In this episode of Professional Punters, we sit down with Evan Semet, a prediction-market trader and quant who has built real edge in some of the strangest and most inefficient corners of modern markets.
Evan studied computer science, mathematics, and economics at Vanderbilt before working across options trading, quantitative research, and systematic execution at firms including Belvedere, the Cutler Group, J.P. Morgan, GTS, and a family office.
He eventually left a traditional quant seat to trade prediction markets full time, building his own statistical-modeling and execution stack to price markets across Kalshi and Polymarket. He was recent recruited by a major market marker to run their prediction markets operation.
Evan made low five-digits a day market-making fifteen-minute crypto binaries, with Sharpe ratios in the 40-50 range. He also found major edges in election arbitrage, AI model leaderboard markets, and insider-looking prediction-market flow.
This conversation is about what it actually takes to find edge in prediction markets: the modeling, the execution, the weird rules, the toxic flow, the oracle disputes, the bankroll constraints, the trades that worked, the trades that stopped working, and the losses that teach you what the rules really mean.
2024 Election Markets
Kalshi and Polymarket both ran “who wins” contracts that settle to a dollar, but Polymarket carried a persistent two-to-three-cent premium on Trump; partly a user-base story (Polymarket’s crypto & Trump-leaning base vs. Kalshi’s). Buy the dollar for 97–98¢ across the two venues and you collect ~2% a week with only exchange risk.
The interesting part isn’t the spread, but why it survived. To trade Kalshi you needed a U.S. entity; to trade Polymarket you needed a non-U.S. one. Almost no one could put on both legs cleanly. Susquehanna potentially could of (Dublin office for the Polymarket leg, a U.S. office for Kalshi, settle internally), but most U.S. prop shops couldn’t capitalize. On a Sharpe basis a 2%/week with minimal vol is almost nonsensical, with around 280% annualized returns.
538 Presidential-Approval Markets
Evan’s first real loss came on the 538 presidential-approval markets. The play looked clean: once the week’s number prints, buy the winning bracket up to 99¢ and sell the others. He’d “bonded” 99¢ for something he was sure was worth a dollar (bonding here refers to the practice of buying high probability events and collecting “safe” payments like one would with a bond). The problem was that he assumed 538 published one value per day whereas they revised, sometimes up to three times in a day. He’d loaded up on shares at 99¢ that were worth zero.
Why didn’t the market warn him with pricing that was significantly below 99¢? Because he was the market. His own 99¢ bids were the only thing holding the price there. Someone chipped away at him periodically, but he wrote it off as a participant freeing up capital, which is a perfectly normal thing to see in these venues because the opportunity cost of capital in prediction markets is enormous (if your book returns 200% a year, you’ll gladly pay 1% to free capital two weeks early). So a steady stream of people hitting your 99¢ bid looks like business as usual, not a red flag.
Latency Edge: Scraping the AI Leaderboard
The first systematic winner was the “top AI model” market on Kalshi, priced off LMArena’s Elo leaderboard (the human head-to-head voting site). The structural quirk exploited was that LMArena pushed updated results to Hugging Face before their own front-end refreshed, sometimes a 15-minute head start. Scrape Hugging Face and waiting for a surprise would have been profitable because other market participants didn’t realize this quirk. The biggest was Gemini’s debut topping the board when nobody expected Google to be competitive (their prior model, Bard, was bad). One book-clearing print on a model update was a ~$30k click; the whole edge added up to ~$50–60k.
Mention Markets & Fine Prints
Evan doesn’t trade mention markets much, but he flagged a trader who’s made a lot of money the rules-are-the-product principle. The example given was a Bernie Sanders town hall that wasn’t carried on a qualifying broadcast, so under the literal rules none of the words said could count. If you’d read the resolution criteria, you knew the market was mispriced. Everyone arguing on the Kalshi Discord hadn’t. His view on settlement: even when an outcome feels against the spirit of a market, the exchange has to honor the written rules, which is exactly why reading them is an edge.
Bitcoin 15-Minute Binaries
The recurring 15-minute (and 5-minute) crypto markets have a single strike, wherever the previous window closed, so it’s a pure up-or-down binary over the next window.
While technically options, these are not volatility products. As time-to-expiry goes to zero, Vega goes to zero (the price barely responds to implied vol), but it’s enormously sensitive to the underlying. And for a binary that’s exactly at-the-money, Vega is literally zero. So your only edge at the money is modeling short-horizon returns; it behaves like a Delta-one product, not an options trade.
Then why not just trade spot directly? Because spot is a different sport. Market-making BTC/USDT perps on Binance puts you against Jump Crypto, Tower, Radix, which is an infrastructure arms race you will lose. The binary venues, by contrast, were still tractable with an AWS EC2 box and good Python, because the heavyweights weren’t there and the flow was price-insensitive.
The microstructure also presents some opportunities not present in spot markets”
Quote to the very end: In the final minute, many makers widen out or stop quoting entirely. Spreads blow out but Kalshi has price-insensitive takers who will cross a 20–30¢ spread in the last few seconds. That’s where the edge is fattest. But don’t quote so tight that another maker picks you off.
Adverse selection is detectable, after the fact. In post-trade analysis you can see it: if you’re marked 10–20ms after a fill, you got picked off. You can use this data to improve your modeling by looking at correlated statistics.
Toxicity isn’t linear in size. You’d think bigger fills mean higher conviction (toxic). Past a point this principle inverts; a huge fill is often just a clueless market order punching through levels because someone can’t read a book.
Multi-level quoting is a real tradeoff. Resting deep in the book catches the punch-through orders, but the back of the book is also where the most adverse fills live. We discussed an old equities project measuring slippage across dozens of quoted layers, where getting run through all of them was extremely toxic. (Caveat: equities size, hundreds of millions, isn’t five-minute Bitcoin size.)
The results: at peak, $10–12k a day. Per-coin Sharpes in the 30–50 range; aggregated across the ~7 coins he quoted, some days hit 50–90. The kind of return graph where, in his words, there’s “no intraday drawdown, it’s just a straight line up.”
Discretionary Book: Trading Against Identity
Evan’s systematic strategies were most of his volume, but he spent a disproportionate share of time on discretionary trades, because they’re more interesting. He thinks sports and elections are where people are most irrational. People are personally attached to candidates the way fans overrate their home team, so no-hope candidates always trade at some non-zero probability. And as results come in, you get a flood of flow from people buying whoever’s “currently leading” without understanding the county-by-county or mail-in breakdown.
Recent wins came from reading exactly that: Peru (Fujimori was going to take the uncounted expat vote even as her lead narrowed on screen, while Polymarket’s comment section panicked), the LA mayoral race (he just bought “no” on a candidate he saw no credible path for), and Hungary (the market implied a ~25% chance Orbán would refuse to leave / contest the result, which he found absurd, inflated partly by U.S. observers feeling sudden skin in the game after JD Vance’s association).
His noted there are only a handful of genuine election sharps on Polymarket; if they trade against you, worry, but most other flow is benign, and some whale accounts are just spectacularly bad (one down ~$5M YTD that he was happy to fade).
Unfair Resolutions
The big loss is a governance story. A market on whether streamer Clavicular would be announced to have gotten someone pregnant said “jokes don’t count”; the streamer (who’s said he’s sterile) joked about it on stream, and UMA’s resolvers called it serious and resolved YES. Evan was long ~50k “no” shares and lost ~$30–35k.
The deeper point: Polymarket settlement runs through UMA’s “decentralized” oracle, but in practice voting power is hoarded, outcomes often come down to a couple of large holders (UMA Rocks and a big whale account). Polymarket can override but rarely does, sometimes letting UMA take the heat. Oracle governance is a real, priceable risk, not a footnote.
Solo vs. Firm
A trading firm eventually recruited him off his Bitcoin work, through Discord connections, not a cold recruiter email. The biggest difference he noticed wasn’t appetite for trade risk (firms have the bankroll to size up, so a given position is relatively less risky for them), it’s their intolerance for system risk. No “vibe code looks good enough to me” on a quoter; PRs get reviewed by multiple people; compliance scrutinizes everything down to whether Perplexity is allowed on a work machine. Solo, you set your own hours and answer to no HR, some days are 30 minutes, some are till 4 a.m., but you also own all the system risk yourself.
The One Rule to Take Away
His closing advice doubles as the thesis of the whole episode. Don’t treat prediction markets as free money, there are millions of recreational bettors, but the sharps are real. When you see a price far from your fair value, first try to explain why the market disagrees with you. With a no-hope candidate, the answer might be “there’s a fan base that buys him at any price,” fine, fade it. But if you can’t explain the gap, you don’t understand the market well enough to be trading it.
That’s the through-line. The money isn’t in predicting outcomes better than everyone else. It’s in knowing exactly who’s on the other side, why they’re there, and whether the structure of the venue lets that mispricing persist.
Evan writes up more of his trades and thinking on his Substack, if you trade these markets, it’s worth the subscribe. He’s on X at @evan_semet, and his discretionary book is public on Polymarket.
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 across equities. 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.
Disclaimer: The information provided on TheLogbook (the “Substack”) is strictly for informational and educational purposes only and should not be considered as investment or financial advice. The author is not a licensed financial advisor or tax professional and is not offering any professional services through this Substack. Investing in financial markets involves substantial risk, including possible loss of principal. Past performance is not indicative of future results. The author makes no representations or warranties about the completeness, accuracy, reliability, suitability, or availability of the information provided.
This Substack may contain links to external websites not affiliated with the author, and the accuracy of information on these sites is not guaranteed. Nothing contained in this Substack constitutes a solicitation, recommendation, endorsement, or offer to buy or sell any securities or other financial instruments. Always seek the advice of a qualified financial advisor before making any investment decisions.
