Some of us utilize modern advanced metrics, while the others go with traditional numbers. This mix is what I believe makes Bleeding Yankee Blue the special place it is, that and of course, you, the readers.
Today, I'm introducing a new post, alittle something different for BYB, but don't worry, we're not going to totally get geeky on you. What we've done is compiled a single page that simply explains the most common statistics. It's our quick and easy database, or, the BYB Sabermetric Encyclopedia featuring some of the most commonly used advanced metrics and I'll do my best to explain each stat so you can judge whether or not you buy it. In a nutshell, anything with a slightly ambiguous name can likely be found in this post, to help guide you through the world of Sabermetrics. If you have a request for an explanation of any statistic not listed, leave a comment and I’ll make sure to help you out.
So here it is...the BYB Sabermetrics Encyclopedia...
Batting Average on Balls In Play is exactly what it sounds like: the likelihood that when a player puts a ball in play, it will be a hit. If a player comes to the plate 10 times, walks once, strikes out twice, gets three hits, and puts it in play for an out four times, their BABIP would be .429. BABIP has become synonymous with luck recently, with analysts often citing a low BABIP as a result of opposing defenders making great plays or the ball never seeming to find the gaps. However, this is only true in the case that a player’s BABIP suddenly dips below their career average while retaining their previous line drive, groundball, and fly ball rates. Similarly, a high BABIP does not necessarily indicate good luck, as some players simply have a knack for getting a hit when the ball is put in play. Derek Jeter has a very high career.355 BABIP, but it’d be foolish to say he’s been lucky his entire career.
I’ve lumped these four together because they are all essentially the same thing with slightly different names. They are all numbers put on a scale in which 100 is league average, accounting for ballparks played in. In ERA+, 100 is average, below 100 is below average, and above 100 is above average. Mariano Rivera is the all-time ERA+ leader with a freakish 206. The other three are just the opposite, below 100 is above average and vice versa. FIP and xFIP obviously measure the f/x part of the line against league average, but the concept is the same. While imperfect like all stats, it is more or less the most accurate measurement of a pitcher’s past performance and is particularly useful for comparing pitchers across different eras.
By far the most commonly used of the advanced pitching metrics, Fielding Independent Pitching works under the assumption that the only three things a pitcher has direct control of are walks, strikeouts, and homeruns. It uses the formula [13*HR+3*(BB+HBP-IBB)-
FIP will often be found along with ERA and xFIP (next up) inan e/f/x line. For example, 3.00/2.88/3.02.
Isolated Power is a measurement of a player’s raw power. It differs from SLG because a high SLG still correlates to a high batting average. ISO is just SLG-BA, so a low batting average won’t drag down a player’s power numbers. In 2008, Mark Teixeira’s SLG was .552, while this past year he slugged only .494. Such a significant drop would normally indicate a loss of power, but his ISO in 2011 was actually higher (.246) than it was in 2008 (.244). It can be used to show if a player is losing power or is getting fewer hits. While we’d all like Teixeira to hit .308 likehe did in 2008, it is at least comforting to know that when he gets a hit these days, it’s just as likely to be for extra bases as ever.
OBP, SLG, and OPS
Easily the most mainstream of all sabermetric stats, I’m sure you’re all accustomed to seeing AVG/OBP/SLG lines, such as Curtis Granderson’s .262/.364/.552 in 2011. On Base Percentage removes the fatal flaw of average: failing to account for a player’s ability to draw walks. The formula used is (H+BB+HBP)/(AB+BB+HBP+
Similar to ERA+, OPS+ is a player’s OPS (see above) adjusted to league average and accounting for ballparks played in. 100 is average, above 100 is above average,and below 100 is below average.
The most comprehensive and complicated of all the pitching stats, SIERA is the closest thing we have to a be all end all pitching statistic. The formula is exceedingly complex:
SIERA = 6.145 – 16.986*(SO/PA) + 11.434*(BB/PA) –1.858*((GB-FB-PU)/PA) + 7.653*((SO/PA)^2) +/– 6.664*(((GB-FB-PU)/PA)^2) +10.130*(SO/PA)*((GB-FB-PU)/
I won’t bother attempting to explain it and I won’t even pretend that I really understand its inner workings. However I can tell you that whenever I go into deep analysis of a pitcher using many other statistics, my resulting conclusion is usually something akin to what SIERA alone could have told me. For more information on it check out Baseball Prospectus.
Ultimate Zone Rating is a measurement of a player’s defensive ability to save runs based on the strength of their arm, ability to turn double plays, their range, and the number of errors they make. Brett Gardner led the entire league in UZR last year with a stellar 25.2 (for reference, 15+ is considered Gold Glove caliber). Due to the fact that players participate in a defensive play less frequently than they do in offensive plays,UZR, and all defensive metrics, are most accurate if used in 3 year increments. If you used only 2011, Curtis Granderson would have been a below average fielder at -5.1 However, using a 3 year sample, he averages out to… dead average at 0, perfectly acceptable for a player of his offensive caliber.
Wins Above Replacement is a measurement of the number of team wins a player is worth over a theoretical standardized AAA replacement player, taking into account performance and value of the position played (i.e.center fielders, catchers, and shortstops are valued more since offense is arare commodity in these positions). There are a few types of WAR, specifically bWAR and fWAR, which stand for Baseball-Reference.com WAR and Fangraphs WAR. I generally prefer fWAR, but for pitchers bWAR is sometimes said to be a better measurement of what happened while fWAR is a more accurate predictor since it uses DIPS (defensive independent pitching statistics) such as FIP. For position players, I find fWAR to be more accurate in nearly all cases. bWAR is broken down into oWAR, offensive WAR, and dWAR, defensive WAR for position players. These are exactly what they sound like, simply showing the value those individual parts of a player’s game provide.
Perhaps the most mainstream of all pitching stats, Walksplus Hits per Inning Pitched is simply the number of base runners a pitcher allows to reach per inning. Somehow MLB Network decided that WHIP was actually number of base runners allowed per 9 innings, so there was the curious scenario in which AJ Burnett was so good that he only allowed an average of 1.434 men to reach base per 9 innings. If this were true we’d be insane to have traded him away, but that is certainly not what it is. Use the relatively simple formula (H+BB+HBP)/IP to find WHIP.
Weighted On BaseAverage takes the flaws of OBP and SLG, which are failing to account for power and plate discipline respectively, and the obvious problem with just sticking a plus sign in between them, and produces a new number which accounts for all events using the formula(0.72*NIBB + 0.75*HBP + 0.90*1B + 0.92*RBOE + 1.24*2B + 1.56*3B + 1.95*HR) / PA. As with SIERA, the inner working of the numbers elude me, but when I spend a long time analyzing players using other, simpler sabermetrics, it often ends up giving me the same data I could have obtained by simply looking at wOBA, so it gets my highest level of recommendation.
wRC+ is the wOBA version of OPS+, measuring a player’s wOBA against league average and accounting for parks played in. It works on the same scale in which 100 is average, above is above average, etc.
Expected Fielding Independent Pitching goes one step further and assumes that pitchers cannot even consistently control the home runs theygive up. The formula is the same as FIP’s, but their home run total is replaced with a league average rate. This can be useful in predicting how a pitcher might fair in a more or less homer friendly ballpark than the one they currently pitch in. For example, Matt Cain finished 2011 with a wonderfully low 2.88 and an equally great 2.91FIP. However, his xFIP of 3.78 indicates that any team which plays in a smaller ballpark should tread lightly when offering Matt Cain a big free agent contract if and when he hits the market.
I hope you enjoy what Casey is bound to call my "Nerd" post. Sure, he breaks chops about Sabermetrics, but I know he respects it. Hopefully this post will be helpful for those of you who are curious about Sabermetrics. Oh yeah... and Let's Go Yankees!