Sports

  • Super Bowl 2020

    A friend has two tickets for the 2020 SUPER BOWL — both box seats. He paid $2,500 for each ticket, but didn't realize last year when he bought them that it was going to be on the same day as his wedding. If you are interested, he is looking for someone to take his place — it’s at St. Dominic's Church in San Francisco at 3pm. Her name is Melissa . She's 5'7 about 140 lbs. She's a good cook, too. She'll be the one in the white dress.
     
    As for the Super Bowl, it seems like this will be a good matchup. The KC Chiefs are in it for the first time in 50 years (and have an amazing offense), while the 49ers are coming off a series of tough seasons (but have an amazing defense). It will also be a Super Bowl without Bill Belichick's scowl gracing our screens. 
    It's not just the fans that are excited about the matchup, it's also the bookies. Two years ago, gamblers set a record by placing $158.6 million in Super Bowl bets. Expect more of the same with gambling now legalized in an additional 13 states beyond Nevada. 
     
    If you're still looking for a bet – there's another wishful thinking approach you could take to find one … it is called the "Super Bowl Indicator."
     

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    The theory is that a Super Bowl win for a team from the AFC foretells a decline in the stock market and a win for the NFC means the stock market will rise in the coming year. So, if you're hoping for a strong S&P you'd be rooting for 49ers. 

    There's one big caveat … it counts the Pittsburgh Steelers as NFC because that's where they got their start (or as a data scientist would caution … they did that to fit the data better). 

    With that "adjustment," at one point the SBI was "right" 33 years out of 41 – an 80% success rate. Sounds good, right?

    Come on … you know better. It's been wrong four of the last four years … another sign of spurious correlation if you weren't sure. 

    Here are some other "fun" stock market fallacies:

     

    Back to Reality

    Rationally, we understand that football and the stock market have little in common, and we probably intuitively understand that correlation ≠ causation. Yet, we crave order and look for signs that make markets seem a little bit more predictable.

    The problem with randomness is that it can appear meaningful. 

    Wall Street is, unfortunately, inundated with theories that attempt to predict the performance of the stock market and the economy. The only difference between this and other theories is that we openly recognize the ridiculousness of this indicator. More people than you would hope, or guess,  attempt to forecast the market based on gut, ancient wisdom, and prayers.

    While hope and prayer are good things … they aren’t good trading strategies.

    As goofy as it sounds, some of these "far-fetched" theories perform better than professional money managers with immense capital, research teams, and decades of experience…

    I have a thought experiment I sometimes ask people. 

    What percentage of active managers beat the S&P 500 any given year?

    … Now, what percentage beat the S&P 500 over 15 years?

    Recently, the answer is about 5% (and that's in a predominantly bull market).  For the record, that's significantly worse than chance. Perhaps that means something they're doing is hurting, not helping. 

     

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     via Gaping Void

    There's simply too much information out there for us to digest, process, rank, and use appropriately.

    Every second you spend looking at a market is a second wasted.

    There are people beating the markets — not by using the Super Bowl Indicator … they're doing it with more algorithms and better technology. 

    There will never be less data or slower markets. A good reminder that if you don't know what your edge is … you don't have one!

    Onwards.

  • A Roller Coaster of Emotions

    I recently saw this video during a presentation.

    It's hilarious – and a great reminder that it doesn't matter how simple something is (if the stakes are high enough) and the environment is distracting enough. 

    That's where automation comes in. Enjoy. 

     

    Eliminating fear and greed are great steps to take in the pursuit of eliminating discretionary mistakes.

    Think about how many other places this video explains the "why" behind disappointing results.

  • What Does The Average NFL Player Look Like (By Position)

    Football season is officially underway! In honor of that, here's a look at each position's composite player!

    As you might expect, different sports have a different ratio of ethnicities. For example, you might expect more Pacific Islanders in Rugby or Asians in Badminton.

    The same is true for different positions on a football team.  Apparently, offensive linemen are more likely to be white while running backs are more likely to be black. 

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

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    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 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 different schemes. 

    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.

    Onwards.

  • Here Are Some Links For Your Weekly Reading – September 1st, 2019

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

    Lighter Links:

     

    Trading Links:

  • Here Are Some Links For Your Weekly Reading – August 4th, 2019

     

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

     

    Lighter Links:

    Trading Links:

  • Here Are Some Links For Your Weekly Reading – June 30th, 2019

    Most people don't spend money on freemium games. These games rely on a small subset of "whales" to survive – players that spend absurd amounts of money on their games. Gacha games let players pay to "roll the dice" to unlock rare characters and collectibles similar to a "Loot Box" mechanic. 

    Now, the US is trying to make this mechanic illegal for taking advantage of children with their "pay-to-win" mechanics. 

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

     

    Lighter Links:

     

    Trading Links:

  • Social Media Is Changing Everything: A Reprisal

    I came across a post from 2009 about social media. 10 years later, with the knowledge of how much data we use today, it's quite a read.

    Here it is in its full glory. 

    Social Media Is Changing Everything: October 18th, 2009

    My son won't use e-mail the way I did. So how will people communicate and collaborate in the next wave of communications?

    Here is a peek into the difference that is taking hold.  I was looking at recent phone use.  The numbers you are about to see are from the first 20 days of our current billing cycle.

      • My wife, Jennifer, has used 21 text messages and 38 MB of data.
      • I have used 120 text messages and 29 MB of data.
      • My son, at college, used 420 text messages, and is on a WiFi campus so doesn't use 3G data.
      • My son, in high school, used 5,798 text messages and 472 MB of data.

    How can that be?  That level of emotional sluttiness makes porn seem downright wholesome. 

    But, of course, that isn't how he sees it.  He is holding many conversations at once.  Some are social; some are about the logistics of who, what, when, where and why … some are even about homework.  Yet, most don't use full sentences, let alone paragraphs.  There is near instant gratification.  And, the next generation of business people will consider this normal.

    Is social media a fad? Or is it the biggest shift since the Industrial Revolution?

    Welcome to the World of Socialnomics.  This video has a bunch of interesting statistics … and is fun to watch. 

     

     

    Other Resources:

    Social Media Is Changing Everything: April 20th, 2019 

    Looking at the stats from 2009 is pretty funny

    • My son was using 472 MB of data a month
    • Hulu had grown from to 373 million total streams in April 2009
    • Only 25% of Americans in the past month said they watched a short video on their phone

    For some context, I looked up the comparative numbers for 2018. 

    • I picked a random month in 2018 … in August my son used 10.85 GB of data. He doesn't text as often – but has sent/received 282,000 snapchats since downloading it 5 years ago.
    • Hulu has over 20 million subscribers who streamed more than 26 million hours a day in 2018 
    • People spend over five hours a day on their smartphones on average. 70% of web traffic happens on a mobile device, and more than 50% of videos are watched on mobile (93% of twitter videos). 

    Here's what happens every minute of every day on the internet

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    via Lori Lewis and Chadd Callahan

     

    A little different than 2009 …

  • March Madness: The Quest For The Holy Grail

    March Madness is in full swing and will have the world's attention for a few more days. As you could guess – most brackets have already busted. Louisville lost, UC Irvine won, and Duke barely weathered UCF's onslaught. 

     

    190324 Duke

    via FanSided

    Lots of skill, lots of adrenaline and lots of natural talents (including amazing physical talents).  Here is a picture of 7'6 Tacko Fall. Tacko still looks taller than his opponents even when kneeling. 

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    via UCF Basketball

    The allure of March Madness is the same as gambling or trading.  These are all fertile grounds for emotion, biases, and statistics. 

    The holy grail is mighty elusive in March Madness (as in most things)  … For example, the odds of getting the perfect bracket is 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.

    Here's some more crazy March Madness Stats: 

     

    via Duke University

    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 3 people that 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 feel, some base their decisions 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 be helpful in making 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 your 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 have the answers to "who will win" and we don't have the answer to "what makes a perfect prediction". 

    Think about it from a market efficiency standpoint. People make decisions on many factors – sometimes irrational ones – and that creates inefficiencies & complexities. It can be hard to find those inefficiencies and capitalize on them – but they're there to be found. 

    In trading, that's where AI and advanced math come in – taking away our biases and identifying 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

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    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 on 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. 

  • Avoiding Death By Snowmobile: Part II

    Last year, I recounted my humbling via snowmobile. 

    I traveled to Saskatchewan, Canada and "sledded" with people who had been doing it since birth. 

    When I got there, everyone was in great spirits. Apparently, this is a sport done with day drinking, lots of laughter, and semi-reckless abandon.

    We split into groups: Insane, Merely Crazy, and the Turtles. I figured I was relatively safe with the Turtles … I was wrong. 

     

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    The machines are capable of gliding over the snow at speeds exceeding 120 miles per hour.  I wasn't going nearly that fast … but the beasts were harder to tame than I expected. 

    Despite crashing numerous times, totaling a sled, and being sore for weeks … I had so much fun on year one that I brought my son with me on year two. 

     

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    Last year, I recognized 2 things: 

    1. Humans are deletion creatures. That means they can hold seven things (plus or minus two) in their memory. While they were focused on fun, I was focused on how to stay on the sled
    2. You're supposed to stay on the sled, and leaning into the turn helps you do that … who'd've thunk it? 

    This year I learned a couple more: 

    1. People don't forget. Everyone remembered my less than skillful sledding. 
    2. Youth is wasted on the young. My son picked it up fast and was speeding as if he'd been on a sled his whole life (despite falls that would cause people with skulls that no longer have soft spots to proceed with caution). 
    3. When you find the right group of people, the fun gets more fun, and struggles seem less challenging. 

     

    While sledding is fun, it's the people that make the trip. 

    Likewise, it's the people you take with you through life that makes it so worth it. 

    Oh, and I did suck less this time … Progress!

     

  • Gil Brandt and the Innovation of Modern Football

    I'm regularly surprised by the levels of innovation and strategic thinking that I see in football.

    I've written posts about how much regular businesses could learn from watching football teams. That post talks about how even high school teams have a position-by-position depth chart. They have a game plan for their next opponent. They practice plays on both offense and defense. They watch game film of their past games and future opponents. And, they coach the team as a team, and the players individually. 

    Often, simple ideas, applied consistently, are enough to win.

    Last night I was at a Dallas Cowboys playoff game (note that is not an oxymoron) … and I took a picture with this guy.

     

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    I see him at every game … and every game he wears a different creative and well-thought-out costume. He sits on the 50-yard line, He wears Ferragamo shoes and a nice watch. Yet, something about this game captures his imagination.

    Yesterday, I also had the honor of meeting Gil Brandt at the game. For those of you not familiar with him, he was the vice president of player personnel (or Chief Scout) of the Dallas from 1960 to 1989.  He helped Dallas grow into one of the most powerful and popular sports franchises in America. His innovative management and personnel systems are standard operating procedure today for many teams at the professional and collegiate levels nationwide. 

      

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    Many consider him the architect of the Dallas Cowboys. I'm not talking about the stadium or headquarters, I'm talking about their teams and ideas that were the catalyst to "America's Team".  I took this from his Wikipedia page.

    He helped pioneer many of the scouting techniques used by NFL clubs today,[2] such as:

     

    Gil Brandt is one step closer to joining the NFL's ultimate pantheon – The Pro Football Hall of Fame.  He has been nominated as a contributor finalist for the Class of 2019. The contributor category recognizes "individuals who made outstanding contributions to professional football in capacities other than playing or coaching."

    He's an inspiration. Not only did he innovate how teams draft and measure talent … he's the guy who figured out how to make cheerleaders an iconic symbol of a team and the game itself.