Field of (Data) Dreams


“I don’t like his babip,” a friend told me the other day.

My friend’s comment and my instant understanding of it – I didn’t wonder if his mouth was numbed by novocaine – shows the pervasiveness of data nerdiness in baseball. Not just in the game itself, but among fans.

My friend and I were discussing preparations for our upcoming fantasy baseball league draft. BABIP, or Batting Average on Balls in Play, is one of the statistical categories we’ve been examining in assessing players. It measures how many of a batter’s balls in play go for hits, which can help determine the effect of luck and other extraneous factors on a batter’s performance.

When our Fantasy league started around 1990, everything from team rosters to player statistics was done by hand on paper. The most sophisticated technology used was a calculator. Team owners selected players based on a small set of traditional numbers – home runs, batting average, wins, strikeouts, etc. – and hunches.

Now, a website handles the league’s business and does all the number crunching. And when deciding which players they think will perform well during the reason, the more savvy Fantasy owners have ventured into an alphabet soup of sophisticated new measurements – not only BAPIB but others such as FIP, FRA, WARP, TAv and PECOTA.

I should point out that our league is made up mostly of former journalism, liberal arts and business majors, not computer scientists or mathematicians. But throw us into a fantasy baseball draft room and we turn into data propeller heads.

They say baseball imitates life, but when it comes to data-driven decision-making, it may be the other way around. “Moneyball,” Michael Lewis’s 2003 book about Oakland A’s general manager Billy Beane and his analytical approach to building a winning team despite a limited salary budget, anticipated the current Big Data fascination across society. It’s no accident that Beane, while he still runs the A’s front office, also is now a regular on the corporate speaking circuit.

Or consider Nate Silver, the statistician who became a household name when, writing for the New York Times, he accurately predicted the winner of all 50 states and the District of Columbia in the 2012 presidential race. Many baseball fans already knew Silver as the inventor of PECOTA, a player performance forecasting system that he managed for Baseball Prospectus, the baseball stat-head’s bible, from 2003 to 2009. Silver now works for ESPN.

Baseball’s love affair with data (which is different from numbers – the game has always loved and lent itself to numbers) just gets deeper. In March, Major League Baseball Advanced Media unveiled what it called “a revolutionary plan for in-ballpark infrastructure designed to provide the first complete and reliable measurement of every play on the field and answer previously unanswerable analytics questions.”

Major League Baseball chose to introduce the new system not at a stadium news conference but at the MIT Sloan Sports Analytics Conference in Boston. According to an article on, the system will provide a datastream to enable new metrics for evaluation by clubs, scouts, players and fans.

“For instance,” the story said, “on a brilliant, game-saving diving catch by an outfielder, this new system will let us understand what created that outcome. Was it the quickness of his first step, his acceleration? Was it his initial positioning? What if the pitcher had thrown a different pitch? Everything will be connected for the first time, providing a tool for answers to questions like this and more.”

BABIP, anyone?

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