Hello, and welcome to The Investor’s Bookshelf.Today’s book is The House Advantage: Playing the Odds to Win Big in Business.So, what exactly is “house advantage”? Simply put, it’s a way of thinking in terms of high probabilities—what you might call a “life algorithm.” Master it, and you can learn to observe the past in order to raise your chances of success in the future. It may sound a bit mystical, but probability thinking is far from superstition. It rests on a solid foundation of statistical logic. The secret behind the wealth of many life-long winners—people like Warren Buffett, George Soros, and Edward Thorp, the “father of quantitative investing”—comes directly from this way of thinking.
The author, Jeffrey Ma, is himself a beneficiary of this mindset.Ma is a Chinese American whose name you may not know, but whose story you may have seen on the big screen—in the film 21. The movie, inspired by his life, tells the tale of a group of MIT whiz kids who took casinos for millions. The protagonist, modeled after Ma, had a signature line at the blackjack table—something along the lines of “Winner, winner, chicken dinner”—which later went viral through a popular video game.
In the mid-1990s, Jeffrey Ma was a student at the Massachusetts Institute of Technology. Every weekend, he and a few classmates would head to Las Vegas or Atlantic City, carrying $100,000 in starting capital, to play blackjack. In a short time, they legally won over $6 million, prompting casinos to restrict their access. Ma himself became well-known in the U.S., nicknamed “the Chinese Blackjack King.”
For Ma, blackjack wasn’t gambling—it was purely a problem of statistical analysis.
By tracking which cards had already appeared, he could predict the probability of winning the next hand. His winning formula was straightforward: the higher the probability of winning, the more money he would bet. By repeating this strategy over and over, the profits grew exponentially.
Ma’s casino exploits are just one example of using high-probability thinking to succeed.
By “high probability,” we don’t mean perfect precision—rather, we mean staying directionally correct. At its core, probability thinking is about using historical observation and analysis to maximize your odds of success in the future.
After leaving the casino world, Jeffrey Ma applied this thinking flexibly across multiple fields, extending his winning streak.
He became a sports prediction analyst for major media outlets, advised companies on technology and management, and founded ventures in industries ranging from family entertainment to transportation, early childhood education, beta-testing platforms, and hotel management. Every company he started either secured multiple rounds of funding or went public successfully.
These experiences became Ma’s first-hand material for this book.
In The House Advantage, he explains in detail the core principles of probability thinking, common pitfalls, and actionable strategies. Each chapter begins with an episode from his casino days, then distills an essential principle of probability thinking and extends it into real-world contexts such as sports betting, finance, business, and the workplace. As Ma himself puts it, the book is “a business analysis course wrapped in gambling and sports stories,” designed to “introduce the mysteries of statistical analysis to people who would never pick up a statistics textbook.”
In the rest of this session, I’ll break down this “life algorithm” of probability thinking using the principles in the book, along with relatable real-life stories.
This discussion will be divided into two parts:
Part One:How to Precisely Define Your Problem.By the end, you may realize that many issues aren’t truly unsolvable—it’s just that we haven’t defined them precisely, which makes them seem unsolvable.
Part Two:How to Use High-Probability Thinking to Solve Problems
Part One – How to Precisely Define Your Problem
In the book, the author emphasizes that only when a problem is well-defined can it lead to the right solution.
Accurately defining the problem is the starting point for action. But how exactly do we define a problem accurately? Here, “accuracy” can be broken down into two requirements: precision and specificity.
That may sound a bit abstract, so let’s try a different approach.
Imagine this: you are about to compete in an international hot dog eating contest. The day before the competition, you are taken into a room with a mirror. The person who brought you there says, “This is a magical mirror—it can answer your questions.” You have two seconds to think. What would you ask it?
Thinking about tomorrow’s contest, you might ask:
“How can I win tomorrow’s competition?”
The mirror replies:
“According to the rules, to win you must eat more hot dogs than anyone else within 12 minutes.”
You roll your eyes—isn’t that obvious? Some people, disappointed, would walk out at this point. Others might press on:
“How can I eat more hot dogs in 12 minutes?”
The mirror answers:
“In the same amount of time, to eat more, you must eat faster.”
Still obvious. About 80% of people would leave here, feeling the mirror was useless. But the remaining 20% might push further:
“How can I eat faster?”
The mirror says:
“The key action that affects eating speed is swallowing. So you should swallow faster.”
Again—too vague for most. All but one person leave. That last person asks:
“How can I swallow faster?”
This time, the mirror flickers and responds:
“Based on timing data for different ways of handling hot dogs, to maximize swallowing speed, you should separate the bun from the sausage, break the sausage in half, soak the bun to soften it, and eat them alternately. Add a little vegetable oil to the water.”
A bit strange, perhaps—but the next day, following this odd strategy, you win the championship.
This story is actually based on a real competitive eater from Japan—Takeru Kobayashi.
In his very first international hot dog contest, he broke the world record. The previous record was 26 hot dogs in 12 minutes; Kobayashi ate 50.
How did he do it?
Think back to our story. Most people, when thinking about winning, asked:
“How can I eat more hot dogs?”
Kobayashi asked a different question:
“How can I make hot dogs easier to swallow?”
That question led him to run experiments, collect timing data for different methods, and continually refine his strategy.
Why was Kobayashi’s question better than everyone else’s?
First, it was specific.
How can you tell if a question is specific? A simple test is this: a specific question’s answer always has action value—meaning, from the answer, you know exactly what to do next.
For example, if you ask, “How can I be successful?” someone might tell you to work hard, be ambitious, stay positive, and so on. But after hearing all that, you still don’t know what to do tomorrow morning. Why? Because “how to be successful” is too complex—no single action will make you successful. Success requires a chain of actions, continually adjusted as time, place, personal conditions, and the external environment change. Answers to such a broad question are either so general you can’t act on them, or so complex you don’t know where to start—either way, the question becomes meaningless.
Kobayashi’s “How can I make hot dogs easier to swallow?” was specific because its answer had to be a method for handling hot dogs. Whatever the method, it was something you could directly try. That made the question actionable and relevant.
Second, Kobayashi’s question was precise.
Look at how the mirror conversation unfolded: from “How can I win?” to “How can I eat more?” to “How can I eat faster?” and finally to “How can I swallow faster?”—this was a process of getting closer to the core of the problem. Our goal was to win, and the question we ended up with was about swallowing—the key factor in winning.
When we say Kobayashi’s question was precise, we mean it pinpointed the critical lever for achieving the goal.
Part Two — How to Use High-Probability Thinking to Solve Problems
Above, we said that to use probability thinking well, you must first learn to define your problem accurately. Once the problem is set, the next step is to solve it with high-probability thinking. Here, I’ll borrow an example from the book and walk you through the process step by step.
Imagine you run a men’s apparel brand with many chain stores. Summer has arrived, and you need to distribute a new short-sleeve shirt to every store. Suppose the shirt comes in eight colors, five fabrics, and four sizes. How should you allocate inventory to each store?
Many people might think: “Isn’t this simple? Just split everything evenly!” That is indeed the most straightforward approach. For instance, if there were only three colors and three sizes, you might ship each store 30 shirts of each color, with 10 shirts of each size within every color.
This was common practice in retail some twenty-odd years ago. Centrally planned chains would periodically send each store garments in identical mixes of color, fabric, and size. But in practice, when the mix is perfectly even, one or two color-size combinations inevitably sell out, while other combinations pile up. Customers who can’t find the color and size they want leave empty-handed; and the overstocked items must be discounted, eroding margins.
If not a perfectly even split, then how should a retailer determine the right mix? This is where probability thinking helps. The author tells us that to solve problems with probability thinking, you must first collect as much historical data as possible. By studying the past, we can discover relatively stable patterns amid changing numbers, and thus better handle uncertainty ahead. As the saying goes, “History often rhymes, even if it doesn’t repeat.”
In retail, large chains have a built-in data advantage, so they were the first to tackle allocation scientifically. Their systems contain decades of inventory, ordering, marketing, and pricing data—the bedrock for analysis and decision-making. Savvier retailers also layer in external data beyond the sales system: neighborhood demographics (age, gender, interests, lifestyle), macroeconomic and market trends, weather forecasts, and event calendars that might affect demand. With these inputs, retailers can forecast sales more accurately, which greatly improves assortment and inventory planning.
One caution: when faced with a flood of data, people sometimes focus on a subset and ignore the rest—classic “cognitive bias.” The author highlights two in particular:
- Confirmation bias: we pay more attention to data that confirms our existing views and discount data that contradicts them—many conspiracy theories spring from this.
- Survivorship bias: we analyze only the “survivors.” For example, when studying what drives startups to succeed, we look mainly at those still alive while neglecting those that died along the way.
To avoid these biases, the historical data you collect must reflect the full picture. That means considering as many relevant indicators as possible, and—crucially—gathering a sufficiently long time series for each indicator.
Throughout the book, the author underscores the value of historical data and urges us to pull the historical lens back far enough. The longer the lookback, the sturdier the patterns we find—and the better our forecasts. In everyday life, we often get the future wrong simply because our historical lens is too short.
For example, you see an athlete who just lost a match, crying on camera, then grinding in practice. As ordinary viewers, we might feel sure they’ll redeem themselves next game. That could happen—but if you scan a long enough record, you might find that this athlete loses thirty-plus of every fifty matches. In that case, the most likely outcome next time is still a loss.
Likewise, suppose a surgeon’s last operation failed, and they carefully debriefed and learned from it. We might think, “Lesson learned—next time will succeed, right?” Probability thinking warns us not to jump to that conclusion. First examine the surgeon’s success rate over a long period for that same type of operation. If it’s high, the last failure was likely a fluke; if it’s low, failure may persist.
It’s like what teachers told us as kids: one or two bad test scores don’t define you; we need to look at the overall pattern. Even with something that seems “one-and-done” like the college entrance exam, many students who underperform that day do just fine ten years later—because their long-run “exam success rate” is high. Over a lifetime of big and small evaluations, they’re still likely to succeed.
So, to solve problems with probability thinking, first extend your historical window and collect enough data. The next step is to process and analyze that data to extract answers.
Back to retail. By examining item-level sales data at specific stores, one brand discovered that medium and large shirts in five colors and three fabrics were standouts. After overlaying demographic and psychographic data, they uncovered the reason: those store trade areas had a relatively large population of single men aged 21–35 who shopped two or three times a month and were interested in fitness and men’s fashion.
Based on the analysis, the brand rolled out a new allocation plan. For those stores, they increased the share of medium and large sizes. Each store would carry 15 small, 60 medium, 75 large, and 30 extra-large. The latest style would come in four colors—lavender, deep red, black, and off-white—and three fabrics—standard, glossy, and textured.
In essence, this processing and analysis step is like running your data through a sieve to wash and sort it—so that durable, actionable patterns can emerge.
There are two kinds of sieve: simple and complex.
The simple sieve is the one your brain can design on its own—what we commonly call intuition. It sounds easy and looks a lot like “gut feel,” but very few people have reliable intuition. (Note: very few, not none.) Consider a story you may have heard: in his later years, Puyi, the last emperor of the Qing dynasty, could tell at a glance whether an antique jade piece was genuine—and he was right time after time. Asked for his secret, he said there was none; he had simply seen too many. Or think of martial-arts novels: an old beggar sizes up a scrappy youth and decides to pass down his life’s skills. (Let’s assume he’s not a swindler.) Why would he do that? Because after a lifetime of meeting people, he can recognize raw talent. In both cases, what we call “intuition” is really a brain-based expert database: years of accumulated domain data etched into memory. In that context, they can call on intuition and make an uncannily accurate prediction.
Most of us, most of the time, don’t have that gift. What then? No problem—you can build a complex man-made sieve. To do that, you’ll need some statistical know-how and, very likely, computers and professional software. If you’re unfamiliar, you can learn in your spare time. There are plenty of courses and tools—Stata, SPSS, Python, and so on. As the author stresses: if you truly want to win, this investment is well worth it.
That, in broad strokes, is how to apply high-probability thinking. In a sense, it supplements and corrects our common-sense thinking. In daily life, we often rely on intuition to decide. But without a long enough look at the past—without data that shows the whole picture—our intuition is often wrong. Probability thinking tells us this: rather than letting “common sense” guide the future by assumption, treat history as a database, mine it for statistical regularities, and use those patterns to get closer to the truth about what comes next.
Part Three — Turning Probability Thinking into Action
By now, from what we’ve covered, you already know how to use probability thinking to arrive at the optimal solution to a problem. But that’s not the end of the story—what comes next is the critical step: translating probability thinking into sustained action.
In the book, the author repeatedly reminds us that whether in the casino or in business, you must truly believe in the power of probability, unwaveringly build your strategy around the optimal solution, look past short-term fluctuations, and commit to a long-term perspective.
This is not easy.
In casinos, in life, and in work, even when the odds are in your favor, you will often encounter losing streaks. For example, imagine you and a friend are playing a dice game, one dollar per round. If the die shows 1, 2, 3, or 4, you win. Clearly, your chance of winning is higher than losing, so you join the game. But if the first five rolls are all above 4, you might start thinking about quitting.
The author is adamant: quitting here would be a huge mistake. On the contrary—you should stake more money and keep playing with your friend. Strictly speaking, as long as you have enough patience, there will come a day when you take all of your friend’s money.
This is not blind faith—it rests on solid theory. In probability theory, the Law of Large Numbers states that under constant conditions, the relative frequency of an event approaches its theoretical probability as the number of trials increases.
In our dice example, if you keep playing for, say, 100,000 rounds, you will win roughly 60,000 of them. Losing the first five was simply a matter of falling into a “negative fluctuation zone.”
Volatility is a concept people struggle with. A streak of losses at a given moment may simply be bad luck—it doesn’t mean the decision was wrong. Yet losses leave emotional scars, and many people abandon their long-term advantage out of fear of short-term swings.
If, in the earlier stage of applying probability thinking to solve a problem, you needed to extend your view far enough into the past, then in turning probability thinking into action, you must extend your view far enough into the future—and commit to playing the long game.
Conclusion
And that, in essence, is the core of The House Advantage I wanted to share with you. This book reminds me of something Warren Buffett once said:
“Take the probability of loss and multiply it by the amount you could lose. Then take the probability of gain and multiply it by the amount you could gain. Subtract the loss from the gain—and that’s the approach we’ve always used.”
When you think about it, Buffett’s path to becoming the “Oracle of Omaha” is essentially a lifetime of consistently practicing probability thinking.
At its core, isn’t the stock market like a vast warehouse filled with countless possibilities? Buffett’s strategy is to first observe the data metrics of large, listed companies, then apply the above probability-profit formula to select a handful of stocks, buy heavily, and hold them for the long term. The model isn’t perfect, but it’s that simple.
Most of us agree with the old saying, “Don’t put all your eggs in one basket”—meaning, diversify to reduce risk. Buffett’s view is the exact opposite: “Put all your eggs in one basket, and then watch that basket very carefully.” In other words, when you spot an exceptional opportunity, go in hard—and once you do, grip it tightly and trust your choice.
Consider this: between 1988 and 1989, Buffett invested $1 billion in Coca-Cola stock—more than a third of his total investments at the time. Fast forward to 2018, when he controlled nearly $200 billion, and two-thirds of that capital was concentrated in just five stocks. He had held those positions for years, never wavering through market volatility or crashes. Reality has proven the wisdom of his long-term strategy: the overwhelming majority of Buffett’s lifetime wealth has come from a dozen or so investments.
Buffett once said: “Most people can’t handle getting rich slowly.”
Yet probability thinking is precisely a “life algorithm” that allows you to get rich slowly. It’s not the exclusive domain of mathematicians or statistical prodigies—it reflects an objective historical perspective, the ability to simplify complexity, and the discipline to think long term. The principle behind it is simple: if every step you take in life follows a strategy with a high probability of success, then over the long run, you maximize your chances of becoming a true winner in life.
And that is exactly what the book’s subtitle—“Winners in life are winners in probability”—is all about.
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