Moneyball, a best-selling novel and award-winning movie, gives readers and viewers a detailed explanation about the importance of analytics in the baseball industry, and how one man’s intuition transformed the scene forever. It is about Billy Beane, The Oakland Athletics’ General Manager, and his non-traditional methodology of using readily available, long-overlooked data to build a champion baseball team.
Oakland, California is not a huge sports market, as Beane had only $44 million to spend on fielding a team. Compare this to the New York Yankees’ $125 million payroll, and it’s clear that the A’s would never been in the same conversation as the Yankees. This could not be further from the truth. Despite spending slightly more than a third of the money, the A’s made the playoffs, and ultimately made it just as far as ‘The Bronx Bombers’. So, what was the difference?
Billy Beane felt the traditional, century-old view of ‘talent’ was one-dimensional, and not necessarily the best way to build a team. Batting average, as well as the number of runs batted in, home runs, and stolen bases, were long-valued measures of success, but in Beane’s view, not ones that would necessarily earn his team a spot in the postseason. So, he took a different approach, and the result seems so simple.
Here’s what he did: he simply asked: How do teams win games? By scoring more runs than the other team. How do they score runs? By getting players on base. He realized that this ignored statistic was a key element to a new analytical model. And that became his strategy. He drafted and traded for players who had high levels of on base percentage (OBP) and slugging percentage (SLG). OBP is simply how often a player reaches base for any reason, and SLG is the total bases a player reaches divided by his number of at bats. Because these were usually ignored metrics, Beane found players that were grossly undervalued in the market, and brought them to Oakland at a very affordable price.
In the baseball world, this newfound methodology is referred to as Sabermetrics. Bill James, a pioneer and advocate for Sabermetrics, defined it as “the search for objective knowledge about baseball”, and can answer questions such as “which player on the Red Sox contributed most to the team’s offense?” [i] Sabermetricians like Beane questioned the inflated importance of batting average, as it does not directly translate to the amount of runs a team scores. For decades, baseball traditionalists only looked at a small set of statistics, which were arbitrarily deemed to be the most significant. However, Beane realized there was a profound set of data that had previously gone unnoticed, and applied it in a tactical manner.
Moneyball highlights one man’s courage to look beyond the way business has always operated—to challenge the status quo, and to utilize old facts in a new way in order to develop unique strategies. It took quite a while, but before other teams adopted this strategy into their front offices, the Oakland A’s had a sizable competitive advantage over other teams in the league. Compare the Moneyball mentality to a new concept—predictive analytics—and it becomes clear that the future of the sports industry is going to be completely revolutionized—examining data that was always available, but never analyzed in such a revolutionary manner.
Predictive analytics can be implemented in nearly any industry—not just sports. For example, say the director of development for a museum system analyzes his or her data and realizes that the number of donors has been in steady decline for the past few years, despite increased amounts of marketing to prospective donors. Using predictive analytics, that same director of development could predict the likelihood that a visitor to a museum would eventually become a high-level donor, allowing his or her efforts to be focused on those customers who already have the ‘look’ of that donor. Additionally, he or she could predict the impact that a specific marketing campaign would have on those prospective donors, not only helping save the museum time and money, but also ultimately meeting business goals. The examples are endless, and being able to answer such questions will help organizations improve decision-making and separate them from their competition.
In Oakland, Billy Beane revolutionized the way teams—specifically general managers—assessed talent. By analyzing the data in a different way, he effectively changed the way the entire baseball industry operated. This is what OThot aims to achieve—by utilizing organizations’ data to not only help them understand the future of their business, but to also understand why—a necessary component in being able to take necessary action in changing that future. For example, using past attendance statistics in order to predict future attendance at a sporting event is useful, but without knowing why that trend exists, it is incredibly difficult for the organization to make calculated decisions that will ensure specific goals are met. By creating a model that analyzes a myriad of potentially overlooked data, OThot’s predictive analytics engines can help nearly any company predict and alter the future of its business. Soon, predictive analytics will be commonplace in almost every industry.
Like Billy Beane, our goal is to change the way that organizations make decisions.