Same-match evidence contract
Pair the forecast, available odds and result before scoring
Every accepted row needs one complete model 1X2 distribution, three finite decimal prices greater than 1, one settled outcome and times proving the forecast and price snapshot were available before kick-off. Invalid or late rows stay visible as exclusions.
- match identity
- Stable unique key so one fixture cannot be counted twice
- publication time
- Zoned time when the tested model probability split was fixed
- odds time
- Zoned time for the exact bookmaker snapshot used as the baseline
- kick-off time
- Strictly later than both pre-match evidence timestamps
- model probability
- Complete home, draw and away distribution on one consistent scale
- decimal odds
- Home, draw and away prices greater than 1 from the same real snapshot
- odds stage
- Self-declared pre-match snapshot or closing label; the browser cannot authenticate it
- source label
- Optional bookmaker and league fields preserve provenance but do not verify the provider
- outcome
- One settled result: home, draw or away
- SHA-256
- Identifies the exact local bytes; it does not independently timestamp them
Two de-vig assumptions
Raw inverse odds are not a probability forecast until the margin is handled
Decimal odds imply raw weights of 1 ÷ odds. Their sum usually exceeds one. Proportional normalization divides every weight by that sum. Shin instead fits z under an insider-trading model and redistributes margin non-proportionally. The fitted parameter is not an observation of insiders. Neither method is ground truth, so the report keeps both.
Transparent and deterministic; assumes the margin scales every outcome equally.
Allows favourite–longshot asymmetry; report fitted z as a model parameter—not observed insiders—and any solver exclusion.
Forecast-quality claim
Score the full probability split, not just who finished top
The model and both market baselines face the same settled matches. Class-averaged Brier score measures squared error over all three outcomes; log loss strongly penalizes tiny probability on what happened. Lower is better for both. Publish paired differences, coverage and exclusions—not only the winning cell. For finite deterministic exports, the implementation floors an actual-outcome probability of zero at 1e-15 before taking the logarithm; that epsilon is included in every JSON and CSV report. Reproduce that boundary on a single 1X2 forecast in the football log-loss lab.
- 01
Same rows
No fixture-mix advantage: every score is calculated on the same accepted matches.
- 02
Two baselines
A superiority claim must survive both proportional and Shin margin removal.
- 03
Paired losses
Inspect per-match score differences and their uncertainty, not unrelated averages.
- 04
Held-out period
Freeze the model and protocol before evaluating a later, untouched match window.
Claim boundary
Better probabilities do not automatically create a profitable strategy
This lab evaluates forecasts against a supplied market snapshot. It does not know whether those prices were executable, which limits applied, how the market moved, or what selection and transaction costs a strategy would face.
- No odds shopping.Combining the best home, draw and away prices from different times or books creates a baseline that never existed.
- No closing-line shortcut.Odds observed after the model publication can contain information the model never had; timestamps must be reported.
- No selective coverage.Omitted leagues, matches or failed forecasts remain part of the completeness question.
- No profit claim.Forecast quality, expected value, executable price and realized return are separate claims.
Primary literature
The benchmark is grounded in scoring rules and declared margin removal
These papers ground the scoring, odds-setting, market-comparison and Shin assumptions. They do not certify this implementation, any uploaded odds, or future model performance.
- Brier (1950), verification of probability forecasts.Original paper and DOI
- Shin (1992), insider trading and favourite–longshot bias.Economic Journal paper and DOI
- Gneiting & Raftery (2007), strictly proper scoring rules.Journal paper and DOI
- Franck, Verbeek & Nüesch (2010), bookmaker and betting-exchange prediction accuracy.International Journal of Forecasting paper and DOI
- Koning & Zijm (2022), bookmaker odds setting and prediction.Annals of Operations Research paper and DOI
Cite this tool as: Football Proof AI. “Football Prediction Model vs Bookmaker Odds Calculator.” Version football-market-benchmark/1.0.0, 13 July 2026, https://footballproofai.com/tools/football-model-vs-bookmaker-calculator. Editorial technical note; not externally peer reviewed.