Moneyball vs. Battle-Scars - May 1, 2026

Do You Go With What The Numbers Are Telling You, Or Do You Rely On Your Gut Instinct

One morning this week I was having a coffee in my living room, trying to decide what my weekly article was going to be about.  I glanced over at some books sitting on our table.  One of those books was “Moneyball”, written by one of my favourite authors, Michael Lewis.  I’m a big fan of this book, and an idea for an article popped into my head - the use of "Moneyball” vs. the reliance on “gut-instinct” to make decisions.  

Let’s start by establishing a common understanding of what "Moneyball" is.  Moneyball is the idea of using data analytics and statistical modelling to find undervalued assets that traditional evaluation methods overlook.  The core concept is to use applied statistics to find real information, where conventional wisdom relies on “noise” or potentially misleading data points. Data analytics are applicable almost anywhere humans make evaluative judgments under uncertainty. The limitation of analytics is usually the amount, or accuracy, of the data availability, and the willingness of organizations to trust the numbers over an experienced expert opinion.  

Seeing the book on my table reminded me that while I have a lot of respect for data analytics, I have relied on my experiences and my gut feeling for people when making decisions, and that this approach has served me well.  I do seek out data when making decisions, and I don’t ignore what the data is telling me, and, but I’m not one to let data analytics override what my experience and instincts are telling me.  Nor do I allow myself to suffer from “analysis paralysis”, where decision-makers can become overwhelmed by data, or keep seeking additional data, because they are afraid to make a decision based on what they do know.  Being a good leader means making timely decisions, based on your experience and whatever information is available, in a reasonable amount of time.  

Michael Lewis popularized “Moneyball” and data analytics with his book, and subsequent movie of the same name, by telling the story of the Oakland A’s professional baseball team management, namely General Manager Billy Beane and Assistant General Manager Paul DePodesta, and their use of data analytics in running the team’s operations.  Since then many teams, not only in Major League Baseball, but the three other major professional sports leagues (the NHL, the NFL and the NBA) have now adopted data analytics as a big part of their management philosophy.  

While Michael Lewis may have popularized “Moneyball”, it is Bill James who is the central figure in the origins of data analytics, and in particular to its use in sports.  Starting in the late 1970s, James self-published his Baseball Abstract annually, developing an entirely new framework for evaluating player performance using statistics. He coined the term sabermetrics (after SABR, the Society for American Baseball Research) and spent decades arguing that conventional baseball wisdom was riddled with bad metrics. Almost everything the book and movie "Moneyball" celebrates, actually flows from James.  Lewis found a compelling story,  an underdog small-market team, the Oakland A’s, beating the well-funded New York Yankees, through brains over money. Lewis used this narrative as a way to explain ideas that had been circulating in baseball's analytical underground for 20+ years. Bill James had a cult following but was largely ignored by MLB establishments. Lewis brought these ideas to a mass audience, with his book and the subsequent movie, in 2003.  Lewis himself has been candid about this. His skill is identifying interesting intellectual stories already happening in the world and translating them for general audiences.  He did the same thing with the subprime mortgage crisis (The Big Short), behavioral economics (The Undoing Project), and government dysfunction (The Fifth Risk).  Lewis gave the concept its catchy name, “Moneyball” and its cultural moment. The ideas themselves had been around for decades.

The use of data analytics has reached other industries as well, such as:

• Finance and Investing - finding mispriced securities and undervalued companies, using data rather than gut instinct.

• Politics & Campaigns -  The Obama campaigns of 2008 and 2012 pioneered data-driven voter targeting and resource allocation, identifying persuadable voters others were ignoring. Noted pollster Nate Silver's electoral modeling is another proponent of data analytics.

• Hiring & Human Resources - Companies such as Google have used structured, data-driven hiring to reduce bias and find talent that traditional résumé/interview screening misses. The idea of "hiring for skills over credentials" is Moneyball logic.

• Medicine & Healthcare -  Using large amounts of data to identify which treatments actually work vs. those which are just conventional wisdom.  Evidence-based medicine, predictive diagnostics, and hospital resource allocation may now all follow this pattern.

• Entertainment - Netflix and Spotify use data analytics to approve content and identify underserved audience tastes. The popular drama House of Cards is cited as an example of a data-driven production bet made by Netflix.

• Military & Intelligence -  Predictive analytics for threat assessment, logistics optimization, and personnel decisions have been applied extensively, particularly after the early 2000s.

• Real Estate -  Algorithmic property valuation (Zillow's Zestimate, iBuyers) and identifying undervalued neighbourhoods before gentrification are other examples of data analytics in use.

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“Moneyball” and data analytics isn’t loved by everyone.  The objections come from several different directions, some intellectual, some more defensive; they include:

1. "You're Measuring the Wrong Things"

This is the strongest critique, and it comes from the data science world itself . Metrics capture what is easily quantifiable, but the most important variables are often the hardest to measure.  Those being: leadership, clutch performance, locker room dynamics, coachability, how a player responds to adversity. If a model omits these, you're optimizing for an incomplete picture. The danger is mistaking measurable for meaningful.

2. "The Market Corrects Itself"

The core “Moneyball” principle, finding undervalued assets, only works if others aren't doing it yet. Once everyone adopts the same analytics framework, the inefficiencies disappear. This has happened in major league baseball: on-base percentage, the key undervalued metric in the original “Moneyball” story, is now universally valued. The arbitrage evaporates. Teams are now in a race to find the next undervalued thing, which gets progressively harder.

3. "Small Sample Sizes"

Sports seasons, business quarters, political cycles; none of these generate enough data to be statistically reliable about many of the conclusions analysts draw from it. Randomness gets misread as a signal. A player has a great year; is that a real trend or noise? Analytics can create false precision around inherently uncertain occurrences.

4. "Human Beings Aren't Reducible to Data"

This is the traditional objection, and while it's sometimes used defensively by people protecting their turf, it contains some real truth. Players, employees, voters, and patients are complex adaptive entities who respond to being measured, gamed, and managed in ways that confuse the models. Teaching to the test is the educational version of this; optimizing for the metric degrades the underlying thing the metric was supposed to capture. This is sometimes called Goodhart's Law: when a measure becomes a target, it ceases to be a good measure.

5. "You're Losing the Experts"

Experienced scouts, coaches, doctors, and business managers have extensive knowledge built from thousands of hours of direct observation, that is genuinely hard to capture in data. Dismissing this expertise entirely, which aggressive analytics cultures sometimes do, means throwing away the real signal. The best organizations find ways to integrate quantitative and qualitative judgment rather than replace one with the other.

6. "It Creates a Monoculture"

If every team, fund, or company uses the same models, they converge on the same strategies, which makes everyone simultaneously vulnerable to the same blind spots and shocks. The 2008 financial crisis had elements of this: everyone's risk models were built on similar assumptions, so they all failed together. Diversity of approach has systemic value that pure optimization ignores.

7. "It Dehumanizes People"

In hiring, medicine, criminal justice (predictive policing, recidivism scoring), and education, reducing people to data profiles can build-in existing biases at scale, strip away individual context, and make consequential decisions feel void of responsibility. "The algorithm said so" becomes a way of avoiding accountability. This critique is especially powerful in high-stakes domains outside sports.

8. "It Optimizes for the Short Term"

Analytics tends to reward what's measurable now, which often means sacrificing long-term development, relationship-building, or resilience for short-term performance metrics. In business, this shows up as quarterly earnings pressure undermines R&D investment.

9. The Meta-Objection

Perhaps the deepest critique is that data analytics can create an illusion of certainty in areas that are fundamentally uncertain. The confidence that comes with a well-constructed model can be more dangerous than acknowledged ignorance, because it stops people from asking whether the model's assumptions are right in the first place; knowing to ask these questions comes from prior experience.  

The Toronto Maple Leafs of the NHL recently fired their General Manager, after the team failed to qualify for the 2026 playoff.  In a press conference held after the firing, the team’s President came out and said, "Our next General Manager will be a numbers guy, someone who believes in data analytics”, in a way blaming the team’s failure to make the playoffs this season on their prior lack of use of analytics. Some Leafs fans would argue the opposite; that management at the time was measuring the wrong thing, and didn’t place enough emphasis on valuable intangible factors, when making some of the personnel decisions they made in prior off-seasons (ie - whom to keep, whom to trade away).  Nonetheless, this example emphasizes the pervasiveness of “Moneyball” in professional sports.  

Data analytics doesn’t measure intangibles, such as: compete factor, heart and leadership qualities.

There is room for both data analytics and reliance on your hard-earned experiences.  “Moneyball” methods can be used to support experience-based decision making.  But the numbers alone won’t help you back-fill for a lack of experience in identifying the valuable intangible qualities of human, financial and physical assets.