]], [1000, 301, 199, 100]],\n   {'e10': 199, 'e23': 651, 'e26': 650})],\n [('regression: tie detection precision', [[['e21', 1497], ['e14', 1493], ['e03', 1500]], [1000, 77]],\n   {'e03': 1000, 'e21': 77}),\n  ('partial repair probe: tie detection precision',\n   [[['e25', 1278], ['e28', 1323], ['e04', 1328]], [501, 250, 199, 2]],\n   {'e04': 501, 'e25': 199, 'e28': 250}),\n  ('second regression',\n   [[['e21', 1323], ['e08', 1326], ['e27', 1323], ['e26', 1333], ['e24', 1278], ['e02', 1326]],\n    [501, 301, 250, 2, 1]],\n   {'e02': 276, 'e08': 275, 'e21': 2, 'e26': 501, 'e27': 1}),\n  ('normal control 1',\n   [[['e21', 1500], ['e22', 1500], ['e15', 1500], ['e08', 1490], ['e23', 1445], ['e01', 1490]], [250, 77]],\n   {'e15': 109, 'e21': 109, 'e22': 109}),\n  ('normal control 2',\n   [[['e07', 1365], ['e11', 1413], ['e15', 1413], ['e01', 1413]], [1000, 199, 100, 77, 1]],\n   {'e01': 433, 'e07': 77, 'e11': 433, 'e15': 433}),\n  ('normal control 3', [[['e07', 1415], ['e09', 1415], ['e05', 1415]], [1000, 500]],\n   {'e05': 500, 'e07': 500, 'e09': 500}),\n  ('normal control 4', [[['e15', 1500], ['e10', 1490], ['e18', 1490]], [500, 500, 301, 2, 2]],\n   {'e10': 401, 'e15': 500, 'e18': 400})],\n [('regression: tie detection precision',\n   [[['e17', 1323], ['e28', 1330], ['e12', 1326], ['e09', 1330], ['e19', 1330], ['e20', 1326], ['e15', 1328],\n     ['e26', 1278]],\n    [501, 500, 100, 100]],\n   {'e09': 367, 'e15': 100, 'e19': 367, 'e28': 367}),\n  ('partial repair probe: tie detection precision',\n   [[['e29', 1413], ['e01', 1420], ['e28', 1365], ['e16', 1410], ['e26', 1420], ['e25', 1420]],\n    [500, 301, 199, 199]],\n   {'e01': 334, 'e25': 333, 'e26': 333, 'e29': 199}),\n  ('second regression',\n   [[['e16', 1415], ['e17', 1415], ['e14', 1413], ['e09', 1420], ['e01', 1420], ['e07', 1420], ['e12', 1420],\n     ['e23', 1417]],\n    [1000, 1000, 250]],\n   {'e01': 563, 'e07': 563, 'e09': 562, 'e12': 562}),\n  ('normal control 1',\n   [[['e09', 1420], ['e03', 1365], ['e23', 1410], ['e21', 1410], ['e05', 1420], ['e17', 1365], ['e25', 1420],\n     ['e13', 1410]],\n    [2, 1]],\n   {'e05': 1, 'e09': 1, 'e25': 1}),\n  ('normal control 2',\n   [[['e12', 1278], ['e27', 1323], ['e23', 1333], ['e01', 1323], ['e26', 1278]], [301, 199, 77]],\n   {'e01': 138, 'e23': 301, 'e27': 138}),\n  ('normal control 3', [[['e17', 1278], ['e01', 1333], ['e06', 1333]], [1000, 250, 250, 199, 1]],\n   {'e01': 625, 'e06': 625, 'e17': 250}),\n  ('normal control 4', [[['e21', 1410], ['e29', 1420], ['e16', 1420], ['e27', 1410]], [1, 1]],\n   {'e16': 1, 'e29': 1})],\n [('regression: tie detection precision',\n   [[['e12', 1445], ['e15', 1493], ['e25', 1493], ['e27', 1445], ['e22', 1497], ['e04', 1445]], [501, 250]],\n   {'e15': 125, 'e22': 501, 'e25': 125}),\n  ('partial repair probe: tie detection precision',\n   [[['e07', 1500], ['e13', 1497], ['e27', 1495], ['e24', 1495], ['e21', 1500], ['e22', 1490]], [77, 2]],\n   {'e07': 40, 'e21': 39}),\n  ('second regression',\n   [[['e20', 1417], ['e06', 1365], ['e19', 1410], ['e15', 1415], ['e05', 1420], ['e28', 1365], ['e29', 1410]],\n    [501, 500, 500, 1]],\n   {'e05': 501, 'e15': 500, 'e19': 1, 'e20': 500}),\n  ('normal control 1', [[['e02', 1420], ['e28', 1410], ['e17', 1410]], [199, 2, 2]],\n   {'e02': 199, 'e17': 2, 'e28': 2}),\n  ('normal control 2', [[['e18', 1330], ['e15', 1330], ['e06', 1330]], [501, 500, 301, 301]],\n   {'e06': 434, 'e15': 434, 'e18': 434}),\n  ('normal control 3',\n   [[['e06', 1500], ['e14', 1500], ['e13', 1500], ['e24', 1500], ['e10', 1500], ['e15', 1500], ['e11', 1500]],\n    [1000, 250, 199, 1]],\n   {'e06': 208, 'e10': 207, 'e11': 207, 'e13': 207, 'e14': 207, 'e15': 207, 'e24': 207}),\n  ('normal control 4',\n   [[['e12', 1500], ['e11', 1490], ['e03', 1445], ['e25', 1500], ['e13', 1500]], [501, 301, 199, 100, 77]],\n   {'e03': 77, 'e11': 100, 'e12': 334, 'e13': 334, 'e25': 333})]]\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-contest-payout-ties-tie-detection-precision","generated_at":"2026-09-29T14:50:36.656980+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Tie splitting in paid contests moves real money; cent remainders and the pooled positions must be exact.","repair":"Group only entries whose hundredths scores are equal.","root_cause":"Tie grouping compares scores truncated to tenths rather than exact hundredths.","sha256":"36f860230767c807d872c8891dd02934d9253417b7e206c67d6edd38a7d5b2d4","title":"Ties decided on the displayed tenths · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":41.135,"exit_code":1,"observations":[{"actual":{"e06":517,"e12":1,"e14":517,"e21":517},"check":"regression: tie detection precision","expected":{"e06":301,"e12":1,"e14":250,"e21":1000},"passed":false},{"actual":{"e02":501,"e09":501,"e10":500,"e26":500},"check":"partial repair probe: tie detection precision","expected":{"e02":500,"e09":501,"e10":501,"e26":500},"passed":false},{"actual":{"e02":331,"e14":331,"e21":331,"e27":331,"e28":331},"check":"second regression","expected":{"e02":77,"e14":1000,"e21":39,"e27":38,"e28":501},"passed":false},{"actual":{"e05":301,"e23":138,"e24":138},"check":"normal control 1","expected":{"e05":301,"e23":138,"e24":138},"passed":true},{"actual":{"e01":77,"e10":1000,"e11":501},"check":"normal control 2","expected":{"e01":77,"e10":1000,"e11":501},"passed":true},{"actual":{"e05":52,"e06":51,"e11":51},"check":"normal control 3","expected":{"e05":52,"e06":51,"e11":51},"passed":true},{"actual":{"e05":199,"e14":100,"e16":1000},"check":"normal control 4","expected":{"e05":199,"e14":100,"e16":1000},"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: tie detection precision\", \"actual\": {\"e06\": 517, \"e14\": 517, \"e21\": 517, \"e12\": 1}, \"expected\": {\"e06\": 301, \"e12\": 1, \"e14\": 250, \"e21\": 1000}, \"passed\": false}, {\"check\": \"partial repair probe: tie detection precision\", \"actual\": {\"e02\": 501, \"e09\": 501, \"e10\": 500, \"e26\": 500}, \"expected\": {\"e02\": 500, \"e09\": 501, \"e10\": 501, \"e26\": 500}, \"passed\": false}, {\"check\": \"second regression\", \"actual\": {\"e02\": 331, \"e14\": 331, \"e21\": 331, \"e27\": 331, \"e28\": 331}, \"expected\": {\"e02\": 77, \"e14\": 1000, \"e21\": 39, \"e27\": 38, \"e28\": 501}, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": {\"e05\": 301, \"e23\": 138, \"e24\": 138}, \"expected\": {\"e05\": 301, \"e23\": 138, \"e24\": 138}, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": {\"e10\": 1000, \"e11\": 501, \"e01\": 77}, \"expected\": {\"e01\": 77, \"e10\": 1000, \"e11\": 501}, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": {\"e05\": 52, \"e06\": 51, \"e11\": 51}, \"expected\": {\"e05\": 52, \"e06\": 51, \"e11\": 51}, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": {\"e16\": 1000, \"e05\": 199, \"e14\": 100}, \"expected\": {\"e05\": 199, \"e14\": 100, \"e16\": 1000}, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":39.805,"exit_code":1,"observations":[{"actual":{"e06":276,"e12":1,"e14":275,"e21":1000},"check":"regression: tie detection precision","expected":{"e06":301,"e12":1,"e14":250,"e21":1000},"passed":false},{"actual":{"e02":500,"e09":501,"e10":501,"e26":500},"check":"partial repair probe: tie detection precision","expected":{"e02":500,"e09":501,"e10":501,"e26":500},"passed":true},{"actual":{"e02":331,"e14":331,"e21":331,"e27":331,"e28":331},"check":"second regression","expected":{"e02":77,"e14":1000,"e21":39,"e27":38,"e28":501},"passed":false},{"actual":{"e05":301,"e23":138,"e24":138},"check":"normal control 1","expected":{"e05":301,"e23":138,"e24":138},"passed":true},{"actual":{"e01":77,"e10":1000,"e11":501},"check":"normal control 2","expected":{"e01":77,"e10":1000,"e11":501},"passed":true},{"actual":{"e05":52,"e06":51,"e11":51},"check":"normal control 3","expected":{"e05":52,"e06":51,"e11":51},"passed":true},{"actual":{"e05":199,"e14":100,"e16":1000},"check":"normal control 4","expected":{"e05":199,"e14":100,"e16":1000},"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: tie detection precision\", \"actual\": {\"e21\": 1000, \"e06\": 276, \"e14\": 275, \"e12\": 1}, \"expected\": {\"e06\": 301, \"e12\": 1, \"e14\": 250, \"e21\": 1000}, \"passed\": false}, {\"check\": \"partial repair probe: tie detection precision\", \"actual\": {\"e09\": 501, \"e10\": 501, \"e02\": 500, \"e26\": 500}, \"expected\": {\"e02\": 500, \"e09\": 501, \"e10\": 501, \"e26\": 500}, \"passed\": true}, {\"check\": \"second regression\", \"actual\": {\"e02\": 331, \"e14\": 331, \"e21\": 331, \"e27\": 331, \"e28\": 331}, \"expected\": {\"e02\": 77, \"e14\": 1000, \"e21\": 39, \"e27\": 38, \"e28\": 501}, \"passed\": false}, {\"check\": \"normal control 1\", \"actual\": {\"e05\": 301, \"e23\": 138, \"e24\": 138}, \"expected\": {\"e05\": 301, \"e23\": 138, \"e24\": 138}, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": {\"e10\": 1000, \"e11\": 501, \"e01\": 77}, \"expected\": {\"e01\": 77, \"e10\": 1000, \"e11\": 501}, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": {\"e05\": 52, \"e06\": 51, \"e11\": 51}, \"expected\": {\"e05\": 52, \"e06\": 51, \"e11\": 51}, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": {\"e16\": 1000, \"e05\": 199, \"e14\": 100}, \"expected\": {\"e05\": 199, \"e14\": 100, \"e16\": 1000}, \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":42.692,"exit_code":0,"observations":[{"actual":{"e06":301,"e12":1,"e14":250,"e21":1000},"check":"regression: tie detection precision","expected":{"e06":301,"e12":1,"e14":250,"e21":1000},"passed":true},{"actual":{"e02":500,"e09":501,"e10":501,"e26":500},"check":"partial repair probe: tie detection precision","expected":{"e02":500,"e09":501,"e10":501,"e26":500},"passed":true},{"actual":{"e02":77,"e14":1000,"e21":39,"e27":38,"e28":501},"check":"second regression","expected":{"e02":77,"e14":1000,"e21":39,"e27":38,"e28":501},"passed":true},{"actual":{"e05":301,"e23":138,"e24":138},"check":"normal control 1","expected":{"e05":301,"e23":138,"e24":138},"passed":true},{"actual":{"e01":77,"e10":1000,"e11":501},"check":"normal control 2","expected":{"e01":77,"e10":1000,"e11":501},"passed":true},{"actual":{"e05":52,"e06":51,"e11":51},"check":"normal control 3","expected":{"e05":52,"e06":51,"e11":51},"passed":true},{"actual":{"e05":199,"e14":100,"e16":1000},"check":"normal control 4","expected":{"e05":199,"e14":100,"e16":1000},"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: tie detection precision\", \"actual\": {\"e21\": 1000, \"e06\": 301, \"e14\": 250, \"e12\": 1}, \"expected\": {\"e06\": 301, \"e12\": 1, \"e14\": 250, \"e21\": 1000}, \"passed\": true}, {\"check\": \"partial repair probe: tie detection precision\", \"actual\": {\"e09\": 501, \"e10\": 501, \"e02\": 500, \"e26\": 500}, \"expected\": {\"e02\": 500, \"e09\": 501, \"e10\": 501, \"e26\": 500}, \"passed\": true}, {\"check\": \"second regression\", \"actual\": {\"e14\": 1000, \"e28\": 501, \"e02\": 77, \"e21\": 39, \"e27\": 38}, \"expected\": {\"e02\": 77, \"e14\": 1000, \"e21\": 39, \"e27\": 38, \"e28\": 501}, \"passed\": true}, {\"check\": \"normal control 1\", \"actual\": {\"e05\": 301, \"e23\": 138, \"e24\": 138}, \"expected\": {\"e05\": 301, \"e23\": 138, \"e24\": 138}, \"passed\": true}, {\"check\": \"normal control 2\", \"actual\": {\"e10\": 1000, \"e11\": 501, \"e01\": 77}, \"expected\": {\"e01\": 77, \"e10\": 1000, \"e11\": 501}, \"passed\": true}, {\"check\": \"normal control 3\", \"actual\": {\"e05\": 52, \"e06\": 51, \"e11\": 51}, \"expected\": {\"e05\": 52, \"e06\": 51, \"e11\": 51}, \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": {\"e16\": 1000, \"e05\": 199, \"e14\": 100}, \"expected\": {\"e05\": 199, \"e14\": 100, \"e16\": 1000}, \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}