Datasets

This folder contains field definitions, samples, and full settled records to make OddsFlow's public logs auditable and reproducible.

Schemas & samples

Schema: schema/signal-log.schema.json
Sample: samples/signal-log.sample.csv

Full datasets

These two datasets have distinct roles — read them differently:

real-money-results/ — THE canonical proof. Real money our AI agents placed at sportsbooks, settled at the book, every row linking a PDF proof (pdf_proof column). All markets included — AH, OU, and 1X2, including the underperforming ones. Overall real-money ROI +10.3% (OU +19.1%, AH +6.7%, 1X2 +1.7%). This is the honest floor — what agents actually captured. See real-money-results/README.md.
settled-predictions/ — signal-level internals, NOT real money. The model's per-bet decision record: every in-play signal with its entry context (minute, scoreline, pressure signal), entry odds, and settled profit/loss. The ROI here is signal-theoretical. Valuable for understanding why a bet fired, but it is not a competing real-money performance claim. See settled-predictions/README.md.

Signal vs real-money: signal-level ROI (+17.55%) is what the models identify on paper; real-money ROI (+10.3%) is what the agents captured at the book. The gap is execution reality — fills, limits, slippage. We publish both.

We publish full settled (post-match) records. We do not publish pre-match or live signals, model parameters, or features — only the settled outcomes needed to verify and reproduce our public performance claims.