The stats we use: wOBA


Just because people term a statistic “advanced” doesn’t mean it requires an overly complex calculation. Last week we examined UZR, and that might have given off the wrong impression. UZR is complex out of necessity. A baseball field contains 78 zones, and to calculate defense we must account for multiple zones per player. This involves not only balls hit into the zones, but also the type of ball hit into the zones and the rate at which other players converted those balls into outs. Offensive statistics, however, are a bit more straight forward. It helps, too, that we’re already familiar with the components.

This week we’re going to dive into weighted on base average, or wOBA. Developed by Tom Tango, the stat attempts to reconcile OBP and SLG, two of the most important offensive statistics. They both measure one thing while ignoring others. OBP measures how many times a player reaches base while ignoring the difference between a walk and a home run. SLG measures total bases while ignoring walks. wOBA weighs both and combines them for an offensive statistic that more accurately represents a player’s value.

What about OPS?

At this point you might be saying, “But they already have OPS. That combines OBP and SLG. So what gives?” True, OPS stands for on-base plus slugging, so why the need for a more advanced calculation? The answer deals with the scales upon which each metric is based.

The denominator in OBP is plate appearances, while the denominator in SLG is at-bats. True, they’re expressed in decimal format, and that might make it easier to slap them together. That doesn’t mean it is correct. Beyond the denominator issue, we also have an issue of scale. OBP is almost always going to be lower than SLG, because OBP is binary. You either reached base or you didn’t, meaning you get a 1 if you succeed and a 0 if you fail. SLG, on the other hand, measures total bases, so a player receives 4 for a home run, 3 for a triple, 2 for a double, and 1 for a single. And, again, it works with a smaller denominator, since at-bats is a subset of plate appearances.

By merely adding together OBP and SLG, we get a number that greatly favors power hitters. High on base guys will still climb the OPS charts, but their lack of power will keep them away from the top. What we want an on-base plus slugging stat to accomplish is to properly weigh these two aspects and provide us a proper valuation of offensive contribution.

How do we weigh events?

Instead of working with OBP and SLG, Tango decided to start from scratch with wOBA. This makes perfect sense. Statistics are just a recording of what happened on the field. OBP examines walks, hits, and outs. SLG examines singles and extra base hits. Why take those two pre-made calculations when the available data allows you to weigh the individual components of these stats before their OBP and SLG calculations? That’s exactly what Tango did.

In last week’s UZR primer, we looked at linear run estimators, which assigns a certain run value to each offensive event. This comes into play again for wOBA. After breaking offense down into its individual components, we can then weigh the value of those components and combine them for a rate stat. With wOBA, however, we’re looking at the runs above the run value of an out, which is zero in an OBP calculation. So here’s an updated linear run estimator, in terms of runs above the value of an out.

Event Value
Single 0.77
Double 1.08
Triple 1.37
Home run 1.70
Walk 0.62

The only step left is to scale the stat to OBP. This isn’t necessary — in fact, the league-average hitter under this situation would be around .300, which seems like a neat, round number. But, since we’re talking about a weighted OBP, Tango decided to scale it so the league average is around .340. That requires adding 15 percent to each weight. Again, not a big deal. It just makes the end result easier to understand.

Stolen bases

Yes, wOBA includes stolen bases. Successful stolen bases weigh at 0.20, while a caught stealing around -0.44. Tango provides a full list of linear weight events here.

Runs above average

Using wOBA, we can easily determine how many runs above average a player contributed. All we have to do is subtract the league-average wOBA from the player’s wOBA. But, because of the 15 percent weight added for scale, we need to divide by 1.15 to strip that out.

No adjustments?

No, there is no wOBA adjustment for park, position, or league. Individuals can make these calculations, but wOBA is meant as a context-neutral stat. Again, just as OPB and SLG are not park adjusted, nor is wOBA.

What about OPS+?

On Big League Stew, Alex Remington is running his own series of articles on advanced statistics. I try not to use what he writes in mine, especially because we’re trying for different purposes. In his wOBA primer he makes a statement with which Tango takes issue. Alex says that wOBA is “superior to non-weighted stats like OPS and OPS+.” The statement is inaccurate, because OPS+ is weighted. But, as Tango notes, it is weighted improperly.

Sean Forman, proprietor of Baseball Reference, leads the charge with OPS+. He essentially weighs OBP to SLG at a 1.2 to 1 ratio. That appears to improperly undervalue OBP, and Tango argues that the ratio should be more like 1.7 or 1.8 to 1. Because OPS+ does park and league adjust, that would make OPS+, as he says, “(almost always) superior to wOBA.” Again, this comes from the man who created wOBA.

To remember

Unlike all-in-one stats like WAR and WARP3, wOBA doesn’t try to tell us everything. What it does try to tell us is the value of a player’s offensive contribution. It doesn’t necessarily favor guys who walk a lot, like OBP, or guys who hit for a lot of power, like SLG and OPS. It breaks down offense into its core events, weighs those events, and then adds up the value. It also adjusts to a familiar scale, making it easier for us to understand, at a glance, the value of a player’s contribution.


wOBA from The Book
Getting to know wOBA
The history of wOBA, part 1 (to which there seems to be no part 2)

Categories : Offense


  1. pete says:

    I <3 RAB

  2. AeroFANatic says:

    Edited by RAB: Completely off-topic

    • Rocky Road Redemption (formerly RAB poster) says:

      Heh. I’m laughing a bit meanly because my ultra-mega-super off topic post from the previous thread has still not been deleted. And, if I’m being honest with myself, it should be.

  3. Because OPS+ does park and league adjust, that would make OPS+, as he says, “(almost always) superior to wOBA.” Again, this comes from the man who created wOBA.

    Isn’t that the point of wRC+?

    To make a park and league adjusted central offensive production stat that is centered around the linear weight model of wOBA, rather than the cruder OBP and SLG-centric model of OPS?

    So, wRC+ >>> OPS+ >>> wOBA >>> OPS >>> SLG and OBP >>> BA?

    • Might be. I haven’t read up too much on wRC or wRC+ yet, but you can be sure they’ll be part of this series.

    • pete says:

      “So, wRC+ >>> OPS+ >>> wOBA >>> OPS >>> SLG and OBP >>> BA?”

      I think that’s a fair assessment, yes.

      What I think is interesting is that when it comes to evaluating prospects and minor leaguers, you’d probably be more inclined to go with something like this, where you’re depending more on raw talent that’s likely to exceed at the next level than pure win-related results evaluation:


      • pete says:

        or would you put OPS+ above wRC+? I suppose you probably would.

      • Meh, it’s more a division between simple, all-in-one stats to compare players amongst their peer groups vs. separate, individual stats to drill down into a single player’s ability to forecast and indentify strengths and weaknesses.

        wOBA and wRC+ are great stats for, say, deciding who to be interested in (or not interested in) amongst 5-10 players on a free agent market, i.e. is Marlon Byrd’s total production worth more or less than Gary Sheffield’s, or are both of them better than/worse than the in-house options we have.

        It’s not the best tool for evaluating minor leaguers, though, because you’re not really interested in comparing Austin Jackson to Curtis Granderson. You’re more interested in what Austin Jackson is AND who he’ll become, and for that level of forecasting you need all the dozens of individual component metrics that go into the big super-stats like wOBA. wOBA is too all-encompassing to notice individual trendlines in things like K/9 or GB/FB or Swing Percentage or stuff like that.

        • pete says:

          well yeah I would certainly agree. I was just ranking how much somebody would care about the listed statistics in a minor league or prospect evaluation setting. For me, for a hitter, I’d probably rank them like this:

          LD%>SLG>K%> anything else

  4. Mattingly's Love Child says:

    RAB = The bees knees

    Thank you guys for this. Sometimes going to Fangraphs is really overwhelming to understand all of these statistics, especially for a idiot like me. I feel I have a much better understanding of wOBA now. The true test is if I can explain it to my 62 year old father while we’re watching games in April.

  5. Chris says:

    Tango provides a full list of linear weight events here.

    Does wOBA consider all of the other events listed, such as reached on error HBP, etc?

  6. pete says:

    One thing i wonder is that why, since wOBA is context-neutral (and anyone in their right mind would look at wRC+ over wOBA when evaluating a player), does it bother with the linear weights? That is not to say that I don’t appreciate the creation of weighted statistics, but if you’re weighting them, then what’s the point of keeping it context-neutral?

    I guess what I’m saying is that while it’s much more useful when it comes to player evaluation at the FO level, I think it would be cool to have a completely neutral combination stat. One where, for example, a walk = a single = a HBP = a SB, etc. In other words, one that simply counts how many bases a player takes minus how many extra outs he makes, against total plate appearances. The formula would look something like this:

    (TB + BB + HBP + SB – CS – GIDP) / TPA

    The reason I bring this up is because I worry that the linear weights could be affected by sample. For example, the weight of a single could be quite accurate, but a HBP not as accurate because there are fewer, and therefore the comparative relationship breaks down, breaking down the whole weight system to begin with. Plus it’d be kinda cool to see exactly how many bases a player was worth without the run/win relationship context. At least I think it would.

  7. [...] their strikeout rate against their weighted on-base average (wOBA,, which Joe explained in detail here). If strikeouts are so bad for hitters, then theoretically the players with the highest [...]

  8. [...] started writing about the stats we use. One concept we saw in both current entries, UZR and wOBA, is linear weights. The idea might sound complex, but it is not. The idea is to assign a value to [...]

  9. [...] of three seasons in New York and hit .256/.376/.424, good for an OPS+ of 113. As we learned when discussing wOBA, OPS+ undervalues OBP a bit, so Johnson actually performed a bit better than his OPS+ mark [...]

  10. [...] Those familiar with the statistics will recognize what it means when we say a player has a .355 wOBA. Those who aren’t, though, might have a bit of trouble determining exactly what that means, [...]

  11. [...] hitting .171-.224-.238 in 118 plate appearances. Bruntlett straight up can’t hit (career .286 wOBA), but at least he can play every position expect pitcher and catcher. It’s just a depth move [...]

  12. [...] It looked like they had really hit their stride on Tuesday when they rocked Roy Halladay. But they managed just four runs in the next two games, including just one last night. The Phillies pitchers did a good job of getting ahead in the count, and that apparently threw the Yanks off their game. That hasn’t dropped the Yanks from their percha top the AL in runs per game, but they have dropped behind the Red Sox in terms of wOBA. [...]

  13. [...] He also made an excellent catch on a Rod Barajas fly ball out by the wall in left. His .378 wOBA is not only right around what Johnny Damon posted last year, but is also .001 ahead of Carl [...]

  14. Nip Tuck is a unique TV Series just like House MD.’`*

  15. Nip/Tuck has some great drama too in the story, the women in this tv series are gorgeous too eventhough most of them are mature”.*

  16. [...] What’s important is that he has gotten better from May through July. He posted a meager .332 wOBA in May, but that rose to .357 in June and .392 in July. In fact, he’s shown much improvement in [...]

  17. [...] all shortstops with 500 plate appearances (.317), ranked second in WAR (47.5), and ranked third in wOBA and OPS behind Alex Rodriguez and Hanley Ramirez. Impressively enough, he managed to do all of this [...]

  18. Nygktvgt says:

    images crickets,

  19. [...] read this site long enough, then you’re probably familiar with the idea of linear weights and wOBA. If not, then I suggest checking out Joe’s primer. In a post at the FanGraphs Community blog [...]

  20. [...] that Alex’s lowest O-Swing% and O-Contact% of a final 3 years was in 2009, his final .400-plus wOBA campaign. Somewhat foresight is Alex’s 27% O-Swing% in 2011 — adult from 2010?s 25.3% and [...]

  21. [...] offensively. That’s relative to his lofty standards of course, because in no world is a .371 wOBA and a 129 wRC+ bad. He he .290+ with 30+ doubles and 25+ homers for the third time in four years, [...]

  22. [...] reasons to re-sign Jones were obvious: he mashes lefties (.400 wOBA vs. LHP in 2011, .401 from 2010-2011), plays solid enough defense in the corner spots, and [...]

  23. [...] It would be a success if Montero winds up having a career as long (13 seasons) and productive (.355 wOBA and 114 wRC+) as Lee has, but they’d go about it in very different [...]

  24. [...] best prospect in the Rockies’ system in last year’s Prospect Handbook. He put together a .392 wOBA for Colorado’s Triple-A affiliate last year, then picked up a pair of knocks in his first taste [...]

  25. [...] has been steadily declining over the last few years, though he can still hit righties some (.322 wOBA) and has pop in his bat (.184 ISO vs. RHP last year). He also doesn’t strike out much against [...]

  26. [...] 34, produced a powerless (.094 ISO) .294 wOBA in 175 plate appearances last season, missing nearly three months with a broken bone in his foot. [...]

  27. [...] would be an upgrade over Martin especially if he shows that last year’s offensive spike (.349 wOBA) is a real thing during his peak years, but the big question is money. I have a feeling Molina’s [...]

  28. [...] chance last year and performed fairly well compared to most utility infielders, producing a .313 wOBA and a 92 wRC+ in 338 plate appearances. When Derek Jeter and Alex Rodriguez went down with injuries [...]

  29. [...] either short or third while Derek Jeter or A-Rod get the day at DH. He’s hit lefties (.317 wOBA) better than righties (.298) in his short big league career, and he has shown a similar split in [...]

  30. [...] 34, posted a .459 wOBA in 19 Triple-A games but hitting his not his forte. He’s a defensive stud capable of manning all [...]

  31. [...] period of time. A-Rod is no longer the historically great hitter he once was, but his .356 wOBA and 121 wRC+ will be very tough to replace. The Yankees also have zero right-handed power in their [...]

  32. [...] helps prevent him from having a significant platoon split — he’s holding lefties to a .222 wOBA (.323 career) and righties to a .301 wOBA (career .291) this [...]

  33. […] 23, is hitting .266/.308/.602 (~149 wOBA) with 25 homers in 292 plate appearances split between High-A Tampa and Double-A Trenton this year. […]

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