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:
- Companies expect $4 Billion of corporate losses due to employees tracking games
- The highest-paid NCAA coach, Kansas's Bill Self, makes a salary of $8.8 Million
- 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
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:
- 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.
Something to think about.
Taking Lessons on AI from my Grandmom
Almost every event I go to nowadays ends up focused on AI. At a recent event, conversations ranged from use cases for generative AI (and the ethics of AI image creators) to the long-term effects of AI and its adoption.
Before I could chime in, the conversation had gone to the various comparisons from past generations. When electricity was harnessed, articles claimed that it would never catch on, it would hurt productivity – and woe be unto the artisans it would potentially put out of business if it were to gain traction.
When the radio or TV was released, the older generation was sure it would lead to the death of productivity, have horrible ramifications, and ultimately lead to the next generation's failure.
People resist change. We're wired to avoid harm more than to seek pleasure. The reason is that, evolutionarily, you have to survive to have fun.
On the other hand, my grandmother used to say: "It's easy to get used to nice things."
Here's a transcript of some of my comments during that discussion:
Ultimately, as you start finding ways to use emerging technologies in a way that excites you, the fear and gloom fade.
The best way to break through a wall isn't with a wide net ... but rather with a sharp blow. You should be decisive and focused.
Commit to using AI in ways that give you energy. And the best way to figure that out is to start using it differently than you do now.
So much of what we do now is anchored in the past. This is an opportunity to transcend the old ways you did things and to shape and transform your future (and perhaps even what you believe is possible).
I was an entrepreneur in the late 90s (during the DotCom Bubble). And I remember watching people start to emulate Steve Jobs ... wearing black faux-turtlenecks and talking about how they were transforming their business to be in an Internet company – or which Internet company would be the "next big thing." Looking back, an early sign that a crash was coming was that seemingly everybody had an opinion about what would be hot, and too many people were overly confident in their views because seemingly everyone was saying the same things.
Human nature has remained stubbornly consistent through many waves of technology.
The point is that almost nobody talks about the Internet with the focus and intensity they did in the late 90s ... in part because the Internet is now part of the fabric of society. At this point, it would be weird if somebody didn't use the Internet. And you don't really even have to think about how to use the Internet anymore because there's a WHO to do almost all those HOWs (and many of them are digital WHOs that do those HOWs for you without you even knowing they were needed or being done).
The same is going to be true for AI. Like with any technology, it will suffer from all the same hype-cycle blues of inflated expectations and then disillusionment. But, when we come out the other side, AI will be better for it ... and so will society.
Understanding the Possibility Scale
It helps to understand how we bring things into existence. To start, it's nearly impossible to manifest something you can't first imagine. And there is a moment just before something happens - when it becomes inevitable.
I created this Possibility Scale to help envision the stages of becoming.
As an aside, before today, I would not have attempted to create an image for a post like this. While I love thinking and writing, image creation was outside my area of expertise or unique ability. But now things are different. Today, I simply asked ChatGPT to create that image. Yes, it took me four tries ... each retry starting with "that was great, now help me improve the prompt to (fix the thing I wanted done differently)." At the end, I asked it to give me the complete prompt I could reuse. Contact me if you want the prompt.
Earlier, I mentioned how long it took to get from "Zero-to-One" with AI. But don't fret; things are speeding up, and we're just at the beginning of the process. If I created a scale to show the capabilities of AI, and set the endpoint at 100 to represent AI's potential when I die - even though I'm in my early 60s, I'd put us at only a 3 or 4 on that 100-point scale. Meaning, we are at the beginning of one of the biggest and most asymptotic curves that you can imagine. That also means that you're not late. You're early! Even the fact that you're thinking about stuff like this now means that you are massively ahead of most.
The trick isn't to figure out how to use AI or some AI tool. The trick is to keep the main thing the main thing. Build your ability to recognize when and how to use these new capabilities to bring the future forward faster.
Investing resources into your company is one thing. Realize that there are 1000s of these tools out there, and many more coming. You don't have to build something yourself. It is often faster and better to acquire a tool than it is to spend money on developing and building it.
Think of the Medici family. They invested in people, which in turn triggered the Renaissance. A key to moving forward in the Age of AI will be to invest in the right WHOs, seeking to create the kind of world you want to see or the types of capabilities you desire. Think of this as a strategic investment into creators and entrepreneurs with a vision or who are on a path that aligns with yours.
As you get better and better at doing that, you'll see increasing opportunities to use tools to engage people to collaborate with and create joint ventures. Ultimately, you will collaborate with technology (like it's your thought partner and then your business partner). We are entering exciting times where AI, automation, and innovation will make extraordinary things possible for people looking for opportunities to do extraordinary things.
As my grandmother said, it's easy to get used to nice things.
Onwards!
Posted at 05:25 PM in Books, Business, Current Affairs, Gadgets, Ideas, Market Commentary, Personal Development, Science, Trading, Trading Tools, Web/Tech | Permalink | Comments (0)
Reblog (0)