Friday, May 18, 2012

Luck Be A Lady Tonight

            So, after a several month hiatus, and I'm sure much to your chagrin, I'm back.  I hope you’ll pardon my prolonged absence in a much more gracious way than I will handle the eventual returns of Ryan Howard and Chase Utley to the Phillies lineup, but such is life.  Things get in the way, Achilles’ tendons tear, we all move on.  I’m endeavoring to slightly reformat the way I do this blog, still writing about my two favorite sports (baseball and football) but doing so in such a way that I part the veil somewhat on the different statistical approaches to the game.  In this way, I hope to give people who are otherwise unwilling to deal with all of the statistical mumbo-jumbo a little better understanding of why people are so immersed in these types of analysis.  I know I’m a bit late on the whole baseball thing at this point, but having just one month’s stats can actually help crystallize my point.

            Despite similar claims from football-centric television networks (just because you’re not NFL-sponsored doesn’t mean that doesn’t include you, ESPN), baseball really is the game of inches.  The result of the interaction between a quickly spinning spherical ball and a swinging rounded bat can change dramatically if even the slightest adjustment is made in the approach angle of either of those objects.  Ever hear announcers talk about a foul ball back to the screen as a case in which the batter “just missed it?”  If the bat were just an inch higher in the zone, that might have gone straight out to center field for a home run.  An inconsistency in the terrain of the infield could cause a ground ball to roll slightly differently, allowing it to evade the outstretched glove of an infielder.  These inconsistencies, combined with the fact that the players are running around and flailing their arms with some degree of uncontrollability, make the task of describing statistically a player’s true ability or performance a difficult one.

            In the past few decades, baseball statisticians have endeavored to factor these incredibly dynamic factors out of their analyses.  Over time, one would expect the strange bounces or muscle twitches to balance each other out when it comes to in-game results, being equally beneficial and harmful across a large enough sample.  Per the statistical axiom of regression to the mean, any result that strongly deviates from expectation is merely a result of sample variance (or luck, if you’d like).  No one expects a coin to come up heads 80 times out of 100, but they might accept 8 out of 10 as plausible.  For example, consider those little dribbling “seeing-eye” singles that just eke their way through the middle of the shortstop and third baseman, or the broken-bat bloopers that just drop in front of the right fielder and behind the second baseman.  The vast majority of the time, if a batter hits a pitch that softly, a fielder will be able to get a glove on it and retire him easily, but in some cases it works out for the hitter.  Additionally, consider the large deviations in the angle that a ball is hit that result from a small difference in the timing of the swing.  If a hitter swings a fraction of a second earlier or drops the bat a fraction of an inch, the trajectory of the ball could change just enough to turn a double in the gap into a line-out, or a home-run into an innocuous fly ball.

            While many of these types of “random” results balance themselves out in terms of whether or not they benefit the player in question, over the course of a small enough sample size (and this could theoretically be as long as a season in some cases), a player can certainly flip 10 heads in a row.  Identifying which players are having more or fewer balls bounce their way can help us understand why a career .270 hitter like Evan Longoria hits .240 for a season, or why Zack Greinke allowed about 1 run per 9 innings more than he really should have.  If used correctly by general managers (or fantasy baseball managers), these tools can help identify players that seem to be on the decline but are just experiencing natural variance and acquire them cheaply.  Or, perhaps, they can just give a former college student something innocuous to obsess about.

            In the next few posts, I'll introduce all the baseball metrics that I've been using for the past couple of years to really help me understand the ebbs and flows of a baseball season.  The primary purpose of many of these will be to separate luck from skill, while some of them will just aim to effectively compare the performances of players, regardless of external circumstances.  The secondary purpose, we will likely discover, is to make half the population fall asleep.  Sweet dreams.

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