In this episode of Professional Punters, we sit down with Sam Rosenstrauch and Jude Rizzo, two quants, and friends since high school, who spent years betting profitable on sportsbooks.
Jude Rizzo studied math, physics, and machine learning at Cornell and is a Stuyvesant alum. He co-founded Carousel Technologies (YC W24), which used AI to build financial models, and was acquired by the market-intelligence platform AlphaSense in October 2025. Before Carousel, he had a brief experience as a quant at Jump Trading.
Sam Rosenstrauch earned his BS in Math from Carnegie Mellon, where he placed top 200 nationally on the Putnam, and qualified for the USA Math Olympiad in high school. He traded options at Susquehanna (SIG) and worked as a Quantitative Research Analyst at Goldman Sachs. He’s now an engineer at Espresso AI, a frontier neo-lab building ML-driven compute optimization.
This conversation is about where edge actually comes from in sports betting markets: the NFL-drive latency trade that turned a small stake into 20x in a couple of months; the half-second window they exploited on hockey goals; the cat-and-mouse of staying under a book’s limits; why Pinnacle welcomes smart money when everyone else bans it; books you bet against versus exchanges like Kalshi and Polymarket; and how life eventually led them out of trading.
The Drives: 20x on a Half-Season
Jude is a lifelong Jets fan, which mostly means he has spent years watching his team punt. During one of those punts he pulled up the sportsbook odds out of curiosity: what’s the real probability the Jets turn this drive into a touchdown? (Low, always.) But looking across books with Sam, something else stood out: during a live game, the price of a given outcome didn’t line up across DraftKings, FanDuel, and the rest.
The mechanic was latency. On live betting, one book moved first, usually DraftKings, then FanDuel, then everyone else followed, and PointsBet in particular would sometimes just not move in time. So you could bet on something that had effectively already happened and still get stale odds.
Consider an example: a drive opens with the punt at −200, the field goal at +300, the touchdown at +200. As the drive develops you watch DraftKings reprice; the touchdown drifts from +200 to +100, which after the vig (the cut the book bakes in to tilt every line slightly in its favor) implies roughly 45%. Meanwhile PointsBet still has the touchdown at +200, about 33% implied. You’re buying something worth ~45% (if you believe that the line moved for good reasons) for ~33%.
Two things made it more than a one-off. First, drives happen many times a game and there are many games a week, so the edge was testable and repeatable enough to size into without betting the account on a single outcome and losing to variance even when you’re right. Second, a drive outcome is a side market. The book concentrates its attention on the money line (a straight bet on who wins) and the point spread (a bet on the margin of victory), where the volume is. The side markets are softer, so the usual assumption that “the book is right” is weakest exactly where you’re trading.
They sized by feel using the Kelly criterion, the formula for the size that maximized your rate of return over repeated bets. (Bet everything and you eventually bust even with an edge; bet too little and you leave money on the table; Kelly is the mathematical optimal). In practice they couldn’t be precise due to the time-sensitive nature of the opportunity, so they kept a small table of common American-odds cases and guesstimated live.
It compounded fast. At ~20% edge pre-vig, closer to 10% post-vig, and pressing hardest whenever the value gap was widest, the stake 20x’d in a couple of months: “basically one half of a season.” Then PointsBet caught on. Which, as it turned out, was the pattern for everything that followed.
Building OddsJam for Themselves
The drives play was one instance of the real engine. Their bread and butter was a giant sheet of every book’s odds across every market: “we basically built OddsJam for ourselves [before OddsJam]” scanning for arbitrage and, more usefully, near-arbitrage. A true arb is when two books disagree enough that hedging both sides locks in a profit; a near-arb is the more common and more interesting signal, because the very existence of the gap makes it more likely that at least one side carries edge.
The hard part is deciding which side is wrong. They started with consensus as a prior, if every book has a team at 40% and one suddenly prints 50%, the crowd is probably closer to the truth than the outlier.
Over time they learned book-specific tendencies: for a stretch you’d find softer underdog odds on FanDuel; BetMGM was consistently off from everyone else on basketball quarter point-spreads. Different books, different algorithms, different blind spots shown to the user to exploit.
Size Limits From Sportsbooks
The first thing to understand about beating a sportsbook is that the book is built to win, and it has defenses. It won’t usually ban a winner outright, but it will limit you. The algo flag your account and suddenly you can only put $8 on a given line. There’s a livable zone in between, smart enough to win but small enough not to trip the wire, and the pair lived there for a while before getting throttled.
The exception is Pinnacle, the sharp offshore book that does the opposite of everyone else: it takes smart money, even publishes an API for its odds, and uses that flow as signal to keep its spreads tighter than anyone’s. As Sam put it, it’s a skill issue.
Books vs. Exchanges
Underneath all of this is a structural split. One kind of book: DraftKings, FanDuel, the US incumbents that were basically the only legal option before Kalshi, has you betting directly against the house. The book doesn’t want you to win; it’s incentivized against you, and it makes its money on the vig. The other kind is an exchange; now Kalshi and Polymarket, where you’re betting against another person and the venue genuinely doesn’t care who wins.
The against-the-house model appears to mostly a regulatory artifact. Everywhere else in finance the two jobs are split: exchanges aggregate liquidity (and the liquidity is the moat; everyone wants to trade the S&P where everyone else trades it), while front ends like Robinhood, Schwab, Interactive Brokers and E-Trade compete for users on top.
User acquisition and liquidity provision are different businesses, run by different companies, for a reason. Sportsbooks fuse them only because the law made them, and now that exchanges like Kalshi and Polymarket exist, they’re pulling flow off the books (and rightfully so, as they are more open and transparent venues).
Hence the irony of DraftKings running ads about how Kalshi isn’t a “real” financial contract, and a founder complaining on Twitter that sizing up on Kalshi walks him through the order book, coming from a business that limits anyone who wins. On an exchange both sides of a trade are happy at the moment it prints; a book that throttles winners while lecturing the exchanges is hard to take seriously. It also set up the next trade; because Kalshi, unlike the books, doesn’t lock its market during a game.
The Half-Second Edge: Hockey Goals on Kalshi
That Kalshi keeps its book live during a game was the whole opening. Some traders leave resting orders up while a major event happens on the ice, because they haven’t seen it on the broadcast yet. That gap is the trade.
To find out how big the gap was, they ran an experiment instead of guessing. They pulled up a Rangers game, coded a scraper that read Kalshi’s WebSocket messages to watch the order book tick by tick, and grabbed a stopwatch app. The moment they could visually react to a goal, they hit the button to timestamp their own reaction; afterward, they lined up their click times against when the market actually moved. The answer: about half a second of wiggle room.
Hockey wasn’t a random choice; it’s close to the optimal sport for this trade. The edge is a latency advantage, and a latency advantage is worth the most when the event it front-runs swings the odds the most and arrives unambiguously. A hockey goal does both. There’s essentially only one event that moves the line (a goal), it moves it a lot, and there aren’t so many goals that any single one is diluted.
Basketball scores constantly, and the game is long enough that no single basket moves much; baseball events (a double, a triple) are harder to interpret in real time. Hocky is nice because there’s no skill in judging whether the puck crossed the line, unlike a jump shot still in the air. That matters because Kalshi’s taker fees (what you pay to cross the spread and hit a resting order) top out around 7% on a sliding scale, so you need a lot of clean edge to clear them.
The ceiling is set by depth, not skill. Pregame, the book is millions of dollars deep; the instant the game starts, that depth collapses. You might get $1–2k down on a goal, maybe $5k on a big game if you work it, but there’s no putting a million to work. Jude’s estimate for what the whole hockey program could earn was mere a few hundred thousand dollars a year. Whether it still works he isn’t sure; the arb may barely exist now, though he’d bet it’s there for anyone willing to grind it.
The 50-Second Mirage
Through a friend, Daniel, who sells data infrastructure to trading firms, the host had come to see that some large firms, Jump among them, only had access to the same broadcast as everyone else which is running about 50 seconds behind live (on select games). Graph the point-spread orders around a goal and you can see it: a tight cluster of small orders over roughly five seconds (people reacting live) then the same cluster again 50 seconds later, bigger, from people on delayed feeds who don’t know they’re behind. Sam confirmed it matched what they saw on Kalshi exactly.
The microstructure that falls out of this:
The first ~5-second cluster is live reactors
The second cluster, ~50 seconds later, is delayed-broadcast money trading a goal that’s already old news.
The smart money realizes the move is in, it fades the second cluster; the late buyers pay a premium and the level evens back out.
Once everyone knows to buy when the goal lands, the market overcorrects, and the natural move is to sell into the overcorrection. Instead of crossing the spread and eating the taker fee, provide liquidity when your live information is better than the market maker’s. Basketball is the clean case: someone watching live knows whether a late basket was a two or a three, while a market maker on a delayed feed does not.
Why There’s Edge on the Table
The recurring question underneath every one of these trades is why the edge survives at all; why the biggest, best-capitalized firms don’t simply take it? Mostly, they can’t be bothered. Imagine you’re Jump: do you care about $1,500 on a hockey game when your trading bot makes a million dollars a day? The compute tokens and trader salaries (upwards of millions a year) aren’t worth it.
Comparison to Options Trading
Scale the same thinking about benign flow and dislocations that it causes, and you arrive at the options market, where the players are larger but the game is identical.
The same instinct, work out exactly who’s on the other side and why, is what professional desks run on, and the guests were candid that the trades there are often less sophisticated than outsiders imagine.
Start with what you can see. After you trade an option, depending on which of the roughly thirteen OCC options exchanges you’re on, you can often see your counterparty, and if it’s a known market maker (Jane Street, Citadel Securities, SIG, Optiver, IMC), you get out of the way, because you assume they know something.
The simplest money came from watching a maker telegraph an institutional order: buy a level up in small clips, then a giant block prints where they sold that high level to a pension fund; or sell it down in twos and fives, then a block prints where they bought from an institution. (To the CFTC this isn’t manipulation; the maker is merely pre-positioning the impact the order would have had.)
Then there’s forced flow, the cleanest edge of all, because it’s on a calendar. Covered-call ETFs, funds that systematically sell call options against their holdings, have a mandate to sell a fixed number every week; come Friday, if a fund has only sold ~60% of its quota, market makers push implied vol down to buy those calls cheaply, knowing the seller has to show up.
For funds that sell calls in enough size it gets blunter: instead of moving vol you can move spot. And this isn’t a small-cap curiosity; it happens on the S&P 500 and ES futures (the S&P’s flagship futures contract). A large block of calls hits in the morning, the S&P ticks down half a percent, certain makers absorb them, and it ticks right back up. Index rebalances are the same idea; enormous, scheduled, entirely predictable flow, and, as the host noted, the biggest provider of that trade may not be the market makers at all but a particular team at a multi-manager (a fund running many independent trading pods under one roof).
The same predictability, the host added, is why companies stay private longer: index funds are forced to buy at IPO, so the move is to grow as large as possible in private markets and then list at a rich valuation the index has no choice but to fund.
From Trading to Startups
Both of guests eventually stopped trading and started building. The skills carried over more directly than you’d expect.
Sam describes his work at Espresso AI: optimizing customers’ compute, as feeling like trading a live game. You’re forced to make fast routing decisions, and you can make them decently good by layering ML on top of interpretable heuristics. The edges even feel like trading. Snowflake bills on a 60-second minimum, so a warehouse switched on for one second and idle for the next 59 still bills the full minute, and batching compute tasks allows cloud bills to therefore be lowered by an order of magnitude. This sort of attention to detail and the fine print is exactly the kind of structural quirk they used to hunt on a sportsbook; now hunted on a cloud bill.
Jude’s company, Carousel, puts AI into Excel. A banker-quality spreadsheet holds far more context than it appears to using formatting that is both load-bearing and ruinous to token budgets, so reading every cell naively doesn’t work. They ended up reading “bricks” of a file (connected units of meaning, not just formulas) and building a dependency graph the hard way, since you can see what a cell references but not what references it until you’ve walked the whole sheet once. Again, the skills of attention to detail and systematic thinking came into good use.
The One Rule to Take Away
Step back, and every edge in the conversation has the same shape. A puck crosses the line and for half a second the order book hasn’t caught up. A broadcast runs fifty seconds behind and a second wave of money trades a goal that’s already history. A covered-call fund has to sell by Friday, and the price moves before the flow arrives. None of it was about predicting the game or the price better than anyone else: a hockey goal is unambiguous, a rebalance date is on a calendar. The edge was being the one person standing in the gap between a fact and its price. The catch is that the gap always closes over time. PointsBet caught on and eventually quant money poured into Kalshi.
Jude now builds at AlphaSense and Sam designs core algorithms at Espresso AI. They're two of the sharpest people I've had the pleasure of knowing, and also two of the most genuinely enjoyable to talk and work with.
Professional Punters is presented by Freeport Markets: Freeport lets users trade 24/7, weekends included, with no KYC and up to 200x leverage across stocks, indices, commodities, crypto, rates, and more, with access to pre-IPO exposure in companies like OpenAI & Anthropic. For traders who already follow sharp sources across Twitter, Substack, hedge fund filings, corporate insider activity, and political trading disclosures, Freeport uses AI to read the sources they trust, connect the dots across markets, and surface trade ideas as narratives develop.
About the Host: Lihong Wang is the founder and CEO of Freeport Markets. Before starting Freeport, he traded discretionary semiconductor names at quantitative market-making firms including Jane Street and IMC Trading, building both systematic and discretionary strategies. Lihong graduated from Duke with a degree in mathematics and statistics, and has raised over $2.5M to build Freeport from investors including Y Combinator, Alliance DAO, and Informed Ventures. For more of his long-form research, visit freeportlogbook.substack.com.
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