## wRC for Pitchers and Koji Uehara’s Dominance

wRC is a very useful statistic.  On the team level, it can be used to predict runs scored fairly accurately (r^2 of over .9).  It can also be used to measure how much a specific player has contributed to his team’s offensive production by measuring how many runs he has provided on offense.  But it is rarely used for pitchers.

Pitching statistics are not so much based on linear weights and wOBA as they are on defense-independent stats.  I think defense-independent stats are fine things to look at when evaluating players, and they can provide lots of information about how a pitcher really performed.  But while pitcher WAR is based off of FIP (at least on FanGraphs), RA9-WAR is also sometimes looked at.  Now, if the whole point of using linear weights for batters is to eliminate context and the production of teammates, then why not do the same for pitchers?  True, pitchers, especially starters, usually get themselves into bad situations, unlike hitters, who can’t control how many outs there are or who’s on base when they come up.  But oftentimes pitchers aren’t better in certain situations, as evidence by the inconsistency of stats such as LOB%.  So why not eliminate context from pitcher evaluations and look at how many runs they should have given up based on the hits, walks, and hit batters they allowed?

To do this, I needed to go over to Baseball-Reference, as FanGraphs doesn’t have easy-to-manipulate wOBA figures for pitchers.  Baseball-Reference doesn’t have any sort of wOBA stats, but what they do have is the raw numbers needed to calculate wOBA.  So I put them into Excel, and, with 50 IP as my minimum threshold, I calculated the wOBA allowed – and then converted that into wRC – for the 330 pitchers this year with at least 50 innings.

Next, I calculated wRC/9 the same way you would calculate ERA (or RA/9).  This would scale it very closely to ERA and RA/9, and give us a good sense for what each number actually means.  (The average wRC/9 with the pitchers I used was 3.95; the average RA/9 for the pitchers I used was 3.96).  What I found was that the extremes on both sides were way more extreme (you’ll see what I mean soon), but overall it correlated to RA/9 fairly closely (the r^2 was .803).

Now, for the actual numbers:

The first thing that jumps out right away is that Koji Uehara had a wRC/9 of 0.08.  In other words, if that was his ERA, he would give up one earned run in about 12 complete game starts if he were a starter, which is ridiculous.  The second thing that jumps out is that most of the top performers are relievers – in fact, 12 out of the top 13 had fewer than 80 innings, with the only exception being Clayton Kershaw.  Also, the worst pitchers by wRC/9 had a wRC/9 much higher than their ERA or RA/9.  Pedro Hernandez, for example, had a wRC/9 of 7.68, and there were 6 pitchers over 7.00.  Kershaw actually has a wRC/9 that is lower than his insane RA/9, so maybe he’s even better than his fielding-dependent stats give him credit for.

But wait!  There’s more!  The reason we have xFIP is because HR/FB rates are very unstable.  So let’s incorporate that into our wRC/9 formula and see what happens (we’ll call this one xwRC/9):

Not a huge difference, although we do see Uehara’s number go down, which is incredible, and Tanner Roark’s – the second-best pitcher by wRC/9 – nearly double.  Also, Tyler Cloyd becomes much worse, and is now the worst pitcher by almost half a run per nine innings.  Kershaw’s wRC/9 goes up by a considerable amount, so much so that his xwRC/9 is now higher than his RA/9.  All in all, however, xwRC/9 actually has a smaller correlation with RA/9 (an r^2 of .638) than wRC/9 does, so it isn’t as useful.

Now, logically, the people who outperformed their wRC/9 the most would have high strand (LOB) rates, and vice-versa.  So let’s look at the ten players who both outperformed and underperformed their wRC/9 the most.  The ones who underperformed:

 IP LOB% RA/9 wRC/9 RA/9 – wRC/9 Danny Farquhar 55.2 58.50% 4.69 2.64 2.05 Charlie Furbush 65 64.40% 4.57 2.96 1.61 Casey Fien 62 69.40% 4.06 2.73 1.33 Andrew Albers 60 60.40% 5.10 3.78 1.32 Nate Jones 78 62.90% 4.62 3.31 1.31 Joel Peralta 71.1 70.20% 3.91 2.67 1.24 Addison Reed 71.1 68.90% 3.91 2.69 1.22 Tom Wilhelmsen 59 69.90% 4.27 3.07 1.20 Jesse Chavez 57.1 66.90% 4.24 3.04 1.19 Koji Uehara 74.1 91.70% 1.21 0.08 1.13

We can see that everyone here – except for Koji Uehara, who had the fourth-highest LOB% out of all pitchers with 50 innings – is below the league average of 73.5%.  Only Uehara and Joel Peralta are above 70%.  Clearly, a low LOB% makes you allow many more runs than you should.  But what about Koji Uehara?  How did he allow all those runs (10, yeah, not a lot, but his wRC/9 was way lower than his RA/9) without allowing many baserunners to score and not allowing many damaging hits?  If you know, let me know in the comments, because I have no idea.

Now for the people who outperformed their wRC/9:

 Rex Brothers 67.1 88.80% 2.14 3.23 -1.09 Donovan Hand 68.1 81.90% 3.82 4.97 -1.15 Stephen Fife 58.1 78.40% 4.47 5.63 -1.16 Jarred Cosart 60 85.90% 2.25 3.41 -1.16 Heath Bell 65.2 82.70% 4.11 5.35 -1.23 Chris Perez 54 82.30% 4.5 5.94 -1.44 Mike Gonzalez 50 80.30% 5.04 6.5 -1.46 Seth Maness 62 84.50% 2.47 4 -1.53 Adam Warren 77 84.70% 3.39 5.01 -1.62 Alex Sanabia 55.1 77.40% 5.37 7.29 -1.93

Just what you would expect:  high LOB%’s from all of them (each is above the league average).  Stephen Fife and Alex Sanabia are the only ones below 80%.

So what does this tell us?  I think it’s a better way to evaluate pitchers than runs or earned runs allowed since it eliminates context:  a pitcher who lets up a home run, then a single, then three outs is not necessarily better than one who lets up a single, home run, then three outs, but the statistics will tell you he is.  It might not be as good as an evaluator as FIP, xFIP, or SIERA, but for a fielding-dependent statistic, it might be as good as you can find.

Note:  I don’t know why the pitchers with asterisks next to there name have them; I copied and pasted the stats from Baseball-Reference and didn’t bother going through and removing the asterisks.

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Jonah is a baseball analyst and Red Sox fan. He would like it if you followed him on Twitter @japemstein, but can’t really do anything about it if you don’t.

Member
Member

The asterisks refer to LHP. Also, I think the fact that most of the big changes happen for relievers reflects that there is an inherited runner bias here. If you put a runner on 2B and then come out of the game and the next guy lets him in, wRC just sees the double but RA9 sees the whole run. On the other hand, for the guy who comes in, we only see the single and not the run. Just something to think about.

Member

actually FIP is based on linear weights for the HR, K and BB.

Guest
Bob

“All in all, however, xwRC/9 actually has a smaller correlation with RA/9 (an r^2 of .638) than wRC/9 does, so it isn’t as useful.”

Obviously it has a lower R^2, it’s a better predictor of future values whenever you adjust for HR/FB, not present. It’s actually more useful.

Why do we care about the wOBA against? It doesn’t really say anything about the pitcher’s skill. It’s taking a luck based number and saying why there’s a difference from this other luck based number. Pitcher’s can’t control their wOBA against.

Guest

Uehara actually had bad luck with sequencing. In fact, according to Fangraphs, seaquencing cost him 7 tenths of a win, which is essentially what you found. It’s important to remember that sequencing is not random — high strikeout and low babip pitchers are going to strand a lot more batters.