wOBA

wOBA flash

Weighted On-Base Average (wOBA) is one of the most important and popular catch-all offensive statistics. It was created by Tom Tango (and notably used in “The Book”) to measure a hitter’s overall offensive value, based on the relative values of each distinct offensive event.

wOBA is based on a simple concept: Not all hits are created equal. Batting average assumes that they are. On-base percentage does too, but does one better by including other ways of reaching base such as walking or being hit by a pitch. Slugging percentage weights hits, but not accurately (Is a double worth twice as much as a single? In short, no) and again ignores other ways of reaching base. On-base plus slugging (OPS) does attempt to combine the different aspects of hitting into one metric, but it assumes that one percentage point of SLG is the same as that of OBP. In reality, a handy estimate is that OBP is around twice as valuable than SLG (the exact ratio is x1.8). In short, OPS is asking the right question, but we can arrive at a more accurate number quite easily.

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. While batting average, on-base percentage, and slugging percentage fall short in accuracy and scope, wOBA measures and captures offensive value more accurately and comprehensively.

Calculation:

The wOBA formula for the 2013 season was:

wOBA = (0.690×uBB + 0.722×HBP + 0.888×1B + 1.271×2B + 1.616×3B +
2.101×HR) / (AB + BB – IBB + SF + HBP)

These weights change on a yearly basis, so you can find the specific wOBA weights for every year from 1871 to the present here.

To calculate wOBA, find the weights for the year you are interested in and multiply each weight by the player’s corresponding statistics. For example, in 2013 Mike Trout had 100 unintentional walks, 9 HBP, 115 singles, 39 doubles, 9 triples, and 27 home runs. If you multiple each by it’s 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 .423, or his wOBA for the season.

Why wOBA:

One of the most common questions people ask when presented with a new statistic like wOBA is why they should use it when the basic triple slash line statistics (average, on base percentage, and slugging percentage) work just fine or work even better when using them to form OPS?

Simply put, OPS and wOBA will lead you to very similar conclusions in most situations, but if you care about determining how well a player contributes to run scoring, wOBA is a more accurate representation of that contribution. OPS undervalues getting on base relative to hitting for extra bases and does not properly weigh each type of extra base hit.

Additionally, individuals do not often calculate statistics by hand and will use a spreadsheet if they like doing it themselves or will make use of a website such as FanGraphs to provide that information. OBP or SLG might be easier to calculate with pencil and paper, but wOBA is extremely easy to find and use on our site, meaning any computational costs of moving to wOBA are minuscule.

How to Use wOBA:

One of the beauties of wOBA is that it is extremely easy to use once you learn the basics. League average wOBA is always scaled to league average OBP, so if you know what a good OBP is, you know what a good wOBA is. Below are specific averages for the current season, but typically an average hitter will finish the season with a wOBA of around .320.

wOBA is also quite easy to convert to Weighted Runs Above Average (wRAA), or the non-park adjusted version of Batting Runs. In other words, you can convert wOBA to a cumulative run value above average quickly. Simply take the player’s wOBA and subtract out the league average wOBA, then divide by the wOBA scale and multiple that by the number of plate appearances. Both league wOBA and the wOBA scale can be found here.

((wOBA-League wOBA)/wOBA Scale)*PA = wRAA

For example, Mike Trout had a .423 wOBA in 716 PA in 2013 and the league wOBA was .314 and the wOBA scale was 1.277.

((.423-.314)/1.277)*716 = 61.1 wRAA

In other words, before making park and league adjustments, Mike Trout’s was worth about 61 more runs than the average offensive player. You can’t make such an easy conversion using OPS.

A good rule of thumb is that 20 points of wOBA is worth about 10 runs above average per 600 PA. This is not a precise measurement and specific calculations are always better, but if you’re looking for an approximate rule of thumb, this may be useful.

Context:

Please note that the following chart is meant as an estimate, and that league-average wOBA varies on a year-by-year basis. It is set to the same scale as OBP, so league-average wOBA in a given year should be very close to the league-average OBP. To see the league-average wOBA for every year from 1901 to the present, check the FanGraphs leaderboards.

wOBA Rules of Thumb
Rating wOBA
Excellent .400
Great .370
Above Average .340
Average .320
Below Average .310
Poor .300
Awful .290

Things to Remember:

● This stat accounts for the following aspects of hitting: unintentional walks, hit-by-pitches, singles, doubles, triples, home runs. Stolen-bases and caught stealing numbers used to be included as well on FanGraphs, but they are now instead accounted for with the stats UBR and wSB. This way, wOBA only accounts for a player’s production at the plate.

● Exactly how much to weigh each of the components of wOBA was determined using linear weights.

● wOBA can be converted into offensive runs above average easily. These are called Weighted Runs Above Average (wRAA). The formula to convert wOBA into wRAA is listed below:

wRAA = ((wOBA – league wOBA) / wOBA scale) × PA
(league-average wOBA can be found here; wOBA scale values can be found here)

● 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.

● wOBA on FanGraphs is not adjusted for park effects, meaning that batters that play in hitter-friendly parks will have slightly inflated wOBAs.

Links for Further Reading:

Custom wOBA and Linear Weights for 1871-2010 – Beyond the Box Score

Calculating wOBA (Datebasa Version) – The Book Blog

A Visual Look at wOBA – FanGraphs

The Joy of wOBA – FanGraphs

Intro to wOBA – Big League Stew

Getting to Know wOBA – The Book Blog

The History of wOBA – The Book Blog

wOBA Calculator – New English D

wOBA As a Gateway Statistic – FanGraphs

The Beginner’s Guide To Deriving wOBA -FanGraphs





Steve is the editor-in-chief of DRaysBay and the keeper of the FanGraphs Library. You can follow him on Twitter at @steveslow.

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CajoleJuice
Guest

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.

Nando
Guest
Nando

It could be user error, but I believe the “calculator” is view-only.

Jack Nugent
Guest

I’m still having some difficulty with this. It doesn’t look like it’ll let me save it…

JB
Guest
JB

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)?

craigtyle
Guest
craigtyle

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).

JB
Guest
JB

Excellent, thank you

williams .482
Member
Member
williams .482

a HBP also mens that the pitcher is more likely to be wild.

RJN
Guest
RJN

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.

Flyingelbowsmash
Member
Flyingelbowsmash

I don’t see in player’s stats the number of times they reached base on an error.

Andrew
Guest
Andrew

Where can one find the Reached on Error stats for individual players?

Jacob
Guest
Jacob

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?

jamie
Guest
jamie

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… Read more »

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SecondHandStore
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SecondHandStore

I don’t understand why wOBA doesn’t consider intentional walks. It’s especially confusing since hit by pitch is considered. I’d think if HBP is factored in, so would intentional walks. What am I missing?

d.t.
Guest
d.t.

my guess would be because IBB are from your teammates putting the other team in position where it is better to put you on base, and this is supposed to try to determine what an individual is doing at the plate by himself.

Jacob Knapp
Guest
Jacob Knapp

But then HBP and reaching on an error shouldn’t count either. (which they probably shouldn’t.)

m3tan
Guest
m3tan

I was a major STAThead back in the late 80s and early 90s before it became semi-mainstream thanks to Internet and Moneyball. Just getting back into it. I developed my own metrics in the past and wonder why some stats are included/excluded here. Maybe I just haven’t thought it through: Why is RBOE relevant? If the player “should” have been out, why reward him? That may be useful for comparing projected versus actual run totals for past seasons, but it does nothing for player evaluation or projecting the future. SF should have some value if a HBP has more value… Read more »

James
Guest
James

Since the formula does not give any credit for intentional walks, shouldn’t you divide by plate appearances minus intentional walks instead of just dividing by plate appearances? Otherwise you are actually punishing a batter who gets intentionally walked. Or am I missing something?

Bryce
Member
Bryce

I think this is actually making a non-negligible difference in the Trout/Cabrera comparison.

Robb
Guest
Robb

I am surprised that HBP are weighted higher than walks in the formula, since getting beaned is more a function of luck than drawing a walk. True, some guys get beaned more, but it’s not a repeatable skill like walking is.

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xj
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xj

The problem with most of these ‘Complex Statistics’ is that they weight them based on their correlation with runs. Sure that sounds great in theory, but the reality is you can go 100 for 100 with 100 triples and not score a run. You could also go 1 for 100 with one single and score a run. Correlating events to runs scored (the most important thing an offense can do) is probably the most irrelevant statistic you can have, when it comes to an individual. Statisticians continually turn a blind eye to this. It does not matter what events correlate… Read more »

obsessivegiantscompulsive
Guest

I’m no expert on all this, but from what I understand, the constants that they use in the wOBA formulation relates to the expected value, in terms of runs, that results from each type of hit, during each season. Taking what you aptly noted in the beginning, those 7 doubles could result in less runs happening when compared to those 10 singles. Extrabases mean nothing if the players behind you don’t drive you or, or if the players ahead of you don’t get on base (or are slow on the bases). The constants in the equation accounts for all those… Read more »

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Joe
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Joe

I need to know why, for example, a BB is worth .69, a HBP is worth .72, a 1B is worth .89, etc. How do you figure the value of each of the ways to get on base? I understand why more value is placed on a home run than a walk, but where do you come up with the numerical value?

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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?