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As an exciting—and unlikely—baseball championship season comes to a head, it’s worth remembering that the baseball fan is a unique creature. True fans marry a love for the game’s pure emotion with a healthy respect for statistics. Sure, they may get teary-eyed recounting Dave Roberts stealing second base in the iconic 2004 ALCS, but can also give you a long-winded treatise on why the “wins above replacement” statistic is the most valuable metric for evaluating a player’s worth. But for all their love of statistics, many fans can’t fight a nagging sensation that sabermetrics, or the application of data science to baseball, is going to kill the joy in the sport.
You can’t fight it: Big Data will transform Major League Baseball. The inevitable tide of change is well underway as the MLB has begun installing sophisticated camera and radar systems in every park to track every single play—and will make the data available to every team in the league. Among the new data points collected will be how long it takes runners to achieve their top speed, the efficiency of catcher's routes to the ball, and angles the balls comes off the bats. Similar tracking has already influenced how defensive players are positioned in the field. Teams are piloting even more advanced data collection, which will include the players using wearable technology.
If you've seen Moneyball, you know where this is headed. Teams will use the data to supplant or enhance human knowledge with statistical modeling. This predictive ability will inform everything from scouting new talent, to the batting lineup, to (team owners fervently hope) determining a better free-agent pricing strategy.
How is data changing baseball?
With every game now generating about seven terabytes of data—and this number is growing as new dimensions are added—baseball fans have some time to adjust before all that data produces a dramatic change. It will take sophisticated data scientists and some hefty storing and processing hardware and software to make any sense of the near-infinite quantity and types of data. In theory, organizations have nearly unlimited ability to use all the information and modeling to make really accurate predictions. The reality is more complicated, as IT data architects know. Integrating the various types of data, processing it and analyzing it in a meaningful way to make a sound decision is an incredibly complicated endeavor.
But when baseball realizes true Big Data maturity—and it will—teams are likely to use it to break the stranglehold of big stars on the sport. You probably won't see another quarter-billion-dollar debacle like Alex Rodriguez’s 10-year contract (full disclosure: I’m a Boston fan). You are likely to see a lot more back-office suits exerting greater control over coaches’ decision making. And many fans wonder, will this scientific approach to baseball kill the joy in the sport? Will a big business tactic sully what’s left of the sport’s purity?
I think the answer is no. As humans, our actions and abilities can be quantified, and even predicted to a certain point, but we’re not slaves to the destiny of statistical modeling. Data can’t measure how a player’s growing maturity or determination will improve his game when it counts, nor does it say how his struggles at home will weigh on his mind and bat. It can’t measure a moment in time when a player gets into the zone, the streak-from-above when he can do no wrong. It doesn't take into account the way team chemistry can bring out something new in a player.
What Big Data can do is level the playing field for richer and poorer teams. Yes, the Yankees and Dodgers will still probably scoop up all the really hot stars, if only for their ability to move tickets. But the rest of the teams can try different combinations of players—ones with untapped potential—and use them in a smart way to get results. The best managers will use a combination of Big Data analysis paired with their instincts about what's going on in the moment. This year, several teams light on big stars have excelled through disciplined coaching and teamwork, proving an expensive ace doesn't always trump a group of players working together. Maybe Big Data will help put the focus back on the team, and take it off the big stars. Now doesn't that seem at least as honest ―as “pure”—as the old way of playing the game?
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Lisa Dare is a marketing writer for TEKsystems who enjoys learning about IT from some of the smartest folks in tech. She frequently blogs about data, career advice and the lighter side of tech.