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Advanced Analytics - Think "Moneyball"

You don't have to have an intimate knowledge of regression analysis, confidence intervals, and sample sizes to appreciate advanced analytics. And you don't need access to a supercomputer. I'll always fondly remember night school classes in Cambridge, in a dingy computer lab that the wealthy undergrads didn't even know existed, much less frequent. Sure we were geeks, but we had a lot of fun and learned mostly just for the sake of learning.

We did projects like predicting the run production of a lineup of Wade Boggs-like singles hitters, compared to Jim Rice-like power hitters. Spoiler alert: the singles hitters lineup always produced more runs. Billy Beane and Paul DePodesta (now the VP of Player Devt for the Mets) did this stuff for real, and developed a complex theory popularly known as Moneyball, where they use analytics to find players who can produce wins on a low budget.

So these advanced analytics are not really intended for the general public, but they can help a GM to understand who really contributes to victories, and where his team's true strengths and weaknesses lie. For a simple example, there are a number of all-stars whose +/- statistics are not great, in some cases below those of his less acclaimed (and far less paid) teammates.

Advanced analytics also allow you to learn new insights, by seeing things from a different perspective. As Billy Beane learned, the conventional wisdom has limits. A lot of interesting insights are counter-intuitive. Remember, conventional wisdom once believed that the world was flat, and that a black man could never be president.




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