How the software runs
How the software runs, and what this page will publish once results have settled. The operational figures below describe what the software does; they are not member results, and no performance record is published yet.
Operational
The operational figures
- Opportunities surfaced per day
- 1,000+Opportunities surfaced per day
- Arbitrage opportunities per day
- 250+Arbitrage opportunities per day
- Sports covered
- 8Sports covered
- Software uptime
- 99.9%Software uptime
- Alert latency
- Under 2sAlert latency
These describe how the software operates: how much it surfaces, how widely it scans, and how quickly it alerts. They are not a record of member results, and they say nothing about profit. Whether any given opportunity is worth taking is always your call.
Today
What the software covers
Scanning runs continuously across these books, in the sports listed above. That list is complete, not a sample.
- Bookmakers scanned
- Sportsbet, TAB, Ladbrokes, Neds, PointsBet, Bet365, Unibet, TopSport
- Sports covered
- Baseball, Basketball, Soccer, Tennis, AFL, NRL, Cricket, Ice Hockey
When there is a record
What this page will publish
- Every alert, not a selection
- Each opportunity the software posts will be recorded, not a curated subset. A record that only lists the ones that worked is marketing, not a record.
- Losing results included
- Losses will be published alongside wins. Individual bets lose; a record that hides that is worthless for judging the software.
- Sourced from what was actually posted
- Entries will be taken from the alerts as they were sent to the members' Discord (the market, the books and the prices at the moment of posting), so each line can be checked against the original alert.
- Settled, not projected
- Only results that have settled will appear. Nothing modelled, nothing estimated, and no figure carried over from a sample too small to mean anything.
We are not putting a date on this. A record is only worth publishing once there is enough settled data for it to mean something, and we would rather show nothing than show a sample chosen to flatter. Past performance does not guarantee future results.
Want to understand how the software finds these opportunities in the first place?