Sabermetrics. The word just looks...really daunting. Metrics, does that mean a lot of numbers. And saber- means magic like some sort of a lightsaber? Or is it closer to the sharp jaws of a Saber-toothed tiger?
Since Moneyball came out in 2009, there has been a narrative that those who embrace the analytical nature of baseball are geeky, stat nerds that could never survive in society when the reality has been the exact opposite: those who have embraced the movement have a deeper understanding of the sport they love. And those who shunned analytics professionally? They consistently make poor decisions because they a) use irrelevant, outdated information to make choices or b) intentionally ignore those who are researching positive influences. A list that includes Tony Reagins, Dave Stewart, and Jack Zduriencik are decision-makers who refused to adapt to the changing environment and as a result, decimated the outlooks of their respective franchises in some capacity.
Don’t get me wrong: traditional statistics can still be useful (BABIP, WHIP anyone?), but they are not nearly as important as modern, advanced stats. Comparing a batter’s batting average to his on-base percentage can tell you how patient of a hitter he is, and computing OPS can generally give you a ballpark sense of how good a player is. But in this scenario, how can we accurately compare a patient, contact-oriented player with poor baserunning versus a power-hitting, average patience, smart baserunning slow player? In other words, how would you compare Yunel Escobar and Albert Pujols?
Well, you can’t, at least not accurately. The traditionally championed RBI means little because the run-driving opportunities are a byproduct of where a player plays in a lineup, rather than the cause. Runs? Well, it’s the same thing. I’m sure we can all agree that Kole Calhoun will score more runs on a rate basis (aka runs/game) as a leadoff man than as the eight-hole hitter.
With relatively new stats such as WAR, wRC+, FIP, UZR/DRS (and more!), we are now able to compare players head-to-head. Thankfully, we can compare any hitter with another with Weighted Runs Created Plus, or wRC+, a park-adjusted and league-adjusted all-inclusive offensive stat where 100 is average and higher is better. We see that Escobar’s is 108 and Pujols’ is 111, a margin so close that it would be silly to say that one had a better offensive year than the other (there’s always a certain margin of error for each stat, so just like you wouldn’t say a pitcher with a 2.90 ERA > 3.00 ERA, one stat being greater isn’t empirical).
We all want to sound smart when discussing our favorite baseball team with our friends, family, and coworkers, but would it be even better if we were to be smart? But, the fact of the matter is that it’s especially difficult for casual observers to come around to change — especially when it comes at the expense of the way something has been done for over a hundred years.
That’s exactly what this series intends to do: to make understanding sabermetrics more accessible and to enrich your understanding of the game we all love. And if you wanted to know how it all works but previously didn’t, now’s your chance! Feel free to ask questions in the comments section as we go along and if not me, someone’s bound to help you out.
As I’ve been gradually learning about sabermetrics since I started writing here at HH, I eventually stumbled upon a fan’s intriguing post on Pinstripe Alley about this very topic. Given this was written nearly seven years ago, do forgive the aggressive tone, lack of organization, and poor punctuation! But the author’s point still stands, even today. The below quote is the main thing that I took away:
...None of us sit there watching the game, thinking about how a player's wRC+ will go up or how they can improve their UZR by reaching balls. Just because we chose to trust stats that are more precise in their evaluations of players doesn't mean that we watch the game any different or enjoy it any less than you. You can feel free to keep using Batting Average as a comparative tool among hitters, but someone who uses a more inclusive statistic will always trump you, just as an electronics company who chose to ignore quantum mechanics would always produce inferior products than one who manipulated the laws of QM. If you chose to ignore the better stats that have been created, you aren't less knowledgeable, but you are less informed, so you put yourself at a disadvantage.
Change is inevitable, my friends. Lest you want to have the gusto of an old man angrily screaming at clouds, I would highly recommend following along. You don’t have to be a believer immediately after reading this, but at least keep an open mind on the subject (there’s not much math to be done). A whirlwind of new facts and figures might seem intimidating at first but that’s why this series has come to exist. You might find it challenging initially, but definitely rewarding in the end.
How interested are you in learning about sabermetrics?
This poll is closed
Very interested — I’d like to learn more
Somewhat interested — I’d like to learn more
Not interested — I’m only sticking to what I know!
Not interested — I already know everything there is to know about sabermetrics!