FAILURE MAP
← Case archive

FA-85191 / Fantasy sports scoring / Open access

Strike rate rounded before band comparison · case 01

A strike rate of 170.4 misses the >170 band.

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

ROOT CAUSE

The strike rate is rounded to an integer before the strict band comparisons.

VERIFIED REPAIR

Compare runs*100 against band*balls exactly.

Unsuccessful approach: Flooring the strike rate still drops fractional parts above band edges.

Case contract

Score a T20 batting innings: 1 per run, +1 per four, +2 per six; only the highest milestone bonus applies (30+ -> 4, 50+ -> 8, 100+ -> 16). A duck (-2) applies when the batter is out for 0 after facing at least one ball, except for BOWL-role players. Strike-rate bonus for non-BOWL players with at least 10 balls, using exact runs*100 vs balls: >170 +6, >150 +4, >=130 +2, >=70 0, >=60 -2, >=50 -4, below -6.

Why this case matters

Cricket fantasy contests hinge on exclusive milestones, duck eligibility and strike-rate bands evaluated exactly.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(inn):
    pts = inn['runs'] + inn['fours'] + inn['sixes'] * 2
    if inn['runs'] >= 100:
        pts += 16
    elif inn['runs'] >= 50:
        pts += 8
    elif inn['runs'] >= 30:
        pts += 4
    if inn['out'] and inn['runs'] == 0 and inn['balls'] >= 1 and inn['role'] != 'BOWL':
        pts -= 2
    if inn['balls'] >= 10 and inn['role'] != 'BOWL':
        r, b = round(inn['runs'] * 100 / inn['balls']), 1
        if r > 170 * b:
            pts += 6
        elif r > 150 * b:
            pts += 4
        elif r >= 130 * b:
            pts += 2
        elif r >= 70 * b:
            pass
        elif r >= 60 * b:
            pts -= 2
        elif r >= 50 * b:
            pts -= 4
        else:
            pts -= 6
    return pts
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: strike rate exactness',
   [{'balls': 47, 'fours': 16, 'out': False, 'role': 'AR', 'runs': 80, 'sixes': 0}], 110),
  ('partial repair probe: strike rate exactness',
   [{'balls': 57, 'fours': 8, 'out': False, 'role': 'BAT', 'runs': 86, 'sixes': 3}], 112),
  ('second regression', [{'balls': 17, 'fours': 2, 'out': True, 'role': 'WK', 'runs': 29, 'sixes': 0}], 37),
  ('normal control 1', [{'balls': 47, 'fours': 5, 'out': False, 'role': 'BAT', 'runs': 46, 'sixes': 2}], 59),
  ('normal control 2', [{'balls': 70, 'fours': 6, 'out': False, 'role': 'BAT', 'runs': 50, 'sixes': 4}], 72),
  ('normal control 3', [{'balls': 60, 'fours': 0, 'out': False, 'role': 'WK', 'runs': 79, 'sixes': 9}], 107),
  ('normal control 4', [{'balls': 9, 'fours': 4, 'out': False, 'role': 'AR', 'runs': 17, 'sixes': 0}], 21)],
 [('regression: strike rate exactness',
   [{'balls': 44, 'fours': 14, 'out': True, 'role': 'WK', 'runs': 75, 'sixes': 2}], 107),
  ('partial repair probe: strike rate exactness',
   [{'balls': 48, 'fours': 12, 'out': False, 'role': 'AR', 'runs': 82, 'sixes': 4}], 116),
  ('second regression', [{'balls': 57, 'fours': 10, 'out': True, 'role': 'WK', 'runs': 97, 'sixes': 9}], 139),
  ('normal control 1', [{'balls': 23, 'fours': 2, 'out': False, 'role': 'BAT', 'runs': 11, 'sixes': 0}], 7),
  ('normal control 2', [{'balls': 47, 'fours': 7, 'out': True, 'role': 'AR', 'runs': 49, 'sixes': 0}], 60),
  ('normal control 3', [{'balls': 9, 'fours': 8, 'out': True, 'role': 'BAT', 'runs': 70, 'sixes': 1}], 88),
  ('normal control 4', [{'balls': 9, 'fours': 2, 'out': True, 'role': 'BOWL', 'runs': 34, 'sixes': 2}], 44)],
 [('regression: strike rate exactness',
   [{'balls': 54, 'fours': 20, 'out': True, 'role': 'WK', 'runs': 92, 'sixes': 0}], 126),
  ('partial repair probe: strike rate exactness',
   [{'balls': 57, 'fours': 18, 'out': False, 'role': 'BAT', 'runs': 86, 'sixes': 1}], 118),
  ('second regression', [{'balls': 57, 'fours': 5, 'out': False, 'role': 'AR', 'runs': 97, 'sixes': 12}],
   140),
  ('normal control 1', [{'balls': 11, 'fours': 0, 'out': False, 'role': 'BOWL', 'runs': 0, 'sixes': 0}], 0),
  ('normal control 2', [{'balls': 0, 'fours': 0, 'out': True, 'role': 'BOWL', 'runs': 0, 'sixes': 0}], 0),
  ('normal control 3', [{'balls': 11, 'fours': 0, 'out': True, 'role': 'BOWL', 'runs': 34, 'sixes': 3}], 44),
  ('normal control 4', [{'balls': 48, 'fours': 8, 'out': True, 'role': 'BOWL', 'runs': 73, 'sixes': 1}], 91)],
 [('regression: strike rate exactness',
   [{'balls': 47, 'fours': 1, 'out': True, 'role': 'WK', 'runs': 80, 'sixes': 4}], 103),
  ('partial repair probe: strike rate exactness',
   [{'balls': 53, 'fours': 4, 'out': False, 'role': 'WK', 'runs': 80, 'sixes': 9}], 114),
  ('second regression', [{'balls': 59, 'fours': 2, 'out': True, 'role': 'AR', 'runs': 89, 'sixes': 12}], 127),
  ('normal control 1', [{'balls': 20, 'fours': 0, 'out': True, 'role': 'BAT', 'runs': 0, 'sixes': 0}], -8),
  ('normal control 2', [{'balls': 10, 'fours': 5, 'out': True, 'role': 'BAT', 'runs': 29, 'sixes': 1}], 42),
  ('normal control 3', [{'balls': 25, 'fours': 4, 'out': True, 'role': 'BOWL', 'runs': 50, 'sixes': 3}], 68),
  ('normal control 4', [{'balls': 0, 'fours': 0, 'out': True, 'role': 'AR', 'runs': 0, 'sixes': 0}], 0)],
 [('regression: strike rate exactness',
   [{'balls': 27, 'fours': 11, 'out': False, 'role': 'BAT', 'runs': 46, 'sixes': 0}], 67),
  ('partial repair probe: strike rate exactness',
   [{'balls': 41, 'fours': 7, 'out': False, 'role': 'AR', 'runs': 70, 'sixes': 4}], 99),
  ('second regression', [{'balls': 58, 'fours': 0, 'out': True, 'role': 'BAT', 'runs': 99, 'sixes': 5}], 123),
  ('normal control 1', [{'balls': 30, 'fours': 9, 'out': False, 'role': 'BAT', 'runs': 40, 'sixes': 0}], 55),
  ('normal control 2', [{'balls': 10, 'fours': 0, 'out': False, 'role': 'AR', 'runs': 49, 'sixes': 4}], 67),
  ('normal control 3', [{'balls': 0, 'fours': 0, 'out': True, 'role': 'BOWL', 'runs': 0, 'sixes': 0}], 0),
  ('normal control 4', [{'balls': 32, 'fours': 0, 'out': False, 'role': 'AR', 'runs': 0, 'sixes': 0}], -6)]]
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 fixtureActualExpectedOutcome
regression: strike rate exactness108110Failed
partial repair probe: strike rate exactness112112Passed
second regression3737Passed
normal control 15959Passed
normal control 27272Passed
normal control 3107107Passed
normal control 42121Passed

SHA-256 / 122ed50261bdcb52d2389dbb0843ae8209c5e641c3264d4434797ef594030fa6

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(inn):
    pts = inn['runs'] + inn['fours'] + inn['sixes'] * 2
    if inn['runs'] >= 100:
        pts += 16
    elif inn['runs'] >= 50:
        pts += 8
    elif inn['runs'] >= 30:
        pts += 4
    if inn['out'] and inn['runs'] == 0 and inn['balls'] >= 1 and inn['role'] != 'BOWL':
        pts -= 2
    if inn['balls'] >= 10 and inn['role'] != 'BOWL':
        r, b = inn['runs'] * 100 // inn['balls'], 1
        if r > 170 * b:
            pts += 6
        elif r > 150 * b:
            pts += 4
        elif r >= 130 * b:
            pts += 2
        elif r >= 70 * b:
            pass
        elif r >= 60 * b:
            pts -= 2
        elif r >= 50 * b:
            pts -= 4
        else:
            pts -= 6
    return pts
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: strike rate exactness',
   [{'balls': 47, 'fours': 16, 'out': False, 'role': 'AR', 'runs': 80, 'sixes': 0}], 110),
  ('partial repair probe: strike rate exactness',
   [{'balls': 57, 'fours': 8, 'out': False, 'role': 'BAT', 'runs': 86, 'sixes': 3}], 112),
  ('second regression', [{'balls': 17, 'fours': 2, 'out': True, 'role': 'WK', 'runs': 29, 'sixes': 0}], 37),
  ('normal control 1', [{'balls': 47, 'fours': 5, 'out': False, 'role': 'BAT', 'runs': 46, 'sixes': 2}], 59),
  ('normal control 2', [{'balls': 70, 'fours': 6, 'out': False, 'role': 'BAT', 'runs': 50, 'sixes': 4}], 72),
  ('normal control 3', [{'balls': 60, 'fours': 0, 'out': False, 'role': 'WK', 'runs': 79, 'sixes': 9}], 107),
  ('normal control 4', [{'balls': 9, 'fours': 4, 'out': False, 'role': 'AR', 'runs': 17, 'sixes': 0}], 21)],
 [('regression: strike rate exactness',
   [{'balls': 44, 'fours': 14, 'out': True, 'role': 'WK', 'runs': 75, 'sixes': 2}], 107),
  ('partial repair probe: strike rate exactness',
   [{'balls': 48, 'fours': 12, 'out': False, 'role': 'AR', 'runs': 82, 'sixes': 4}], 116),
  ('second regression', [{'balls': 57, 'fours': 10, 'out': True, 'role': 'WK', 'runs': 97, 'sixes': 9}], 139),
  ('normal control 1', [{'balls': 23, 'fours': 2, 'out': False, 'role': 'BAT', 'runs': 11, 'sixes': 0}], 7),
  ('normal control 2', [{'balls': 47, 'fours': 7, 'out': True, 'role': 'AR', 'runs': 49, 'sixes': 0}], 60),
  ('normal control 3', [{'balls': 9, 'fours': 8, 'out': True, 'role': 'BAT', 'runs': 70, 'sixes': 1}], 88),
  ('normal control 4', [{'balls': 9, 'fours': 2, 'out': True, 'role': 'BOWL', 'runs': 34, 'sixes': 2}], 44)],
 [('regression: strike rate exactness',
   [{'balls': 54, 'fours': 20, 'out': True, 'role': 'WK', 'runs': 92, 'sixes': 0}], 126),
  ('partial repair probe: strike rate exactness',
   [{'balls': 57, 'fours': 18, 'out': False, 'role': 'BAT', 'runs': 86, 'sixes': 1}], 118),
  ('second regression', [{'balls': 57, 'fours': 5, 'out': False, 'role': 'AR', 'runs': 97, 'sixes': 12}],
   140),
  ('normal control 1', [{'balls': 11, 'fours': 0, 'out': False, 'role': 'BOWL', 'runs': 0, 'sixes': 0}], 0),
  ('normal control 2', [{'balls': 0, 'fours': 0, 'out': True, 'role': 'BOWL', 'runs': 0, 'sixes': 0}], 0),
  ('normal control 3', [{'balls': 11, 'fours': 0, 'out': True, 'role': 'BOWL', 'runs': 34, 'sixes': 3}], 44),
  ('normal control 4', [{'balls': 48, 'fours': 8, 'out': True, 'role': 'BOWL', 'runs': 73, 'sixes': 1}], 91)],
 [('regression: strike rate exactness',
   [{'balls': 47, 'fours': 1, 'out': True, 'role': 'WK', 'runs': 80, 'sixes': 4}], 103),
  ('partial repair probe: strike rate exactness',
   [{'balls': 53, 'fours': 4, 'out': False, 'role': 'WK', 'runs': 80, 'sixes': 9}], 114),
  ('second regression', [{'balls': 59, 'fours': 2, 'out': True, 'role': 'AR', 'runs': 89, 'sixes': 12}], 127),
  ('normal control 1', [{'balls': 20, 'fours': 0, 'out': True, 'role': 'BAT', 'runs': 0, 'sixes': 0}], -8),
  ('normal control 2', [{'balls': 10, 'fours': 5, 'out': True, 'role': 'BAT', 'runs': 29, 'sixes': 1}], 42),
  ('normal control 3', [{'balls': 25, 'fours': 4, 'out': True, 'role': 'BOWL', 'runs': 50, 'sixes': 3}], 68),
  ('normal control 4', [{'balls': 0, 'fours': 0, 'out': True, 'role': 'AR', 'runs': 0, 'sixes': 0}], 0)],
 [('regression: strike rate exactness',
   [{'balls': 27, 'fours': 11, 'out': False, 'role': 'BAT', 'runs': 46, 'sixes': 0}], 67),
  ('partial repair probe: strike rate exactness',
   [{'balls': 41, 'fours': 7, 'out': False, 'role': 'AR', 'runs': 70, 'sixes': 4}], 99),
  ('second regression', [{'balls': 58, 'fours': 0, 'out': True, 'role': 'BAT', 'runs': 99, 'sixes': 5}], 123),
  ('normal control 1', [{'balls': 30, 'fours': 9, 'out': False, 'role': 'BAT', 'runs': 40, 'sixes': 0}], 55),
  ('normal control 2', [{'balls': 10, 'fours': 0, 'out': False, 'role': 'AR', 'runs': 49, 'sixes': 4}], 67),
  ('normal control 3', [{'balls': 0, 'fours': 0, 'out': True, 'role': 'BOWL', 'runs': 0, 'sixes': 0}], 0),
  ('normal control 4', [{'balls': 32, 'fours': 0, 'out': False, 'role': 'AR', 'runs': 0, 'sixes': 0}], -6)]]
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 fixtureActualExpectedOutcome
regression: strike rate exactness108110Failed
partial repair probe: strike rate exactness110112Failed
second regression3537Failed
normal control 15959Passed
normal control 27272Passed
normal control 3107107Passed
normal control 42121Passed

SHA-256 / c5bf9c6dd732fcb82a1f1d57eabcee44e01400ce94d3edc6504814f0c66a38c6

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(inn):
    pts = inn['runs'] + inn['fours'] + inn['sixes'] * 2
    if inn['runs'] >= 100:
        pts += 16
    elif inn['runs'] >= 50:
        pts += 8
    elif inn['runs'] >= 30:
        pts += 4
    if inn['out'] and inn['runs'] == 0 and inn['balls'] >= 1 and inn['role'] != 'BOWL':
        pts -= 2
    if inn['balls'] >= 10 and inn['role'] != 'BOWL':
        r, b = inn['runs'] * 100, inn['balls']
        if r > 170 * b:
            pts += 6
        elif r > 150 * b:
            pts += 4
        elif r >= 130 * b:
            pts += 2
        elif r >= 70 * b:
            pass
        elif r >= 60 * b:
            pts -= 2
        elif r >= 50 * b:
            pts -= 4
        else:
            pts -= 6
    return pts
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: strike rate exactness',
   [{'balls': 47, 'fours': 16, 'out': False, 'role': 'AR', 'runs': 80, 'sixes': 0}], 110),
  ('partial repair probe: strike rate exactness',
   [{'balls': 57, 'fours': 8, 'out': False, 'role': 'BAT', 'runs': 86, 'sixes': 3}], 112),
  ('second regression', [{'balls': 17, 'fours': 2, 'out': True, 'role': 'WK', 'runs': 29, 'sixes': 0}], 37),
  ('normal control 1', [{'balls': 47, 'fours': 5, 'out': False, 'role': 'BAT', 'runs': 46, 'sixes': 2}], 59),
  ('normal control 2', [{'balls': 70, 'fours': 6, 'out': False, 'role': 'BAT', 'runs': 50, 'sixes': 4}], 72),
  ('normal control 3', [{'balls': 60, 'fours': 0, 'out': False, 'role': 'WK', 'runs': 79, 'sixes': 9}], 107),
  ('normal control 4', [{'balls': 9, 'fours': 4, 'out': False, 'role': 'AR', 'runs': 17, 'sixes': 0}], 21)],
 [('regression: strike rate exactness',
   [{'balls': 44, 'fours': 14, 'out': True, 'role': 'WK', 'runs': 75, 'sixes': 2}], 107),
  ('partial repair probe: strike rate exactness',
   [{'balls': 48, 'fours': 12, 'out': False, 'role': 'AR', 'runs': 82, 'sixes': 4}], 116),
  ('second regression', [{'balls': 57, 'fours': 10, 'out': True, 'role': 'WK', 'runs': 97, 'sixes': 9}], 139),
  ('normal control 1', [{'balls': 23, 'fours': 2, 'out': False, 'role': 'BAT', 'runs': 11, 'sixes': 0}], 7),
  ('normal control 2', [{'balls': 47, 'fours': 7, 'out': True, 'role': 'AR', 'runs': 49, 'sixes': 0}], 60),
  ('normal control 3', [{'balls': 9, 'fours': 8, 'out': True, 'role': 'BAT', 'runs': 70, 'sixes': 1}], 88),
  ('normal control 4', [{'balls': 9, 'fours': 2, 'out': True, 'role': 'BOWL', 'runs': 34, 'sixes': 2}], 44)],
 [('regression: strike rate exactness',
   [{'balls': 54, 'fours': 20, 'out': True, 'role': 'WK', 'runs': 92, 'sixes': 0}], 126),
  ('partial repair probe: strike rate exactness',
   [{'balls': 57, 'fours': 18, 'out': False, 'role': 'BAT', 'runs': 86, 'sixes': 1}], 118),
  ('second regression', [{'balls': 57, 'fours': 5, 'out': False, 'role': 'AR', 'runs': 97, 'sixes': 12}],
   140),
  ('normal control 1', [{'balls': 11, 'fours': 0, 'out': False, 'role': 'BOWL', 'runs': 0, 'sixes': 0}], 0),
  ('normal control 2', [{'balls': 0, 'fours': 0, 'out': True, 'role': 'BOWL', 'runs': 0, 'sixes': 0}], 0),
  ('normal control 3', [{'balls': 11, 'fours': 0, 'out': True, 'role': 'BOWL', 'runs': 34, 'sixes': 3}], 44),
  ('normal control 4', [{'balls': 48, 'fours': 8, 'out': True, 'role': 'BOWL', 'runs': 73, 'sixes': 1}], 91)],
 [('regression: strike rate exactness',
   [{'balls': 47, 'fours': 1, 'out': True, 'role': 'WK', 'runs': 80, 'sixes': 4}], 103),
  ('partial repair probe: strike rate exactness',
   [{'balls': 53, 'fours': 4, 'out': False, 'role': 'WK', 'runs': 80, 'sixes': 9}], 114),
  ('second regression', [{'balls': 59, 'fours': 2, 'out': True, 'role': 'AR', 'runs': 89, 'sixes': 12}], 127),
  ('normal control 1', [{'balls': 20, 'fours': 0, 'out': True, 'role': 'BAT', 'runs': 0, 'sixes': 0}], -8),
  ('normal control 2', [{'balls': 10, 'fours': 5, 'out': True, 'role': 'BAT', 'runs': 29, 'sixes': 1}], 42),
  ('normal control 3', [{'balls': 25, 'fours': 4, 'out': True, 'role': 'BOWL', 'runs': 50, 'sixes': 3}], 68),
  ('normal control 4', [{'balls': 0, 'fours': 0, 'out': True, 'role': 'AR', 'runs': 0, 'sixes': 0}], 0)],
 [('regression: strike rate exactness',
   [{'balls': 27, 'fours': 11, 'out': False, 'role': 'BAT', 'runs': 46, 'sixes': 0}], 67),
  ('partial repair probe: strike rate exactness',
   [{'balls': 41, 'fours': 7, 'out': False, 'role': 'AR', 'runs': 70, 'sixes': 4}], 99),
  ('second regression', [{'balls': 58, 'fours': 0, 'out': True, 'role': 'BAT', 'runs': 99, 'sixes': 5}], 123),
  ('normal control 1', [{'balls': 30, 'fours': 9, 'out': False, 'role': 'BAT', 'runs': 40, 'sixes': 0}], 55),
  ('normal control 2', [{'balls': 10, 'fours': 0, 'out': False, 'role': 'AR', 'runs': 49, 'sixes': 4}], 67),
  ('normal control 3', [{'balls': 0, 'fours': 0, 'out': True, 'role': 'BOWL', 'runs': 0, 'sixes': 0}], 0),
  ('normal control 4', [{'balls': 32, 'fours': 0, 'out': False, 'role': 'AR', 'runs': 0, 'sixes': 0}], -6)]]
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 fixtureActualExpectedOutcome
regression: strike rate exactness110110Passed
partial repair probe: strike rate exactness112112Passed
second regression3737Passed
normal control 15959Passed
normal control 27272Passed
normal control 3107107Passed
normal control 42121Passed

SHA-256 / b98ee106ae96428513c868abee3b938cb964263f243b217623570f739f35514a

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 2026-09-29T14:50:38.029265+00:00.

Case digest / f9f2d7af453839cdc762c66668f74418cdea13c603075552a4bdd14a4f2cbff2