A model calls every match before kickoff and logs every result after, so an agent can act with confidence instead of a guess. Football, basketball, tennis. Wins and losses. Nothing hidden.
The same question, two ways
We ran this on real settled matches with every name and date stripped out, so the AI could not look up the result. Same match, same model, one difference: whether it saw our probabilities.
A real match from our set, names removed so the AI can’t look it up. Same agent, asked twice.
Honest footnote: our model roughly matches the betting market’s own accuracy — we don’t claim to beat the bookmakers. The measured edge is over an agent with no data, which is exactly what an agent is without a tool like this. How we track it →
Next, this becomes an MCP any agent can call for sport predictions — the way a travel agent can check real fare history instead of guessing a price. Grounded data in, an agent that reasons from evidence instead of a hunch.
How it works
Everything is automated and timestamped, so the record can't be edited after the fact.
The model forecasts each match and posts its pick, with a clear confidence level, before the game starts.
After the final whistle, the result goes up: correct or wrong. No cherry-picking, no deleting the misses.
Every result updates a running win/loss tally, broken down by confidence, so you judge it on real numbers.
In your pocket
A pick arrives before kickoff with its confidence. The result follows after the match, with the running record attached. Wins and losses alike.
21:04
Saturday, 16 August
Coverage
Eleven competitions across football, basketball and tennis. Counts are live upcoming matches.
On the slate
The fixtures our model is calling. The picks go out in the channel before kickoff.
Settled today
Every match we called today, next to how it finished. Wins and losses, nothing removed.
Play
Call the close games. Nail 6 of 10 and win a week of our top picks. Your picks are saved on this device.
What are we
People bet on sports with data, instinct, and a memory of what happened last time. An agent has none of that. It has confident-sounding guesses. We give it the missing piece: a prediction it can trust, with the receipts.
Every pick is posted before kickoff and every result after, wins and losses. The track record is public and cannot be edited later.
We tested it: an agent guessing hits 47%, the same agent with our data hits 62%. We publish the method, not just the headline.
Next it becomes an MCP any agent can query for a grounded prediction, so it acts on evidence instead of a hunch. The trust sits between the agent and the bet.