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Tuesday, March 16, 2010

How much can a pitcher’s road parks affect his stats?

By , 04:12 AM

A lot!  We sometimes talk about a team’s strength of schedule and how it might affect its win/loss record (not much, I think).  We sometimes even talk about a team’s strength of schedule in terms of how it affects individual players.  Of course, if you use BPF and PPF (batter park factors and pitcher park factors), like what you see on B-R, you are automatically adjusting for the pool of pitchers (for batters) and batters (for pitchers) that a player plays against, but even those numbers assume two things which are not true:  One, that there is a balanced schedule, which there isn’t, and two, that players on the same team play an equal number of games against the same opponents, which they don’t, especially pitchers.

Anyway, I am not here to talk about the strength of a player’s pool of opponents, although that is an important discussion in and of itself.  What about opponent park factors, or the average park factor that a player faces on the road?  The same thing that I said above is true for road park factors.  For one thing, the unbalanced schedule means that, for example, the Dodgers, Giants, and the Padres play a lot of games in ARI and COL, the two most hitter friendly parks in the NL.  And pitchers, especially starters, because they don’t pitch every day, may play an inordinate number of games in one park or parks or another.  This can make a big difference in terms of their raw, unadjusted (by their road parks) stats.

IOW, when park adjusting player stats, you really have to know their “average road park PF” rather than just using the “other parks factor” (which is 1 minus your home park PF divided by 13 or 15) which is traditionally used in park factor adjustments.

For example, let’s say that the Padres PF is .85 .  That means of course that you would adjust a home stat (in runs) by dividing it by .85.  Traditionally, for the road stats, you would use the “other parks factor” of 1+ .15/15, or 1.01.  IOW, if the Padres have a PF of .85, all the other parks in the NL must have PF’s of 1.01 for everything to equal 1.00.

But, as I said, because they play lots of games against the D-Backs and Rockies, they play lots of games in two parks that have PF’s of around 1.10 and 1.20 and not 1.01 (the average park in the NL other than ARI).

Anyway, what I did was to compile the average road park factor for all players in 2009.  You will be surprised at some of the results:

Here are some Padre pitchers and the average park run factor of all the road parks they played in prorated by the number of TBF in each of those road parks:

Peavy 1.00 So he did in fact play in average road parks (actually not the 1.01 that you would expect)

Mujica 1.03 So if his road ERA were 5.00, that would actually be 4.85 after park adjusting it, which would make a difference of .05 runs in ERA overall (as compared to if you used the generic 1.01 for his road parks)!

Chris Young 1.04 Besides sucking due to a large decrease in velocity, he also played in heavy hitters’ parks on the road, costing himself .075 in overall park adjusted ERA.

Gaudin 1.03

What about some COL pitchers?  If you used the traditional “other park factor” you would adjust (divide) their road ERA’s by a factor of .988, since COL has a PF of 1.18.

DeLaRosa was .97, one reason why his stats were so good!  He is benefiting overall by about .05 in ERA.

A bunch of relievers actually had road park factors of .94 and .95, even in a decent number of TBF, like Rincon, Daley, and Peralta.

Poor Glendon Rusch, aside from being a bad pitcher, his road park factor was 1.03!

Arizona pitchers have an “other park factor” (again, assuming that they play an equal number of games in all NL parks) of .993.  Here are some pitchers who were pretty far from that:

Buckner .95
Petit 1.01
Vasquez .96

For batters, since they tend to play every day, the variations in road PF are not that great.

Here are the players, pitchers and batters, who were helped or hurt the most by their road PF’s, minimum of 150 TBF or PA in their road games:

Batters

Hurt

Carlos Gonzalez COL .96
Fowler COL .97
Iannetta COL .97
Carlos Ruiz PHI .97
J Hamilton TEX .97
A Jones TEX .97

Helped

Delmon Young PIT 1.03
McCutchen PIT 1.03
G Jones PIT 1.03

Pitchers

Hurt

Troncoso LAN 1.04
C Morton PIT 1.04
C Young SDN 1.04

Helped

B Buckner ARI .95
J Martin WAS .96
Sonnanstine TBA .96
Hunter TEX .96
Nippet TEX .96

Once you throw in the weather issues that I talked about the other day, and a player’s personal SOS (the quality of the pool of opponents they happen to face), I think you have quite a bit of context for a lot of players that is not accounted for by traditional park adjustments.  Given the ease that these things can be computed with simple database queries, I would hope that sites like B-R will eventually include then in their OPS+, wOBA+, ERA+ and other stats.


#1    Peter Jensen      (see all posts) 2010/03/16 (Tue) @ 09:56

And don’t forget about home plate umpires for pitchers.  I was wondering when somebody else was going to realize what a big factor these unequal distributions were for projecting starting pitchers.


#2    Greg Rybarczyk      (see all posts) 2010/03/16 (Tue) @ 10:03

Cool topic, MGL!

Sorry to complicate this even more, though, but I think you might want to look into how much the clustering effect of pitcher’s and hitter’s parks affects the park factors themselves.

For example, the Cubs played in a division in 2009 that had 4 parks with factors below 1.00 (three of which were a lot below 1.00), 1 barely above it and their own park which was way above 1.00.  Because the park factors themselves are calculated by comparing home and road numbers, Wrigley Field’s number is biased by having more Cubs’ road games played in pitcher’s parks, which would then amplify Wrigley’s hitter’s park status.

If you then come along and adjust individuals’ numbers for these “amplified” park factors, you might end up overdoing it…

What do you think?


#3    Tangotiger      (see all posts) 2010/03/16 (Tue) @ 10:20

First off, great stuff.  Exactly the kind of stuff we need.  Which leads me down memory lane…

***

“I think you have quite a bit of context for a lot of players that is not accounted for by traditional park adjustments.  “

Finally!

Some seven years ago, I wrote this:
http://tangotiger.net/parks.html

1 - static conditions: ...
2 - dynamic conditions: ...
3 - other parks: ...
4 - the tendencies of hitters/pitchers: ...
5 - the quality of hitters/pitchers: ...
6 - the game context…
..
So, your “park factor” is made up of several factors, each of which needs to be analyzed on its own. For some, you can use multi-years, and others you need single-years, etc, etc.
...

And I always said that park factors, AS WE’VE CURRENTLY SEEN THEM, is always a first step, not the last step.

And at the time, and still today, I can’t stand the way park factors are used, because it’s always treated as some last step.  “Oh, there, I applied a generic park factor for everyone at Coors.  Done.”

And so, I have resisted trying to apply park factors because it just seems to cut off the dialogue.  Basically, there’s alot more we don’t know than we do know about how 3com affects Barry Bonds or how Coors affects Juan Pierre and Dante Bichette.  If I adjust them, I want to put a big asterisk saying “I really changed their stats alot, and I’m not even sure if I’m right.”

The argument I always had with MGL, and he counters back was: some park factor is better than no park factor.

And, my counter is to only worry about the park factor in extreme cases, and not apply it to most players.

***

Anyway, lots of great potential here for aspiring saberists.


#4          (see all posts) 2010/03/16 (Tue) @ 10:37

I think you have a typo, Delwyn Young(PIT) not Delmon Young(MIN).


#5    Michael      (see all posts) 2010/03/16 (Tue) @ 10:37

I’ve wondered how things like BP’s WARP2 handle the strength of schedule adjustment. I can’t imagine using single-year data to adjust strength of schedule for a player, that seems inappropriate. Off the top of my head I would have figured calculating a Marcel projection for each opposing batter/pitcher and finding how those players rate compared to the league average to find the “opponent/SoS” factor.


#6    Tangotiger      (see all posts) 2010/03/16 (Tue) @ 12:11

Michael: I’m sure that’s exactly how MGL would do it.  And you’d both be right.


#7    MGL      (see all posts) 2010/03/16 (Tue) @ 13:25

Michael, right, or just use multi-year numbers preferably using “surrounding” years to minimize aging problems.


#8    statzombie      (see all posts) 2010/03/16 (Tue) @ 17:13

How exactly are park factors calculated? My understanding has always been it is (team home stat) / (away team stat) for some given team and stat. However, this has always seemed incorrect to me, for roughly the reason provided here. It will essentially make the extremes too extreme, as the away parks will be biased towards the other extreme.


#9    Karl from NY      (see all posts) 2010/03/16 (Tue) @ 17:14

Could there also be a playing-time bias between starting and relief pitchers?  In a pitcher’s park, starters will go longer.  In a hitter’s park, more runs are scored and relievers will come in sooner and get a greater portion of IP in hitters’ parks.  (Perhaps a reason to gauge pitching on batters faced rather than IP, but I digress.)

4 of your 5 “helped” pitchers are starters and Nippert was half-and-half (20 G 10 GS.) Although only Troncoso from your “hurt” list is a reliever and the other two are starters.  Would be interesting to see the starter/reliever proportions of a bigger segment of the lists.


#10    statzombie      (see all posts) 2010/03/16 (Tue) @ 17:35

Karl, I think you have a good point, but possibly for another reason. Relief pitchers pitch more often, and in any given series, are fairly likely to pitch at least once. Thus their schedule is much more balanced than a starter, who could potentially miss out on pitching at the good parks simply due to the schedule.

Not sure if there’s anything to back this up, just my initial thought. Morton at least had to pitch in Arizona, Colorado, and Wrigley twice without many pitcher’s parks.


#11    Karl from NY      (see all posts) 2010/03/16 (Tue) @ 18:27

Good point statzombie.  I think there’s two effects there.  Starters will cluster at both ends, having a wider variance in personal park factor from fewer appearances.  And the personal park factor will be higher collectively for relievers than for starters, since a greater portion of reliever IP and BF will occur in higher-offense parks.


#12    MGL      (see all posts) 2010/03/16 (Tue) @ 20:54

"And the personal park factor will be higher collectively for relievers than for starters, since a greater portion of reliever IP and BF will occur in higher-offense parks.”

Interesting and probably true, although I’m not sure it will be much of a difference.  If true, relievers are better, relative to starters, then we think they are!  If both relievers and starter both have ERA’s of 4.30, the relievers are actually better if as greater proportion of their IP or TBF are in hitters parks.  Keep in mind that all my numbers are prorated to TBF and not IP.


#13    Zach      (see all posts) 2010/03/26 (Fri) @ 00:43

I have a question about how to park adjust certain stats.

Let’s say a hitter has 120 non-HR hits in 410 BIP (AB - K - HR) for a BABIP of .293. His home park has a BABIP park factor of .97, so his park-adjusted BABIP is .302.

Now, let’s say his other stats are this:
600 PA, 100 BB, 70 K, 20 HR, 140 total H

Let’s say his BB and HR park factors are both 1.00, so they are the same (so his AB stays the same too), but because of his K park factor (which would have to be 0.42, FWIW), his BIP jumps from 410 to 450.

If you wanted to know his park-adjusted batting average (for instance), would you calculate non-HR hits as .302 * 410 (his original BIP), or .302 * 450 (his park-adjusted BIP)?

If you calculate it the first way, his park-adj. AVG is .288, up from his original .280 AVG.

But if you use the second formula, his AVG jumps to .312 because he has fewer K’s and more balls in play.

(Sorry for the length of the question.)


#14    MGL      (see all posts) 2010/03/26 (Fri) @ 01:05

Depends on how you calculate the park factors in the first place.  You have to adjust the player’s stats in the same way and the same order as you calculated the park factors.  So, in your example, first park adjust the player’s K rate.  Then adjust everything else.  Then park adjust everything else. So you do it the second way (302*450). If I play in a park that increases K rates, the assumption is that if I had played in a neutral park, I would have fewer K, and thus more BIP.  So I would park adjust my BIP based on the new number (or rate) of BIP.


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