MatchWiz

StackLab

Pick a game from tonight's slate, lock the legs you like, and the model fills the rest. Every price is a real one a book is posting right now.

Book
Target payout
Legs
Same-game parlay0 legs · NY @ BOS
—
Lock a leg, or hit Fill the rest
  • Nothing picked yet
Leg pool139 legs at DraftKings · 423 rungs folded in · 76 not simulated
PlayerPropPriceOursBookEV
Mikal BridgesGAssists 3.5++22848%—+57.8%
Payton PritchardGAssists 5.5++24144%—+51.1%
Derrick WhiteGRebounds 4.5++19548%—+40.6%
Andre DrummondCRebounds 9.5++27837%—+38.8%
Neemias QuetaCPts+Reb 19.5++25539%—+37.8%
Payton PritchardGPts+Ast 24.5++21242%—+31.2%
Andre DrummondCPts+Reb 19.5++36328%—+28.9%
OG AnunobyFReb+Ast 9.5++34029%—+28.5%
Mikal BridgesGRebounds 4.5++25736%—+28.0%
Payton PritchardGPts+Reb 24.5++24138%—+27.9%
Payton PritchardGReb+Ast 7.5+-12169%51%+26.6%
Neemias QuetaCRebounds 8.5++19842%—+26.2%
Payton PritchardGPts+Reb+Ast 29.5++25735%—+25.8%
Mikal BridgesGReb+Ast 5.5+-12971%54%+25.6%
Payton PritchardGAssists 3.5+-16177%58%+25.3%
Paul GeorgeFRebounds 3.5+-21083%—+22.1%
Mikal BridgesGThrees 2.5++34426%—+17.3%
Jalen BrunsonGThrees 3.5++33727%—+16.8%
Payton PritchardGRebounds 2.5+-23582%—+16.6%
Jalen BrunsonGRebounds 4.5++31928%—+16.1%
Neemias QuetaCPoints 5.5+-30387%—+15.8%
Payton PritchardGThrees 3.5++26232%—+15.2%
Derrick WhiteGPoints 16.5+-10358%—+14.6%
Paul GeorgeFAssists 2.5+-17873%—+14.1%
Derrick WhiteGReb+Ast 8.5+-12363%50%+13.8%
Josh HartGAssists 5.5++23034%—+13.7%
Mikal BridgesGPts+Ast 14.5+-11961%49%+12.8%
Derrick WhiteGPts+Reb 24.5++23434%—+12.6%
Paul GeorgeFAssists 3.5++13448%—+12.5%
OG AnunobyFAssists 2.5++20137%—+11.6%
Josh HartGRebounds 9.5++31827%—+11.5%
Mitchell RobinsonCRebounds 8.5++18539%—+11.2%
Mikal BridgesGPts+Reb+Ast 17.5+-12862%51%+10.9%
Payton PritchardGRebounds 3.5+-10156%—+10.8%
Derrick WhiteGPts+Ast 24.5++17141%—+10.3%
Paul GeorgeFReb+Ast 4.5+-70096%—+9.9%
Derrick WhiteGReb+Ast 7.5+-21074%—+9.9%
Paul GeorgeFRebounds 2.5+-46990%—+9.7%
Payton PritchardGAssists 2.5+-42989%—+9.3%
Paul GeorgeFRebounds 4.5+-10556%—+8.9%
Paul GeorgeFReb+Ast 7.5+-12260%51%+8.5%
Jalen BrunsonGRebounds 2.5+-16266%59%+7.3%
Derrick WhiteGRebounds 2.5+-26778%—+7.1%
Neemias QuetaCRebounds 5.5+-22674%—+6.9%
Payton PritchardGPts+Reb+Ast 19.5+-27978%—+6.5%
Neemias QuetaCPts+Reb 9.5+-55789%—+5.5%
Josh HartGAssists 2.5+-38783%—+4.9%
Karl-Anthony TownsCRebounds 9.5+-23973%—+3.9%
Josh HartGPts+Reb 17.5+-12057%51%+3.9%
Payton PritchardGReb+Ast 4.5+-80092%—+3.6%
Payton PritchardGPts+Reb 14.5+-35681%—+3.3%
Mikal BridgesGPts+Reb+Ast 14.5+-28075%—+2.4%
Josh HartGRebounds 4.5+-51286%—+2.3%
Mikal BridgesGRebounds 2.5+-20068%—+1.9%
Josh HartGAssists 3.5+-16162%58%+0.9%
Neemias QuetaCRebounds 4.5+-41581%—+0.7%
Josh HartGReb+Ast 9.5+-24071%—+0.7%
Mikal BridgesGPts+Ast 9.5+-56085%—+0.1%
Josh HartGReb+Ast 7.5+-65087%—+0.0%
Neemias QuetaCRebounds 3.5+-86089%—-0.2%
Landry ShametGPoints 7.5+-10651%—-0.2%
Mitchell RobinsonCRebounds 4.5+-46782%—-0.8%
Paul GeorgeFPts+Reb 14.5+-85088%—-1.1%
Josh HartGPts+Reb+Ast 14.5+-91089%—-1.3%
OG AnunobyFThrees 3.5++31824%—-1.4%
Derrick WhiteGPts+Reb+Ast 19.5+-40479%—-1.4%
Payton PritchardGPts+Ast 14.5+-45281%—-1.5%
Karl-Anthony TownsCRebounds 6.5+-95089%—-1.5%
Mikal BridgesGThrees 0.5+-39378%—-1.8%
Derrick WhiteGPts+Reb 14.5+-41379%—-1.8%
Mitchell RobinsonCRebounds 5.5+-24570%—-2.1%
Karl-Anthony TownsCRebounds 7.5+-57483%—-2.3%
Jalen BrunsonGReb+Ast 9.5+-11251%50%-2.5%
Josh HartGPts+Ast 9.5+-73086%—-2.7%
Payton PritchardGPoints 9.5+-64084%—-2.8%
Mikal BridgesGPts+Reb 19.5++24328%—-2.9%
OG AnunobyFRebounds 2.5+-74085%—-3.1%
OG AnunobyFReb+Ast 4.5+-47580%—-3.4%
Derrick WhiteGPoints 9.5+-73085%—-3.5%
Josh HartGRebounds 5.5+-26670%—-3.7%
Karl-Anthony TownsCThrees 0.5+-46879%—-3.9%
Derrick WhiteGAssists 3.5+-37276%—-4.1%
Josh HartGPoints 10.5+-10950%48%-4.1%
Derrick WhiteGPts+Ast 14.5+-69084%—-4.1%
Derrick WhiteGPoints 14.5+-16359%—-4.3%
Jalen BrunsonGAssists 7.5++16836%—-4.5%
OG AnunobyFRebounds 3.5+-30572%—-4.5%
Derrick WhiteGPts+Ast 19.5+-17560%—-5.0%
Derrick WhiteGAssists 4.5+-15858%57%-5.1%
Jalen BrunsonGThrees 1.5+-21364%—-5.4%
Mikal BridgesGPts+Reb 9.5+-66082%—-5.5%
Landry ShametGThrees 0.5+-54280%—-5.6%
Andre DrummondCRebounds 5.5+-21765%—-5.7%
Landry ShametGThrees 2.5++21730%—-5.8%
Josh HartGPts+Reb+Ast 19.5+-24567%—-6.1%
OG AnunobyFThrees 1.5+-22765%—-6.1%
Derrick WhiteGAssists 4.5+-16458%—-6.4%
Karl-Anthony TownsCPts+Reb+Ast 24.5+-66081%—-6.5%
Derrick WhiteGThrees 1.5+-34772%—-7.1%
Mitchell RobinsonCPts+Reb 12.5+-12351%50%-7.1%
Payton PritchardGThrees 1.5+-26167%—-7.3%
Jalen BrunsonGPts+Reb+Ast 24.5+-99084%—-7.3%
Paul GeorgeFPts+Ast 19.5+-18160%—-7.4%
Karl-Anthony TownsCPoints 11.5+-65080%—-7.5%
Josh HartGPts+Reb 14.5+-31370%—-7.6%
Mitchell RobinsonCPts+Reb 9.5+-33171%—-7.9%
Andre DrummondCRebounds 3.5+-76081%—-8.3%
Karl-Anthony TownsCPts+Reb 24.5+-32970%—-8.3%
Paul GeorgeFPts+Ast 14.5+-64079%—-8.8%
Karl-Anthony TownsCPts+Reb 19.5+-84081%—-9.1%
Derrick WhiteGThrees 2.5+-11749%52%-9.4%
Paul GeorgeFPts+Reb+Ast 19.5+-48275%—-9.4%
Derrick WhiteGPts+Reb+Ast 29.5++21129%—-9.6%
Jalen BrunsonGReb+Ast 7.5+-31068%—-9.6%
Josh HartGPts+Ast 14.5+-12650%51%-10.3%
Jalen BrunsonGAssists 3.5+-92080%—-11.1%
Jalen BrunsonGPts+Reb 19.5+-82079%—-11.1%
OG AnunobyFPoints 11.5+-47373%—-11.5%
OG AnunobyFPts+Reb 14.5+-72078%—-11.7%
Karl-Anthony TownsCDouble double-21760%—-11.8%
Karl-Anthony TownsCPts+Ast 14.5+-82078%—-12.2%
Paul GeorgeFPts+Reb 19.5+-24062%—-12.8%
Paul GeorgeFPts+Ast 20.5+-12348%51%-12.8%
Karl-Anthony TownsCPoints 14.5+-30265%—-13.0%
Jalen BrunsonGPts+Reb 24.5+-27163%—-13.2%
Karl-Anthony TownsCReb+Ast 11.5+-50072%—-13.3%
Jalen BrunsonGAssists 5.5+-19657%—-13.3%
Josh HartGThrees 0.5+-49972%—-13.3%
Jalen BrunsonGPts+Reb+Ast 29.5+-35067%—-13.4%
Jalen BrunsonGPoints 25.5+-11146%—-13.4%
Paul GeorgeFThrees 3.5++15035%—-13.4%
Paul GeorgeFPts+Reb 22.5+-10544%48%-13.6%
Mitchell RobinsonCPoints 5.5+-12448%49%-14.1%
OG AnunobyFPts+Reb+Ast 29.5++23226%—-14.2%
Karl-Anthony TownsCThrees 1.5+-10744%—-15.7%
OG AnunobyFPts+Reb 24.5++13136%—-16.9%
Paul GeorgeFPts+Reb+Ast 24.5+-16250%—-19.1%
Josh HartGThrees 2.5++27021%—-21.9%
OG AnunobyFPts+Ast 24.5++26220%—-26.8%

One leg per player. Three rungs of the same man's ladder are the same bet three times — they win and lose together, and no book will take them in one slip. You get his best rung in each price band rather than every rung, for the same reason. Book is the de-vigged market price, or a dash where the market is quoted one way only and the hold can't be stripped out.

Why the number isn't a multiply

A multiply is wrong in basketball, and it's wrong in both directions at once. Which one you get depends on whether your legs are one player or two.

Stack one man's night and the legs ride together. We measured it on 79,638 real player-games across three seasons: his points and his assists both going over hits 28.7% more often than the multiply says, and his rebounds and made field goals 36.2% more often. Pair his points with his points-rebounds-assists and it's 92.4% — the same night counted twice. A multiply prices that slip as a much longer shot than it is.

Two teammates fight instead. One ball, about a hundred possessions, so a teammate's big night comes partly out of yours: two players both going over on points lands 4.7% less often than the product, and on made field goals 4.2% less. Here the multiply flatters the slip, which is the trap, because that's the stack that looks diversified.

Opponents barely move at all — within half a percent of the plain product on points and rebounds. The pace both teams play lifts both benches a little, and that's the whole of it.

So StackLab doesn't multiply. Every game here is simulated thousands of times off the players' own game logs, and each posted line carries a record of which runs it hit. The chance a whole slip lands is the share of runs where every leg hit at once.

What the EV number means

EV is our win probability times what the slip pays, minus one. Positive means our model makes the parlay worth more than it costs. Negative means the book is ahead of us, and that's the common answer — it should be. Books hold a margin on every leg, and a parlay multiplies it.

Which is why the leg-count strip matters. Inside a fixed payout band, each extra leg multiplies the same hold again, so EV falls as the count rises. The strip shows that shape on the pool in front of you.

Fill the rest won't pick a leg we can't stand behind. 6 of the 20 legs we can compare sit more than 10 points from the book's own number, and our pricer calls a gap that size a stale line or an injury we don't know about rather than an opportunity. The model is unbiased on average here and wide game to game, so those are the rows where it's likeliest to be the one that's wrong — and ranking by EV would otherwise reach for them first.

They're still on the board, and you can still lock one. That's your call to make with the number in front of you; what the model won't do is choose it for you and call the result an edge.

Every leg here is one a book is quoting

A leg only appears when a sportsbook is posting that exact line and we hold simulated runs behind it. The percentage beside a player is the same one his prop board shows — one model, one number, two pages.

The runs come from his own games. A player's line is priced off the shape of his last 40-plus box scores rather than a bell curve, because a counting stat isn't shaped like one, and the simulation draws whole box scores rather than one stat at a time. That's why his points and his rebounds move together by exactly as much as they really do.

We also publish how the model does against the market, and on NBA player props the market wins. Nothing here is sold as a pick. It's our number next to the book's, and a payout that's honest about what the legs do to each other.