Keep it Simple: Lessons from Moneyball

*This week's post is brought to you by [Jim Bryant](https://www.linkedin.com/pub/jim-bryant/8/454/893)*. Many of you may have seen the movie [Moneyball](http://www.sonypictures.com/movies/moneyball/) released in 2011 starring Brad Pitt and Jonah Hill. The subject of the movie being a former player and now the Oakland A's general manager Billy Beane's successful attempt to assemble a baseball team on a lean budget by employing computer-generated analysis to acquire new players. More than likely, anyone who has interviewed and selected new hires immediately thought; I can apply these principles to my job. Just the other morning I saw a new book, ["Moneyball for Government"](http://moneyballforgov.com/), written by Jim Nussle and Peter Orszag which takes a similar approach to Government performance. One comment from the writers of the morning TV show, [Morning Joe](http://www.msnbc.com/morning-joe) on this book, is that it doesn’t require Washington to reach a consensus on health-care reform or defense spending. But it does require that we introduce more objective evidence and data into our policy-making process and budget decisions, even as we continue to debate what those policies and dollar allocations should be. Our choices should be more informed by what will have the most impact and have the best results. I had to pause after hearing this and think how long had it been since I attended a meeting, responded to an RFP or completed contract negotiations that outcomes, analytics, return on investment, dashboards, ad hoc reporting, key performance indicators and performance penalties were not part of doing business. A quick search on the Internet reveals many articles, research papers and studies on how Moneyball strategies have been considered for many Human Resource functions. One article in particular written by Eric Krell, a freelance writer based in Austin, Texas does a great job in analyzing how Moneyball relates to HR in his article, ["How 'Moneyball' Measures Up As An HR Approach."](http://www.weknownext.com/workforce/how-moneyball-measures-up-as-an-hr-approach) Another blog by J. Scott Armstrong, marketing professor at the Wharton School of the University of Pennsylvania entitled, ["Moneyball for Managers: Paul Meehl's Legacy,"](http://whartonmagazine.com/blogs/moneyball-for-managers-paul-meehls-legacy/) discusses work done by the late Professor Paul Meehl, whose research was based on findings that statistical models do better than unaided judgments for personnel decisions. *Why is this important?* Mr. Armstrong writes that Billy Beane used two key procedures to select and retain his baseball players: the first relates to developing statistical models, and the second to ensuring the models are properly used. He then analyzed the data on players’ performance and used the models’ outputs to select players. He understood that opinions should be used only as inputs to a model. What is really interesting as pointed out by Mr. Armstrong is that Professor Meehl was completing this research in the 1950’s and in reviewing the evidence on predictions about people, he concluded in 1956 that, “it almost looks as if the first rule to follow in trying to predict the subsequent course of a student’s or patient’s behavior is to carefully avoid talking to them, and the second rule is to avoid thinking about them.” Armstrong also notes, “change occurs slowly.” Moneyball is a great book, a good movie and sound approach to using data to predict performance. As I found out there are many articles written on the subject, and the research started decades before the ideas were made popular by the movie. Remember, when applying these techniques to business, keep it simple, Billy Beane used two key procedures to select and retain his baseball players that business professionals involved in hiring need to consider: + Develop statistical models + Ensure the models are properly used Finally, according to Professor Meehl do not revise the models’ recommendations based on opinions. Follow these guidelines and you’ll have the initial framework of how to gain [insights](http://www.optis.com/data-services) into the information you need.