FA-84646 / Betting odds conversion / Open access

Void selection kills or wins every line containing it · case 01

Lines containing a non-runner return nothing, or pay the non-runner price.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Void selections are treated as losers.

THE FAILURE

Void selections are treated as losers.

Unsuccessful approach: Pricing the void at its original price pays it as a winner.

Case contract

Full-cover system bets. system: trixie (3 selections: doubles and treble), patent (3: singles, doubles, treble), yankee (4: doubles, trebles, fourfold), lucky15 (4: singles through fourfold). Wrong selection count returns "invalid". Every combination is a line staked unit_cents; a line with a losing selection returns 0; void selections count as price 1. Return [lines, total stake, total return rounded down].

Why this case matters

Bookmaker settlement expands system bets into combination lines before pricing each one.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
import itertools
import math
N = 1
observations = []
def solve(selections, unit_cents, system):
    SYS = {'trixie': (3, [2, 3]), 'patent': (3, [1, 2, 3]), 'yankee': (4, [2, 3, 4]), 'lucky15': (4, [1, 2, 3, 4])}
    n, sizes = SYS[system]
    if len(selections) != n:
        return 'invalid'
    lines = [c for k in sizes for c in itertools.combinations(range(n), k)]
    ret = Fraction(0)
    for combo in lines:
        m = Fraction(1)
        for i in combo:
            price, result = selections[i]
            if result in ('lose', 'void'):
                m = Fraction(0)
                break
            if result == 'win':
                m *= Fraction(price)
        ret += unit_cents * m
    return [len(lines), unit_cents * len(lines), math.floor(ret)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
def run(args):
    try:
        return solve(*args)
    except Exception as exc:
        return 'raised ' + type(exc).__name__
cases = [[('control trixie all win',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 2000]),
  ('control yankee line count',
   ([['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'yankee'),
   [11, 1100, 0]),
  ('boundary void selection',
   ([['2.00', 'void'], ['3.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 1700]),
  ('boundary patent single winner',
   ([['5.00', 'win'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'patent'),
   [7, 700, 500]),
  ('boundary wrong count',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'yankee'),
   'invalid'),
  ('regression: void selection',
   ([['5.92', 'lose'], ['4.13', 'lose'], ['5.96', 'void']], 100, 'patent'),
   [7, 700, 100]),
  ('variant scenario 1',
   ([['3.65', 'win'], ['3.16', 'win'], ['2.64', 'win'], ['4.07', 'win']], 100, 'lucky15'),
   [15, 1500, 35598]),
  ('variant scenario 2',
   ([['4.68', 'lose'], ['2.86', 'lose'], ['2.47', 'lose'], ['4.63', 'win']], 100, 'lucky15'),
   [15, 1500, 463])],
 [('control trixie all win',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 2000]),
  ('control yankee line count',
   ([['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'yankee'),
   [11, 1100, 0]),
  ('boundary void selection',
   ([['2.00', 'void'], ['3.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 1700]),
  ('boundary patent single winner',
   ([['5.00', 'win'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'patent'),
   [7, 700, 500]),
  ('boundary wrong count',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'yankee'),
   'invalid'),
  ('regression: void selection',
   ([['2.01', 'void'], ['2.91', 'void'], ['2.01', 'lose']], 100, 'trixie'),
   [4, 400, 100]),
  ('variant scenario 1',
   ([['3.09', 'win'], ['4.05', 'void'], ['5.96', 'lose'], ['1.77', 'lose']], 50, 'yankee'),
   [11, 550, 154]),
  ('variant scenario 2',
   ([['2.18', 'lose'], ['4.86', 'win'], ['3.65', 'win']], 100, 'trixie'),
   [4, 400, 1773])],
 [('control trixie all win',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 2000]),
  ('control yankee line count',
   ([['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'yankee'),
   [11, 1100, 0]),
  ('boundary void selection',
   ([['2.00', 'void'], ['3.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 1700]),
  ('boundary patent single winner',
   ([['5.00', 'win'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'patent'),
   [7, 700, 500]),
  ('boundary wrong count',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'yankee'),
   'invalid'),
  ('regression: void selection',
   ([['4.32', 'win'], ['5.81', 'lose'], ['1.95', 'void'], ['3.80', 'win']], 100, 'yankee'),
   [11, 1100, 4095]),
  ('variant scenario 1',
   ([['2.12', 'win'], ['3.26', 'win'], ['2.15', 'lose']], 50, 'patent'),
   [7, 350, 614]),
  ('variant scenario 2',
   ([['5.95', 'win'], ['2.71', 'lose'], ['3.63', 'lose']], 25, 'patent'),
   [7, 175, 148])],
 [('control trixie all win',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 2000]),
  ('control yankee line count',
   ([['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'yankee'),
   [11, 1100, 0]),
  ('boundary void selection',
   ([['2.00', 'void'], ['3.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 1700]),
  ('boundary patent single winner',
   ([['5.00', 'win'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'patent'),
   [7, 700, 500]),
  ('boundary wrong count',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'yankee'),
   'invalid'),
  ('regression: void selection',
   ([['2.47', 'lose'], ['4.09', 'win'], ['4.37', 'win'], ['2.66', 'void']], 100, 'yankee'),
   [11, 1100, 4420]),
  ('variant scenario 1',
   ([['3.59', 'lose'], ['4.34', 'win'], ['3.89', 'win'], ['2.34', 'win']], 25, 'yankee'),
   [11, 275, 1891]),
  ('variant scenario 2',
   ([['2.72', 'win'], ['5.80', 'lose'], ['2.62', 'lose']], 25, 'trixie'),
   [4, 100, 0])],
 [('control trixie all win',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 2000]),
  ('control yankee line count',
   ([['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'yankee'),
   [11, 1100, 0]),
  ('boundary void selection',
   ([['2.00', 'void'], ['3.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 1700]),
  ('boundary patent single winner',
   ([['5.00', 'win'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'patent'),
   [7, 700, 500]),
  ('boundary wrong count',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'yankee'),
   'invalid'),
  ('regression: void selection',
   ([['4.84', 'lose'], ['1.69', 'win'], ['5.48', 'win'], ['5.81', 'void']], 50, 'yankee'),
   [11, 550, 1284]),
  ('variant scenario 1',
   ([['3.45', 'win'], ['4.45', 'void'], ['3.65', 'win'], ['2.52', 'win']], 100, 'lucky15'),
   [15, 1500, 14467]),
  ('variant scenario 2',
   ([['5.21', 'lose'], ['5.61', 'win'], ['5.91', 'void'], ['1.84', 'lose']], 25, 'lucky15'),
   [15, 375, 305])]]
for label, args, expected in cases[N - 1]:
    check(label, run(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
control trixie all win	`[4, 400, 2000]`	`[4, 400, 2000]`	Passed
control yankee line count	`[11, 1100, 0]`	`[11, 1100, 0]`	Passed
boundary void selection	`[4, 400, 600]`	`[4, 400, 1700]`	Failed
boundary patent single winner	`[7, 700, 500]`	`[7, 700, 500]`	Passed
boundary wrong count	`invalid`	`invalid`	Passed
regression: void selection	`[7, 700, 0]`	`[7, 700, 100]`	Failed
variant scenario 1	`[15, 1500, 35598]`	`[15, 1500, 35598]`	Passed
variant scenario 2	`[15, 1500, 463]`	`[15, 1500, 463]`	Passed

SHA-256 / 5c0039b1d71fe0e7c33deb581087697f80b027f57a826f58f2690c446512d90e

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
import itertools
import math
N = 1
observations = []
def solve(selections, unit_cents, system):
    SYS = {'trixie': (3, [2, 3]), 'patent': (3, [1, 2, 3]), 'yankee': (4, [2, 3, 4]), 'lucky15': (4, [1, 2, 3, 4])}
    n, sizes = SYS[system]
    if len(selections) != n:
        return 'invalid'
    lines = [c for k in sizes for c in itertools.combinations(range(n), k)]
    ret = Fraction(0)
    for combo in lines:
        m = Fraction(1)
        for i in combo:
            price, result = selections[i]
            if result == 'lose':
                m = Fraction(0)
                break
            if result in ('win', 'void'):
                m *= Fraction(price)
        ret += unit_cents * m
    return [len(lines), unit_cents * len(lines), math.floor(ret)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
def run(args):
    try:
        return solve(*args)
    except Exception as exc:
        return 'raised ' + type(exc).__name__
cases = [[('control trixie all win',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 2000]),
  ('control yankee line count',
   ([['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'yankee'),
   [11, 1100, 0]),
  ('boundary void selection',
   ([['2.00', 'void'], ['3.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 1700]),
  ('boundary patent single winner',
   ([['5.00', 'win'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'patent'),
   [7, 700, 500]),
  ('boundary wrong count',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'yankee'),
   'invalid'),
  ('regression: void selection',
   ([['5.92', 'lose'], ['4.13', 'lose'], ['5.96', 'void']], 100, 'patent'),
   [7, 700, 100]),
  ('variant scenario 1',
   ([['3.65', 'win'], ['3.16', 'win'], ['2.64', 'win'], ['4.07', 'win']], 100, 'lucky15'),
   [15, 1500, 35598]),
  ('variant scenario 2',
   ([['4.68', 'lose'], ['2.86', 'lose'], ['2.47', 'lose'], ['4.63', 'win']], 100, 'lucky15'),
   [15, 1500, 463])],
 [('control trixie all win',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 2000]),
  ('control yankee line count',
   ([['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'yankee'),
   [11, 1100, 0]),
  ('boundary void selection',
   ([['2.00', 'void'], ['3.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 1700]),
  ('boundary patent single winner',
   ([['5.00', 'win'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'patent'),
   [7, 700, 500]),
  ('boundary wrong count',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'yankee'),
   'invalid'),
  ('regression: void selection',
   ([['2.01', 'void'], ['2.91', 'void'], ['2.01', 'lose']], 100, 'trixie'),
   [4, 400, 100]),
  ('variant scenario 1',
   ([['3.09', 'win'], ['4.05', 'void'], ['5.96', 'lose'], ['1.77', 'lose']], 50, 'yankee'),
   [11, 550, 154]),
  ('variant scenario 2',
   ([['2.18', 'lose'], ['4.86', 'win'], ['3.65', 'win']], 100, 'trixie'),
   [4, 400, 1773])],
 [('control trixie all win',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 2000]),
  ('control yankee line count',
   ([['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'yankee'),
   [11, 1100, 0]),
  ('boundary void selection',
   ([['2.00', 'void'], ['3.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 1700]),
  ('boundary patent single winner',
   ([['5.00', 'win'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'patent'),
   [7, 700, 500]),
  ('boundary wrong count',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'yankee'),
   'invalid'),
  ('regression: void selection',
   ([['4.32', 'win'], ['5.81', 'lose'], ['1.95', 'void'], ['3.80', 'win']], 100, 'yankee'),
   [11, 1100, 4095]),
  ('variant scenario 1',
   ([['2.12', 'win'], ['3.26', 'win'], ['2.15', 'lose']], 50, 'patent'),
   [7, 350, 614]),
  ('variant scenario 2',
   ([['5.95', 'win'], ['2.71', 'lose'], ['3.63', 'lose']], 25, 'patent'),
   [7, 175, 148])],
 [('control trixie all win',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 2000]),
  ('control yankee line count',
   ([['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'yankee'),
   [11, 1100, 0]),
  ('boundary void selection',
   ([['2.00', 'void'], ['3.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 1700]),
  ('boundary patent single winner',
   ([['5.00', 'win'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'patent'),
   [7, 700, 500]),
  ('boundary wrong count',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'yankee'),
   'invalid'),
  ('regression: void selection',
   ([['2.47', 'lose'], ['4.09', 'win'], ['4.37', 'win'], ['2.66', 'void']], 100, 'yankee'),
   [11, 1100, 4420]),
  ('variant scenario 1',
   ([['3.59', 'lose'], ['4.34', 'win'], ['3.89', 'win'], ['2.34', 'win']], 25, 'yankee'),
   [11, 275, 1891]),
  ('variant scenario 2',
   ([['2.72', 'win'], ['5.80', 'lose'], ['2.62', 'lose']], 25, 'trixie'),
   [4, 100, 0])],
 [('control trixie all win',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 2000]),
  ('control yankee line count',
   ([['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'yankee'),
   [11, 1100, 0]),
  ('boundary void selection',
   ([['2.00', 'void'], ['3.00', 'win'], ['2.00', 'win']], 100, 'trixie'),
   [4, 400, 1700]),
  ('boundary patent single winner',
   ([['5.00', 'win'], ['2.00', 'lose'], ['2.00', 'lose']], 100, 'patent'),
   [7, 700, 500]),
  ('boundary wrong count',
   ([['2.00', 'win'], ['2.00', 'win'], ['2.00', 'win']], 100, 'yankee'),
   'invalid'),
  ('regression: void selection',
   ([['4.84', 'lose'], ['1.69', 'win'], ['5.48', 'win'], ['5.81', 'void']], 50, 'yankee'),
   [11, 550, 1284]),
  ('variant scenario 1',
   ([['3.45', 'win'], ['4.45', 'void'], ['3.65', 'win'], ['2.52', 'win']], 100, 'lucky15'),
   [15, 1500, 14467]),
  ('variant scenario 2',
   ([['5.21', 'lose'], ['5.61', 'win'], ['5.91', 'void'], ['1.84', 'lose']], 25, 'lucky15'),
   [15, 375, 305])]]
for label, args, expected in cases[N - 1]:
    check(label, run(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
control trixie all win	`[4, 400, 2000]`	`[4, 400, 2000]`	Passed
control yankee line count	`[11, 1100, 0]`	`[11, 1100, 0]`	Passed
boundary void selection	`[4, 400, 2800]`	`[4, 400, 1700]`	Failed
boundary patent single winner	`[7, 700, 500]`	`[7, 700, 500]`	Passed
boundary wrong count	`invalid`	`invalid`	Passed
regression: void selection	`[7, 700, 596]`	`[7, 700, 100]`	Failed
variant scenario 1	`[15, 1500, 35598]`	`[15, 1500, 35598]`	Passed
variant scenario 2	`[15, 1500, 463]`	`[15, 1500, 463]`	Passed

SHA-256 / 1725b4a9702436356e79d9b553b705fe85dd58423708fd4326957f3283a4031f

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 8 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

Member access is invitation-based. Sign in with your invited account to inspect the repair.

Verification & scope

Stipulated, bounded toy contract stated in the contract field; not a claim of conformance with any operator, exchange or regulator rule set. 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 2026-09-29T14:50:32.991059+00:00.

Case digest / 85cd6be868fef9d4e6a4886949931c23b039d85a8791eb6596ca9104c348964a