A Tale of Two Analysts

The best of times for one, the worst of times for the other?

This is the story of an analyst. This analyst had a talent of sifting through large amounts of information and using it for a variety of purposes. One day, this analyst figured out how to take that information and use it to create a reasonably accurate prediction of events to come. This analyst gained some fame because of it, thanks to like minded people who appreciated and respected what this analyst did. But there were those who derided what this analyst did, and summarily dismissed the ideas this analyst put forth.

This is the story of another analyst, much like the first. This other analyst had a talent of sifting through large amounts of information and using it for a variety of purposes, much like the first analyst. One day, this other analyst figured out how to take that information and use it to create a reasonably accurate prediction of events to come, using a system that was very similar to the first analyst. This other analyst gained even more fame because of it, thanks to like minded people who appreciated and respected what this other analyst did. There were few, if any, of these like minded people who derided this other analyst’s approach.

Similar stories, yet different outcomes. How did these two analysts get there?

Well, for one, this isn’t a story of two analysts, but of one and his forays into two distinct realms. This is the story of Nate Silver, the brains behind the PECOTA projection system at Baseball Prospectus and of FiveThirtyEight.com, the electoral projection site that made him a media darling up through November 4. It highlights the central issue of this piece: why do parts of baseball still seem so combative to this kind of statistical analysis while other areas of society embrace it?

Let’s step back and look at how and why statistical analysis and forecasting is used in different non-sporting arenas. In politics, they love polls. That’s their way of getting a pulse on the will of the people without having to talk to all the people themselves.  In insurance, statistical models predict the amount of a risk a certain person has to making the insurer pay money for the insured’s missteps and misfortunes, as well as predicting how much money that will be. It is the lifeblood of how rates are set. For a retailer who mails catalogs, statistical models predict who will actually place an order and, in some cases, for how much.

Why is there an acceptance in these arenas? Because the leaders of each field have utilized the superior information that is provided by good statistical analysis to attain their goals, be it winning an election or making more money. One major success using statistics in these fields has lead to many people using the same tools in hopes of emulating that success.

Wait a second. Why isn’t baseball like this? Why isn’t every team employing a crew of sabermetricians to find ways to be the best using the wealth of numerical information that is available?

In some ways, baseball is like any other well-established organization, in that it takes a long time for some revolutionary ideas to take hold. This is only part of the equation. Yes, those that are in charge of such decisions need to have an open mind for such ideas, but sabermetricians haven’t done themselves many favors by taking on a more adversarial stance.

Too often, sabermetrics are used to criticize those in baseball in a very harsh manner. This, in many ways, is a part of the sabermetric culture, thanks to The Godfather. But some of today’s leading voices go way beyond the lamenting that comes with good ideas falling on deaf years that Bill James encountered. Much of it is to the point where some sabermetricians have this “I’m smarter than any GM out there” attitude, rather than treating individuals with respect while debating the point.

Simply put, if sabermetrics is to advance into more front offices, the tone of sabermetrics needs to be one of “Hey, we can help you win baseball games, and maybe even the World Series!” rather than “Mr GM, you’re a dumbass, and I can prove it.” Let’s start that here, and now.