Excerpt

Three years out of a PhD in physics in 1953, John Kelly, Jr. published a breakthrough paper about insider information in horse racing in an unlikely place: the Bell Labs Technical Journal. By the time it was in print, the paper’s title had been scrubbed of its references to gambling—the AT&T executives didn’t care for Bell Labs to be so directly associated with horse racing—but the content remained. Kelly had not just cracked the mathematics underlying a type of gambling, but he had also revealed deeper patterns about the nature of prediction.

When the odds posted by the track are different from the odds determined using insider information, Kelly’s formula explains how to take those differences and place the best bets possible, mathematically speaking. The formula is powerful in its simplicity. It tells us to put money on every horse for which we have an informational or statistical edge, and then calculates exactly what fraction of our bankroll to bet on each horse, depending on the strength of that edge.

While this basic idea had long been known—the larger the difference in the track odds and the real odds, the bigger the opportunity for the gambler—Kelly quietly revolutionized the practice of prediction by writing down the optimal exchange rate between knowing something that others do not and the benefits of that knowledge.

Today, racetracks are less popular, but the principles remain the same. Asymmetries in the power to predict the future statistically are the bread and butter of finance around the world, for example. But predictions underpin more than financial markets alone.

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