Remember the days when watching a baseball game meant nothing more than going out to the ballpark, spending some time with friends and family, eating hot dogs, and rooting for your favorite player to hit a home run, or for your favorite team to win? Remember the magic that came from seeing a rookie make a spectacular diving catch, or the veteran jumping to rob the opposing batter of a homer? Remember when the game was solely about the love of baseball, and nobody worried about properly calculating stats?
Yeah, me neither.
For the past 3 decades, statistics new and old have come to dominate how we view position players, pitchers, and teams overall. At the forefront of this mathematically-minded revolution is the raging debate between regular stats versus sabermetrics, tradition versus change. After all this time, stats like batting average for a batter or wins and losses for a pitcher don't satisfy as proper indicators of how good that player is. There's WAR (Wins Above Replacement), UZR (Ultimate Zone Rating), FIP (Fielding-Independent Pitching), and a slew of other confusing acronyms that mask formulas even more perplexing. Managers and front office workers are now lauded by their ability to create not the best baseball team, not even the most popular baseball team, but the most efficient baseball team. Billy Beane, Oakland Athletics GM and notorious wunderkind, is now hailed with the likes of Bill James (the godfather of sabermetrics) and Earnshaw Cook (whose 1964 book Percentage Baseball was the first book dedicated to promoting the usage of advanced statistics in baseball) because of his ability to spend low on players that are, based solely off these stats, theoretically create the most dominant team in baseball. Now, we all know how his approach has worked out so far (the A's haven't gone deeper into the postseason than the Division Series other than in 2006), but it's not for lack of trying. And, in Beane's defense, the numbers told him they'd be a good team, and for what he paid, he got a solid team, just not one tooled for the postseason.
Even at 21 years old, I'm an old school baseball fan. I abide more by the ideology that anything can happen on a baseball field, regardless of what the numbers say. In all fairness, though, I'm delving myself into the practice of sabermetrics, and I have to say, I'm being impressed with a lot of it. These math geeks who now influence many team owners and GMs have really figured their stuff out: the formulas are solid, and plugging in data from the past can often be a very good indicator of how a player will do in the future (you know I love you, Derek Jeter, but the naysayers may not be so wrong when it comes to your defensive abilities). The most intriguing sabermetric I've found so far is the Pythagorean expectation, a formula created by Bill James to figure out how many games a team "should" have won, based on how many runs they scored and allowed. I won't go into explicit detail, since the formula looks like something you'd expect in a masters-level engineering class (and I'm an undergrad majoring in journalism, do the math...so I don't have to), but this stat seems very cocky, in a way. Predicting the number of wins a team SHOULD have had? Give me a break. If a team SHOULD have had those wins, they SHOULD have gotten them. Albert Pujols SHOULD have hit 10 more home runs than he did. Felix Hernandez SHOULD have thrown more strikeouts. The Cubs SHOULD have won a title in the last 100 years. There's no true stat that can accurately account for what SHOULD happen, or SHOULD have happened. Bill James could calculate that the Red Sox SHOULD have won 110 games last year instead of 90, but they DIDN'T. MLB isn't going to redact the Cardinals' World Series win and award it to Boston solely on merit, and the result of this formula.
Baseball, like it or not, isn't a game of numbers. You can't completely predict what a ballplayer will do based on math. Sure, a lot of it CAN be properly calculated. Stats accumulated at the end of a season can certainly be used in sabermetrics to determine how good of a season the player had, but can't possibly state to the exact number, or even close, to how that same player will perform next year. Take Jered Weaver, Angels ace and 2011 Cy Young runner up: in 2009, his FIP (which is calculated, bear with me, as number of home runs allowed multiplied by 13 PLUS number of walks allowed multiplied by 3 MINUS number of strikeouts multiplied by 2, all of which is DIVIDED by number of innings thrown, then the entire equation has 3.10 added to it so that the result resembles a traditional ERA) was 4.04, not bad for a starter. With that kind of FIP, one might expect him to perform at an average level, but not be a formidable ace. In 2010, though, he improved quite handily: his FIP dropped almost an entire point, to 3.06, and even though his record worsened (16-8 to 13-12), his ERA dropped, as did the number of hits, runs, home runs and walks he allowed, while his innings count and strikeouts increased handily. While this isn't extreme, there doesn't seem to be a whole lot of mathematical evidence to explain it. The sabermetrics predicted he'd be a middle of the pack starter, but for all intents and purposes, his performance then helped to make him one of the hottest hurlers in 2011.
There is a lot of merit in these stats and sabermetrics, don't get me wrong. And I'm going to keep exploring them and their applications to this great game. But I'm just saying, for all of you out there who use them religiously and rely on them to help you in every argument...it's been said before because it's true, baseball is a magical game. You can't account for everything. Players coming through in the clutch, the force of the fans, even the weather can make or break any team, regardless of what the math says.
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