So far, we have covered the most well-known offensive metrics and discussed their strengths and weaknesses. More recently, we started to dive into the more advanced categories with OPS and OPS+. In this video, we continue going down the path of advanced metrics with Weighted-On Base Average, or simply, wOBA.
wOBA Explained
If you recall our video about On Base Percentage, you’ll remember that while it’s important to include all of the ways a hitter can reach base, it’s not enough to measure that alone, since we know that certain events impact the potential for run-scoring differently.
Similarly, in our video about Slugging Percentage, we learned that we need to differentiate between batted ball outcomes. We know that using “total bases” to determine hit values isn’t the most accurate way to do so. For example, a home run is more valuable than a single, but the question at hand is, is it exactly four times as valuable?
Like OPS and OPS+, wOBA attempts to capture a hitter’s contribution to their team in one singular metric. What makes wOBA more useful, however, is its ability to properly weigh each outcome to give evaluators a more accurate representation of a hitter’s ability.
Made popular by sabermetrician Tom Tango, wOBA is represented generally by this equation:
wOBA = (0.69×uBB + 0.72×HBP + 0.89×1B + 1.27×2B + 1.62×3B + 2.10×HR) / (AB + BB – IBB + SF + HBP)
While this may appear complicated at first glance, it is actually much simpler than it looks. The coefficients assigned to each outcome are called linear weights and are designed to represent each of their impacts on run scoring.
These weights may change slightly from year to year depending on the overall league run-scoring environment. For example, in 2014 when home runs were down, the weight assigned to home runs was 2.135. In the home run record-breaking year of 2019, the weight for home runs was 1.940 because they were so prevalent their value was not as great. You can find these coefficients on Fangraphs.com.
Reviewing wOBA is similar to On Base Percentage where the league average is roughly .320, with .380 being above average and anything above .400 being elite.
Summary
One of the reasons we may use wOBA over other advanced metrics is that it properly provides credit to players who are skilled at drawing walks (or getting on base) and accounts more accurately for hit values. By using weighted stats, we can get the best possible look at a player within a particular level based on their run-scoring environment compared to others.
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