Wake up, nerd. Baseball is not math class.

Advanced analytics are killing the poetry and romance in baseball. Everything is now distilled, quantified, and commodified.

And no, I am not anti-data. I want to make that very clear, so I avoid being cast as a casual fan and baseball heretic.

But I do believe that, like many revolutions that began in the name of progress, the proliferation of advanced analytics has gone too far. It is hurting the beautiful game of baseball. There is a time for data, but experience has its own place, too. There is a place for multiple perspectives. A lot of fans agree that they are feeling a bit apprehensive about the direction baseball is taking nearly 20 years after Michael Lewis’s revolutionary book "Moneyball" came out.

In recent years, when the topic of famous advanced analytics-induced managerial gaffes are brought up, the Tampa Bay Rays come to mind for most. For context, Tampa Bay was down 3-2 in the World Series against the Los Angeles Dodgers. Game 6 was do or die, so Tampa Bay had their Cy Young-winning ace Blake Snell on the bump. Snell was shoving. Through 5 ⅓ innings, he had given up two hits and struck out nine batters while only throwing 73 pitches. In other words, Snell was dominant and efficient.

Yet, when he gave up his second hit of the ballgame, Tampa Bay manager Kevin Cash pulled Snell much to the chagrin of the baseball world. Cash’s reasoning was that Snell was going to face the three best batters in Los Angeles’s order, so the analytics suggested to pull him. Yet, Snell had struck out each of those three batters — twice. Yes, Tampa Bay was already down in the series, but this move effectively sealed their fate. Snell was replaced by relief pitcher Nick Anderson, who immediately coughed up a double to Mookie Betts, a wild pitch, and a fielder’s choice, giving Los Angeles a 2-1 lead that they would never surrender. One could say that Tampa Bay’s computers were the MVP for Los Angeles.

The argument against this is that Tampa Bay's use of an advanced analytical strategy was what ultimately brought them to the World Series. So, where is the line in the sand with analytics? I would be a complete nimrod to make the case against analytics entirely. It works for Tampa Bay (usually). It is valuable to a point.

In baseball, I believe human discretion is often overlooked in favor of an unduly strict adherence to mathematics. You need to have faith in human abilities. This is emphasized even more in the postseason. The playoffs are a different animal, and analytics typically go out the window. Having a feel for the game is what elevates teams, not a computer.

The pre-Moneyball era of baseball is criticized by big stat-heads as being ignorant and antiquated. However, by doing so, it seems to me that we are swapping one form of ignorance for another.

At times, using imagination is a better alternative than statistical knowledge. Because knowledge is limited. So if a team has that really creative and innovative person managing during the game and constructing the roster, they therein have a competitive advantage.

Every team is using analytics; everyone has hopped on the bandwagon. There is a cap on what can be discerned by data, and there will always be silly new statistics created, like VORP, LIPS, and PECOTA, to name a few. In the end, every team is still using the same models and figures.

We are in the analytics era. Yes, there are things we can derive from them, but it is time to backpedal a little because the right person is the advantage in baseball.