# Pitchers’ Runs Created

June 26, 2005 5 Comments

Later, I will introduce a new Win Shares type system that solves many of the problems found in Bill James’ first attempt at producing one number, measured in wins, to characterize player value. Right now, however, let me introduce you to an important part of the system I will unveil, pitchers’ runs created.

My biggest problem with WS is that they do not actually measure absolute value, which is what James claims they do. Rather, they measure a player’s value over roughly a .170 W% level. The problem that James ran into is that while measuring absolute value for hitters is easy–just use runs created–there is no such number for pitchers. Thus, to get around this problem, James devised marginal runs, which look something like this:

RS – LG/2 = marginal offensive runs (where Lg is the league average)

1.5*LG – RA = marginal defensive runs

But what he’s doing here (and this, in my mind, is his first and most pivotal mistake) is comparing players not to a zero baseline, as he claims to do, but rather to a baseline of 1.5*Lg or Lg/2, which is bad, but not zero.

Again, the simple problem is that there is no number like RC for pitchers, a number where everything is equal to zero or more and where a higher number is better. But how do we come up with such a number? After some brainstorming, it hit me, like a pile of bricks or a once-in-a-lifetime idea: Why not convert runs allowed into runs scored?

Think about it: we know how runs scored and runs allowed interact with W%, so why not convert RA into RS by using W%? Using an average baseline for runs scored, we can predict a W% for any team or player on its/his runs allowed. More so, we can convert that W% into runs *scored* by converting it back using an average baseline for runs allowed. So if a player allows 3 R/G when the average team scores 4.5 R/G has a .692 W%, he can be said to be scoring 6.75 R/G, as a team that scores that much and allows 4.5 R/G will also have a .692 W%.

Using this, we can come up with defensive runs created (DRC), which will be on the same baseline as runs created for hitters. Of course, they must still be split between pitchers and fielders after. I will provide the formulas that I use for this adjustment a little later, but I would like to keep this article as devoid of technicalities as possible.

Pitching Runs Created can be used to judge Cy Young races, Greatest of All-Time debates, and trades. They’re easy to use and understand, and the great thing is, since they’re put on a zero baseline, they will measure *absolute* value.

Now, without further delay, let me present the top-10 for each league this year:

__AL__

1. Johan Santana – 58.88

2. Roy Halladay – 56.44

3. Mark Buehrle – 52.09

4. Bartolo Colon – 48.61

5. Matt Clement – 47.70

6. Dan Haren – 46.13

7. John Lackey – 44.60

8. Chris Young – 42.66

9. Jeremy Bonderman – 42.18

10. Randy Johnson – 41.25

__NL__

1. Pedro Martinez – 58.54

2. Chris Carpenter – 58.48

3. Dontrelle Willis – 53.95

4. Jake Peavy – 51.10

5. Roger Clemens – 50.95

6. John Smoltz – 50.25

7. Roy Oswalt – 50.12

8. Livan Hernandez – 49.69

9. A.J. Burnett – 46.81

10. Andy Pettitte – 43.22

The top-two in each league are very close, so it should be a good race through. Santana’s great season has been marred by a strangely horrible defense; while his BABIP is low (.276), he’s still allowing about .6 more runs than his peripherals (HR, BB, K) would indicate. Also, because of all the batters he strikes out, Santana gets more credit for his stellar pitching. In the NL, Pedro Martinez is similarly higher up than he is on other lists because of his high strikeout rate. His fielders have actually been above average. NL wins leader Dontrelle Willis is close behind in third, and could still finish first before all is said and done. On the other hand, AL wins leader John Garland is nowhere to be found on this list, with 33.46 PRC. The White Sox’s incredible defense has contributed much to his record. Roger Clemens, who most statheads would have likely picked as the best pitcher in the NL, is in fifth place, as his fielders make him look almost a full run better than he actually is.

Great idea. Let me just get one thing straight – the number next to Santana’s means what, exactly? How many runs below average he has saved?

It’s literally runs created. If you want to convert it into above/below average, you could find the average runs a pitcher will create per 9 innings (about 3 this season, 3.3 last) and then convert it into runs above or below average. But this is literally runs above a 0 baseline, like runs created are for hitters.

I absolutely love this idea, but I still don’t understand how you got the numbers for the top 10’s. I found Santana’s runs/game to be somewhere in the ballpark of 7, so how does 58.88 tie into that?

Steve,

This is an old chart. If you find the R/G, all you have to do is multiply by IP/9 and adjust for K-rate.

IeXjjW bedfardrjqjm, [url=http://pscnhzwigcta.com/]pscnhzwigcta[/url], [link=http://kgybiimkxawk.com/]kgybiimkxawk[/link], http://fpokafvomkyu.com/