If you watch any of the four major professional sports leagues, be it the NFL, NBA, NHL or MLB, you have probably heard the term “sports analytics.” Simply put, sports analytics is the systematic analysis of statistics within professional sports. While statistical analysis has been part of sports for decades, the fact that all of today’s major sports teams hire a group of statisticians to support their operations is a relatively new phenomenon.
Interest in sports analytics climbed sharply due to the 2003 book, Moneyball by Michael Lewis later made into an Academy Award nominated film of the same name starring Brad Pitt. Mr. Lewis’ book introduced “sabermetrics” to the world at large. In major league baseball, sabermetrics is the analysis of baseball statistics originating from the Society of American Baseball Research (SABR), founded in 1971. It was not until the late 1990s, however, that major league baseball personnel began to take heed of the large trove of data that SABR afforded. As technology progressed, more data became available to those who wanted to analyze it.
One such devotee of statistical baseball data was Billy Beane, the general manager of the Oakland Athletics from 1997 to 2016. As the general manager, Beane was in charge of scouting and drafting players. He became disillusioned with conventional measures of a player’s potential, as he felt they did not accurately predict the players they would later become.
Some of these traditional metrics to evaluate players included: batting average, runs batted in, home runs, and earned run average. Anyone who has ever collected baseball cards is intimately familiar with these statistics. They were the bedrock of baseball analysis for decades. It is for this very reason, though, that Billy Beane questioned their validity. He believed that with all the new data available, there must have been something that other people were not seeing.
After conferring with programmers that the Athletics already had on staff to cover IT functions, Beane arrived at what he thought were better predictors of future performance. While some of these statistics are quite arcane, others were simply overlooked for various reasons. One example a player’s ability to adjust to different ballparks.
Making Beane’s GM job more difficult was that upon being hired, he was tasked with slashing the current payroll. With limited financial resources, the Oakland Athletics needed an advantage wherever they could find it. This edge turned out to be what other coaches and managers were overlooking – a staff solely dedicated to analyzing the extensive amount of data available.
Under Beane’s front-office leadership, the Athletics were able to exploit this edge. Having reached the playoffs in four straight years from 2000 to 2003, they became the first team in the 100+ years of American League baseball to win 20 consecutive games. Further, in terms of “value,” Beane also delivered. In 2006, for example, the Athletics ranked 24th of 30 major league teams in player salaries but had the 5th best regular-season record.
As the Athletics began to attract national attention due to their success, it was inevitable that Beane’s analytics would soon be copied by other general managers across baseball. As this began to happen, Oakland found it more difficult to keep their edge over their competition. Even during their successful years with Beane, the Athletics still had a below-average payroll, so they were always fighting an uphill battle.
The takeaways from the Oakland Athletics in their “Moneyball-era” have many parallels to those in quantitative investing. Perhaps chief among these, is the idea of following where the data takes you and in turn identifying an edge.
Beane recognized that his competition was all looking at the same statistics and analyses and had been for years and years. Simply put, he believed better data would guide him to a better solution, and ultimately better results.
At Q3, our investment philosophy is predicated on this concept. Scrutinizing the data is paramount in identifying our edge. As a manager that emphasizes the importance of managing downside risk, one might equate our style to that of a baseball player who hits mostly singles and doubles, but not a lot of home runs. We may not be flashy, but we hit for a high batting average. We believe that this approach gives our investors the best chance to ride out the ups and downs of the stock market and ultimately win the game.