A few years ago, I read an interview with Zack Britton, who as the Orioles’ closer in 2016 had one of the greatest seasons by a reliever ever. Traded to the Yankees by a rebuilding Baltimore in 2018, Britton was reportedly amazed at everything a data-loving analytics team could teach him. I hated that interview. It wasn’t just garden-variety “my ex has found happiness with someone else” bitterness. It was also the reminder that Baltimore was “numbers-averse,” which probably explained their longtime ineptitude at developing pitchers.
Well, times change. The Orioles are now a data-literate franchise, embracing analytics, sabermetrics, machine learning, modern motion-analysis, and a whole lot of other stuff. This helps explain how, even with the 28th lowest payroll in baseball, the Orioles have the best record in the AL.
With the times, the relevant baseball statistics change, too. Just like Moneyball taught us about OBP and to “Buy wins, not players!”, the sabermetrics revolution brought us wOBA. Invented by Tom Tango, Mitchel Lichtman and Andrew Dolphin in their 2006 book, The Book: Playing the Percentages in Baseball, wOBA is a sabermetrician’s stat. It looks complicated, but it starts from a simple idea: not all hits are created equal, so we shouldn’t treat them equally!
Fangraphs has been collecting this stat since 2008, so here, straight from the horse’s mouth, is their handy explanation of what it is, why it’s good, and how to calculate it:
Unlike batting average, wOBA combines all the different aspects of hitting into one metric—walks (not counting intentional ones, hence the “u”), HBPs, singles, doubles, triples and home runs. And it weights each in proportion to its actual run value. As you see from the formula, a home run is worth about three times a walk in terms of run creation. SLG (slugging percentage) tries to do this, but it does it crudely: is a single worth half as many eventual runs as a double? (Nope. It’s more like 60% of one.) Meanwhile, OPS (on-base percentage plus slugging) does combine different aspects of hitting into one metric, but it’s built on SLG so it suffers from the same weighting problem.
(One caveat about wOBA’s accuracy: it’s context-neutral, so it doesn’t take game situation into account (RISP, etc) nor park effects, meaning hitter-friendly parks tend to produce slightly inflated wOBAs. wRC+ (weighted runs created) does include park factors, but its output is a little less intuitive, so I won’t talk about it here.)
As Fangraphs notes, the actual wOBA weights vary slightly year-to-year depending on the run-producing environment in MLB that season. Fangraphs calculates and keeps the specific weights for every season from 1871 to the present.
So, say you wanted to calculate Cal Ripken Jr.’s wOBA in his MVP season in 1991. The wOBA formula for the 1991 season was:
wOBA = 0.707×uBB + 0.739×HBP + 0.908×1B + 1.279×2B + 1.647×3B + 2.133×HR / (AB + BB – IBB + SF + HBP)
In 1991, the Iron Man had 38 unintentional walks, 5 HBP, 125 singles, 46 doubles, 5 triples, and 34 home runs. If we multiply each by its corresponding weight and then divide that number by the sum of his at-bats, walks (excluding IBB), hit by pitches, and sacrifice flies, you get .404, his wOBA for the season.
wOBA—to my knowledge—hasn’t yet taken off among the average baseball fan or beat writer, but because it’s scaled to league average OBP, there’s a certain intuitiveness to it. If you know what a good OBP is, you know what a good wOBA is. As the Fangraphs chart below shows, an average hitter will typically finish the season with a wOBA of around .320, and anything over .400 is excellent.
Now let’s see what wOBA can tell us about the current Orioles squad. Here’s a chart showing the MLB’s Top 1, 10, 20 and 30 hitters by wOBA, their rank, then their OPS and rank, and the corresponding stats for the Orioles starting lineup.
MLB Leaders (wOBA and OPS)
|Player||Team||wOBA||MLB Rank (wOBA)||OPS||MLB Rank (OPS)|
|Player||Team||wOBA||MLB Rank (wOBA)||OPS||MLB Rank (OPS)|
One thing to notice: as good as this team has been, they have no one in the Top 30. Shohei Ohtani leads MLB, at .434 (Excellent), Pete Alonso comes in 20th at .361 (Above Average), and Adolis García is 30th at .355 (also Above Average).
The Orioles’ leader, meanwhile, is Ryan O’Hearn, at .355. He just misses the MLB Top 30 list and, to go by Fangraphs’ Rules of Thumb, he’s the only O’s hitter in the Above Average – Great category. After O’Hearn, there are two hitters in the Above Average group: Adley Rutschman and Gunnar Henderson. Then four more who somewhere between Average – Above Average: Ryan Mountcastle, Anthony Santander, Austin Hays and Cedric Mullins.
That’s seven hitters above the .320 cutoff for an Average hitter. Is that a lot? Well, for comparison, the Texas Rangers have nine. The Atlanta Braves, too. The Yankees: just two. So I guess it does have some offensive predictive power.
Just for kicks, the Orioles’ top offensive months by an individual player: Jorge Mateo’s April (.429), Anthony Santander’s May (.445), and Ryan Mountcastle’s July-August (.454). (It’s nuts to think that Shohei has sustained this, or nearly, for the whole season—while pitching. And that, apparently, he’s done pitching for the season (sad face).)
I included OPS and OPS Rank for comparative sake. Compared to wOBA, OPS overweights power relative to getting on base. It’s not a huge difference, especially at the top of the mountain. Note, though, that Adley is better using wOBA, because of his on-base percentage, while the other four above-average O’s hitters—Henderson, Santander, Mountcastle and Hays—all do better in OPS because of their power.
For what it’s worth, too, the wOBA/OPS comparison shows more generally that the Orioles are a power-hitting team, not a high-OBP one. (Apologies if you knew that already.) Taking .710 as an Average OPS, “known home run hitter” Adam Frazier is also average, which gives them eight hitters in the black. That’s enough to field a dangerous lineup, up and down the batting order, which is what these Orioles usually do.
Conclusions: The Orioles offense is solid and cohesive, not top-heavy. They should walk more. Ryan O’Hearn continues to be an incredible find. wOBA has its uses, even if it’s not anywhere close to explaining the Orioles’ startling success this season.