The Human Element: Responding to Zay Amaro on Market Certainty
January 26, 2026
I recently read Zay Amaro’s post, "Markets, Metrics, and the Myth of Certainty," where he responds to my earlier thoughts on Kalshi. Zay makes a compelling point: while I view prediction markets as a tool for "reading reality," he sees them as a way we try (and often fail) to put a price tag on randomness. He specifically points to the "10% void"—the space where human grit and unpredictable "Black Swan" events happen.
The Illusion of the Percentage
Zay is right to warn against the "fluency illusion." When we see a contract on Kalshi trading at $0.90, our brains want to believe the event is a certainty. We treat the market as the "Oracle." But as Zay points out using NFL analogies, a 90% win probability doesn't account for the "intentionality" of a team or a locker room's character. In the world of finance and politics, this translates to the "hidden variable"—the piece of information that hasn't been "priced in" yet because it hasn't happened.
This conflict touches on a classic debate in economics: the Efficient Market Hypothesis (EMH). The EMH suggests that at any given time, prices fully reflect all available information. However, as Zay argues, "information" is often just data, and data cannot measure the human spirit. If the market is 90% data and 10% "void," then the trader who can decode that 10% has a massive advantage over the algorithms.
The Psychology of the "Black Swan"
In my previous post about mastering tendencies, I talked about "pulling out" when the thesis breaks. This is exactly what Zay is talking about when he mentions "Black Swan" events. A prediction market is an aggregation of current knowledge. It is not a crystal ball. According to Investopedia, a Black Swan is an event that is beyond normal expectations and has potentially severe consequences. Prediction markets are actually the best tools we have for identifying when people are becoming too complacent about these risks.
Behavioral Economics: Why the "Void" Exists
Why does the market fail to predict the "human element"? The answer lies in Behavioral Economics. As Nobel Prize winner Daniel Kahneman famously explored, humans are not "rational actors." We are prone to biases like overconfidence and loss aversion. On Polymarket, we see this when "Yes" shares for a popular candidate stay high even when the data suggests they are losing. This isn't the market being "wrong"—it's the market reflecting the collective bias of the traders. As Zay correctly notes, the "myth of certainty" is often a mask for collective delusion.
The Market as a Sentiment Tool, Not a Fact
I agree with Zay that we shouldn't have blind faith in the stats. However, I argue that the "beautiful, random moments" Zay looks for in sports are exactly what make prediction markets so profitable for the observant trader. If the "experts" and the algorithms are wrong because they ignored the "human element," the market price will eventually collapse or skyrocket. As traders, we aren't just betting on the data; we are betting on whether the rest of the world has correctly interpreted that data.
Zay’s perspective reminds me that while I'm looking at the "tape" and the "drift," I shouldn't lose sight of the "reality" that lives off-screen. The "cost of human relevancy" is indeed the price of being unpredictable, and perhaps the most successful traders are the ones who recognize when a market has become too "automated" and lost touch with the human chaos it's trying to predict.
Ultimately, prediction markets don't eliminate the "10% void" that Zay describes—they just force us to put a dollar value on it. And in that valuation, we find a different kind of truth: not what will happen, but what we fear might happen.
Check out the ENGL 170 Blog Network Dashboard to see more of this debate between data and human intuition.