Welcome to Sox Stat Attack! In these articles, my goal is to pair with my good friend Beefloaf and bring the analytical minds and traditionalists together. I will highlight the “Sabermetric” stat that I will talk about, talk about its pros and cons, and compare it to some of the related “Traditional” stats. And, if the article is too technical for you, the good news is that at the end will be short videos (for this article, coming shortly) I’ve made to explain the basic concepts of the stat up for discussion.
So, without further ado, let’s begin….
What is wOBA?
Weighted On-Base Average (wOBA) is considered a “catch-all” offensive statistic. It is meant to measure a hitter’s overall offensive value to his team based on the “value” of each event that occurs as a result of an at-bat. The equation is shown here:
The official equation for wOBA (via FanGraphs)
Everyone’s first thought is usually the same: that looks rather similar to SLG, right? Yes, as there is a value, or “weight” for each result of an at-bat. However, there are two important differences in just the top part of the equation:
- Unlike SLG, which only accounts for singles, doubles, triples, and home runs, wOBA also accounts for the two other ways a batter can reach base safely (outside of an error): walks and hit-by-pitches.
- Also notice the weight associated with each ball put in play: they are a bit different than SLG. This number (I won’t bore you with too many details) is founded on the concept of “Run Expectancy” – how many runs you are expected to score based on the number of outs in an inning and the number of runners on base. Remember the old saying, “A Walk is as good as a Hit”? In terms of run scoring, this doesn’t hold true.
Simply put, a team needs to score runs to win a baseball game. A single doesn’t provide as much value towards scoring a run as a home run does. How many times have we heard Hawk over the years say, “Got the leadoff guy on, but couldn’t do anything with it,” in reference to a leadoff single? In the same vein, how many times have we Hawk say, “You can put it on the board,” in reference to a home run?
The difference in a hit’s ability to be productive and help lead to run scoring is what wOBA is trying to capture.
Why Not Just AVG, OBP, SLG, and OPS?
This is a very natural question to ask, especially considering that one of the first things most people do is compare the equations of SLG and OPS to wOBA. Simply put, your standard triple slash line (AVG/OBP/SLG) is helpful, but it lacks details. Let’s start with AVG. Not all hits are created equal. However, AVG assumes that they are. So does OBP, but it goes a step further than AVG and includes walks and HBP (Moneyball‘s “Because he gets on base” comes to mind here). SLG takes both of these metrics a step further and begins the concept of adding “weights”. However, the math nerds have run the numbers and determined that a double isn’t worth 2x as much as a single – again, I won’t bore you with the details of how they determined that (but again, think Run Expectancy). In additions, OPS just adds OBP and SLG, assuming that a point in OBP is just as valuable as a point in SLG. This once again does not prove to be the case (I’d rather have a .300 OBP and .400 SLG hitter than a .200 OBP and .500 SLG hitter… extreme example, but hopefully you see the point). So, given these statistics, OPS is asking the right question and approaches the answer fairly well compared to the others. However, wOBA is the most accurate way to comprehensively represent all that is comprised in these four statistics.
So, How Do I Use It?
The beauty of wOBA is that it has been created to be on the same scale as OBP – so, a good OBP value is also considered a good wOBA value. The breakdown (via FanGraphs) is shown below:
For example, Yoan Moncada‘s wOBA last year was .311. So, he was a below average hitter last year – something I think we can all agree on. The curious thing: Tim Anderson got a lot of credit for his 20-20 season last year. Yet his wOBA was just .294. This emphasizes one of the biggest shortcomings of TA’s game – he does not walk a lot, and when he’s not hitting, he doesn’t create much value in terms of run creation.
The cool thing about wOBA is that you can take it a step further and calculate a specific number for how many runs above/below average a player created in a given season. This metric is called Weighted Runs Above Average (wRAA), and is derived directly from wOBA. For example, Yoan Moncada was worth 2 runs less than the average offensive player in 2018 (again, highlighting his very average to below average season). Daniel Palka, on the other hand, was worth 5 more runs than the average offensive player.
This extra little runs created step at the end helps turn a more abstract idea like wOBA into something more concrete and valuable for conversation amongst fans at the bar or in the seats.
What Are the Limitations?
wOBA, for as accurate and comprehensive as it is, does have some shortcomings. First of all, despite what it seems like, wOBA is a rather simple equation. It really doesn’t account for a player’s speed at all. Tim Anderson is an excellent example of this. He is probably worth more runs than would be determined by wOBA, simply because of his speed – he can turn a lot of singles into doubles and doubles into triples on balls in the gap or via the stolen base. It also doesn’t account for a player’s position. Much like we would prefer a corner infielder to slug closer to .400, we would prefer a corner infielder to have a wOBA around .370. Yet, For a middle infielder, a .340 or even .330 wOBA would be considered pretty darn solid. Finally, wOBA doesn’t account for the fact that different players play in different ballparks throughout the year. Some ballparks are just naturally easier to hit in.
So, in short, you have to always consider the context of these numbers when evaluating and comparing players at different positions.
Conclusion
To sum this all up, wOBA attempts to wrap up a whole bunch of useful, but flawed traditional statistics nicely into one catch-all statistic. The important thing to learn here is that if you are someone who loves traditional statistics, that is absolutely fine – in fact, “nerds” like me love traditional stats too. They are often the basis for Sabermetrics, as is the case with wOBA. That’s not to say that wOBA isn’t flawed – as indeed I detailed how it is – but, hey, we are all striving to get a little bit better at evaluation whenever we can, right?
This is the biggest takeaway that I hope all fans get from these articles: Sabermetrics and stat nerds are not attempting to destroy the game as we know it. The goal is to advance the game, analyze it in new ways, and open it up to a new audience. We all want to see baseball continue to grow and continue to be something that we can pass down to our children (or grandchildren). I hope that even if everyone doesn’t love the new age of baseball data, that everyone can at least grow to respect why some of us look at the game the way we do.
It’s just like with cell phones: your mom’s (or, depending on your age, your) old Motorola RAZR phone got the job done, but the iPhone can just do so much more! But, if you want to continue using your old flip phone, that’s fine too! Just understand why some people might want to use the iPhone.
Author’s Note: Several times, I said “I won’t bore you with the details”. If you would like to know more details behind the numbers, reach out to me on Twitter and I’d be happy to explain them further to you!
What did you think of Volume 1? Let me and SoxOn35th know on Twitter! Look for Volume 2 to come out some time this week!
Featured Edit: Brandon Anderson
Thanks for the explanation.