In the fall of 2001, the Oakland Athletics were in trouble.
They just lost to the New York Yankees in the American League Division Series. After the season, they lost three of their best players to free agency — Jason Giambi, Johnny Damon and Jason Isringhausen.
It seemed unlikely that the Athletics would repeat their success without their two best hitters and strongest pitcher.
What happened in the months leading up to the 2002 baseball season not only became the stuff of Hollywood lore, but an introduction to the trend that, 21 years later, has become the biggest buzzword in investing.
Today, I’m going to tell you how a strategic shift in America’s pastime led to an explosion in the use of data science … from Wall Street to Main Street.
The Athletics, Sabermetrics and Artificial Intelligence
Billy Beane — a former Major League Baseball prospect — was the general manager of the Athletics in 2001.
He was faced with the daunting task of replacing three of his best players with little to no money to do it.
After meeting with teams around the league in search of prospects, Beane met Paul DePodesta — a Harvard economics graduate working with the Cleveland Indians.
That meeting sparked a revolution in the game of baseball and set us on the path to using artificial intelligence (AI).
DePodesta was a firm believer in using sabermetrics — live game data — to evaluate players and tactics.
Up until then, finding the right baseball player was more about a scout or manager’s instinct and less about tracked performance.
But DePodesta, with his sports and economics background, believed looking at in-game data can help predict future performance.
That was just the edge Beane needed since his budget for big players was tight.
So Beane and DePodesta used sabermetrics to find lesser-known (or even past their prime) players whose performance added up to what the team lost.
In the 2002 season, the Athletics not only won their division, but also set the record for the most consecutive games won in a regular season.
All despite losing their top talent the year before.
Sabermetrics and the Stock Market
Sabermetrics evolved the game of baseball. We’re seeing the same today as AI evolves how we invest.
It’s not about what direction the wind is blowing or how something “feels.” It’s about what the data tells us.
This is no different than using sabermetrics to see that a player is more likely to get on base during a 1-2 count versus a 0-2 count.
Like Beane and the 2002 Oakland Athletics, it wasn’t about always swinging for the fences, but more about consistently getting on base and scoring runs.
Using machine learning — where computers are trained on huge amounts of data to perform certain tasks — AI can tell us a lot about the underlying data for stocks.
Machine learning can scan social media to gauge sentiment around stocks and examine market patterns to find the best time to execute a trade.
The possibilities are endless, which is not lost on us here at Money & Markets.
Our own chief investment strategist, Adam O’Dell, has been working with TradeSmith CEO Keith Caplan and his team on “Project An-E” — a trading platform that uses AI to find stocks with the greatest potential.
Adam and Keith launched their findings this week and you can get an exclusive, behind-the-scenes look at this transformational endeavor right here.
If you’re at all interested in using AI to gain an investing edge, I encourage you to check out what TradeSmith, Keith and Adam are doing with An-E.
Bottom line: Billy Beane faced the monumental task of replacing big-name baseball players with almost no money on hand.
He also faced the uphill battle of traditional baseball purists who relied on gut instinct over data.
Thanks to the introduction of AI, through sabermetrics, he conquered both.
And I believe AI is going to challenge — and beat — our conventional wisdom on investing.
Until next time…
Matt Clark, CMSA®
Chief Research Analyst, Money & Markets