The math
Follow one ticket from price to payout. These worked examples separate what a ticket is expected to earn, what you put at risk, and what you actually get.
Start with the ticket cash flows
Expected value (EV) is what a ticket earns on average: each possible result weighted by its chance. For a standard house-side ticket, before fees:
The estimate is only as good as the probability behind it. Actual profit depends on the real result and costs. A market midpoint (halfway between the best buy and sell prices) is an estimate, not a known probability.
Multiplying independent probabilities
If the legs (picks) are independent and their probabilities are accurate, the chance all of them win is their product. At 60% per leg:
The engine skips related legs it detects, unless same-game pricing is on for that sport, and checks the order books behind each price. Correlations it misses and pricing errors can still throw off the estimate.
Margin is a markup
Your margin is a markup on the parlay’s fair value, the engine’s estimate of its true price. It is a percentage of that value, not of your amount at risk. Margins are shown in bps (basis points): 100 bps = 1%.
A leg-count target is a margin on the whole parlay, not one added to every leg. Autopilot’s final margin can also reflect its cost floor and other adjustments.
The same EV, another way
Sixty fills like this one add up to $30 of estimated EV before fees. Multiplying the 5% markup by the $39.50 at risk would give the wrong answer. EV adds up across tickets, but shared outcomes still widen the range of real results.
The same prices can produce different nights
Suppose 40 different tickets each match the example: $10.50 stake, $50 payout, and an estimated 20% chance to hit. The book takes in $420 and, under that model, expects eight payouts.
One winning pick shared by several tickets can make them pay together. More fills do not always spread your risk, and trading more does not remove model error. Check the shared-outcome view alongside each ticket.
What the limits bound
Each limit caps something different. Per ticket and Total open cap how much is at risk. Fills per day caps a count. The daily and weekly loss limits are the ones that respond to losses. None of them promises a profit.
Choose limits that match losses you can afford. Check settled profit and loss as the night goes on, and remember that separate tickets can share the same risk.
Shared outcomes change the book
Multiplying assumes independence. For related events, the real chance both happen can be higher or lower than the product, depending on how they are related.
By default, Pregame skips requests with duplicate legs, or legs from the same game, event, or team. If you turn on same-game pricing for a sport (Autopilot only), the engine can price some moneyline, spread, and total combinations with a measured correlation model, leaning toward a higher price. Across separate tickets, concentration still matters: one result can affect many tickets.
Small price errors can be large relative errors
Neither error is automatically harmless; the ticket’s size sets the dollars. A $10 stake at a 2¢ quote buys a $500 payout and puts $490 of your money at risk before fees. Your parlay price range and dollar limits cap different parts of that risk.
Price and fill rate belong together
A wider margin can earn more per fill but win fewer auctions. Compare EV per fill times the chance of getting that fill, on similar requests and costs.
Autopilot learns from past auction results and aims for your target win rate, never below its cost floor. The pricing panel shows where its target came from and when it is using a fallback. Manual control uses the base margin you set instead.
One ticket, end to end
On the Tickets page, the bettor’s stake and your amount at risk describe each ticket. Chance to pay is a live estimate; only a confirmed settlement records the result.
What to check at a glance
- Engine state: has the desk confirmed it is quoting, or stopped?
- Room left: how much is at risk, and how close is it to your limits?
- Pricing source: is the target learned, a fallback, or your manual value?
- Concentration: which shared outcomes affect the most tickets?
- Data freshness: are prices and settlement checks current?
- Settled results: how does settled profit compare with your estimates and costs?