Moneyball was a popular book (2003), and a subsequent movie (2011), that recounted the story of how the low-budget Oakland A’s baseball team, led by general manager Billy Beane, found a way to compete with teams with far higher budgets. Beane drafted young and inexpensive players who were good enough to contend with teams from far larger markets in New York, Boston, and elsewhere. In addition to being a great story, the key ideas also have implications for K-12 education and policy, which I’ll set up here and explore in more detail in the next post. The book didn’t just tell a scrappy underdog story, although this theme made for an excellent movie arc. The A’s success wasn’t based on the luck of finding those underdogs who could compete. Instead, Beane identified baseball talents that were being undervalued by other teams. For example, all teams would pay a premium for players with high batting averages. But Beane realized the truth in “a walk is as good as a hit” and saw that teams were not paying very much for players who walked often but had low batting averages. He was able to get these players easily by drafting or trading for them, and he paid them less than the players with high batting averages. These guys were almost as good as—and sometimes better than—the players with high batting averages, but cost far less.

At the core, Beane was building a team by finding and exploiting market inefficiencies, much as the most successful investors such as Warren Buffett have done. But unlike Buffett, who maintains some structural advantages in his investing, Beane had created a problem.  By acquiring a certain type of player and growing them into high-value assets, he was signaling to every other team what sort of player was being undervalued. Therefore, his advantage in selecting players who walked often, or were particularly good at defense, was short lived because other teams saw his strategy and invested in this method too—which then drove up the price of those players.

Because Beane then needed a new strategy, the Oakland A’s subsequently became one of the leading teams in the use of advanced statistical analyses to predict players’ outcomes. What he and others found was that many of the traditional baseball statistics were accurate in describing what had happened but poor at predicting the future. For example, a pitcher’s Earned Run Average (ERA) counts how many earned runs he gives up per nine innings—which is a pretty good description of results. But it turns out that pitchers’ ERAs vary quite a bit from season to season due in part to luck. Instead, Beane and other baseball analysts found that other statistics such as strikeouts, walks, and the rate at which the pitcher gives up home runs were far better than ERA at predicting future results. Teams that used these and other complex metrics could avoid overpaying for pitchers who were likely to regress to a lower level of performance, and to find the bargain pitchers whose results were likely to improve.

Why am I describing all this on an education blog? First, when someone as successful as Billy Beane shifts direction, it’s worth watching what he thinks is important now and in the future.  In a recent article in the Wall Street Journal, Beane describes his latest direction: an increasing reliance on big data as central to the team’s competitive advantage. The public announcement of his strategy suggests that he believes that it’s not going to be easy—that the predictive power of big data has promise, but it is hard to get right.

Second, the way that most K-12 education treats data analysis and statistics is like pre-Moneyball baseball—at best. Education policy and practice simply have not kept up with the increasing focus on data collection and analysis that is now common to many fields.

Within these ideas lie several important lessons for education. The growing, sophisticated use of data is increasingly common, and is arguably the most important developing issue in many fields from medicine to agriculture. But using data well is not easy for anyone, including schools, and many current practices and policies are impeding the move to better analytics.

The next post will further explore how these ideas apply to education.