March Madness is in full swing and will have the world's attention for a few more days. As you can guess, almost no one has a perfect bracket anymore. McNeese beat Clemson, Drake beat Mizzou, and Arkansas handed Kansas its first first-round loss since 2006. On Friday, the NCAA said that of the over 34 million brackets submitted at the start of March Madness, approximately 1,600 remained perfect. That's less than .1% after the first day. The first game of the tournament - Creighton vs. Louisville - busted over half of the brackets.
The holy grail is mighty elusive in March Madness (as in most things). For example, the odds of getting the perfect bracket are 1 in 9,223,372,036,854,775,808 (that is 1 in 9.223 quintillion if that was too many zeros count). If you want better odds, then you can have a 1 in 2.4 trillion chance based on a Duke Mathematician's formula that takes into account ranks). It's easier to win back-to-back lotteries than picking a perfect bracket. Nonetheless, I bet you felt pretty good when you filled out your bracket.
In 2018, it was estimated that March Madness generated $10 Billion in gambling (twice as much as the Super Bowl)
Feeding the Madness
"Not only is there more to life than basketball, there's a lot more to basketball than basketball." - Phil Jackson
In 2017, I highlighted three people who were (semi) successful at predicting March Madness: a 13-year-old who used a mix of guesswork and preferences, a 47-year-old English woman who used algorithms and data science (despite not knowing the game), and a 70-year-old bookie who had his finger on the pulse of the betting world. None of them had the same success even a year later.
Finding an edge is hard - Maintaining an edge is even harder.
That's not to say there aren't edges to be found.
Bracket-choosing mimics the way investors pick trades or allocate assets. Some people use gut feelings, some base their decisions on current and historical performance, and some use predictive models. You've got different inputs, weights, and miscellaneous factors influencing your decision. That makes you feel powerful. But knowing the history, their ranks, etc., can help make an educated guess, and they can also lead you astray.
The allure of March Madness is the same as gambling or trading. As sports fans, it's easy to believe we know something the layman doesn't. We want the bragging rights for the sleeper pick that went deeper than most expected, our alma mater winning, and for the big upset we predicted.
You'd think an NCAA analyst might have a better shot at a perfect bracket than your grandma or musical-loving co-worker.
In reality, several of the highest-ranked brackets every year are guesses.
The commonality in all decisions is that we are biased. Bias is inherent to the process because there isn't a clear-cut answer. We don't know who will win or what makes a perfect prediction.
Think about it from a market efficiency standpoint. People make decisions based on many factors — sometimes irrational ones — which can create inefficiencies and complexities. It can be hard to find those inefficiencies and capitalize on them, but they're there to be found.
In trading, AI and advanced math help remove biases and identify inefficiencies humans miss.
Can machine learning also help in March Madness?
“The greater the uncertainty, the bigger the gap between what you can measure and what matters, the more you should watch out for overfitting - that is, the more you should prefer simplicity” - Tom Griffiths
That said, people have tried before with mediocre success. It's hard to overcome the intangibles of sports—hustle, the crowd, momentum—and it's hard to overcome the odds of 1 in 9.2 quintillion.
Two lessons can be learned from this:
People aren't as good at prediction as they predict they are.
Machine Learning isn't a one-size-fits-all answer to all your problems.
Casinos only offer to play games that they expect to win. In contrast, gambling customers play even though they know the odds are against them.
Why does this happen? The rush of a win, the chance of a big win, and random reinforcement are common factors that incentivize people to play the lotto, go to a casino, or try to trade.
Chemicals like adrenaline and dopamine play a part as well. Even in a sea of losses, your body can't help but crave the chemical reward of even a small win.
The "House" knows this and engineers an experience that takes advantage of it.
In the case of casinos, every detail is meticulously crafted to extract you from your money - from carpet patterns to the labyrinthian layouts, the music, the lights, and even the games themselves.
Most people aren't gamblers ... the fear of losing big inhibits them. However, when people were instructed to "think like a trader," they showed considerably less risk aversion when gambling. And I bet you have no problem filling out a March Madness bracket, even if you put money on the line.
The illusion of control convinces us we can overcome the statistics.
When you almost get it right - when you guess the first round of March Madness correctly, when you miss the jackpot by one slot on a slot machine, when you just mistime a trade to get a big win - you're more likely to play longer, and place bigger bets ... because you're "so close."
It's human nature to want to feel in control.
This is why you find a lot of superstitious traders & gamblers. If you wear this lucky item of clothing ... if you throw the dice in this particular way ... if you check your holdings at this time every day ... you have control.
There is a big difference between causation and correlation.
It is not hard to imagine that, for most traders, the majority of their activities do little to create a real and lasting edge.
Skill vs. Luck
There are games of skill, and there are games of chance.
In a casino, poker, and blackjack are considered games of skill. In contrast, slot machines are considered a game of chance.
In trading, predicting markets is much different than using math and statistics to measure the performance of a technique.
Much of what we do is to figure out how to eliminate the fear, greed, and discretionary mistakes humans bring to trading.
In trading, "Alpha" is the measure of excess return attributed to manager skill, rather than luck or taking on more risk.
We believe in Alpha-by-Avoidance ... Meaning much of what we do is figure out what to ignore or avoid so that more of the games we play are games of skill rather than games of chance.
Twice this week, I heard someone say, “Start with Why.”
As a tech entrepreneur, I often ignore that aphorism.
Someone who embodies it (and made it famous) is Simon Sinek. He is a motivational speaker and organizational consultant who gained widespread recognition after his 2009 TED Talk, “How Great Leaders Inspire Action,” which remains one of the most-viewed TED Talks ever, with almost 70 million views.
This talk introduced his core framework: The Golden Circle, the concept that catapulted him to fame. It is a simple but powerful model for understanding why some leaders and organizations inspire while others don’t. It consists of three concentric circles, like a bullseye. At the center is Why, the middle ring is How, and the outermost ring is What.
Most people and organizations focus on the outermost circle first—what they do—and then work inward. Sinek flips this approach, arguing that great leaders and companies start from the inside out: start with Why.
Why Start with Why?
Here’s an analogy: Think of a magnet. The strongest force comes from its core. Similarly, in leadership and business, the Why is your core—it’s what attracts people to you. It’s not just about selling a product; it’s about sharing a belief or vision that resonates emotionally with others.
For example:
Apple doesn’t just sell computers (What). They believe in challenging the status quo and thinking differently (Why). Their How—innovative design and user-friendly technology—flows naturally from this belief.
Martin Luther King Jr. didn’t say, “I have a plan.” He said, “I have a dream.” His Why inspired millions because it connected with their values and emotions.
The Biological Connection
Sinek ties this idea to how our brains work. The outer layer of the brain (the neocortex) processes logical information like facts and figures (What), but decisions are driven by the limbic brain, which controls emotions and instincts (Why). When you lead with Why, you speak directly to people’s feelings, inspiring trust and loyalty.
Simplified Takeaway
Think of it like this: If you want people to join your cause or buy into your vision, don’t just tell them what you’re selling or how great it is. Tell them why it matters—to you and to them. Starting with Why connects hearts before minds, creating a lasting impact.
In short, the Golden Circle isn’t just a business strategy; it’s a way to inspire action by leading with purpose.
I met Simon through friends before his first book came out.
Then, in 2009, he gave a speech to the Dallas Chapter of EO, and then visited my office to speak with our team afterwards. I still remember how well-received he was. It was right at the beginning of his meteoric rise, two short months after the release of his famous book "Start With Why."
Who do you believe will do a better job, someone who takes a job because of the salary and benefits ... or someone truly inspired to accomplish the job's purpose?
Phrased that way, of course, you know the answer. Still, how can you leverage this to better select customers and employees?
For example, Simon uses the story of Sir Ernest Shackleton to illustrate this concept. Shackleton was preparing to lead the first expedition across Antarctica in 1914. Legend has it that when seeking crew members for his journey, Shackleton placed the following ad in a newspaper:
"MEN WANTED FOR HAZARDOUS JOURNEY. SMALL WAGES, BITTER COLD, LONG MONTHS OF COMPLETE DARKNESS, CONSTANT DANGER, SAFE RETURN DOUBTFUL. HONOUR AND RECOGNITION IN CASE OF SUCCESS. - SIR ERNEST SHACKLETON"
When the expedition became stuck in the ice and could not be rescued for 22 months, not a single man was lost. The reason Simon gave for their unlikely survival was that Shackleton hired survivors that could deal with the situation and were aligned with the mission and purpose.
Can you imagine writing an ad like that to attract the right people to your cause?
Watch This Video.
Here is a video of Simon speaking at a TED Conference. It is an excellent intro to his stuff.
Other Resources:
Here is a link to Simon's Blog. (2023 Note: this now links to his old blog, which is poorly formatted but interesting to see. His new website/blog can be found here.)
It's now more than ten years later, and Simon is one of the most prominent leaders in leadership development and has published five books, to much acclaim.
Part of his success is the charisma and pith with which Simon speaks and writes - but a large part is his focus on what makes humans human. He's not preaching a leadership mantra focused on the bottom line and revenue; he's focused on the aspects of human nature that don't change. He's focused on purpose and the elements of leadership that apply to everyone - not just CEOs.
As we move into an era of increased volatility – both in markets and business - these leadership principles will become more important.
Understanding your "WHY" is vital if you want to make a difference (and not be replaced by an AI). It’s also vital in making discipline the easier choice.
But Skype might be even more surprising. Skype was so ubiquitous that it became a verb and eponymous with video calling. As a world traveler, Skype also used to be the go-to international calling app.
That’s what Skype did - and it’s not the first tech business to fail similarly...
Thinking Linearly in an Exponential Age
Humans can’t do a lot of things. Honestly, the fact that we’re at the top of the food chain is pretty miraculous.
We’re slow, weak, and famously bad at understanding large numbers or exponential growth.
Making matters worse, our brains are hardwired to think locally and linearly.
It’s a monumental task for us to fathom exponential growth … let alone its implications.
Think how many companies have failed due to that inability … RadioShack couldn’t understand a future where shopping was done online – and Kodak didn’t think digital cameras would replace good ol’ film. Blockbuster couldn’t foresee a future where people would want movies in their mailboxes because “part of the joy is seeing all your options!” They didn’t even make it long enough to see “Netflix and Chill” become a thing. The list goes on.
Human perception is linear. Technological growth is exponential.
There are many examples. Here is one Peter Diamandis calls “The Kodak Moment” (a play on words of “a Kodak Moment”... the phrase Kodak used in advertising to mean a “special moment that’s worth capturing with a camera”).
In 1996, Kodak was at the top of its game, with a market cap of over $28 billion and 140,000 employees.
Innovation is a reminder that you can’t be medium-obsessed. Kodak’s goal was to preserve memories. It wasn’t to sell film. Blockbuster’s goal wasn’t to get people in their stores, it was to get movies in homes.
Henry Ford famously said: “If I had asked people what they wanted, they would have said faster horses.” Steve Jobs was famous for spending all his time with customers, but never asking them what they wanted.
Two of our greatest innovators realized something that many never do. Being conscientious of your consumers doesn’t necessarily mean listening to them. It means thinking about and anticipating their wants and future needs.
Meanwhile, despite Skype having several features that Zoom still hasn’t implemented, Zoom recognized an opportunity during COVID and capitalized. When Microsoft bought Skype, they focused on adding several new features and expanding the range of services instead of improving the quality of their audio or video. Meanwhile, when Zoom entered the space, they brought much better servers and the ability to have much larger rooms. More attendees meant a wider variety of use cases and quicker adoption and referral cycles. They also made it easy to join a Zoom room. Instead of getting your e-mail up front and forcing you to create an account to use it, they let you join a meeting without an account. You only needed an account to host a meeting.
They focused on making it easy to use their service and on having a clear identity instead of trying to ride every wave and become unfocused. Of course, at the same time, Microsoft stopped focusing on the tool, with an increased focus on their new competitor to Zoom, Teams.
Tech and AI are creating tectonic forces throughout industry and the world. It is time to embrace and leverage what that makes possible. History has many prior examples of Creative Destruction (and what gets left in the dust).
Opportunity or Chaos … You get to decide.
Don’t forget ... you don’t have to be the first mover to win in the end.
Many of our best decisions, timeliest course corrections, or significant innovations occur after a seemingly disastrous occurrence. That's why many psychologists and self-help gurus encourage people to focus on the hidden gift that many of these experiences provide.
It's there if you look for it. That painful event becomes the catalyst for either something new, a better way, or a level-up.
The goal isn't just to survive - it's to thrive. While a robust business can withstand shocks and a resilient business can recover from them, an anti-fragile business improves and grows stronger when exposed to volatility, randomness, disorder, and stressors. The interesting thing about this concept is that it doesn't mean not fragile. It means things that weaken other systems are actually the things that strengthen you.
Of course, that's not the case for everyone or every event ... It takes the right mindset and the right actions to turn a trial into a triumph.
As we see the world changing rapidly, both through AI and through Trump's presidency, I think back to 2008 and how a prior incarnation of algorithms fared against it (spoiler alert: not nearly as well as this time). They say the things that don't kill you make you stronger. Here's my trial into triumph story about that.
Too many people become victims of their circumstances instead of choosing to be the master of their destinies.
Life is harder for people who live a life of least resistance. Doing the hard things and making the most of bad times makes life better and, ultimately, easier.
Tony Robbins calls this the Threshold of Control. If you push through the fear and the struggle ... as you persevere, eventually, what was scary becomes easy. You've increased your threshold, and that's often a permanent improvement.
Here is a list of the seven steps I use to transform almost any situation.
Seven Best Practices for Uncertain Times.
Accept Reality: We are where we are. Focus on being complete with what happened before this – and think about this as a new beginning with an even bigger future.
Do Something Positive: Take action and build momentum and confidence. Big wins are great. Yet, in scary times, even small items are worth noting, building upon, and stacking. Let progress build positive momentum for you.
Take Care of Yourself: Increase your physical activity, meditation, and massage. Take time to eat and sleep well. Many studies show decision-making suffers when you're stressed. Caring for yourself goes a long way to improving many other things.
Communicate More: The natural tendency is to hide or to recuperate in private. Instead, be open and receptive to help and ideas from friends, partners, or wherever it may come.
Creative Destruction: The old game and the old ways of thinking are over. Shift your energy to what is working. Commit to the result you want rather than the process.
Increase Your Options: It often takes a different level of thinking to solve a problem than the level of thinking that got you there in the first place. So, be open to opportunities, new possibilities, and more ways to win.
Choose a Bigger Future: Instead of resigning yourself to playing small and doing with less, recognize that a clearing creates space for something even better. Choose what you want and call it into existence through your thoughts and actions.
They say everything happens for a reason. The secret is that you get to choose the reason, what it means to you, and what you're going to do about it. Choose well, and someday, you could look back on this time as one of the best things that ever happened to you.
The S&P 500's price-to-earnings ratio (CAPE) has recently been nearing historic highs. Traders think that signals that market valuations might be overheated.
In December of last year, it hit 37.9, over double its long-term average of 17.6. For context, it has only exceeded that level during the Dot-Com bubble and in 2021.
Overheated prices mean that there's a significant gap between company earnings and stock prices. That disparity translates to speculation and hope driving the stock price instead of more quantitative data.
For some historical perspective, after the Dot-Com bubble, the S&P declined by 40% in the following two years. And after its 2021 peak, the S&P sank almost 20%.
While AI enthusiasm has brought a spark to the markets, the question is, is that hype hiding deeper issues?
On a broader note, my message to you would be that if you don't know what your edge is, you don't have one. Investors and traders should understand market indicators, economic trends, and other world factors – mainly because it's important to be educated (or at least informed). Of course, merely understanding these things does not translate to a reliable trading strategy or an edge in today's environment.
Lastly, just because something has been true in the past does not mean it predicts the future. In trading, we use the phrase "past performance is not indicative of future results" to remind us that there is a difference between a coincidence and a correlation. Indicators like CAPE study the past, so it is dangerous to assume you can use them to predict the future. For better or worse, whether markets go up or down is based on much more than earnings and stock prices.
While I mainly discuss entrepreneurship and technology trends, I still have a soft spot for trading, which remains a large part of what we do at Capitalogix. While we've broadened the industries in which we use our Capitalogix Insight Engine, it was originally built with trading technologies in mind. We have exciting new partnerships there, including a new fund.
As we look forward, I thought it was a good time to look back as well.
"...the change in the pit isn't a harbinger of death for futures trading; it's the signal of a new era."
At the time, it didn't feel like a bold statement to me – because what was coming felt inevitable. And it has proven to be true. Markets have changed radically since 2016. And you can bet that the changes aren't done, as AI and exponential technologies promise to transform markets and trading again.
The process of trading and clearing is moving beyond human capabilities. As the old duties of the Exchange fade away, the focus must be on the dangers, opportunities, and strengths of a bigger future. That means new games to play, new risks to navigate, and a new set of rewards to capture.
Nearly empty CME trading pits in 2016 (specifically, the S&P and Eurodollar)
The new game involves not only new players, methods, and markets ... but also a new geography.
Yes, as more things become digital, geography still matters.
Texas is rapidly becoming an even larger economic hub. It boasts the highest number of NYSE listings, Nasdaq recently established a large base here, and companies representing more than $3.7 trillion in market value list Texas as their headquarters.
No, I'm not talking about how fast the DOGE team is terraforming government.
I'm talking about how fast the insights of exponential technologies are compounding the real-world implications of where we are and where we are going.
In past issues, we've talked about how quickly the world is changing, how fast innovations happen, and why it's not about today's tools but rather the value and capabilities of the foundational assets we build upon ... and, ultimately, the things that makes possible.
Today's commentary is different from our usual posts. Yes, the inspiration came from my weekly curation of links selected based on what captured my attention or imagination. However, today's post is about the sheer volume and density of groundbreaking innovations competing for mindshare and investment dollars. And while commercial success is a great way to keep score, we'll explore what this accelerating pace of innovation means for our future and the world.
So, here is a list of some of the things that made headlines this week.
Nvidia and Arc Institute launched a new AI Bio model - Evo 2 - trained on over 100k species. This doesn't just analyze genomes ... it creates them.
Sakana.AI introduced the AI CUDA Engineer, an agentic AI system that automates the production of CUDA kernels 10-100x faster than with common machine learning,
Some may not matter to you now. Try re-reading the list while letting yourself be amazed at what is happening!
Any one of these is a momentous achievement that would have sounded like science fiction even 10 years ago. Now, that's one week of achievement.
As someone whose company invents things for a living, I understand that none of these breakthroughs were actually invented last week. Obviously, a long and winding road leads to each of those announcements. However, it's remarkable to see so many significant innovations reaching the stage of public announcement simultaneously.
It's hard to quantify the impact of these innovations on not only the tech industry - but the world.
Think about the implications. Google's co-scientist is already solving problems that humans haven't been able to solve for decades. Clone is building robots that will use the next generations of AI to transform how we think about what artificial intelligence looks like.
Not to mention the improvement in quantum computing and nuclear fusion, industries that I've been paying attention to since the 90s.
While any of these topics would have made a good article, in my opinion, the whole is more impressive than the sum of its parts.
If I had to pick one of those topics to highlight, I think it's now time to start focusing more on quantum computing.
Most of you probably aren't interested in watching the whole thing, but here are some of the highlights.
They've created a new state of matter called a topological superconductor.
The qubits created with topological superconductors are fast, reliable, and small ... very small.
These new qubits are 1/100th of a millimeter, meaning we now have a clear path to a million-qubit processor.
To put that in perspective, imagine a chip that can fit in the palm of your hand yet can solve problems that even all the computers on Earth today combined can't!
Satya doesn't believe in making claims about how quickly AGI is coming.
However, he believes it is useful and productive to set a benchmark of making the world 10% better.
He also believes the topological superconductor breakthrough makes quantum computing a practical reality that can happen in a few years - not decades +.
From 2023-2024, over $26 billion flowed into the sector - including big deals like Inflection, xAI, and Anthropic.
While many of the biggest investments were in foundational models and infrastructure, some money is now moving into targeted AI applications.
AI isn’t just for researchers and the tech giants anymore ... it’s becoming more commercial.
Realistically, AI is overhyped – and there is a lot of competition. Yet, few firms have operationalized AI in a meaningful way.
With that said, here is a question worth considering.
Where are the AI applications capable of generating returns that justify the infrastructure, investment, and focus?
The next battle will likely be in the AI Applications space. To keep it short, why hasn’t it happened yet ... and what will likely create the value we’re looking for?
Why Haven’t AI Apps Taken Off Yet?
• Cost vs. Value Gap: Many AI applications are still experimental or add only incremental value.
• Enterprise Hesitation: Many companies are still figuring out how to integrate AI into their operations in a way that delivers real ROI.
Where Might the Value Come From?
For AI investments to pay off, applications must solve big problems, not just serve as experimental tools. The highest-value areas likely include:
• AI copilots and automation (Enterprise AI reducing labor costs and bottlenecks)
• Autonomous systems (AI for analytics, compliance, and logistics)
• AI-driven discovery (Accelerating breakthroughs in capabilities and performance)
• Next-gen digital assistants (LLMs with memory, context, and long-term utility)
Right now, AI apps are where mobile apps were in 2008 — there is plenty of potential, but only a handful of genuinely indispensable use cases.
Companies like Capitalogix that crack the code on industrial-grade AI applications, will drive the next wave of value creation.
It’s fun and rewarding to watch artificial intelligence become available to everyone.
As the cost of “intelligence” decreases, let’s hope more people take advantage of the opportunity.
However, the sad truth is the opposite is also more likely. As AI becomes more available, it becomes easier for it to become a distraction.
Remember the Internet? When it first started, most of the uses were academic. Now, despite there being functionally infinite ways to use the internet to improve your life and make you smarter, most people use it for memes and distractions.
When you think about AI, don’t just think about artificial intelligence ... Think about amplified intelligence. That is the term I use to distinguish between the technology and what people really want ... which is the ability to make better decisions, take smarter actions, and continuously improve performance.
AI isn’t about taking away the humanity from your business or automating away the things you love. It’s about allowing you to be more human – doing more of the things you’re best at - that give you energy and bring you joy.
Unsurprisingly, almost half of consumer spending goes toward housing and transportation.
While this has slightly outpaced inflation, it hasn’t by much.
Meanwhile, spending in some areas surged well beyond wage and overall inflation levels. For example, Americans spend 21% more on food than in 2021. A closer look shows that the cause isn’t just inflation. Food and beverage companies increased their operating profits by 79% from 2019-2023.
Educational spending and healthcare spending are also rising.
How do you think the Trump administration’s actions will impact the economy and the wallets of typical Americans?
Many of my close friends and advisors are optimistic about the Trump administration’s actions and expected impacts. However, as I’ve often noted regarding technological change, people are good at noticing big turning points – but struggle with predictions about the second and third-order consequences of these shifts.
Mad About March Madness
March Madness is in full swing and will have the world's attention for a few more days. As you can guess, almost no one has a perfect bracket anymore. McNeese beat Clemson, Drake beat Mizzou, and Arkansas handed Kansas its first first-round loss since 2006. On Friday, the NCAA said that of the over 34 million brackets submitted at the start of March Madness, approximately 1,600 remained perfect. That's less than .1% after the first day. The first game of the tournament - Creighton vs. Louisville - busted over half of the brackets.
Before 24/7 sports channels, people watched the weekly show "The Wide World of Sports." Its opening theme promised "The thrill of victory and the agony of defeat!" and "The human drama of athletic competition." That defines March Madness.
The holy grail is mighty elusive in March Madness (as in most things). For example, the odds of getting the perfect bracket are 1 in 9,223,372,036,854,775,808 (that is 1 in 9.223 quintillion if that was too many zeros count). If you want better odds, then you can have a 1 in 2.4 trillion chance based on a Duke Mathematician's formula that takes into account ranks). It's easier to win back-to-back lotteries than picking a perfect bracket. Nonetheless, I bet you felt pretty good when you filled out your bracket.
via Duke University
Here's some more crazy March Madness Stats:
Feeding the Madness
In 2017, I highlighted three people who were (semi) successful at predicting March Madness: a 13-year-old who used a mix of guesswork and preferences, a 47-year-old English woman who used algorithms and data science (despite not knowing the game), and a 70-year-old bookie who had his finger on the pulse of the betting world. None of them had the same success even a year later.
Finding an edge is hard - Maintaining an edge is even harder.
That's not to say there aren't edges to be found.
Bracket-choosing mimics the way investors pick trades or allocate assets. Some people use gut feelings, some base their decisions on current and historical performance, and some use predictive models. You've got different inputs, weights, and miscellaneous factors influencing your decision. That makes you feel powerful. But knowing the history, their ranks, etc., can help make an educated guess, and they can also lead you astray.
The allure of March Madness is the same as gambling or trading. As sports fans, it's easy to believe we know something the layman doesn't. We want the bragging rights for the sleeper pick that went deeper than most expected, our alma mater winning, and for the big upset we predicted.
You'd think an NCAA analyst might have a better shot at a perfect bracket than your grandma or musical-loving co-worker.
In reality, several of the highest-ranked brackets every year are guesses.
The commonality in all decisions is that we are biased. Bias is inherent to the process because there isn't a clear-cut answer. We don't know who will win or what makes a perfect prediction.
Think about it from a market efficiency standpoint. People make decisions based on many factors — sometimes irrational ones — which can create inefficiencies and complexities. It can be hard to find those inefficiencies and capitalize on them, but they're there to be found.
In trading, AI and advanced math help remove biases and identify inefficiencies humans miss.
Can machine learning also help in March Madness?
The data is there. Over 100,000 NCAA regular-season games were played over the last 25+ years, and we generally have plenty of statistics about the teams for each season. There are plenty of questions to be asked about that data that may add an extra edge.
That said, people have tried before with mediocre success. It's hard to overcome the intangibles of sports—hustle, the crowd, momentum—and it's hard to overcome the odds of 1 in 9.2 quintillion.
Two lessons can be learned from this:
Something to think about.
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