A conference held in Reno and Las Vegas beginning in the 1970s brought together two groups who rarely met: professional advantage gamblers, people who made their living beating bookmakers and casinos through mathematical systems, and academic statisticians who published papers about probability and markets but would not wager a nickel on their own conclusions. The productive friction between those two groups is the origin story behind Aaron Brown‘s new book, Wrong Number: How to Extract Truth from a Blizzard of Quantitative Disinformation, and the central question it asks of any statistic or study is the same question those conferences eventually forced the academics to confront, namely whether the person presenting the finding believes it enough to put their own money on it. Brown, a longtime risk manager in the hedge fund space, joined Dan Proft on Chicago’s Morning Answer to discuss the book and what thirty-five years of making money off other people’s wrong numbers on Wall Street taught him about statistical literacy.
The conference was founded by Bill Eadington, a professor at the University of Nevada Reno who died about ten years ago, and who had the unusual idea that academics who study probability and markets should be made to sit across the table from people who actually bet on probability and markets. The gamblers included Ed Thorp, who invented blackjack card counting, and others who had developed systematic approaches to sports betting and casino games. Many of them later migrated to finance, Brown said, because finance allows you to put down much larger bets and get rich considerably faster, though the edges are smaller. The academics learned that some of the practitioners were applying serious mathematical insight and making real money doing it. The gamblers learned that some of the academics were genuinely smart and had valuable ideas, but could not distinguish their good ideas from their bad ones because they never had to pay a price for being wrong.
Brown demonstrated his own version of the practitioner’s edge in 2006 when an ESPN producer who attended the conference proposed a documentary pitting professors against advantage gamblers at predicting NFL game results against the spread. Brown built a simple system using only public data, published it openly on the internet, and bet real money on it every week for seventeen years. The system produced steady profits, roughly twelve thousand dollars over the full period, not enough to live on but enough to demonstrate that consistently beating NFL lines does not require inside information, a statistics degree, or a complicated model. It requires discipline and a willingness to act on your analysis. The professors’ claim that you could only beat bookmakers with exotic information or sophisticated models turned out to be wrong, and Brown said the more interesting finding was the obstacle he kept running into on the other side: it is not hard to make winning picks, but it is very hard to keep finding people willing to keep writing you checks when you keep winning. That is why most advantage gamblers with serious edges migrate to finance, where the institutional infrastructure exists to deploy larger amounts of capital.
He said he spent his Wall Street career making money off wrong numbers, and after retiring began applying the same analytical framework to major public policy debates through a video series at Reason, looking at gun control, climate change, and minimum wage statistics the same way he would look at a mutual fund pitch. The premise is that you do not need to be an expert to identify statistical disinformation. You need only ask whether the number being presented is even plausible on its face. He offered a published study claiming USAID saved ninety-one million lives since 2002 as an example that appeared in one of the world’s most prestigious medical journals and was amplified by the Washington Post, New York Times, Bloomberg, and NPR. The problem is that ninety-one million lives is more than the entire decline in global mortality over the relevant period, which means the claim requires believing that USAID, representing less than eight percent of total foreign aid, prevented more than one hundred percent of all global deaths. Five minutes of arithmetic reveals it is absurd, yet it circulated without challenge through major institutional channels because nobody applied the basic skepticism they would apply to a financial sales pitch.
He applied the same framework to COVID policy, saying that public health officials in the early days of the pandemic had no choice but to make guesses with incomplete information and deserve forgiveness for initial mistakes. What is harder to forgive is the failure to update as contradicting evidence accumulated, the active suppression and censorship of people raising legitimate questions, and the political theater masquerading as science that produced rules like New York allowing people to go to the beach but only in the water while California allowed the beach but not the water. He cited David Zweig’s book as documenting that Andrew Cuomo’s inner circle, which was receiving national acclaim for aggressive pandemic management, never once asked whether any of the policies were actually working or consulted a scientist about the evidence. They were making it up based on political optics and never subjected their choices to the basic test that any advantage gambler would apply automatically, which is whether the prediction is good enough to put real money behind.
Brown’s book contains thirty-one case studies of major public issues that received widespread institutional endorsement and press amplification and that a few minutes of careful thinking would reveal as complete nonsense. His prescription is not that everyone needs to become a statistician but that everyone should be willing to look at a number and ask the fundamental question: is this even possible, and would the person telling me this bet their own money on it?