wOBA
Description:
Weighted On-Base Average (wOBA) is based on a simple concept: not all hits are created equal. Batting average would have you believe they are, but think about it: what’s more valuable, a single or a homerun? Batting average doesn’t account for this difference and slugging percentage doesn’t do so accurately (is a double worth twice as much as a single? In short, no). OPS does a good job of combining all the different aspects of hitting (hitting for average, hitting for power, having plate discipline) into one metric, but it weighs slugging percentage the same as on-base percentage, while on-base percentage is more valuable than slugging.
Weighted On-Base Average combines all the different aspects of hitting into one metric, weighting each of them in proportion to their actual run value.
Context:
wOBA is put on the same scale as OBP, so any score that would be a great OBP is also a great wOBA. League-average is typically around .330, although it varies from year to year.
2010 Values
Things to Remember:
- This stat is context-neutral, meaning it does not take into account if there were runners on base for a player’s hit or if it was a close game at the time.
- These are the aspects of hitting that wOBA accounts for: non-intentional walks, hit-by-pitches, singles, reached on error, doubles, triples, and homeruns.
- wOBA can be converted into offensive runs above average easily. These are called Weighted Runs Above Average (wRAA).
- wOBA on FanGraphs is not adjusted for park effects, but the wOBA on StatCorner is. Take your pick.
- Exactly how much to weigh each of the components of wOBA was determined using linear weights. See that page for more information.
Links for Further Reading:
Intro to wOBA – Big League Stew
1
Heads up, the link to the wOBA calculator is broken (delete the “0″ after “spreadsheets” in the URL).
Besides that, this entire saber library is amazing work, Steve.
Thanks…it should be fixed now. Hopefully that works.
It could be user error, but I believe the “calculator” is view-only.
All right, it’s public on the web now. The only trick is if you want to edit it, you need to save your own copy and do it that way. I want to make sure the formula stays intact and it doesn’t get changed by accident.
I’m still having some difficulty with this. It doesn’t look like it’ll let me save it…
Haha, dang, why do I suck so much at this? Now it’s public and anyone can edit. Hopefully that does the trick.
Yeah it’s working, except now it looks like if there is more than one person viewing the document, that they can potentially both be punching in their own numbers at the same time.
Geez. Okay, I’ll mess around with it some tonight.
No sweat. Looking forward to having that here though
Oooo. A fourth tier of responses. Should be global.
Apologies if I’m being a bit lazy, cause I’m sure this answer is somewhere in this excellent library, but why are HBP weighted more than NIBB (.75 v .72)?
HBPs have a slightly better linear weighting than do NIBBs. The reason for this is that pitchers have somewhat more control over NIBBs, and thus they are more frequent in those situations in which their impact is somewhat lessened (e.g., runners on second and/or third, first base open) and less frequent when their impact is greatest (e.g., bases loaded).
Excellent, thank you
a HBP also mens that the pitcher is more likely to be wild.
The 25th percentile wOBA is really only .004 below the 50th percentile, but the 75th percentile is .045 above? It doesn’t make intuitive sense that such a large number of players would be bunched just below the 50th percentile but not just above it.
This graph is for WAR, but the same general concept is the same. There’s a big bunch of mediocre players once you get to a certain level.
http://assets.sbnation.com/assets/483944/war_distro_2010.jpg
Also, this is why I call these “estimates”. Technically the 50th percentile is a bit higher than what’s listed, but I wanted to express the mean instead of the 50th percentile. Makes it a tad weird.
Howdy! Quick question that’s entirely off topic. Do you know how to make your site mobile friendly? My weblog looks weird when viewing from my apple iphone. I’m trying to find a theme or plugin that might be able to resolve this issue. If you have any suggestions, please share. Thanks!
I don’t see in player’s stats the number of times they reached base on an error.
This will be a fantastic blog, would you be interested in doing an interview regarding how you designed it? If so e-mail me!
Audio started playing as soon as I opened up this internet site, so annoying!
Where can one find the Reached on Error stats for individual players?
Perhaps I missed something but I did my own calculation for a specific player, Ian Kinsler, and came out a little off. Do the linear weights change from year to year?
Q#1: why is this called weighted-onbase-AVERAGE? This is not an average.
If this is an average, what does 1.000 represent (other than awesome!)? what does Ellsbury’s 0.402 2011 wOBA mean? he’s just over 40% of what?
Q#2: Who came up with the name? Do you really think people want to use this in public (man, have you seen his woba)?
I love the stat itself. Love the concept. And what you guys have done to provide us with great tools for analysing players performances, but Im not going to use a stat that sounds like a drunk jedi knight (Obi-woba kenobi?)
Q#3: why is this matched up to look like OBP numbers? Isnt this a kind of replacement for slugging% (which also isnt a percentage)?
what is the fascination with creating new stats and then trying to make them look like other stats? I believe the idea is that your afraid if you keep creating new stats with new numbers, we’ll get fed-up. We’re baseball-fanatics… we’ll never get fed-up of these numbers! I think the opposite has happened, as I for one don’t like viewing a new stat that just hides itself behind the appearance of an old one. Give them there own individual look and they’ll be more widely accepted.
Im currently taking your wOBA results and multipling them by 1.4271886648681 (as close as I can get it without knowing ‘reached base on error’ results) so that 1.000 matches up with the greatest single season performance ever – Babe Ruth in 1921 – and Ive called the stat BRaverage (pronounced brave-erage), meaning the Babe Ruth average. The numbers now have a meaning. How close is the player to the greatest ever season? Jacoby Ellsbury? in 2011 scored 0.574 in BRaverage.
Of course, using Babe Ruths 1921 season is just an example, but my point is, lets make these stats express a point, have some kind of scale that we understand… And a name we can all pronounce (publically and literally) – though BRaverage could also be shortened to BRA, which might be even more publically embarrassing… have you seen Prince Fielders BRA?