"Leicester City are champions of the Premier League! The greatest story ever told has its happy ending… The ultimate underdog is now the undisputed top dog!"

That was the moment in May 2016 when a 2-2 draw between Chelsea and Tottenham sealed Leicester's fate as the 2015/2016 Premier League champion! The season prior, Leicester had only just achieved promotion, with chances of winning the league set at 5,000 to 1—the ultimate underdog story.

A fairy-tale run? Sure. But behind the magic was something more—strategy, data, and an approach eerily similar to MoneyBall.

The Oakland A’s and the Birth of MoneyBall

Before Leicester’s miracle, baseball had already experienced its own data-driven revolution. The Oakland A’s had just lost their top three players to free agency, leaving General Manager Billy Beane (played by Brad Pitt in MoneyBall) scrambling for solutions.

Enter Paul DePodesta, a Harvard-educated economist (played by Jonah Hill), who challenged traditional scouting. Instead of chasing expensive, big-name players, he relied on linear regression models to predict a different way of winning—identifying undervalued free agents with high On-Base Percentage (OBP) and Slugging Percentage (SLG) instead of the "outdated" Batting Average (BA) metric.

At first, DePodesta's strategy didn’t work. In the second month of the season, the A’s had a 20-25 wins to loss ratio, and head coach Art Howe refused to play the team according to DePodesta’s statistical recommendations. And can you blame him? If you were an experienced manager and someone suddenly told you, “Everything about your current approach is wrong. Here’s a spreadsheet. Do it this way instead.” Would you oblige? Probably not.

The media trashed Beane’s MoneyBall approach, describing it as an epic failure. But with the season slipping away, Beane doubled down and took a massive gamble—trading away the team’s starting first baseman, Carlos Peña, to force the coach’s hand in playing the way Beane had wanted. Left with no choice, Coach Howe finally adjusted.

The result? A historic 20-game winning streak, the longest in MLB history. The A’s didn’t win the World Series, but the impact of MoneyBall was undeniable. A year later, the Boston Red Sox adopted a similar statistical approach and went on to win their first championship in 86 years!

The MoneyBall revolution had already begun.

Could MoneyBall Work in Football?

Baseball is a numbers-heavy sport, making it ideal for statistical optimization. Football, on the other hand, is more fluid, with complex player interactions. But does that mean a MoneyBall-style approach would not work? Not necessarily.

Consider Sevilla FC, the most successful club in Europa League history (7 titles). Yet, as of March 4, 2025, Sevilla sits 12th in La Liga—far from their former glory. Post-COVID financial struggles, coupled with La Liga’s strict Financial Fair Play (FFP) rules, have severely limited the club's spending power.

So, what if Sevilla applied MoneyBall principles, to identify undervalued players through advanced statistical models, and to rebuild their squad while staying within financial constraints?

Finding Hidden Gems: The Borussia Dortmund Model

A few clubs are already ahead of the curve. Borussia Dortmund, for instance, has built a reputation for scouting world-class talent and flipping these players for massive profits.

Dortmund’s approach? Identify undervalued talent early, use analytics to predict their peak performance, and sell at the perfect time.

Unlike Dortmund, Leicester wasn’t known for shrewd player sales. But they got it right leading up to the 2016 season, using data to build a title-winning squad on what you could call a shoestring budget.

With the 17th lowest wage bill in the league (£38M), Leicester somehow outperformed big-spending clubs like Chelsea FC (£104M).

How? Smart scouting and injury prevention. They signed overlooked players like N’Golo Kanté, whose elite interception stats made him the perfect fit for the physically demanding Premier League. Purchased for €9M, Kanté became a key part of Leicester’s title run before being sold for €35.8M—a 398% return.

But the club also used a data-driven fitness tracking approach to minimize injuries, ensuring their best XI stayed on the pitch. The result? A historic title win that defied all logic. Except the logic of statistics!

Other Applications of MoneyBall?

Imagine the application of similar principles in basketball. The Los Angeles Lakers, for example, are chasing their 18th NBA championship, led by LeBron James and Luka Dončić.

Could a MoneyBall-style statistical approach identify the missing pieces needed to secure the Los Angeles Laker another ring? Maybe that’s already been done, and, if so, then this might be the year that LeBron "The Goat" James claims his fifth and potentially final NBA championship.

MoneyBall is Really Value-a-Ball!

At its core, MoneyBall is about maximizing value. The same applies in sports, business, or investing—success comes from finding hidden opportunities, making data-driven decisions, and identifying undervalued assets, before the market catches on.

So, who will be the next team to rewrite history with MoneyBall? Time will definitely tell!

Who made the first apple pie? Where did it originate? Honestly, I have no clue! But you know who might? Lauren Cabral from BackThenHistory. Here’s what she has to say:

“Like apples, apple pie isn’t as American as you might think. It actually originated in Europe, influenced by multiple culinary traditions, including British, French, Dutch, and Ottoman cuisines.”

An anonymous Due South writer adds: “The earliest recorded apple pie recipe dates back to 1381 in a cookbook called The Forme of Cury, compiled by King Richard II’s master cooks.”

So, apple pie was first documented in a royal cookbook? Makes sense! Back then, rich and extravagant flavors were reserved for the elite. But does it really matter where apple pie came from? What truly matters is that this sweet and savory dish—apples blended with sugar, flour, and other ingredients—has become one of the world’s most beloved treats.

Where to Begin?

To create our version of this classic treat—once exclusive to the elite but now mastered by a select few—we’ll blend financial apple instruments, sugar-like correlations, and statistical flour packages. Then, we’ll mix these ingredients in our integrated development environment (IDE) bowl and bake them using our application programming interface (API) oven, hoping to serve up some profitable results in the markets.

As newcomers to the business, we first need the right setup. Our tools of choice? PyCharm as our IDE bowl and Interactive Brokers’ Web API as our API oven. (For context, an IDE is where we write and run code, while an API connects us to live financial data from our brokerage account.)

The Challenge: Competing in the Market

Pie markets, like financial markets, are volatile. There are countless pies and even more pie makers, making it tough to stand out. Powell’s Pies will be competing with seasoned players who dominate the industry—J Pie Morgan, MorgPie Stanley, and GoldPie Stacks.

But what if Powell’s Pies received a $100,000 investment? Could we compete then? Maybe. Let’s consider how another pie business, Samy’s Pies, thrives.

A Strategic Approach: Lessons from Samy’s Pies

Samy’s Pies sells year-round but sees peak revenue during the holidays. By offering seasonal specialties—apple peach pies in spring and green apple pies for St. Patrick’s Day—Samy capitalizes on untapped market segments.

Algorithmic trading follows a similar logic. Competing with financial giants requires a unique strategy, much like running a new pie shop—leveraging available capital to exploit overlooked opportunities. Sounds like Business 101!

Finding Profit in Market Movements

Matt Levine’s Bloomberg newsletter, The SEC Was Busy Last Week, explains a simple trading strategy:

1. Most investors hold 60/40 portfolios—60% stocks, 40% bonds.

2. If stocks rise 2% and bonds fall 2%, the portfolio shifts to 61/39.

3. To rebalance, investors must sell stocks and buy bonds.

4. This buying and selling impacts market prices, creating an opportunity to profit.

A clever approach to pie sales follows the same principle—sell directly to pie companies that struggle to meet demand at certain times. Ethical? Maybe. Profitable? Definitely.

The Dark Side of Sweetening the Pot

Levine also discusses the SEC’s enforcement case against Two Sigma, a quantitative hedge fund. Two Sigma researchers allegedly manipulated their firm’s algorithmic trading model, prioritizing their own signals to boost performance and increase their personal payouts.

Hedge funds rely on trading signals—ingredients that enhance their strategy. These signals feed into an IDE, guiding investment decisions based on factors like market trends or even political events, such as a Trump tweet affecting the U.S. dollar.

In Two Sigma’s case, researchers manipulated correlation—a measure of how assets move together—to artificially boost returns. Their actions led to a $400 million gain for some funds but a $165 million loss for others. While the firm netted a $235 million profit, the deception harmed investors in the underperforming fund. As a result, Two Sigma paid $165 million in restitution and an additional $90 million to the SEC, bringing their total penalty to $255 million. That’s a costly lesson in ethics.

The Key Takeaway: Balancing Ingredients for Success

In trading, as in baking, too much correlation (or sugar) can be harmful. Manipulating data to obscure true risk is even worse. If Powell’s Pies advertised a specific sugar content but secretly overloaded its recipes, regulatory bodies—let’s call them the PIE-SEC—would step in.

This example highlights a crucial principle: The weighting of our algorithmic trading ingredients matters. A well-structured portfolio with low correlation among assets leads to optimal risk-adjusted returns.

In our next post, we’ll explore how to achieve this ideal balance. Until then, let’s keep our pies—and our trades—both sweet and sound!

America has some of the most advanced medical technology in the world—yet its healthcare system is notoriously expensive, inefficient, and inaccessible. Every year, millions of Americans struggle to afford basic care, while hospitals and providers battle skyrocketing costs. Sure, cutting-edge treatments and high-tech interventions improve patient outcomes, but at what cost? And more importantly, who gets left behind?

Rising medical costs are fueled not just by inflation, but by expensive technology, costly medications, and growing provider wages. [1] Without serious innovation, the U.S. will continue to fall behind peer nations in life expectancy and overall health outcomes. [2]

So, what’s the solution? Smarter technology, not just more expensive technology. By leveraging tools like telehealth services and Care Operating Systems, we can save lives, reduce costs, and make high-quality healthcare accessible to more people.

In this post, we’ll dive into real-world examples of how health technology is cutting costs without cutting care—and why the future of healthcare depends on getting this balance right. Let’s explore what’s working, what’s next, and how these innovations can reshape American healthcare for the better.

A Brief History of Health Technology

One of the earliest health technologies, the Electronic Health Record (EHR), was first implemented at the Mayo Clinic in Rochester, Minnesota, in the 1960s. However, due to its high costs, widespread adoption was limited and required government collaboration. [3] It wasn’t until 2004 that the U.S. made a concerted push toward digital healthcare records. President George W. Bush appointed Dr. David Brailer, MD, PhD, as the nation’s first National Health Information Technology Coordinator, with the goal of achieving widespread interoperability of EHR systems by 2014. [4]

By 2013, under Dr. Brailer’s leadership, 40% of non-federal acute care hospitals and one-third of office-based physicians had adopted EHR systems. This success laid the foundation for further advancements in digital healthcare, including telehealth services, remote patient monitoring, and medical billing software. [5]

Over time, these innovations have not only improved patient care but also increased operational efficiencies, reducing overall healthcare costs while enhancing access to quality services. Numerous studies have explored the financial and clinical benefits of these technologies in hospitals, urgent care clinics, and other healthcare facilities.

What the Research Says: Telehealth Saves Lives and Reduces Costs

A 2016 study titled “Cost-Benefit Analysis of Telehealth in Pre-Hospital Care” evaluated the impact of the ETHAN telehealth system, which triages eligible patients who do not require emergency department transport. The study found that this system saved $928,113 annually for patients and providers in Houston, Texas. Using the study’s cost-saving formula, I developed a Telehealth Cost Savings Model to estimate the potential financial benefits at the state level. The results indicated that telehealth programs could reduce state healthcare expenditures by up to 16.22% annually, with states like Maine and Nevada experiencing savings of over 10%.

Similarly, a 2023 study, “Costs and Cardiovascular Benefits of a Fourth-Generation Synchronous Telehealth Program on Mortality and Cardiovascular Outcomes”, examined the impact of telehealth on patients with Atrial Fibrillation (AF)—a heart condition that causes irregular, rapid heartbeats. Patients in an advanced telehealth program had their biometric data monitored by nurses and on-call cardiologists 24/7. The results showed reduced mortality rates and lower stroke risk at a comparable cost to traditional care. [6]

These studies highlight the significant potential of telehealth systems to improve patient outcomes while reducing healthcare spending.

Recent Innovations: Care Operating Systems Enhance Efficiency and Patient Care

Inspired by operational advancements in manufacturing, nuclear energy, and commercial aviation, Care Operating Systems have emerged as a way to streamline provider workflows and enhance care delivery. These systems use clinician feedback to develop automated, structured workflows, reducing administrative burdens and allowing providers to focus on patient care. [7]

For example, Jefferson Health’s Abington Hospital (Pennsylvania) implemented the OnPoint Care Operating System to reduce safety incidents and improve patient engagement. Similarly, Prisma Health (South Carolina) and the Greater Baltimore Medical Center (Maryland) have adopted Care Operating Systems to enhance efficiency and lower costs. By reducing operational inefficiencies, these systems help lower provider expenses, enhance patient experiences, and ultimately improve healthcare delivery. [8]

Challenges and Considerations for Widespread Adoption

While health technologies offer substantial benefits, they also come with challenges:

Addressing these challenges requires improving the implementation process, conducting more studies on financial and patient benefits, and securing additional funding to support technological adoption.

Final Thoughts: Investing in Smarter Healthcare Solutions

While medical technology innovations often drive up costs, other technologies can reduce operational overhead and improve care quality while maintaining or lowering overall expenditures. By investing in cost-effective solutions like telehealth and Care Operating Systems, the healthcare industry can expand access, improve patient outcomes, and reduce financial burdens on both providers and patients.

These innovations have the potential to revolutionize U.S. healthcare, making high-quality care accessible to those who need it most. Now is the time to prioritize scalable, efficient solutions that enhance care without inflating costs.

We’ve all faced tough times. Many factors contribute, both external (war, economic instability, environmental disasters) and internal (mental, emotional and physical health challenges). Challenging times make it hard to succeed, and experiencing failure only exacerbates the problem. Resulting negative emotions may lead to procrastination—resulting in drops in productivity, which only contribute to future stress. [1]

Difficult times, failure and resulting negative emotions may cause us to fall behind in achieving success in our professional or personal goals.

Some of us may bounce back fairly quickly, but others, not so much. This post is for the latter: people who struggle to move forward, especially when navigating difficult times.

“Positive Override It”

Billy Oppenheimer speaks of legendary filmmaker and director, Quentin Tarantino’s experience with disappointment:

Tarantino goes on to say “I was under the impression that we were making this really amazing thing”, “It was my dream project…And it ended up being nothing. Absolutely nothing!”

After brooding for a few weeks Tarantino rewatched his film, noticing the final scenes showed noticeable improvements.

When speaking of his previous failures, Tarantino mentions realizing he could “positive override” his initial reactions, resulting in two things:

1. Tarantino could transform feelings of disappointment into a positive outlook—allowing him to recognize areas of improvement.
2. Tarantino learned to view failures as learning opportunities—in this case, how not to make a movie. [2]

Learning from his failure, Tarantino would go on to produce record breaking films, including the widely acclaimed, Pulp Fiction and Reservoir Dogs. Over the course of his career, receiving 171 wins and 284 nominations in movie awards across the globe, Tarantino is now renowned as one of the greatest directors and screenwriters of modern times.

Psychologist, Dr. John Gottman, coined the term, “Positive Sentiment Override”, describing couples’ reactions to their partners when experiencing adversity. From his study:

Applied to oneself, “positive [sentiment] overriding” one’s reactions can help in navigating difficult times. Negative emotions can result in feeling hopeless, making it difficult to continue working towards our goals. “Positive overriding” our initial reactions can help us recognize our strengths and identify the next step towards achieving our goals.

Recognizing that we are in a negative headspace is one of the first steps to “positive [sentiment] override”.

“A negative headspace is more than just a bad mood; you might feel like a failure, consumed with thoughts about how things didn’t go your way or with stress about the future—the results of which include negative self-talk and low self-esteem, causing us to feel frustrated and unhappy.”

When experiencing negative emotions in difficult times, it may help to ask [some of] the following questions:

1. What strengths did I display?
2. What was I most proud of?
3. What did I learn not to do for the next time?
4. How would/could I do it differently?

Not shying away in difficult times (while continuing to make progress) combats negative emotions and inspires us to believe in ourselves. Eventually, successfully navigating troubling times then becomes a matter of patience and consistency.

As we trust ourselves more, (seeing that we’re able to turn adverse situations into opportunities for success) we end up procrastinating less as we become more excited and less scared of what the future might hold.

“Positive override” can help us make steady improvements towards achieving our future goals—not by being overly optimistic, but by facing difficult situations with a positive and opportunistic mindset. Rather than continuing to be hard on yourself for failing, experience the feelings of disappointment and challenge yourself, to notice the strengths you currently possess. “Positive override” your initial reactions in times of disappointment, to efficiently and effectively move towards your goals.