As a follow up to the first Better Know a Stat entry which explained OPS+, wOBA is an advanced statistic which gives different weights to on base percentage and to slugging percentage, as opposed to OPS gives equal weight to the two. 

From Fangraphs

wOBA is a linear weight formula presented as a rate statistic scaled to On Base Percentage. Essentially, what that means is that average wOBA will always equal average OBP for any given year. If you know what the league’s OBP is, you know what the league’s wOBA is. Usually, league average falls in the .335 range

The beauty of wOBA lies in linear weights. Essentially, every outcome has a specific run value that is proportional to other outcomes – a home run is worth a little more than twice as much a single, for instance. What wOBA does, as all linear weights formulas do, is value these outcomes relative to each other so that they are properly valued.

Simply speaking, the ability to get on base is the most important quality a hitter can have due to the rules of baseball. To paraphrase from Moneyball, unlike any other American sport, baseball is an un-timed game. So long as there are additional outs, a team is never out of the contest. As such, those outs are the most precious resource the hitting team has.  With every out, the game is shortened, and every time a batter leaves the batters box without an out being recorded they have extended the game. 

The fact that OBP is more important than SLG seems to be fairly instinctual within the TBLA community, and perhaps the player that most people point out the failings of OPS is when discussing Russell Martin.  Martin certainly had a rough stretch at the plate last year, and his OPS fell from .781 in 2008 to .680 in 2009. Suffice it to say, an OPS of .680 is pretty bad for a catcher-- actually its pretty bad for basically any position. In fact, it ranks 31st out of 45 catchers with at least 200 plate appearances in 2009. However, Martin still posted a quite respectable OBP of .352 (good for 8th amongst catchers in 2009 with at least 200 plate appearances) so the most damaging aspect of his OPS was the very poor SLG of .329 (37th out of 45!)  As such, wOBA provides a useful tool when measuring Martin who has such a sharp split with how he stacks up amongst his peers.  Once giving more weight to OBP, as wOBA does, Martin posted a wOBA of .307, which is, frankly,  still pretty bad (26th out of 45.)

Looking towards 2010 for Martin, one could argue that absent improvements in his SLG, he will have a hard time maintaining his stellar OBP.  By posting a SLG less than his OBP, pitchers will be more inclined to challenge Martin as 2009 showed that Martin is having a hard time hitting for power.  That’s not to say that 2010 will be the same struggles for Martin of course, as their have been reports that Martin added 20 pounds of muscle in the offseason.  But wOBA should expose the fallacy that Martin’s strong OBP should forgive his inability to hit for power.  One last and somewhat surprising note,  Martin’s wOBA of .307 in 2009 compares unfavorably to favorite TBLA whipping boy Juan Pierre’s career wOBA of .325.

For the third installment of the TBLA "Better Know a Stat" I am going to take a look at a stat that builds on Fielding Independent Pitching (FIP) and functions as a better predictor for future pitching performance.

From The Hardball Times:

Expected Fielding Independent Pitching is an experimental stat that adjusts FIP and "normalizes" the home run component. Research has shown that home runs allowed are pretty much a function of flyballs allowed and home park, so xFIP is based on the average number of home runs allowed per outfield fly. Theoretically, this should be a better predictor of a pitcher's future ERA.

x-FIP begins at the same point as the FIP, which is to say it eliminates the defensive contributions (or deficiencies) in place behind the pitcher. Strikeouts and walks are handled essentially the same way as they are individual matchups between the pitcher and the batter.  The third component of FIP, homeruns, is looked at differently in this model.  As the definition above states, x-FIP argues that home runs are largely a function of the number of flyballs surrendered by the pitcher and the park they play in.  The idea that home runs are largely impacted by the home park is not terribly revolutionary.  For years, people have been discounting the offensive production of sluggers who compete at Coors Field (regardless of how true that remains today, but that’s a different discussion for a different day.) 

As for home runs being a function of fly balls allowed, this seems to me to be rather intuitive as well. A pitcher can’t surrender a home run without surrendering a fly ball. The more fly balls you allow, the more opportunities for home runs.  As their was some good conversation regarding FIP in the previous column, where certain pitchers are "ground ball" pitchers, no pitchers in MLB consider themselves fly ball pitchers.   The more fly balls the pitcher surrenders, the more likely they are to surrender a home run.

Continue reading this post »

For the second installment of the TBLA "Better Know a Stat" series, I wanted to take a look at the pitching statistic which attempts to isolate the pitcher’s responsibility for the runs allowed using only walks, strikeouts and homeruns. 

The FIP formula is (HR*13+(BB+HBP-IBB)*3-K*2)/IP, plus a league-specific factor that scales FIP to match league average ERA for a given season and league.

http://www.fangraphs.com/blogs/index.php/pitcher-win-values-explained-part-two

Note: The league specific factor I have used for FIP in the past is 3.20.

The basic argument for using FIP as opposed to ERA and WHIP is the realization that the pitcher has very little control for where the ball goes once it is hit by the batter.  As I’ve watched baseball, its easy to notice that some times the ball is struck well yet right at a fielder, whilst other times the ball appears to be a routine inning ending double play, only to find the baseball rolling past Jeff Kent playing second base.   As a result, a pitcher with a better defense behind them will be less likely to surrender hits (and ultimately runs) than the pitcher will the below average defense. 

Continue reading this post »

Inspired by Stephen Colbert and his Better know a congressman series, TBLA is launching our new "Better Know A Stat" series and today's debut is the baseball reference stat OPS+. OPS+ is a great stat to start with as we use it just about every day, and it incorporates several stats that everyone should know. OB%, Slug%, and OPS. From The Baseball Page Website

OPS+ is OPS adjusted for the park and the league in which the player played, but not for fielding position. An OPS+ of 100 is defined to be the league average. An OPS+ of 150 or more is excellent, and 125 very good, while an OPS+ of 75 or below is poor.

A common misconception is that OPS+ closely matches the ratio of a player’s OPS to that of the league. In fact, due to the additive nature of the two components in OPS+, a player with an OBP and SLG both 50% better than league average in those metrics will have an OPS+ of 200 (twice the league average OPS+) while still having an OPS that is only 50% better than the average OPS of the league.

Many of you understand the definition above but for those that don't let us take a look at each of the statistics being used in the formula, which is basically:

OPS+ = (OBP / lgOBP + SLG / lgSLG - 1) * 100

More information about OPS+ can be found at Baseball-Reference.com, as well as more information about how their park and league factors are calculated.

On Base Percentage

OB% is measured by the sum of hits (H), base on balls (BB), and HBP (hit by pitch)  divided by the sum of times at bat (AB), (BB), sacrifice flies (SF), and HBP. The formula looks like this (H + BB + HBP) / (AB + BB + SF + HBP). If you have been here anytime at all you know that we usually use OB% instead of batting average when discussing the offensive potential of a player.  Each person has loyalties to certain statistics, some have discounted the batting average as obsolete but it is hard for some of us to give up that ghost, however OB% factors in every time that a hitter reaches base safely, not just the time the batter gets a hit and is much more indicative of a the offensive value then batting average. Using Matt Kemp in our example:

(180+52 + 3) / (606+52+6+3) = .352

.350 is good not great but when combined with Kemp's power you have a valuable player. Jamey Carroll and Russell Martin have good OB% but a weak slug% with little speed to go with it, making them much less valuable from an offensive standpoint.

Slugging Percentage

The 2nd part of the OPS is the slug%  which takes the total bases (singles (+1), doubles (+2), triples (+3), home runs (+4) ) divided by at bats. The forumula for slug% would be (Total Bases ) / (AB). Again using Matt Kemp as an example Matt had 122 singles, 25 doubles, 7 triples, and 26 home runs.

((122*1) + (25*2) + (7*3) + (26*4)) / 606  = .490

.490 is great for a center fielder. Only Tori Hunter had a higher slug% then Matt Kemp in 2009 for centerfielders.

On-Base Plus Slugging (OPS)

Adding OB% with Slug% gets you OPS. This statistic is credited to Bill James,  It is designed to merge a player's OBP, which measures how often he gets on base, and his Slugging Percentage (which measures ability to hit for average and power).  Until more advanced metrics came into play this is the stat that many used and still use when trying to determine the offensive value of a player. It has some good things and bad things. Stolen bases are completely ignored and it weights the OB% the same as Slug%. Also it does not normalize for era's and ballpark effects. For example Wes Parker had an OPS in 1970 of only .850 which at first glance is a good year for a first baseman but not really a great year. If you were to sort LA Dodger first baseman by OPS his .850 would be the 10th best offensive season. However when you use a normalized stat like OPS+ his season is actually tied for 3rd best. It was one hell of a season but the numbers are masked behind the tough hitting environment of Dodger Stadium.

That is why we love to use OPS+, it uses OPS but then normalizes the numbers for era and ballpark effects allowing you to compare what someone did in 1970 with what someone did in 2009.  OPS+ still has the same problem of weighting OB% and Slug% equally and more complex metrics have become available to address this but for a quick and dirty way to compare players across leagues and era's, it makes for a great metric.

Below are the 17 greatest Los Angeles Dodger seasons according to OPS+.

                                                                              
Player           OPS+  TB Year Age  AB HR RBI  BB   BA  OBP  SLG   OPS     Pos
Mike Piazza       185 355 1997  28 556 40 124  69 .362 .431 .638 1.070    *2/D
Pedro Guerrero    181 281 1985  29 487 33  87  83 .320 .422 .577  .999 *7538/9
Gary Sheffield    176 322 2000  31 501 43 109 101 .325 .438 .643 1.081    *7/D
Reggie Smith      167 281 1977  32 488 32  87 104 .307 .427 .576 1.003    *9/8
Mike Piazza       166 308 1996  27 547 36 105  81 .336 .422 .563  .985      *2
Gary Sheffield    164 300 2001  32 515 36 100  94 .311 .417 .583 1.000   *7/D9
Adrian Beltre     163 376 2004  25 598 48 121  53 .334 .388 .629 1.017    *5/6
Reggie Smith      161 250 1978  33 447 29  93  70 .295 .382 .559  .942    *9/8
Eddie Murray      158 290 1990  34 558 26  95  82 .330 .414 .520  .934      *3
Pedro Guerrero    156 308 1982  26 575 32 100  65 .304 .378 .536  .914    *985
Shawn Green       154 325 2002  29 582 42 114  93 .285 .385 .558  .944    *9/D
Shawn Green       154 370 2001  28 619 49 125  72 .297 .372 .598  .970   *9/83
Kal Daniels       154 239 1990  26 450 27  94  68 .296 .389 .531  .920      *7
Pedro Guerrero    154 294 1987  31 545 27  89  74 .338 .416 .539  .955     *73
Mike Piazza       152 307 1993  24 547 35 112  46 .318 .370 .561  .932    *2/3
Jimmy Wynn        151 266 1974  32 535 32 108 108 .271 .387 .497  .884      *8
Pedro Guerrero    150 310 1983  27 584 32 103  72 .298 .373 .531  .904    *5/3

Provided by Baseball-Reference.com: View Play Index Tool Used
Generated 12/30/2009.

The purpose of this series is not for our regular commentators who are already well versed in modern statistics but for those of who have found our site, hang around but don't quite know what we are talking about. This series would be a good time to ask questions and we will do our best to answer them.

Also I'm no expert so if I have made any mistakes or misrepresentations, please let me know so I can fix them.