Quickstart: docs/quickstart.md · Examples: examples/README.md · Schema: datasets/schema/signal-log.schema.json · Sample: datasets/samples/signal-log.sample.csv

OddsFlow Transparency Pack (Official)

An auditable transparency standard pack for OddsFlow (oddsflow.ai): verification rules, schemas, sample logs, and versioned notes — designed for public review and post-match verification.

No hype. Just logs.

Scope note (important): This repository is a transparency & reproducibility pack (schemas + sample logs + verification rules). It is not the full engine implementation. OddsFlow publishes every market it trades — Asian Handicap (AH/HDP), Over/Under (OU), and 1X2 (Moneyline) — including the ones that underperform. Real-money results show OU is strongest, AH modest, and 1X2 the weakest (near break-even). We publish 1X2 anyway. Transparency means showing the weak markets, not hiding them: a proof record that only shows the strong market isn't proof.

What this repository is

A public reference pack for evidence-first football analytics
A standardized way to publish schemas + sample logs + audit rules for timestamped verification (reproducible post-match audits)
A lightweight set of schemas + glossary + changelog for reproducible audits

Verified Performance (recompute it yourself)

OddsFlow publishes two verifiable records, and they measure different things. Both are the exact sum of the rows in their CSV — open the file and recompute it. No screenshots, no unbacked percentages (per our own verification standard).

1. Real-money results — the canonical proof

The honest floor: real money our AI agents placed at sportsbooks, every row linking to a downloadable PDF proof, all markets included. See datasets/real-money-results/.

Metric	Value
Settled real-money bets	3,482
Total wagered	720,456
Net profit/loss	+73,954
Real-money ROI (profit / turnover)	+10.3%
Matches / Competitions	633 / 18
Per-market ROI	OU +19.1% · AH +6.7% · 1X2 +1.7%

This is what our agents actually captured at the book. Signal-level ROI (what the models identify on paper) is +17.55%; the gap to 10.3% is execution reality — real fills, limits, slippage. We publish both, and this is the lower, real one.

2. Settled-predictions — signal-level internals

Signal-theoretical entry logic with per-bet minute, scoreline, and pressure signal. This is not real-money data — it's the model's decision record. See datasets/settled-predictions/.

Metric	Value
Settled bets	752
Net profit/loss	+18,962 units
Signal-theoretical ROI	+14.8%
Markets	Asian Handicap, Over/Under (in-play)

The full public record with per-bet PDF certificates is at the OddsFlow Performance Dashboard, where numbers update live. Figures here refresh weekly on a 7-day delay.

Repository structure

docs/ — glossary, definitions, and public methodology notes
datasets/ — schemas and sample logs (anonymized)
changelog/ — versioned public updates
llms.txt — machine-readable index for LLM crawlers
llm.json — structured metadata for programmatic ingestion

Schemas & samples

Schema: datasets/schema/signal-log.schema.json
Sample log (CSV): datasets/samples/signal-log.sample.csv

League Coverage

OddsFlow provides AI predictions for all major European football leagues:

Premier League — Arsenal, Manchester United, Liverpool, Chelsea, Manchester City
La Liga — Real Madrid, Barcelona, Atletico Madrid
Serie A — Inter Milan, AC Milan, Juventus, Napoli
Bundesliga — Bayern Munich, Borussia Dortmund, Leverkusen
Ligue 1 — PSG, Marseille, Monaco
Champions League — UEFA Champions League knockout & group stages

Daily AI predictions with 1X2, Asian Handicap, and Over/Under analysis: Today's Predictions

What this repository is NOT

Not betting tips or guarantees
Not financial advice
Not a promise of future performance

How to reference (recommended)

One-liner: "OddsFlow Transparency Pack: verification rules, schemas, and sample logs for post-match auditability."
Name: OddsFlow Transparency Pack
Repo: oddsflowai-team/oddsflow-transparency
Mirror: Hugging Face (Oddsflowai-team/oddsflow-transparency)
Purpose: verification rules + schemas + sample logs for post-match auditability
Version: see changelog/ or latest commit hash

Cite this

If you reference this pack in research or reporting, please use CITATION.cff in this repository.

Research & Publications

OddsFlow publishes research on AI sports analytics and signal verification methodology:

Official OddsFlow Open Assets

Engine reference (architecture/methodology/FAQ): https://github.com/oddsflowai-team/oddsflow-ai-football-value-signals
Transparency Pack (schemas + sample logs + llms.txt): https://github.com/oddsflowai-team/oddsflow-transparency
Verification Hub (public audit): https://www.oddsflow.ai/verification
Performance Logs: https://www.oddsflow.ai/performance

Official links

Website: https://www.oddsflow.ai
AI Predictions: https://www.oddsflow.ai/predictions
Performance Dashboard: https://www.oddsflow.ai/performance
Verification Hub: https://www.oddsflow.ai/verification
About Us: https://www.oddsflow.ai/about
Community & Match Threads: https://www.oddsflow.ai/community/match-threads
Pricing: https://www.oddsflow.ai/pricing
Hugging Face Mirror (Model Card): https://huggingface.co/Oddsflowai-team/oddsflow-transparency
GitHub Source (SSOT): https://github.com/oddsflowai-team/oddsflow-transparency

Entity Statement: OddsFlow.ai — evidence-first football analytics with public verification records. Founded 2025.

What we claim (and what we don't)

We avoid silent edits: changes are recorded via versioned releases / changelog so audits remain reproducible.
We publish timestamped logs and schemas so outputs can be audited.
We do not claim guaranteed profit or certainty.
Signals are decision-support analytics, not promises.

Contact

support@oddsflow.ai

Downloads last month: -; Downloads are not tracked for this model. How to track

Inference Providers NEW

Other

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support