Despite all of the rhetoric and talk-radio bluster, sabermetric principles and statistics aren’t actually very complicated. It might take a sharp statistician or savvy programmer to derive perfect park factors, but it doesn’t take anything more than a curious mind to understand and apply the basics. In my time working to help spread these principles, one of the most common and useful questions I get is about which few statistics a person should learn when trying to get into the world of advanced stats.
On Wednesday during my chat I got such a question. Here’s how I responded:
I’ve handed out this advice before and I think it holds up pretty well. If you can master those three or four concepts, everything else falls into place pretty easily. Baseball is a constant struggle to score more runs that the other team and unpacking the events to determine their specific contribution to that act is really all you have to do. Not only will Weighted On Base Average (wOBA) help you do that, but it will unlock many more sabermetric doors.
Often times you might hear a sabermetrician tell you why batting average isn’t as useful as wOBA, but instead let’s pretend you’ve been watching baseball your entire life and you’ve somehow never seen an offensive statistic and have been tasked with building one. I suspect, if you had the proper tools, you would create wOBA before you even thought to create batting average or OPS.
If you were to build a rate statistic you would want something that included all offensive actions (walk, hit by pitch, single, double, etc) and you would want to weigh those actions based on their relative contributions to run scoring. You would want to assign some value for a walk and then some greater value for a single and so on. That’s intuitive. That’s simple. That’s wOBA.
Essentially, the only other steps Tom Tango used when he invented wOBA were to scale it to OBP (so that we could more easily transition to the new number) and adjust the values so that the value of an out is equal to precisely zero.
As a result of this rather straightforward thought process, you now have a statistic that properly weighs all offensive actions and is already scaled in a way that is quite familiar. Why would you only want to know how many hits a batter has in the plate appearances in which he doesn’t walk, get hit, or sacrifice? Why would you want a statistic that treats a home run and a single equally? If you can do better, you wouldn’t.
wOBA = (0.690×uBB + 0.722×HBP + 0.888×1B + 1.271×2B + 1.616×3B +
2.101×HR) / (AB + BB – IBB + SF + HBP)
The wOBA formula (shown above for 2013) might look a little intense, but it only requires basic multiplication and division, and it’s not like you ever calculate a player’s stats by hand anymore.
What’s so great about wOBA, beyond its tangible analytic value, is that it can set you up for success across the sabermetric kingdom. You can turn wOBA into runs above average by subtracting out the league average wOBA and dividing by the wOBA scale (both found here) and then multiplying by plate appearances. wOBA doesn’t include park factors, but with a few adjustments wOBA turns into wRC+, which is the most comprehensive offensive rate statistic we have.
Not only is wOBA the foundation of quality offensive measurement, the process by which we found wOBA teaches us to think like sabermetricians. You have to think about the value of every individual action. How much does Event A change the odds that a run will be scored and how does that relate to Event B?
If that last paragraph seemed painfully obvious, it’s because the fundamentals of sabermetrics are extremely simple and wOBA is a great illustration of that point. It might be a little tricky to calculate a player’s Wins Above Replacement (WAR), but the actual building blocks are simple. How many runs do his actions collectively add to his team compared to a freely available player? wOBA teaches you to think in terms of relative value and in the language of runs and wins. If you can master those two concepts, you have essentially passed Sabermetric Theory 101.
It takes more skill to become a quality analyst with the ability to unpack performance and project into the future, but if you’re only looking to measure past performance, learning wOBA more or less does the trick on offense and provides you with the thought process necessary to interpret the rest of a player’s game.
So while more information and more data is always better, wOBA is really one of the statistics at the heart of sabermetrics. It’s simple, intuitive, useful, and it sets you up to learn other statistics quite easily.
Have a question about wOBA? Ask it in the comments section!
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