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Zack Greinke and Tim Lincecum by LOWESS

Today it was announced that Tim Lincecum would join Zack Greinke as this year's second Cy Young award winner. Both had excellent seasons. While Greinke likely has the edge due to Lincecum's more favorable league and park, both exhibited no real weakness. Both struck out more than a batter per inning, both walked fewer than three batters per nine innings, and both allowed fewer than a dozen home runs.

After Greinke's furious start, there was some concern that his superlative season might be lost as he bumped slightly in the middle of the season. Lincecum, by comparison, appeared to be more consistent: he had no string of dominance as convincing as Greinke's April, but neither did he have any periods in which he struggled.

I decided to calculate each pitcher's single-game FIP and xFIP (based on batted ball data from FanGraphs) and run it through the R CLI. I then applied a LOWESS regression to each pitcher's season to give an idea of how each pitcher's fortunes changed from month to month. Here they are, by FIP (click to enlarge):

Fipcomparison_medium

xFIP comparison below the jump.

Star-divide

Xfipcomparison_medium

Interestingly enough, because of Greinke's higher FB percentage (40.5% to Lincecum's 33.3%), Lincecum's xFIP LOWESS regression is lower than Greinke's for most of the year.

It's worth noting that there is a great deal of noise in all of these plots. What LOWESS does is apply a local polynomial regression; you can think of it as an improved moving average. For those who are curious, I used a smoothing factor of 2/3. 

Is this a helpful way to visualize a pitcher's season? I'm curious what you think!

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I like this a lot

Graphs showing weekly or monthly averages jump around too much to be of any use. These make more sense.

Greinke definitely was helped a lot by a low HR/FB rate. Will this continue? I would say no, but I said the same thing about Cliff Lee coming into this year.

by lookatthosetwins on Nov 19, 2009 7:39 PM EST reply actions  

Last reply, promise

Royals’ HR/OFFB park factor was 0.89, which was 6th lowest in baseball.

Worst were the White Sox, Rockies, Blue Jays, Cubs, and D-backs, in that order.
-j

by JinAZ on Nov 19, 2009 8:51 PM EST up reply actions  

Thanks.

I have been thinking of a xxFIPS where instead of using just flyballs, its flyballs per park

Jeff Zimmerman - Protecting the world from RBI's and Wins from my mom's guest house.

by Jeff Zimmerman on Nov 19, 2009 8:58 PM EST up reply actions  

Sounds interesting

I’d like to see how large of a difference there would be between xFIP and xxFIP.

by lookatthosetwins on Nov 19, 2009 10:22 PM EST up reply actions  

LIPS has all batted ball types, instead I just want fly balls to OF

Jeff Zimmerman - Protecting the world from RBI's and Wins from my mom's guest house.

by Jeff Zimmerman on Nov 20, 2009 12:37 AM EST up reply actions  

Curious,

why would you disregard the rest of the batted ball types? Do they have more noise than OFB?

by lookatthosetwins on Nov 20, 2009 2:14 AM EST up reply actions  

Why does xFIPS ignore them. I am just looking for the 3 true outcomes SO, BB and HR

Jeff Zimmerman - Protecting the world from RBI's and Wins from my mom's guest house.

by Jeff Zimmerman on Nov 20, 2009 9:23 AM EST up reply actions  

I heart LOWESS regressions

Good use of them, too. Might be the first time I’ve seen them in a saber post, and is regardless is the first time I’ve seen them in about 5 years anyway. Kudos. :)
-j

by JinAZ on Nov 19, 2009 8:55 PM EST reply actions  

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