Inverse Moneyball: A Qualitative Comeback

In 2002, something unusual happened in American baseball. The Oakland A’s, a small-market team, refused to compete on the same terms as the big spenders. While other clubs threw fortunes at superstar players, the A’s focused on something else. They used data. By the end of the season, they reached the finals with a budget that was only a third of the champion’s. Their unconventional approach changed baseball forever.

We are about to see this happen again. But this time, it is not about baseball.

Many of the accepted truths in fields like recruitment, marketing, and business are nearing their expiration date. A small number of people have started to notice the shift and are already moving. They are racing toward the edge of the rug, hoping to be the first to pull it out from under the rest.

This will be another Moneyball moment.

Let me explain.

The reason Moneyball became a phenomenon was that the A’s strategy seemed to appear out of nowhere. Michael Lewis, who wrote the book, often tells stories of people who notice something hidden and turn it into a major advantage. The Big Short and The Blind Side follow the same idea. What ties these stories together is a single question: Can you spot value where no one else is looking?

That is what the A’s did. They realized something others had missed. Every baseball game had a massive amount of recorded data. Instead of trusting gut instinct, they studied the numbers. They paid attention to undervalued metrics like on-base percentage and slugging stats. This changed how they judged players. They were not looking for stars. They were looking for results per dollar.

This sounds normal to us now. That is because the numbers-first mindset has become the default in every industry. Today, we measure everything. If we cannot measure it, we try to find a way to turn it into a number. We use ratings, test scores, performance metrics, and endless dashboards.

We are no longer ignoring the numbers. If anything, we are too obsessed with them.

When I say we are about to experience another Moneyball, I am not talking about finding new value in numbers. The numbers have already been mined. Everyone is using them. The next shift is in the opposite direction. It will be about rediscovering what numbers cannot capture.

Before the A’s brought in data, teams relied on talent scouts. Experienced men made judgment calls based on what they saw and felt. Players who looked the part were often overvalued. Players with awkward styles were overlooked, even if they got results. This gap between perception and performance is what the A’s exploited.

We now agree it was foolish to ignore the numbers. But maybe we have gone too far the other way. Maybe we have started to ignore "the scouts" entirely.

This is not just about baseball. It is about how we make decisions. Since the early 2000s, we have moved from intuition to calculation. From stories to spreadsheets. From judgment to metrics. In baseball, this shift made sense. The game is clear. The rules are known. The stats reflect the outcome.

But the world is not baseball.

Most areas of life are not governed by fixed rules. They are open-ended and unpredictable. In leadership, for example, not everything that matters can be measured. Some things are hard to quantify. Others are impossible. In complex environments, numbers can give the illusion of certainty. They can make us feel in control. But if we rely on them too much, they can lead us in the wrong direction.

So why do we keep using them?

Because qualitative methods are slow and expensive. They require time, attention, and human judgment. Numbers are faster. With the internet, we have access to huge amounts of data. With software, we can extract insights instantly. Quantitative tools feel efficient. Qualitative tools feel outdated.

But Moneyball reminds us that what works now may not work forever. When baseball was first invented, computers did not exist. Scouts were the best option at the time. But once better tools became available, they were not adopted right away. People clung to the old ways until the old ways stopped working.

Now we are clinging to something else. Everyone is analyzing data. Everyone is using the same tools. The edge must lie elsewhere.

The next advantage will not come from better spreadsheets. It will come from a shift in perception. We are due for a new method. One that sees what numbers cannot. One that values what cannot be easily measured.

And just like before, it will look obvious only in hindsight.

I think the emergence of AI (LLMs), has offered qualitative methods the opportunity to do the funniest thing ever...

(more on this later)

A Perceptual Revolution

The Observatory