Mike Friere - So, What Is wOBA?

In an effort to make "all that is old, new again", I have resurrected an old series of articles that I put together in a previous "Mack's Mets" lifetime that focused on the new wave of statistical analysis that has shaped baseball scouting and player rankings.  Some would refer to them as "Sabermetrics" and others would argue that it isn't necessarily new anymore. 

Both of those statements are true, as we are all getting older by the minute, right?

However, to keep things somewhat fresh, I will go over a new statistic each week and then I will attempt to relate that measure to our favorite team and one or more of our current players to see how we rate, so to speak. In the third installment of this series, we took a look at BABIP and how it can be used to identify players (pitchers and hitters) that are under OR over performing their skill set.  The data generated with that statistic can be used to evaluate AND prognosticate which is pretty cool.

For today’s installment, we will shift our focus back to the batter(s) and introduce wOBA and the following is a great definition of what it is intended to measure (with thanks to Fan Graphs, of course);

wOBA, which stands for Weighted On Base Percentage, is based on a simple concept, which is “not all hits are created equal”, yet simple statistics like Batting Average (BA) and/or On Base Percentage (OBP) basically assume that they are.   Additionally, secondary measures like Slugging Percentage (SLG) and On Base Plus Slugging Percentage (OPS), are better but still make assumptions such as a point of SLG being similar to a point of OBP, when they are not. 

So, wOBA combines all of  the different aspects of hitting into one metric, weighting each of them in proportion to their actual run value, so it measures offensive value more accurately and comprehensively then other statistics.

For those of you who are statistically inclined, are not afraid of math and actually enjoy figuring things out by hand (that pretty much eliminates all of us), here is the formula that is used to figure out wOBA;

wOBA = (0.690×uBB + 0.722×HBP + 0.888×1B + 1.271×2B + 1.616×3B +2.101×HR) / (AB + BB – IBB + SF + HBP)

                                      

What the damn hell, right?   Just keep in mind that the factor used to calculate wOBA can change from year to year, so the statistic tends to vary.  The listed formula is from 2013, so it isn’t too far into the past and let’s be honest, none of us are going to screw around with the actual math when we can simply look up the numbers in question.

Understanding a particular statistic is one thing, but providing a frame of reference is also necessary.  As such, this formula begs the question(s) how does the resulting value translate to “success”, if you will?  Glad you asked, here is a basic scale for your review;

Excellent =     .400

Great =           .370

Above Avg = .340

Average =     .320

Below Avg = .310

Poor =           .300

Awful =         .290

This statistic is, quite simply, a clearer view of the actual impact an offensive player has on his team while at the plate and it is done by factoring in unintentional walks, hit by pitch(es), singles, doubles, triples and home runs (by weight). 

So, last year, the top five players in all of baseball with regards to wOBA were as follows;

Trout =             .437

JD Martinez =  .430

Judge =           .430

Votto =            .428

Harper =         .416

Not a huge surprise at the top (and he could have been a MET), but the players in the second and fourth spot are interesting and not necessarily included in the same conversation among baseball fans.

What about the Mets?

Conforto =    .392

Cespedes =  .369

Duda =          .365

Bruce =         .353

Nimmo =      .348

Again, the top of the list shouldn’t be a surprise, but seeing Lucas Duda AND Brandon NImmo on the list should raise a few eyebrows.  Makes you wonder if the Mets should commit to finding regular playing time for BN, right?

So, much like any statistic, wOBA can assist with player evaluations, but should not be used as the only factor in an evaluation.   

What do you think?