FA-85331 / Fantasy sports scoring / Open access

Passers collect the big-play bonus · case 01

A quarterback earns 0.5 for every 20-yard completion.

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

ROOT CAUSE

The big-play bonus is not limited to rushing and receiving plays.

VERIFIED REPAIR

Exclude passing plays from the big-play bonus.

Unsuccessful approach: Limiting the bonus to rushes drops receptions.

Case contract

Per-play bonus tenths for one player: a touchdown of 50+ yards earns 3, a touchdown of 40-49 yards earns 2 (exclusive tiers); every rushing or receiving play (not passing) of 20+ yards earns 0.5 whether or not it scored; three or more touchdowns in the game earn 2 once.

Why this case matters

Long-play bonuses are layered rules where tier order and play-type filters are easy to get wrong.

1 / The failure

Exit 1

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

N = 1
observations = []
def solve(plays):
    bonus = 0
    for kind, yards, td in plays:
        if td and yards >= 50:
            bonus += 30
        elif td and yards >= 40:
            bonus += 20
        if yards >= 20:
            bonus += 5
    tds = sum(1 for _, _, td in plays if td)
    if tds >= 3:
        bonus += 20
    return bonus
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: big play type filter',
   [[['rush', 40, True], ['rec', 50, False], ['rec', 51, True], ['rec', 51, True], ['rec', 40, False],
     ['pass', 41, False]]],
   125),
  ('partial repair probe: big play type filter', [[['rec', 20, True], ['rec', 41, True]]], 30),
  ('second regression', [[['pass', 50, True], ['rush', 41, False]]], 35),
  ('normal control 1', [[['pass', 19, False]]], 0), ('normal control 2', [[['rush', 19, False]]], 0),
  ('normal control 3', [[['rec', -3, False], ['rush', 20, False], ['rush', 51, False], ['rush', 40, False]]],
   15),
  ('normal control 4', [[['rush', 49, False], ['rec', 5, True], ['rec', -3, True]]], 5)],
 [('regression: big play type filter', [[['pass', 39, True]]], 0),
  ('partial repair probe: big play type filter',
   [[['rec', 19, False], ['rec', -3, False], ['rec', 50, True], ['pass', 51, False], ['rec', 5, False],
     ['pass', 49, True]]],
   55),
  ('second regression', [[['pass', 41, False]]], 0),
  ('normal control 1', [[['rec', 5, True], ['pass', -3, True]]], 0),
  ('normal control 2', [[['pass', -5, True], ['rush', 19, True], ['pass', 4, False]]], 0),
  ('normal control 3', [[['rush', 39, True]]], 5), ('normal control 4', [[['rec', 5, True]]], 0)],
 [('regression: big play type filter',
   [[['rush', 61, False], ['rec', 51, False], ['pass', 41, True], ['rush', 20, True]]], 35),
  ('partial repair probe: big play type filter', [[['rec', 20, False], ['rec', 41, False]]], 10),
  ('second regression',
   [[['rush', 5, False], ['rush', 5, True], ['rec', 41, False], ['pass', 41, False], ['rush', 50, True]]],
   40),
  ('normal control 1', [[['rush', 40, False]]], 5), ('normal control 2', [[['pass', 19, False]]], 0),
  ('normal control 3',
   [[['rec', 5, False], ['rush', 41, True], ['rush', 40, True], ['rush', 49, False], ['rush', 40, True]]],
   100),
  ('normal control 4', [[['rush', 40, True]]], 25)],
 [('regression: big play type filter', [[['rec', 40, False], ['rec', 51, True], ['pass', 40, False]]], 40),
  ('partial repair probe: big play type filter',
   [[['pass', 23, False], ['rush', 13, True], ['pass', 77, True], ['rec', 41, True]]], 75),
  ('second regression', [[['pass', 20, True], ['rush', 41, True]]], 25),
  ('normal control 1', [[['pass', 5, True], ['rec', 19, False]]], 0),
  ('normal control 2', [[['rush', 40, False], ['rush', 39, True]]], 10),
  ('normal control 3', [[['pass', 5, False], ['rec', 5, True]]], 0),
  ('normal control 4',
   [[['rush', 19, False], ['rush', -3, False], ['rush', 5, False], ['rush', 40, True], ['rush', 5, True]]],
   25)],
 [('regression: big play type filter', [[['pass', 39, True], ['rec', 19, False]]], 0),
  ('partial repair probe: big play type filter',
   [[['pass', 19, False], ['rush', 5, True], ['rush', 40, True], ['rush', 5, True], ['rec', 39, False],
     ['rec', 19, False]]],
   50),
  ('second regression', [[['pass', 41, False], ['rush', 10, True], ['rush', 20, False]]], 5),
  ('normal control 1', [[['rush', 51, False]]], 5),
  ('normal control 2',
   [[['rec', -3, False], ['rush', 51, True], ['rush', -3, False], ['rush', -3, True], ['rush', 19, True]]],
   55),
  ('normal control 3', [[['rec', 19, False], ['rush', 5, False]]], 0),
  ('normal control 4', [[['rush', 39, False], ['rush', 51, False], ['rush', 48, False], ['rec', 5, True]]],
   15)]]
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: big play type filter	`130`	`125`	Failed
partial repair probe: big play type filter	`30`	`30`	Passed
second regression	`40`	`35`	Failed
normal control 1	`0`	`0`	Passed
normal control 2	`0`	`0`	Passed
normal control 3	`15`	`15`	Passed
normal control 4	`5`	`5`	Passed

SHA-256 / 33dbf156d42cfa325931edf3aa2c4403ed4c9718763255045cfdc5b60e0fa804

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(plays):
    bonus = 0
    for kind, yards, td in plays:
        if td and yards >= 50:
            bonus += 30
        elif td and yards >= 40:
            bonus += 20
        if kind == 'rush' and yards >= 20:
            bonus += 5
    tds = sum(1 for _, _, td in plays if td)
    if tds >= 3:
        bonus += 20
    return bonus
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: big play type filter',
   [[['rush', 40, True], ['rec', 50, False], ['rec', 51, True], ['rec', 51, True], ['rec', 40, False],
     ['pass', 41, False]]],
   125),
  ('partial repair probe: big play type filter', [[['rec', 20, True], ['rec', 41, True]]], 30),
  ('second regression', [[['pass', 50, True], ['rush', 41, False]]], 35),
  ('normal control 1', [[['pass', 19, False]]], 0), ('normal control 2', [[['rush', 19, False]]], 0),
  ('normal control 3', [[['rec', -3, False], ['rush', 20, False], ['rush', 51, False], ['rush', 40, False]]],
   15),
  ('normal control 4', [[['rush', 49, False], ['rec', 5, True], ['rec', -3, True]]], 5)],
 [('regression: big play type filter', [[['pass', 39, True]]], 0),
  ('partial repair probe: big play type filter',
   [[['rec', 19, False], ['rec', -3, False], ['rec', 50, True], ['pass', 51, False], ['rec', 5, False],
     ['pass', 49, True]]],
   55),
  ('second regression', [[['pass', 41, False]]], 0),
  ('normal control 1', [[['rec', 5, True], ['pass', -3, True]]], 0),
  ('normal control 2', [[['pass', -5, True], ['rush', 19, True], ['pass', 4, False]]], 0),
  ('normal control 3', [[['rush', 39, True]]], 5), ('normal control 4', [[['rec', 5, True]]], 0)],
 [('regression: big play type filter',
   [[['rush', 61, False], ['rec', 51, False], ['pass', 41, True], ['rush', 20, True]]], 35),
  ('partial repair probe: big play type filter', [[['rec', 20, False], ['rec', 41, False]]], 10),
  ('second regression',
   [[['rush', 5, False], ['rush', 5, True], ['rec', 41, False], ['pass', 41, False], ['rush', 50, True]]],
   40),
  ('normal control 1', [[['rush', 40, False]]], 5), ('normal control 2', [[['pass', 19, False]]], 0),
  ('normal control 3',
   [[['rec', 5, False], ['rush', 41, True], ['rush', 40, True], ['rush', 49, False], ['rush', 40, True]]],
   100),
  ('normal control 4', [[['rush', 40, True]]], 25)],
 [('regression: big play type filter', [[['rec', 40, False], ['rec', 51, True], ['pass', 40, False]]], 40),
  ('partial repair probe: big play type filter',
   [[['pass', 23, False], ['rush', 13, True], ['pass', 77, True], ['rec', 41, True]]], 75),
  ('second regression', [[['pass', 20, True], ['rush', 41, True]]], 25),
  ('normal control 1', [[['pass', 5, True], ['rec', 19, False]]], 0),
  ('normal control 2', [[['rush', 40, False], ['rush', 39, True]]], 10),
  ('normal control 3', [[['pass', 5, False], ['rec', 5, True]]], 0),
  ('normal control 4',
   [[['rush', 19, False], ['rush', -3, False], ['rush', 5, False], ['rush', 40, True], ['rush', 5, True]]],
   25)],
 [('regression: big play type filter', [[['pass', 39, True], ['rec', 19, False]]], 0),
  ('partial repair probe: big play type filter',
   [[['pass', 19, False], ['rush', 5, True], ['rush', 40, True], ['rush', 5, True], ['rec', 39, False],
     ['rec', 19, False]]],
   50),
  ('second regression', [[['pass', 41, False], ['rush', 10, True], ['rush', 20, False]]], 5),
  ('normal control 1', [[['rush', 51, False]]], 5),
  ('normal control 2',
   [[['rec', -3, False], ['rush', 51, True], ['rush', -3, False], ['rush', -3, True], ['rush', 19, True]]],
   55),
  ('normal control 3', [[['rec', 19, False], ['rush', 5, False]]], 0),
  ('normal control 4', [[['rush', 39, False], ['rush', 51, False], ['rush', 48, False], ['rec', 5, True]]],
   15)]]
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: big play type filter	`105`	`125`	Failed
partial repair probe: big play type filter	`20`	`30`	Failed
second regression	`35`	`35`	Passed
normal control 1	`0`	`0`	Passed
normal control 2	`0`	`0`	Passed
normal control 3	`15`	`15`	Passed
normal control 4	`5`	`5`	Passed

SHA-256 / 98e4c3e0f5cd2b53e8a68bc28da6aa9c1f6f437dda7ed4befbd1042e8441d161

3 / The verified repair

Exit 0

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

N = 1
observations = []
def solve(plays):
    bonus = 0
    for kind, yards, td in plays:
        if td and yards >= 50:
            bonus += 30
        elif td and yards >= 40:
            bonus += 20
        if kind != 'pass' and yards >= 20:
            bonus += 5
    tds = sum(1 for _, _, td in plays if td)
    if tds >= 3:
        bonus += 20
    return bonus
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: big play type filter',
   [[['rush', 40, True], ['rec', 50, False], ['rec', 51, True], ['rec', 51, True], ['rec', 40, False],
     ['pass', 41, False]]],
   125),
  ('partial repair probe: big play type filter', [[['rec', 20, True], ['rec', 41, True]]], 30),
  ('second regression', [[['pass', 50, True], ['rush', 41, False]]], 35),
  ('normal control 1', [[['pass', 19, False]]], 0), ('normal control 2', [[['rush', 19, False]]], 0),
  ('normal control 3', [[['rec', -3, False], ['rush', 20, False], ['rush', 51, False], ['rush', 40, False]]],
   15),
  ('normal control 4', [[['rush', 49, False], ['rec', 5, True], ['rec', -3, True]]], 5)],
 [('regression: big play type filter', [[['pass', 39, True]]], 0),
  ('partial repair probe: big play type filter',
   [[['rec', 19, False], ['rec', -3, False], ['rec', 50, True], ['pass', 51, False], ['rec', 5, False],
     ['pass', 49, True]]],
   55),
  ('second regression', [[['pass', 41, False]]], 0),
  ('normal control 1', [[['rec', 5, True], ['pass', -3, True]]], 0),
  ('normal control 2', [[['pass', -5, True], ['rush', 19, True], ['pass', 4, False]]], 0),
  ('normal control 3', [[['rush', 39, True]]], 5), ('normal control 4', [[['rec', 5, True]]], 0)],
 [('regression: big play type filter',
   [[['rush', 61, False], ['rec', 51, False], ['pass', 41, True], ['rush', 20, True]]], 35),
  ('partial repair probe: big play type filter', [[['rec', 20, False], ['rec', 41, False]]], 10),
  ('second regression',
   [[['rush', 5, False], ['rush', 5, True], ['rec', 41, False], ['pass', 41, False], ['rush', 50, True]]],
   40),
  ('normal control 1', [[['rush', 40, False]]], 5), ('normal control 2', [[['pass', 19, False]]], 0),
  ('normal control 3',
   [[['rec', 5, False], ['rush', 41, True], ['rush', 40, True], ['rush', 49, False], ['rush', 40, True]]],
   100),
  ('normal control 4', [[['rush', 40, True]]], 25)],
 [('regression: big play type filter', [[['rec', 40, False], ['rec', 51, True], ['pass', 40, False]]], 40),
  ('partial repair probe: big play type filter',
   [[['pass', 23, False], ['rush', 13, True], ['pass', 77, True], ['rec', 41, True]]], 75),
  ('second regression', [[['pass', 20, True], ['rush', 41, True]]], 25),
  ('normal control 1', [[['pass', 5, True], ['rec', 19, False]]], 0),
  ('normal control 2', [[['rush', 40, False], ['rush', 39, True]]], 10),
  ('normal control 3', [[['pass', 5, False], ['rec', 5, True]]], 0),
  ('normal control 4',
   [[['rush', 19, False], ['rush', -3, False], ['rush', 5, False], ['rush', 40, True], ['rush', 5, True]]],
   25)],
 [('regression: big play type filter', [[['pass', 39, True], ['rec', 19, False]]], 0),
  ('partial repair probe: big play type filter',
   [[['pass', 19, False], ['rush', 5, True], ['rush', 40, True], ['rush', 5, True], ['rec', 39, False],
     ['rec', 19, False]]],
   50),
  ('second regression', [[['pass', 41, False], ['rush', 10, True], ['rush', 20, False]]], 5),
  ('normal control 1', [[['rush', 51, False]]], 5),
  ('normal control 2',
   [[['rec', -3, False], ['rush', 51, True], ['rush', -3, False], ['rush', -3, True], ['rush', 19, True]]],
   55),
  ('normal control 3', [[['rec', 19, False], ['rush', 5, False]]], 0),
  ('normal control 4', [[['rush', 39, False], ['rush', 51, False], ['rush', 48, False], ['rec', 5, True]]],
   15)]]
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: big play type filter	`125`	`125`	Passed
partial repair probe: big play type filter	`30`	`30`	Passed
second regression	`35`	`35`	Passed
normal control 1	`0`	`0`	Passed
normal control 2	`0`	`0`	Passed
normal control 3	`15`	`15`	Passed
normal control 4	`5`	`5`	Passed

SHA-256 / b23e561a36dd893e1317ccb72c79c6874862f607478acac88753f7b85e12ed57

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 2026-09-29T14:50:39.331619+00:00.

Case digest / b5137a424066e9128e6fa9bf89bc7af784ce87c658ab26bf25231cd3373e4569