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What We Can Learn From A Cambridge Economist

This is a guest post from Tom Murphy. Tom blogs at A View From the Cave and helps run the DAWNS Development Digest.

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An economist educated at a highly regarded university in Cambridge, MA pored over the research and past measures of a particular subject and realized that the measures used were inefficient. The economist learned that unpopular methods of measurement that were already in place could lead to a better understanding of the most cost-effective way to achieve success. With so much money being spent in the economist’s field of study, it was important to determine how to use the resources available in a given organization in order to maximize impact.

The economist started to get noticed and featured in books and news articles. Younger people flocked to learn what this economist was doing and began applying the new methods in their organizations.  The old guard rejected the change, holding to traditional measures.  When asked about what the economist was doing, they said that trying to use data to predict the actions of individuals was foolish and impossible.

You might think I’m referring to famed MIT economist Esther Duflo, but the economist in question is Paul DePodesta (Harvard University undergrad). Never heard of him? You’re about to learn a lot more: a character based on him is now being played by Jonah Hill in the film version of Moneyball.

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Moneyball is a book written by Michael Lewis about the surprising success of the turn-of-the-century Oakland Athletics (A’s). With a payroll of only $41 million in 2002 (the Yankees spent $125 million), DePodesta and General Manager Billy Beane were able to field a competitive team by tossing out all of the old measurements of players and looking at it in a new way.

In baseball, statistics like batting average and runs batted in (RBI) have been held in high regard. The last player to bat over .400 in a season was Ted Williams in 1941. When looking at the great ball players of yore, you will find that many had exceptionally high batting averages and RBI totals over their careers.  When trying to win, it would make sense to have hitters who could hit safely on base and knock in runs at a high frequency.

This idea did not sit well with Bill James, who established the Society for American Baseball Research (SABR) in 1971. Outside of the traditional measures, general managers would chalk up the success or failure of a player to intangibles. Because so much goes into a single pitch, they thought it was too hard to have a clear prediction of what would happen. By putting together a team of excellent hitters, it was believed that they would be able to mitigate any intangibles.

James did not buy into that idea, saying that there must be a way to measure and compare players. From his research institute SABR metrics were born. With wild measurements like batting average on balls in play, value over replacement player, wins above replacement player, and on base plus slugging percentage, the SABR researchers were able to determine better ways to compare players and measure their value to a team.

DePodesta and Beane read up on some of the SABR metrics and devised a system that would bolster their team by using players that were of value to them but might not be considered by others. The most significant change was to look at on-base-percentage. They figured that players who were on base more often would be more likely to provide a scoring opportunity. A player who might have a slightly lower batting average but who gets on base often (through walks) is better than a player with a higher batting average and less walks.

It's not just about homeruns anymore.

The A’s used other tactics to maximize their investments which all contributed to their success. Although they did not win the World Series, that misses the point. With the publication of Moneyball and the success of the A’s, baseball teams have begun to accept and put to use SABR metrics. One notable success story is the Boston Red Sox who ended an 86 year World Series drought in 2004 and followed up with another win in 2007.

Baseball was not aware of it at the time, but a statistical revolution was in motion that changed the way players are valued and teams are formed.

Aid and development are on the same track. The evaluation revolution is well underway.  The critics of the randomized control trial have good reason to speak up, but like the A’s and SABR in the world of baseball, this is only the beginning of future innovations. The A’s have not been successful as of late because many of their top players became too expensive to keep; they are also now competing to sign the same players. Evaluations can predict the success of a group of individual actors; it is being used in all professional sports and business. We must embrace the change and look towards ways to improve it.

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