Posted by Mack Ade at 2:00 PM
Last week, in Part Three of this series, we looked at FIP and how it can evaluate how effective pitchers were, despite of, or as a result of the defensive abilities of the team surrounding them. In that piece, we learned that there may be plenty to get excited about in the near future for Jon Niese.
In this week’s installment, I want to touch on a statistic called “weighted on base average” or wOBA.
Most of you are probably familiar with a similar statistic called “on base percentage”, otherwise expressed at OBP. It is expressed as a percentage, meaning that it is on a scale from zero to a maximum of 1.000 (which would be very good, by the way). Basically, OBP is a measure of how many times a batter gets on base, divided by the total number of opportunities that same batter had to get on base.
This statistic usually comes up when talking about leadoff hitters, since you want your leadoff hitter to get on base as much as possible. But, it is a useful statistic when analyzing any batters‘ effectiveness over time. An average OBP for most players would fall around 0.330, but that can vary from one season to the next. An excellent OBP would be approaching 0.400 and anything under 0.300 is usually considered subpar.
Well, wOBA is calculated and then properly scaled, so that the expression of the statistic is similar in composition to OBP. The scaling procedure is done so that the average fan can relate to the expression and that a standard league average can be established for player comparison purposes. In other words, a wOBA of 0.330 would be roughly league average. However, this is where the similarities end between OBP and wOBA.
Simply put, all hits are not created equal. Batting average (BA) measures hits, but it does not differentiate between singles and home runs, for example. Slugging percentage (SLG) does account for the different style of hits, but the process can be flawed (i.e. a double is not always twice as effective as a single with regards to the impact on a particular offensive scenario).
This is where our newest favorite statistic comes in. Developed by George Lindsey and Pete Palmer, wOBA is “an attempt to properly value a player’s contributions in each at bat by weighting each possible outcome with regard to the number of additional runs that player’s team can expect to score as a result.”
Ugh! More simply put by Dave Cameron of FanGraphs, wOBA is most helpful “when you want to know how a batter did at the plate, regardless of who was on base or what the score was at the time”. It is a more precise measure (compared to OPS, covered in an earlier edition of this series) of who is the best hitter, which is what we all really want to know, right?
So, you may be asking “OK, smart guy, how is wOBA calculated then?” I am not sure you will like the answer, but here is the basic formula;
wOBA = (0.72xNIBB) + (0.75xHBP) + (0.90x1B) + (0.92xRBOE) + (1.24x2B) + (1.56x3B) + (1.95xHR)
Total Number of Plate Appearances
NIBB = Non-intentional base on balls
HBP = Hit by pitch
1B = Single
RBOE = Reached base on an error
2B = Double
3B = Triple
HR = Home Run
See, I told you so. Are many of you going to run out and grab a calculator, so you can figure out this crazy stat? I doubt it. But, seeing how it is constructed and knowing what it is intended to measure, helps you understand the statistic when you see it in the future, right?
As we mentioned above, wOBA is calculated and scaled so that it is expressed similarly to other percentage statistics that fans are used to (like BA, OBP). Now that we understand a bit more about wOBA, here are some additional facts that will help define the results (with a thank you to FanGraphs for the basic parameters).
In 2011, the league average for all batters was approximately 0.321, which is simple enough. So, anyone under that figure is below average, and anyone above it is, well......you get the picture. The best player in all of baseball for 2011 was Josh Hamilton who had an impressive wOBA of 0.447 (letting him go was not one of the Rays better personnel moves) and the worst player in 2011 was Cesar Izturis who registered a wOBA of 0.248 (yikes, Rey Ordonez has been cloned).
Using the two parameters, you can calculate a percentile figure for every player, with the higher percentile representing the better player (expressed as a percentage, meaning you divide the wOBA in question by the best overall figure, which was Hamilton’s for 2011). So, if Shin-Soo Choo (like that name, but he should have been a train conductor) of the Indians had a wOBA of 0.388, he would be in the 90th percentile, or better then 90 percent of all hitters.
What about our very own Mets? Let’s use David Wright (since another, nameless player with dread locks has been spoken about enough) and see where he falls. David had a wOBA of 0.342 this past year, which as we know is above average, despite a batting average of only 0.254 (which was suppressed by a low BABIP of 0.302, featured a couple weeks ago in this series).
David’s wOBA falls in the 76th percentile, which means he was better then three quarters of the hitters in baseball, despite a down year by his standards. Can you see how certain “old school” statistics like batting average (BA) can be misleading?
Going further, David’s career wOBA is 0.383, which would have fallen in the 86th percentile this past year (and shows that he underperformed by his own standards). I personally think a new year, with shorter fences at Citi Field and hopefully better health, will return David to the elite level, where he belongs.
In closing, I think using wOBA in conjunction with the overall percentile measurement, is a pretty good indicator of a player’s impact on a specific team. As free agency continues to unfold, use the statistics yourself to evaluate each offensive player for yourself. Chances are, it may be different then what the media “experts” will have to say.