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The Basics in Hitter Valuation

Let’s break down wOBA and understand just how hitters are providing value

Colorado Rockies v Arizona Diamondbacks Photo by Christian Petersen/Getty Images

Recently, I posted Sabermetrics Hitting 101 and 202, which covered the basic and intermediate hitting stats. Of particular focus were OPS and wOBA, which were attempts to unify batting lines into a singular stat that valued all outcomes for a hitter and assigned an overall run value rate.

To recap, remember that there are two primary means by which a hitter can provide value with the bat: by getting on base (avoiding outs) and by hitting for power (different hits have different values). To accomplish this, OPS was created and I broke down OPS to show these two elements at play:

Keep in that mind that this was only approximate, as OBP and OPS have different denominators and that the run values for each type of outcome were reasonable but not accurate. Luckily for us, wOBA has a single denominator so it makes it easier to break out the components:

(for reference uBB = unintentional walks = BB - IBB)

So, the on-base portion is a factor in all ouctomes for a hitter while the “power” portion only comes in the form of doubles, triples, and homers (which have had the “singles” run value stripped and moved over to wOBA_onbase). However, you can see that the power portion has large values and can still add up significantly on its own. I will give some examples at the end of this post to show how different types of hitters provide value.


So let’s talk about outs. Outs are bad and want to be avoided at all costs. And w0BA makes it quite simple to see why outs are bad. Every time you record an out, you increase your ABs by 1 without increasing any other stat in w0BA. So, every time an out is recorded, your denominator increases by 1 while your numerator doesn’t change (aka, it’s your current wOBA + 0/1). So, 1/1 (1.000) becomes 12 (.500) after an out is recorded.

One assumption to recognize: by wOBA, all outs are treated equally. A strikeout = groundball out = flyball out. In terms of run value, there is VERY LITTLE difference between the type of out. Any additional outcomes of a potential out (e.g. double plays or reaching on an error) are already included into the linear weights of the hit values, but their impact is very very small.

A common theme is that “strikeouts are bad”. Yes, strikeouts are bad, but so is any out. A player that strikes out a lot is no less valuable than a player that never strikes out but makes an equal amount of really bad contact. The real reason that a high strikeout rate is seen as a concern is because it limits the amount of chances you have to put a ball in play. So, in order to succeed in the MLB with a high strikeout rate, you need to have solid BABIP, power, and/or walk skills. Citing “strikes out a lot” as a negative for a player doesn’t accomplish much without context. There is a positive correlation between strikeout rates and power (faster bat speed = more power but also means the bat has less time to make contact with the ball), which is why many of the best hitters (like Goldy) still have average or worse strikeout rates. Strikeouts are bad but need to be put into context, just like any other type of out.

So how do we avoid outs?

BABIP, or getting a hit

The first way to avoid an out is to get a hit. As I mentioned in Hitting 101, batting average is the effective “floor” for on-base percentage as OBP ~=~ BA + BB%. To measure a player’s overall talent in getting hits, we look at BABIP. BABIP is literally the batting average on balls in play. So every time you make contact (that isn’t a home run), how often is a player getting a hit? The best hitters typically make the best contact (line drives, deep fly balls, etc.) and as a result, have a higher BABIP than average (and the opposite for bad hitters). There are some exceptions: speedy high groundball hitters will have a higher BABIP than expected whereas high flyball HR sluggers will have a lower BABIP than expected. In the case of the HR sluggers, they will have a lower BABIP but their average will remain strong as home runs are included in BA but not in BABIP.

The general rule of thumb for BABIP is that league average is right around .300 and has been pretty consistent throughout the years. The best hitters are typically around .350 and it is pretty difficult to sustain a BABIP higher than that for an extended period of time (e.g. 600+ PA). On the low side, the lowest that is commonly seen among non-pitchers is generally around .240. Keep this in mind in regards to regression.

A very simplistic way to approximate batting average is to subtract K% from 1.000 and multiply this by the hitter’s BABIP; this is effectively taking the strikeouts out of the average and then multiplying the resultant (which is essentially “balls in play”) by the BABIP. So, BA ~=~ (1.000 - K%) x BABIP. Keep in mind that this does not include home runs, but it is a good way to conceptualize what BABIP means.


Ah yes, good ol’ fashioned walks. The stat that statisticians love and traditionalists tend to not be such a fan of. Walks were a very underrated part of the game for a long time but they’ve quickly grown in importance the past few decades, especially with the growth of Moneyball.

But if you look at wOBA, walks are only rated at about .69 runs, which is only about 77% the value of a single. And it makes sense that a single is more valuable than a walk - after all, a single has the capability of moving runners already on base by more than one base (either from first to third or from second to home). The true benefits to walks are going to be a bit more subtle than what is seen simply in wOBA.

The main reason why walks are so important is really rooted in BABIP. Consider two batters, one that puts the ball in play every time (Player BA) with an average BABIP of .300 versus one that walks every time (Player BB). Player BB is going to have a wOBA of .690 ( (.69*BB)/PA and PA = BB in this case). Player BA’s wOBA will vary based on the hits he gets, but remember than 70% of the time, he’s making an out. So that means in order to achieve an average of .69 runs per plate appearance, he needs to actually average 2.3 runs per hit (0.69/0.30), which is not possible without hitting home runs and messing up our assumption regarding BABIP (home runs are not included in BABIP).

That’s right: a player with a BABIP of .300 but hits a triple every time he gets a hit is less valuable that a player that walks every PA.

Keep in mind that this is an extreme example, but what this is really showing is that even though walks are the lowest run value component of wOBA, they are still incredibly important to a batter’s ability to avoid outs because they are not subject to the whims of BABIP, which is an out more than 60% of the time.

Player Example: Paul Goldschmidt (Elite overall hitter)

To better understand player valuation, let’s look at a few different sluggers. We’ll start with our own Paul Goldschmidt and his .418 wOBA in 2015.

Goldy wOBA Components 2015

Stat Run Value % of wOBA
Stat Run Value % of wOBA
BB 0.0923 22.1%
HBP 0.0022 0.5%
1B 0.1459 34.9%
2B 0.0726 17.4%
3B 0.0049 1.2%
HR 0.1042 24.9%

What may come as a bit of a surprise is that the largest component of Goldy’s wOBA comes from his singles (which is also considerably higher if you take the first-base component of doubles/triples/homers as I do in wOBA_onbase above). Combined, walks and singles make up nearly 60% of Goldy’s overall hitting ability and the power component, which is very good to begin with, makes up about 40% of the total value.

Player Example: Yasmany Tomas (High-K Slugger)

Next up, let’s review Yasmany Tomas - a player with very poor walk skills but elite levels of power. We’ll use his current 2017 stats as of the start of May 2nd, which has him sitting on a .376 wOBA and a .291 ISO.

Tomas wOBA Components 2017

Stat Run Value % of wOBA
Stat Run Value % of wOBA
BB 0.0303 8.1%
HBP 0.0000 0.0%
1B 0.1076 28.6%
2B 0.1116 29.7%
3B 0.0178 4.7%
HR 0.1154 30.7%

Tomas, like Goldy, is still getting a large value of his wOBA from singles. But where Tomas really comes around is his value in doubles and homers. Almost 65% of his wOBA comes from his power. The downside here is that the vast majority of Tomas’s value comes in the form of batted balls, so in the event of a season with a below-average BABIP or K%, Tomas’s value can really plummet. Fortunately, Tomas hasn’t shown exceptionally bad strikeout rates (though still worse than league average) and is one of the very rare sluggers with good BABIP skills, so that actually helps offset much of his volatility.

Player Example: Ender Inciarte (High contact, low power hitters)

Now, let’s take the time machine back again to 2015, this time to look at Ender Inciarte, a prime example of a batter with great contact skills (low K%), low power, and a little better than average BABIP skills. In 2015, Ender posted a .325 wOBA.

Inciarte wOBA Components 2015

Stat Run Value % of wOBA
Stat Run Value % of wOBA
BB 0.0321 9.9%
HBP 0.0052 1.6%
1B 0.1926 59.3%
2B 0.0613 18.9%
3B 0.0145 4.5%
HR 0.0225 6.9%

Ender is getting an insane amount of value from his singles. Nearly 60%! Beyond that, it’s doubles and then walks, which combine for almost 80% of his wOBA. Again, note that a player like Ender Inciarte is also going to be suspect to the same BABIP and K% risks as Tomas - a bad BABIP season would absolutely trash Ender’s value. A good example of this would actually be Jean Segura - bad seasons in Milwaukee thanks a low BABIP and an incredible season last year in AZ thanks to a great BABIP. Generally speaking, these high contact type of hitters tend to be very athletic and will often have good defense or baserunning value to help offset their middle-of-the-pack offensive numbers.

So with that, we’ll call it a day when it comes to valuing hitters. Keep in mind that this is primary for measuring past performance and that this evaluation that we’re doing is not very indicative of future performance. We’ll tackle the “predicting the future performance” portion of hitting at a later date, as that is where the analysis starts to get really complicated.

Hope you enjoyed this read, please give me your feedback and criticisms down below!

Edit 5/3: Corrected various typos