Capitalogix

Here Are Some Links For Your Weekly Reading - October 19th, 2024

Here are some of the posts that caught my eye recently. Hope you find something interesting.

Lighter Links:

• Pope Francis and the Vatican Just Created an “AI Bible” Reshaping Faith in the Digital Age. (TheBrighterSide)
• People Think They Already Know Everything They Need to Make Decisions. (ArsTechnica)
• 8 Expert Tips for Getting Started With NotebookLM. (Google)
• Chinese Humanoid Robot is the 'Fastest in the World' Thanks to One Surprising Addition. (UniladTech)
• Copy of US Constitution Found in Filing Cabinet Sells for $9M. So, Somebody Does Still Value the Constitution. (BBC)

 

Trading Links:

• OpenAI's Advantage Over Rival AI Companies Vanishes, Report Finds. (Fortune)
• Nvidia’s Shocking Truth vs. The Dot-Com Bubble. (JoshBelanger)
• The Next Wave of AI Won’t Be Driven by LLMs. Here’s What Investors Should Focus on Instead. (Fortune)
• Amazon Announces Nuclear Power Deals as Tech Giants Scramble for More Clean Energy. (GeekWire)
• ForecastTrader – Futures-Based Prediction Market for the Presidential Election and Other Things. (ForecastTrader)

 

The Law Of Diminishing Returns

At some point, more of the same stops paying off ... it is called the law of diminishing returns.

 

via Sketchplanations

Nature (and common sense) reminds us that equilibrium is important. For example, when you exercise too much, you get injured; when you drink too much water, you get poisoned; etc. 

This concept applies almost everywhere.

• It's why diversification is so important in portfolio construction theory. 
• Or, why you don't want to put all your eggs in one basket (concentrating your risk).
• And, my favorite, it's also why you shouldn't only eat vegetables.

A related nugget of wisdom from the extreme ... Too much of a good thing is a bad thing! 

And of course ... Be moderate in everything, including moderation.

Imaginary Centenarians: An Ig Nobel Pursuit

The 34th Annual Ig Nobel Prize ceremony took place last Thursday at MIT. Every year since 1991, around the time the recipients of the genuine Novel Prizes are announced,  the Ig Nobel Prize awards the ten achievements that "first make people laugh, and then make them think. They're meant to celebrate the imaginative and encourage more interest in the sciences. 

The first award for Biology was awarded to Robert Klark Graham, for pioneering development of the Repository for Germinal Choice, a sperm bank that accepted donations only from Nobellians and Olympians. While I jokingly tell my kids they have "superior genetic potential," this type of research and belief can have serious consequences. 

In 1996, Physicist Robert Matthews earned a prize for his paper "Tumbling Toast, Murphy's Law and the Fundamental Constants," which sought to explain why toast tends to fall buttered-side down.

  

 

In 2002, another physicist, Arnd Leike, earned the honor after using the law of exponential decay to explain the behavior of beer foam. 

And in 2017, Marc-Antoine Fardin earned the honor for using fluid dynamics to finally answer the eternal question: "Can a cat be both a solid and a liquid?" In case you are squeamish, no blender was involved in that experiment.

While these are funny examples, they're rooted in real science. As is the focus of this article, this year's Ig Nobel Prize for Demography. 

In my circles, it is becoming more common to discuss how to live past 100 — and not just how to live beyond that number ... but to do so with a high quality of life.

A popular concept around that subject is Blue Zones - areas where people seem to live longer and healthier. There's even a Netflix documentary on the subject. Notable places include Sardinia, Italy; Okinawa, Japan; and Ikaria, Greece. 

Saul Justin Newman challenged that belief with his research, which found that extreme age records tend to come from areas with no birth certificates, rampant clerical errors, pension fraud ... and even short life spans. 

While longevity in the zones has primarily been attributed to diet, community, and genetics, Newman found that many of these claims were based on errors - or outright fraud. Instead, these regions are actually characterized by the opposite of what you would expect ... low incomes, low literacy, high crime, and short lifespans. 

To a certain extent, it makes sense. In areas where you're struggling to make ends meet ... why wouldn't you commit pension fraud? In fact, in 2010, the Japanese government realized that over 80% of the people aged over 100 were actually dead. Part of what made this possible was that America bombed the halls of records in that area during the war. 

Here's an interview with Saul Justin Newman on the subject. He's tracked over 80% of the people aged over 110 worldwide. Almost none of them have a birth certificate. Only about 10% have a death certificate. 

What does this mean for human longevity?

While the stories of these 110-year-olds may mostly be fake - as mentioned in my recent article - longevity is on the rise, and there are many modalities to increase your lifespan. 

 

 

The goal isn't just to stay alive longer; it's to live life to its fullest for as long as possible.

There are people living to 100, and there are plenty of people living healthily into their 70s, 80s, and 90s. 

We're taking steps in the right direction. Technology and medicine are both evolving quickly. 

But, like with longevity data, improvements in any space need to be met with a grain of salt. 

If it sounds too good to be true ... it generally is. Not always. But, generally. 

The Average NFL Player ...

When I woke up this morning, I saw that Dak Prescott, the star quarterback for the Dallas Cowboys, signed a new contract, making him the highest-paid player in NFL history. His new four-year, $240 million deal makes him the league’s first $60 million per year player. 

If you are curious, here is a list of the highest-paid NFL players in 2024.

Each of the 32 teams has an active roster of 53 players. That is 1,696 active roster players. Add the practice squad and account for injuries, and in a typical season, you end up with about 2,100 players per season.

Now that the NFL Football season is officially underway, I thought it would be interesting to look at each position’s composite player.

As you might expect, different sports have a different ratio of ethnicities, builds, and features. The same is true for different positions on a football team.  For example, you might expect more Pacific Islanders in Rugby or Asians in Badminton. You expect NBA players to be taller, swimmers to have longer arms, and football players to have more muscle. 

Here is a visualization that shows what happens when you average the top players’ faces in various positions.

  

osmutiar via Reddit

Composites are interesting.

While you may be thinking, “This player must be unstoppable,”... statistically, he’s average.

The “composite” NFL player would be the 848th-best player in the league. He’s not a starter, and he plays on an average team. 

We found the same thing with our trading bots. The ones that made it through most filters weren’t star performers. They were the average bots that did enough not to fail (but failed to make the list as top performers in any of the categories). The survivors were generalists, not specialists.

In reality, you need both.

In an ideal world with no roster limits, you’d want the perfect lineup for each granular situation. You’d want to evaluate players on how they perform under pressure, on different downs, against other players, and with various schemes. 

On a related but slightly different note, I recently read a post called “Why Generalists Own the Future.” It says that, in the age of AI, it’s better to know a little about a lot than a lot about a little. But part of that rationale is that it is easy to find or create digital specialists to do the things people used to do.

That’s what technology lets you do with algorithms. You can have a library of systems that communicate with each other ... and you don’t even have to pay their salary (but you will need data scientists, researchers, machines, data, alternative data, electricity, disaster recovery, and a testing platform).

You won’t find exceptional specialists if your focus is on generalized safety. Generalists are great, but you also have to be able to respond to specific conditions.

How ‘Bout Them Cowboys!

Some Perspective: "I'm Way Closer To Lebron Than You Are To Me!"

With the NFL starting its regular season this week, I was thinking about the talent and effort it takes to compete as a professional athlete.

Take a second to reflect on the journey required to become a pro. In general, the top players from a youth league became standouts in high school and then were star players in college before eventually making it to a professional team. But here's the kicker ... in any other context, they're elite, but unless they're superstars in the NFL, they're considered average or worse. Think about it. By definition, half of the players are below average. 

Meanwhile, I recently came across an example from the NBA that illustrates this concept in a funny but profound way.

There's a relatively famous quote from NBA journeyman, Brian Scalabrine, who said: 

"I'm way closer to Lebron (James) than you are to me!" - Brian Scalabrine

For context, as a USC Trojans men's basketball player, Scalabrine was the top scorer and a leader in field goals and rebounds. He then played 11 years in the NBA ... but at no point in that time was he a star. He didn't put up great (or even good) stats, he wasn't a household name (though he did pick up the nickname "White Mamba"), and he is nowhere near the caliber of player that Lebron James is. In fact, throughout his career, he averaged just 3.1 points, 2.0 rebounds, and .8 assists a game. But, remember, he was good enough to play at a pro level for 11 years.

As a result, many unprofessional (weekend warrior) athletes thought he was an easy target. One day, Brian was playing a rec league game where he dropped 60 points. He tweeted about it, and a bunch of people started tweeting back to the now 40+ years old former NBA player that they could beat him. There are funny YouTube videos about this.

Brian responded by replying:

"Listen. I may suck for an NBA player. Those guys are pretty good. But I don't suck compared to you. You suck compared to me." - Brian Scalabrine

He then accepted their challenge by asking them to send in videos of their play and committed that he would go 1-on-1 against the best of them.

Scalabrine then went on to play 4 of the best players who responded, with one of them having NCAA D1 experience.

The end result?

He outscored them 44-6 - with two of the players scoring 0 points. 

 

via YouTube

It should be self-explanatory, but it seems to be a concept many people struggle with. Any given pro player has been the best of the best throughout their journey. They're the 1% of the 1%. 

But that is probably true for you too. If you're reading this article, you're likely killing it compared to the average Joe. 

Many of us are in rooms with phenomenal business owners and operators. When you meet people like Peter Diamandis, Ray Dalio, or Richard Branson, it's easy to focus on the distance between you and them.

Recognize that it is still a huge accomplishment to be a Brian Scalabrine rather than a Joe Schmo. 

They don't recognize what you and me do recognize. When you're in the NBA, there's all kinds of tells, right? Like if a guy puts his hand like that, you know what he's gonna do. If a guy does a hesitation, you know what he's gonna do. All that stuff is like in real time in the NBA, you got to be so on top of the reads. It's not speed. You can't look at me and say my brain is slow. My brain is fast. My body might be slow, but I have to read whether a guy's gonna shoot, drive, go to the middle, pass. If you're not reading those things, you're not playing in the NBA. - Brian Scalabrine

A helpful reminder.

Keep it up – and as always ... Onwards!

Capturing Gold

The Olympics have officially concluded.  For many, it was the ultimate display of the thrill of victory and the agony of defeat.  For others, it's a way to boast about their country's medal count.  If you're curious, here are the final medal standings.

One of the most captivating moments occurred in the men's 100-meter sprint.  It made me reflect on how years of preparation can come down to a few thousandths of a second determining the difference between Gold, Silver, and Bronze — or virtual irrelevance.

Last week, Noah Lyles broke a 20-year U.S. drought in the men's 100m final, winning gold with a 9.784 time.

Lyles came into the race ranked No. 1 in the world, but he had to run his fastest time ever to win the Olympic gold medal, and he did so by the slimmest of margins — 0.005 seconds.

In that race, Lyles achieved an average speed of 25.7 mph, and his max speed hit 27.84 mph.

Surprisingly, Lyles didn't lead the race until the final and most important moment.  Many thought that Jamaica's Kishane Thompson had the gold ... but advanced technology showed that Lyles surged ahead in the final stretch, edging Thompson out by a split second to claim victory.  Here is the photo finish.

 

via ESPN

It took over half a minute (much longer than the race itself) for the judges to announce the winner - it was that close. 

As a tech nerd, what I found most interesting about the win was the camera used to certify the win. 

Omega, which has been the official Olympics timekeeper for decades, released a new camera that shoots 40,000 frames per second, aimed directly at the finish line.

It reminded me of the facial recognition technology NFL teams like the Dallas Cowboys use to track - theoretically - every person who steps into a stadium.  The cameras are so good that when a crime is committed, they can completely track the perpetrator as they travel throughout the stadium.  The Cowboys' security office boasts that their camera system surpasses even the ones used by Las Vegas casinos to catch cheaters at the gaming tables.  However, it seems like Omega has taken things to a whole new level at the Olympics with its advanced camera technology this year.

Many think the 200m race is Lyles' specialty.  His personal best of 19.31 seconds in the 200m is the American record, making him the third fastest in the event.

Unfortunately, Lyles couldn't grasp gold in the 200.  He got Bronze instead.  However, after the race, he revealed that he ran the race with COVID ... which might explain his drop in performance.  Still, it stands as a testament of will to me.

As a side note, while the International Olympic Committee does not pay athletes for winning at the Olympics, many countries do!

 

via Voronai

While the U.S. isn't near the top of the list - American athletes who get gold bring home $37,500.  A silver nets you $22,500, and a bronze nets you $15,000. 

Of course, these medals can also lead to other compensation and endorsements - but the payout table was still interesting.

The achievements of athletes like Noah Lyles create national pride and open doors to numerous opportunities and rewards.

Meanwhile, the integration of advanced technology in the Olympics highlights the importance of innovation in sports.  As we celebrate these victories, we also look forward to the future advancements that will continue to shape the world of athletics and the world itself.

2024 Update: What Happens In An Internet Minute

The Internet is both timeless and timely in an interesting way.  While what's popular changes seemingly instantly, and what we're capable of doing on it continues to grow exponentially.  Ultimately, the Internet is the digital town square of a global village, where all types and professions gather. 

In 2011, I first wrote about what happens in 60 seconds on the Internet. 

I've since updated the article a few times.

• 2014
• 2018

Each time I write the article, I'm in awe at the amount of data we create and how much it has grown.  For example, looking back to 2011, I was amazed that users created 600+ new videos and 60 new blog posts each minute.  Those numbers seem quaint today. 

 

via DOMO

Shortly after I started sharing the articles, Data Never Sleeps started standardizing the data, which is helpful. 

Today, the Internet reaches 5.4 billion people.  Most of them also use social media. 

To add some more perspective, 

• In 2008, 1.4 billion people were online; in 2015, we were at 3 billion.  Now, that number has almost doubled again. 
• In 2008, Facebook only had 80 million users, and Twitter (now X) had 2 million users.
• In 2008, there were 250 million smartphones, and now there are almost 7 billion of them!

It is mind-blowing to consider what happens each minute on the Internet today.  For example, the 104,000 hours spent on Zoom represents a significant societal shift ... and the over 500 hours of video uploaded to YouTube highlights the incredible amount of content that's being created to share. 

In 2023, the world created approximately 120 zettabytes of data ... which breaks down to approximately 337,000 petabytes of data a day.  Broken down even further, it calculates to more than 15 Terabytes of new data created per person. 

The calculations about what happens in an Internet minute will change rapidly again because of AI.  Consider the amount of computing power and data it takes to power all of these new GPTs. Now, imagine the amount of new data that AI is creating.  Then, try to imagine the challenge we'll have figuring out what's real, what's made up, and what is simply wrong or intentionally misleading.

In addition, as more devices and digital WHOs start creating and sharing data, it's hard to fathom the ramifications and sheer increase in data. 

I'm curious about what the next five years have in store for us as we approach the 40th anniversary of the World Wide Web. 

The Law (And Flaw) Of Averages

The law of averages is a principle that supposes most future events are likely to balance any past deviation from a presumed average.

Take, for example, flipping a coin.  If you happen to get 5 "Heads" in a row, you'd most likely assume the next one should be "Tails" ... even though each flip has a 50/50 chance of landing on either. 

Even from this example, you can tell it's a flawed law.  While there are some reasonable mathematical uses of the law of averages, in everyday life, this "law" mostly represents wishful thinking. 

It's also one of the most common fallacies succumbed to by gamblers and traders. 

The concept of "Average" is more confusing and potentially damaging than you might suspect.

Perhaps you heard the story about how the U.S. Air Force discovered the 'flaw' of averages by creating cockpits based on complex mathematics surrounding the average height, width, arm length, etc., of over 4,000 pilots.  Despite engineering the cockpit to precise specifications, pilots crashed their planes on a too-regular basis. 

The reason?  With hindsight, they learned that very few of those 4,000 pilots were actually "average".  Ultimately, the Air Force re-engineered the cockpit and fixed the problem. 

It's a good reminder that 'facts' can lie, and assumptions and interpretations are dangerous.  It's why I prefer taking decisive action on something known, rather than taking tentative actions about something guessed. 

via ReasonTV

Our Brains and the Illusion of Balance

Our brains are wired to find patterns, even in random events.  This tendency, known as apophenia, can lead us to see connections where none exist.

The Misleading Law of Averages

It's this very tendency that fuels the misconception of the law of averages.  We expect randomness to "even out" because we see patterns in short sequences.  This can be tempting to believe, especially when dealing with chance events.

The law of averages is a common idea that suggests future events will even out past results to reach some average outcome.  For instance, going back to our earlier coin-flipping example,  after getting five heads in a row, it's natural to assume the next flip is "due" to be tails.  However, that's not how probability works.  Each coin flip is an independent event (with a 50% chance of landing on heads or tails), regardless of previous flips.  The coin doesn't "remember" what happened before.

Apophenia isn't limited to coin flips.  For instance, you might see your lucky number appearing repeatedly throughout the day, leading you to believe it has a special meaning – even though each instance is completely independent.

This natural desire for order and predictability can lead us astray when dealing with chance events.

Why is it Flawed?

The law of averages often leads to a misconception called the gambler's fallacy.  This fallacy is the belief that random events can somehow "correct" themselves to reach an average.  In reality, every coin flip, roll of the dice, or spin of the roulette wheel is a fresh start with its own discrete probabilities.  The odds remain the same no matter how long the losing streak persists.

Are there ever times when it applies?

It's important to distinguish the law of averages from the law of large numbers, a well-established statistical principle.  The law of large numbers states that as the number of random events increases, the average outcome gets closer to the expected probability.  This applies in situations where many trials happen, and while past results of individual events are independent, the law describes the behavior of averages over a large number of trials.  For instance, the average weight of a large sample of apples will likely be close to the expected average weight of an apple, even if some individual apples are heavier or lighter than expected.

However, in everyday situations (with a limited number of events), the law of averages is generally not a helpful way to think about chance or probabilities.

Understanding these misconceptions can help us make better decisions and avoid false expectations based on flawed reasoning.

Psychological Reasons Behind the Belief

Human decision-making suffers from a range of tendencies and biases.

Earlier, we discussed the tendency to find patterns, even where none exist.  Next, we will consider cognitive bias.  In our coin-flipping example, it is the representativeness heuristic that makes us assume that small samples should resemble the larger population they come from.

Emotional factors also play a role.  The desire for control in uncertain situations can make us latch onto the law of averages as a comforting notion.  Believing that things will "even out" gives us a sense of predictability and fairness in an otherwise random world.

Additionally, social influences can reinforce these beliefs.  Stories and anecdotes about streaks ending or luck changing often circulate among friends and family, further embedding the misconception into our collective consciousness.

Understanding these psychological reasons helps explain why the law of averages persists despite its flaws.  Recognizing these biases can empower us to think more critically about probability and chance events.

Improving Decision-Making in Gambling and Investing

Recognizing the fallacy of the law of averages can significantly enhance decision-making, particularly in gambling and investing.  Understanding that each event is independent can help participants make more rational choices.  Instead of chasing losses with the hope that a win is "due," savvy speculators understand their odds remain constant and may choose to walk away or set strict limits on their betting behavior.

In investing, this knowledge is equally crucial.  Many factors influence markets.  Nonetheless, believing that a stock "must" rebound after a series of declines too often leads to poor investment decisions.  Investors who grasp that past performance does not dictate future results are better equipped to evaluate investments based on fundamentals rather than emotions or flawed expectations.

By dispelling these misconceptions, you can approach gambling or investing with a clearer mindset, reducing the risk of substantial losses driven by erroneous beliefs about probability and chance.

You can also eliminate fear, greed, and discretionary mistakes by relying on algorithms to calculate realtime expectancy scores and take the road less stupid.  Take a different kind of chance.  Just ask our AI Overlords; they'll tell you what to expect!

Father's Day Weekend Reflections

My adult son took me to a Pixar movie and Dallas' version of NY Deli today for Father's Day. 

Pixar movies never cease to amaze me.  Whether you're a child, a teenager, or an adult, there's always something to enjoy and take away from them.

I especially enjoyed watching it next to my 31-year-old son and noticing that he responded emotionally to the same scenes I did.  On one hand, it felt good to see what he processed and how he internalized things similar to the way I do.  On the other hand, I thought, genetics is a bitch.

Jokes aside, having great kids is a double blessing.  It's nice to be proud of who your kids are and the things they do.  It's also nice to feel proud of the small part you played in helping them become who they are.

In addition, this weekend, I spent some time thinking about my father and what a terrific influence he had on so many lives.

  

 

My Dad was incredibly loving ... yet he was also incredibly demanding.

For example, after winning the State Championship in the shot put, I watched him run down from the stands.  I figured he was coming down to celebrate.  Instead, he looked deeply into my eyes and asked whether I was disappointed that I did not throw a personal best that day?  I replied: "But Dad, I won." He smiled and recognized that winning was important too ... then he reminded me that the other throwers were not my real competition.  To be and do your best, the competition is really with yourself ... and we both knew I could do better.

My Dad believed in setting high standards.  He explained that most people's lives are defined by their minimum standards.  Why?  Because once those standards get met, it is easy to get distracted by other things and how to meet the minimum standards for them as well.

The point is to set a higher standard and to have a better life.

Here is another one of his favorite sayings.  "The difference between good and great is infinitesimal."  This applies to many things.  For example, people who are good take advantage of opportunities; people who are great create them. 

Here is something else worth sharing.  "It's not over until we win!"  This concept underscores the importance of resilience, commitment, and grit.  My Dad emphasized that many people quit when they're on the brink of victory, simply because they don't realize how close they are.  

This has led me to develop several practices.  For example, if I pick up a book, I won't put it down until I finish a chapter.  If I start a game, I can't stop until I exceed a specific score or level.  And when I exercise, there's no way I'd ever stop before finishing a set.

Integrating these concepts involves aligning your head, heart, and feet.  What I mean is that it's one thing to know the saying.  It's another to make it a value or belief.  And it's another thing altogether to make it a practice. 

Well, that should explain a little of my dysfunction ...  but, if you can't mess up your own kids, whose kids can you mess up?

Hopefully, you had a happy Father's Day weekend.

March Is Always 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.  Yale beat Auburn, James Madison beat Wisconsin, Michigan State beat Mississippi State, and by the end of day 1, only 2,000 brackets remained intact.  That's .008% of all brackets submitted. 

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 (2.4 trillion based on a Duke Mathematician's formula that takes into account rank).  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: 

• 80 million tournament brackets were completed
• 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 $9.6 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 of that sleeper pick, of our alma mater winning, of the big upset. 

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 being 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 1 in 9.2 quintillion odds. 

Two lessons can be learned from this:

1. People aren't as good at prediction as they predict they are.
2. Machine Learning isn't a one-size-fits-all answer to all your problems.

Something to think about.

 

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 of that sleeper pick, of our alma mater winning, of the big upset. 

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 being 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 1 in 9.2 quintillion odds. 

Two lessons can be learned from this:

1. People aren't as good at prediction as they predict they are.
2. Machine Learning isn't a one-size-fits-all answer to all your problems.

Something to think about.