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 · DEN @ OKC
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Lock a leg, or hit Fill the rest
  • Nothing picked yet
Leg pool159 legs at FanDuel · 771 rungs folded in · 23 not simulated · 2 one-sided
PlayerPropPriceOursBookEV
Marvin Bagley IIIFPts+Reb 14.5++35044%—+96.9%
Marvin Bagley IIIFPoints 9.5++39038%—+83.8%
DeMar DeRozanGPts+Reb+Ast 24.5++35038%—+72.2%
Ajay MitchellGPts+Reb 19.5++33035%—+51.4%
DeMar DeRozanGPoints 18.5++37032%—+50.9%
Ajay MitchellGPts+Reb+Ast 22.5++28039%—+49.9%
DeMar DeRozanGPts+Reb 19.5++25542%—+49.0%
Isaiah HartensteinCRebounds 11.5++40030%—+48.4%
Isaiah HartensteinCReb+Ast 14.5++35033%—+47.6%
DeMar DeRozanGRebounds 3.5++29036%—+39.6%
Ajay MitchellGPoints 13.5++17251%—+37.9%
Marvin Bagley IIIFPoints 4.5+-15884%—+37.8%
DeMar DeRozanGReb+Ast 5.5++11463%44%+34.7%
Marvin Bagley IIIFRebounds 2.5+-25094%—+31.3%
Ajay MitchellGRebounds 4.5++36028%—+29.6%
Jamal MurrayGAssists 8.5++40025%—+26.4%
DeMar DeRozanGPts+Ast 19.5++20042%—+25.9%
Ajay MitchellGPts+Ast 19.5++30031%—+24.0%
Cason WallaceGSteals 2.5++27033%—+22.0%
DeMar DeRozanGReb+Ast 4.5+-15273%57%+20.8%
Cason WallaceGAssists 3.5++32029%—+20.6%
Nikola JokicCAssists 11.5++27032%—+20.1%
Jalen WilliamsGAssists 6.5++32029%—+20.0%
Chet HolmgrenCPts+Reb+Ast 32.5++37025%—+18.9%
Jamal MurrayGThrees 4.5++37025%—+18.9%
Ajay MitchellGPts+Reb+Ast 14.5+-24083%—+17.7%
Christian BraunGRebounds 5.5++24035%—+17.5%
Chet HolmgrenCRebounds 8.5++10856%48%+17.1%
Alex CarusoGRebounds 3.5++22036%—+16.8%
Aaron GordonFPts+Ast 15.5+-11863%51%+16.0%
Jamal MurrayGRebounds 5.5++23534%—+15.5%
Isaiah HartensteinCPoints 7.5+-11863%51%+15.5%
Ajay MitchellGAssists 4.5++29030%—+15.4%
Isaiah HartensteinCPts+Reb 15.5+-13266%53%+15.3%
Chet HolmgrenCPts+Reb 24.5++11254%—+14.5%
Ajay MitchellGPoints 9.5+-18875%—+14.4%
Nikola JokicCAssists 8.5+-15269%—+14.1%
Isaiah HartensteinCPts+Reb+Ast 19.5+-11561%51%+13.9%
Ajay MitchellGPts+Reb+Ast 13.5+-31086%—+13.6%
Shai Gilgeous-AlexanderGAssists 7.5++19638%—+13.6%
Aaron GordonFPoints 13.5+-10658%48%+13.0%
Ajay MitchellGReb+Ast 4.5+-29083%—+11.8%
Ajay MitchellGPts+Ast 9.5+-50093%—+11.6%
Ajay MitchellGAssists 2.5+-17270%59%+11.4%
Jamal MurrayGPts+Ast 34.5++30028%—+10.4%
Chet HolmgrenCRebounds 7.5+-15467%57%+10.0%
DeMar DeRozanGPts+Reb+Ast 16.5+-17870%—+9.9%
Ajay MitchellGPoints 7.5+-36086%—+9.8%
Isaiah HartensteinCPts+Ast 10.5+-11859%51%+9.7%
Jamal MurrayGPts+Reb 25.5+-11559%51%+9.6%
Cason WallaceGReb+Ast 5.5++11052%46%+9.5%
Chet HolmgrenCBlocks 2.5++24032%—+9.4%
Marvin Bagley IIIFRebounds 1.5+-70095%—+8.9%
Jamal MurrayGPts+Reb+Ast 30.5+-15466%—+8.1%
Christian BraunGPts+Reb+Ast 22.5++32025%—+6.9%
Ajay MitchellGPts+Reb+Ast 11.5+-60092%—+6.8%
Chet HolmgrenCReb+Ast 9.5+-12259%—+6.7%
Cason WallaceGRebounds 3.5++15043%—+6.4%
Aaron GordonFReb+Ast 7.5+-13260%53%+6.3%
Jalen WilliamsGRebounds 5.5++16240%—+6.1%
Ajay MitchellGPts+Reb 9.5+-43086%—+5.9%
Aaron GordonFPts+Reb 18.5+-12258%51%+5.8%
Jamal MurrayGPoints 26.5++24031%—+5.5%
Isaiah HartensteinCReb+Ast 9.5+-18869%—+5.3%
Chet HolmgrenCAssists 1.5++11848%—+5.3%
Isaiah HartensteinCRebounds 6.5+-25075%—+5.0%
Ajay MitchellGAssists 1.5+-55089%—+4.9%
Jamal MurrayGPts+Reb+Ast 36.5++19436%—+4.8%
Isaiah HartensteinCPts+Reb 14.5+-20570%—+4.6%
Christian BraunGRebounds 3.5+-16865%59%+4.4%
Jamal MurrayGPts+Ast 24.5+-23072%—+3.9%
Chet HolmgrenCPoints 14.5+-13660%53%+3.7%
Shai Gilgeous-AlexanderGReb+Ast 9.5+-17866%—+3.5%
Chet HolmgrenCPoints 13.5+-18267%—+3.3%
Isaiah HartensteinCAssists 2.5+-12056%54%+3.1%
Aaron GordonFPts+Reb+Ast 21.5+-10853%49%+3.0%
Chet HolmgrenCPts+Reb+Ast 22.5+-20569%—+2.6%
DeMar DeRozanGPts+Reb+Ast 13.5+-39081%—+2.4%
DeMar DeRozanGAssists 1.5+-43083%—+2.0%
DeMar DeRozanGPoints 10.5+-22570%—+1.6%
Isaiah HartensteinCPts+Reb+Ast 14.5+-44083%—+1.6%
Cameron JohnsonFReb+Ast 4.5+-20068%—+1.5%
Nikola JokicCAssists 6.5+-50084%—+1.3%
Nikola JokicCPts+Reb+Ast 39.5+-36079%—+1.2%
Chet HolmgrenCPoints 9.5+-70089%—+1.2%
Ajay MitchellGRebounds 1.5+-47083%—+0.8%
Ajay MitchellGPoints 5.5+-100092%—+0.7%
Isaiah HartensteinCAssists 1.5+-38080%—+0.7%
Jalen WilliamsGSteals 1.5++16438%—+0.6%
Isaiah HartensteinCPoints 7.5+-16663%—+0.2%
Alex CarusoGPts+Reb 14.5++36022%—-0.5%
DeMar DeRozanGPts+Ast 9.5+-75088%—-0.7%
Cason WallaceGSteals 0.5+-60085%—-0.7%
Shai Gilgeous-AlexanderGThrees 0.5+-67086%—-1.1%
DeMar DeRozanGPts+Reb+Ast 11.5+-70086%—-1.2%
Chet HolmgrenCPts+Ast 14.5+-22068%—-1.4%
Shai Gilgeous-AlexanderGAssists 5.5+-20566%—-1.4%
Cason WallaceGReb+Ast 4.5+-18464%—-1.5%
Nikola JokicCPoints 19.5+-35077%—-1.5%
Chet HolmgrenCPts+Reb+Ast 17.5+-80087%—-1.7%
Jamal MurrayGPoints 17.5+-28072%—-1.7%
Nikola JokicCPts+Ast 24.5+-90088%—-1.9%
Nikola JokicCPts+Reb+Ast 38.5+-42079%—-1.9%
Shai Gilgeous-AlexanderGReb+Ast 10.5+-10450%50%-1.9%
Cameron JohnsonFRebounds 1.5+-85088%—-2.0%
Cameron JohnsonFAssists 1.5+-17062%—-2.3%
Isaiah HartensteinCPts+Ast 9.5+-22067%—-2.3%
Jamal MurrayGAssists 3.5+-90087%—-2.9%
Chet HolmgrenCPts+Reb+Ast 18.5+-58083%—-3.0%
Cason WallaceGRebounds 1.5+-50081%—-3.0%
Nikola JokicCPoints 17.5+-60083%—-3.0%
Chet HolmgrenCPts+Reb 19.5+-29572%—-3.0%
Chet HolmgrenCPoints 11.5+-34075%—-3.3%
Jamal MurrayGPts+Reb+Ast 26.5+-34075%—-3.3%
Nikola JokicCPts+Reb 29.5+-46079%—-3.6%
Cameron JohnsonFRebounds 3.5++10248%48%-3.7%
Christian BraunGRebounds 2.5+-49080%—-3.7%
Aaron GordonFRebounds 4.5+-30072%—-3.9%
Isaiah HartensteinCPoints 5.5+-36075%—-4.2%
Nikola JokicCPts+Reb+Ast 34.5+-100087%—-4.3%
DeMar DeRozanGPoints 7.5+-65083%—-4.6%
Cameron JohnsonFRebounds 2.5+-24568%—-4.7%
DeMar DeRozanGPts+Reb 9.5+-59081%—-4.7%
Isaiah HartensteinCRebounds 5.5+-46078%—-4.9%
Shai Gilgeous-AlexanderGSteals 0.5+-42077%—-5.0%
Isaiah HartensteinCPoints 4.5+-60081%—-5.2%
Cameron JohnsonFThrees 0.5+-100086%—-5.2%
Jalen WilliamsGPts+Reb+Ast 22.5+-49079%—-5.4%
Cameron JohnsonFReb+Ast 5.5+-10248%51%-5.6%
Chet HolmgrenCRebounds 5.5+-58080%—-5.7%
Christian BraunGReb+Ast 4.5+-34073%—-5.9%
Cason WallaceGPts+Reb+Ast 8.5+-75083%—-6.0%
Aaron GordonFThrees 0.5+-65081%—-6.1%
DeMar DeRozanGPoints 6.5+-100085%—-6.3%
Aaron GordonFRebounds 5.5+-14856%56%-6.5%
Nikola JokicCPoints 16.5+-80083%—-6.5%
Cason WallaceGPts+Reb+Ast 9.5+-49078%—-6.5%
Jamal MurrayGPts+Reb 19.5+-43076%—-6.5%
Aaron GordonFPts+Reb+Ast 17.5+-55079%—-6.6%
Jamal MurrayGPoints 14.5+-62080%—-6.6%
Jamal MurrayGPoints 13.5+-90084%—-6.8%
Aaron GordonFAssists 2.5++10047%48%-7.0%
Nikola JokicCReb+Ast 19.5+-23565%—-7.4%
Nikola JokicCPts+Ast 29.5+-29069%—-7.4%
Cason WallaceGRebounds 2.5+-17259%—-7.5%
Jamal MurrayGPts+Ast 19.5+-75082%—-7.5%
Shai Gilgeous-AlexanderGPts+Ast 29.5+-70080%—-8.3%
Alex CarusoGPoints 4.5+-26066%—-8.3%
Nikola JokicCPts+Reb+Ast 44.5+-16657%—-8.4%
Jalen WilliamsGSteals 0.5+-31069%—-8.7%
Nikola JokicCPts+Ast 33.5+-12551%51%-8.8%
Jalen WilliamsGAssists 3.5+-28067%—-9.5%
Shai Gilgeous-AlexanderGRebounds 3.5+-16656%—-10.6%
Jalen WilliamsGPoints 14.5+-40071%—-10.7%
Jalen WilliamsGPts+Ast 19.5+-35069%—-11.2%
Jalen WilliamsGAssists 2.5+-80079%—-11.6%
Jalen WilliamsGRebounds 2.5+-80079%—-11.6%
Nikola JokicCPts+Reb 34.5+-18457%—-11.8%
Jalen WilliamsGPts+Reb+Ast 24.5+-30064%—-14.3%

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. 9 of the 34 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.