TroyCuban

friend is building on @Replit... from prison

For this week's episode, @Sports__Proj and I interview @troycuban . A Kalshi trader closing in on $1m in public profits who "hasn't originated a thing in my life". Troy is active in RFQs along with other strategies - leave some questions below. This is for the top-downers!

someone needs to get 9951 on a pod

@s0ggysausag3 @goldenpants013 @Sports__Proj ball knowledge lol. I wish

@goldenpants013 @Sports__Proj @troycuban Is he 9951? Also does he quote everything or focus on specific markets/edges?

This is like the former CEO of Marlboro going on a multi day rant about the evils of vaping. This guy was is the fun version of Gouker. Hope he sticks around.

Vibe coded my first project today! Let me know what you guys think ❤️

I don’t really dabble in sports “trading”, but I had this idea: Sports books have the chance to spoof tf out of prediction market exchanges. It’s somewhat well known that one can just regress over prices of books with weights scaling with historical brier’s of the books. Suppose pinnacle and another exchange or two “open” up the line 4-5% from their fair (cooperation would be needed), this probably isn’t enough for the average degenerate sports gambler to notice some crazy mispricing, but would be enough to meaningfully shift the lines on kalshi and poly. These books could then size up massively on these exchanges that don’t block institutions from trading and they’d be able to make some pretty big +EV coin flips. Now of course it goes without saying that they can’t do this every time or else people will rely on their pricing less; there’s some GTO mixed strategy here. Someone who knows the space better please correct me if it is obvious this would not work (ignore legal/moral reasons of course).

SIG's CFTC comment includes two actual examples of hedging on Kalshi sports contracts. Full letter below.

Sharp takers have so much more aura than makers

@goldenpants013 @Sports__Proj got another one. In the big 2026, how much of an originator's edge comes from factoring in data no one else has vs. out analyzing the data that everyone has? (Respective of the liquidity of the market you're trying to originate)

Hey all - me and @Sports__Proj are looking for questions for this weeks episode. Feels like a crazy amount of news these last two weeks, looking forward to this one.

if you're a booking parlays, how do you go about managing the risk? It's not as easy as making your max liability per parlay quarter kelly and calling it a day. You could have a ton of 2 to 20 leg parlays that all share 1 or more legs. Maybe some of those legs shared with it already hit. Maybe you have some parlays that contain the opposing side of that leg that net out. Maybe the common leg is a huge favorite so if that wins it wont really tank the value of the rest of the legs that bad.

For those keeping score of sports betting numbers... lot of headlines these days about 'cannibilization' due to prediction markets. And while it is certainly a factor contributing to y/y decrease in recent months, I wouldn't be quick to dismiss other factors such as promo levels coming down y/y (look at the data), the sheep continually getting slaughtered with SGPs, general malaise in the consumer due to inflation and job market... lot of moving parts to consider is the point... ☕️

@Novig elite ball knowledge. Cody Paul #5 would be a sick bottle service sign

Tom Brady sat as a rookie Brett Favre sat as a rookie Jalen Hurts sat as a rookie Drew Brees sat as a rookie Jordan Love sat as a rookie Philip Rivers sat as a rookie Steve Young sat as a rookie Carson Palmer sat as a rookie Aaron Rodgers sat as a rookie Patrick Mahomes sat as a rookie Ben Roethlisberger sat as a rookie Who will be the starting QB in Vegas?👀

Inside the mind of an ex-SIG quant trader who can't turn off the EV brain - even for his kid's school choice Andrew Courtney (@andrewcourt1) ran the International ETFs Trading Desk at Susquehanna International Group for ~15 years before leaving in 2023. He now runs Kalshionomics (@Kalshinomics), a prediction markets analytics tool, and writes the Whirligig Bear, one of the sharpest prediction markets Substacks out there. "I think of everything as a bet. I kind of don't understand how you talk to normal people — they do not do that." SIG trains their junior traders with poker, spending 2hrs/day turning over cards after every hand, justifying every decision quantitatively AND qualitatively. 15 years later, Andrew views prediction markets the same way: read who's on the other side, size accordingly, fold when the whale comes back at you 10x. We cover: - Why SIG pays junior traders to play poker for 2hrs/day — & what happens after every single hand - The "one eye on the market, always" attention tax that destroys most people's careers - How to find edge in prediction markets by asking: who am I actually trading against? - Why meme-heavy, overhyped markets (Taylor Swift at the Super Bowl) might be the juiciest trades - The insider trading debate in prediction markets — & why it's "socially corrosive" - Floor trading vs. upstairs quant: why the transition saved his career - 40 connections after ~15 years at one of the world's best firms — the hidden cost of prop trading - Why he doesn't have collision insurance on his car (& the EV math behind it) Thank you so much @andrewcourt1 for coming on the pod! Timestamps: 00:00 Intro 05:00 Floor trading vs. electronic trading 06:28 What makes an upstairs trader 10:16 Poker as trader training 13:00 Thinking in bets as a mental framework 15:11 Decision trees in real life 16:40 Where prediction markets actually have edge 19:00 Why the LLM forecasting layer falls short 19:40 Liquidity incentives and trading low-volume markets 22:00 Limiting downside even when the model is wrong 24:32 Executing in illiquid markets 25:44 Fair value vs. directional conviction 27:11 Bayesian updating when liquidity responds 28:40 Fading hype and crowded narratives 31:07 Longshot bias vs. fanbase bias 34:20 How to judge whether you really have edge 36:40 Building analytics tools for prediction markets 38:20 The temporary edge for smart amateurs 40:35 Where prediction markets fit best 41:20 Markets that shouldn’t exist 43:20 Why insider trading corrodes incentives 46:52 Are prediction markets a net good or bad 50:47 Minimizing degeneracy and maximizing signal 53:32 A simple EV mindset anyone can use

“I don’t know what to build” 👉 Look through TrustMRR, ProductHunt, Kickstarter. Rip the best ideas that you’re interested in & make them better. TrustMRR *literally* tells you how much money each company makes… “I don’t know how to market” 👉 start posting on social media