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FA-84346 / Betting odds conversion / Open access

Moneyline magnitude truncated or banker-rounded · case 01

A 2.005 price shows +100 instead of +101.

Verified by executionVariant 1 · 10 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The magnitude is truncated toward zero.

THE FAILURE

The magnitude is truncated toward zero.

Unsuccessful approach: Python's round() on the exact Fraction rounds exact halves to even.

Case contract

Convert a decimal price string to an American moneyline string. Prices at or below 1 return "invalid". For d >= 2 the line is "+" followed by 100 * (d - 1); for d < 2 it is "-" followed by 100 / (d - 1). The magnitude is computed exactly from the decimal text and rounded half up to an integer. Evens (2.00) is "+100".

Why this case matters

Sportsbooks display the same price in decimal and American formats and must agree at every boundary.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
import math
N = 1
observations = []
def solve(decimal):
    d = Fraction(decimal)
    if d <= 1:
        return 'invalid'
    if d >= 2:
        v = (d - 1) * 100
        sign = '+'
    else:
        v = 100 / (d - 1)
        sign = '-'
    n = int(v)
    return sign + str(n)
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 underdog', ('2.50',), '+150'),
  ('control favourite', ('1.50',), '-200'),
  ('boundary evens', ('2.00',), '+100'),
  ('boundary half-cent rounds up', ('2.005',), '+101'),
  ('boundary no-profit price', ('1.00',), 'invalid'),
  ('control short favourite', ('1.08',), '-1250'),
  ('regression: magnitude rounding', ('2.217',), '+122'),
  ('regression: magnitude rounding', ('2.005',), '+101'),
  ('variant scenario 1', ('11.70',), '+1070'),
  ('variant scenario 2', ('2.00',), '+100')],
 [('control underdog', ('2.50',), '+150'),
  ('control favourite', ('1.50',), '-200'),
  ('boundary evens', ('2.00',), '+100'),
  ('boundary half-cent rounds up', ('2.005',), '+101'),
  ('boundary no-profit price', ('1.00',), 'invalid'),
  ('control short favourite', ('1.08',), '-1250'),
  ('regression: magnitude rounding', ('2.005',), '+101'),
  ('variant scenario 1', ('1.078',), '-1282'),
  ('variant scenario 2', ('11.34',), '+1034')],
 [('control underdog', ('2.50',), '+150'),
  ('control favourite', ('1.50',), '-200'),
  ('boundary evens', ('2.00',), '+100'),
  ('boundary half-cent rounds up', ('2.005',), '+101'),
  ('boundary no-profit price', ('1.00',), 'invalid'),
  ('control short favourite', ('1.08',), '-1250'),
  ('regression: magnitude rounding', ('2.125',), '+113'),
  ('variant scenario 1', ('7.99',), '+699'),
  ('variant scenario 2', ('9.28',), '+828')],
 [('control underdog', ('2.50',), '+150'),
  ('control favourite', ('1.50',), '-200'),
  ('boundary evens', ('2.00',), '+100'),
  ('boundary half-cent rounds up', ('2.005',), '+101'),
  ('boundary no-profit price', ('1.00',), 'invalid'),
  ('control short favourite', ('1.08',), '-1250'),
  ('regression: magnitude rounding', ('3.125',), '+213'),
  ('variant scenario 1', ('7.98',), '+698'),
  ('variant scenario 2', ('2.77',), '+177')],
 [('control underdog', ('2.50',), '+150'),
  ('control favourite', ('1.50',), '-200'),
  ('boundary evens', ('2.00',), '+100'),
  ('boundary half-cent rounds up', ('2.005',), '+101'),
  ('boundary no-profit price', ('1.00',), 'invalid'),
  ('control short favourite', ('1.08',), '-1250'),
  ('regression: magnitude rounding', ('1.538',), '-186'),
  ('regression: magnitude rounding', ('2.125',), '+113'),
  ('variant scenario 1', ('2.490',), '+149'),
  ('variant scenario 2', ('1.074',), '-1351')]]
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 fixtureActualExpectedOutcome
control underdog+150+150Passed
control favourite-200-200Passed
boundary evens+100+100Passed
boundary half-cent rounds up+100+101Failed
boundary no-profit priceinvalidinvalidPassed
control short favourite-1250-1250Passed
regression: magnitude rounding+121+122Failed
regression: magnitude rounding+100+101Failed
variant scenario 1+1070+1070Passed
variant scenario 2+100+100Passed

SHA-256 / e6a22faba0b9804620b776a30f4c968c587afab5fe6f055c64a3e0981afd985f

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
import math
N = 1
observations = []
def solve(decimal):
    d = Fraction(decimal)
    if d <= 1:
        return 'invalid'
    if d >= 2:
        v = (d - 1) * 100
        sign = '+'
    else:
        v = 100 / (d - 1)
        sign = '-'
    n = round(v)
    return sign + str(n)
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 underdog', ('2.50',), '+150'),
  ('control favourite', ('1.50',), '-200'),
  ('boundary evens', ('2.00',), '+100'),
  ('boundary half-cent rounds up', ('2.005',), '+101'),
  ('boundary no-profit price', ('1.00',), 'invalid'),
  ('control short favourite', ('1.08',), '-1250'),
  ('regression: magnitude rounding', ('2.217',), '+122'),
  ('regression: magnitude rounding', ('2.005',), '+101'),
  ('variant scenario 1', ('11.70',), '+1070'),
  ('variant scenario 2', ('2.00',), '+100')],
 [('control underdog', ('2.50',), '+150'),
  ('control favourite', ('1.50',), '-200'),
  ('boundary evens', ('2.00',), '+100'),
  ('boundary half-cent rounds up', ('2.005',), '+101'),
  ('boundary no-profit price', ('1.00',), 'invalid'),
  ('control short favourite', ('1.08',), '-1250'),
  ('regression: magnitude rounding', ('2.005',), '+101'),
  ('variant scenario 1', ('1.078',), '-1282'),
  ('variant scenario 2', ('11.34',), '+1034')],
 [('control underdog', ('2.50',), '+150'),
  ('control favourite', ('1.50',), '-200'),
  ('boundary evens', ('2.00',), '+100'),
  ('boundary half-cent rounds up', ('2.005',), '+101'),
  ('boundary no-profit price', ('1.00',), 'invalid'),
  ('control short favourite', ('1.08',), '-1250'),
  ('regression: magnitude rounding', ('2.125',), '+113'),
  ('variant scenario 1', ('7.99',), '+699'),
  ('variant scenario 2', ('9.28',), '+828')],
 [('control underdog', ('2.50',), '+150'),
  ('control favourite', ('1.50',), '-200'),
  ('boundary evens', ('2.00',), '+100'),
  ('boundary half-cent rounds up', ('2.005',), '+101'),
  ('boundary no-profit price', ('1.00',), 'invalid'),
  ('control short favourite', ('1.08',), '-1250'),
  ('regression: magnitude rounding', ('3.125',), '+213'),
  ('variant scenario 1', ('7.98',), '+698'),
  ('variant scenario 2', ('2.77',), '+177')],
 [('control underdog', ('2.50',), '+150'),
  ('control favourite', ('1.50',), '-200'),
  ('boundary evens', ('2.00',), '+100'),
  ('boundary half-cent rounds up', ('2.005',), '+101'),
  ('boundary no-profit price', ('1.00',), 'invalid'),
  ('control short favourite', ('1.08',), '-1250'),
  ('regression: magnitude rounding', ('1.538',), '-186'),
  ('regression: magnitude rounding', ('2.125',), '+113'),
  ('variant scenario 1', ('2.490',), '+149'),
  ('variant scenario 2', ('1.074',), '-1351')]]
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 fixtureActualExpectedOutcome
control underdog+150+150Passed
control favourite-200-200Passed
boundary evens+100+100Passed
boundary half-cent rounds up+100+101Failed
boundary no-profit priceinvalidinvalidPassed
control short favourite-1250-1250Passed
regression: magnitude rounding+122+122Passed
regression: magnitude rounding+100+101Failed
variant scenario 1+1070+1070Passed
variant scenario 2+100+100Passed

SHA-256 / 882e3e13725d57eb2462beea6a490d0e583b322f1dd745dc92937dab62f8dcec

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This mechanism has 10 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.

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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 2026-09-29T14:50:29.873678+00:00.

Case digest / 6fa00e3619b4701d94bb8da5af45796c02efdcaca29930197b64c6b081ff6b25