Greene and parks, part deux

Yesterday’s post received some interesting comments. I figured I’d try to address some of them here (okay, there were only 3 not counting my own) and ramble on about some similar topics.

Here’s what Kevin (from Padres Nation) said in comment #1:

Petco suppresses all players, or at least the theory goes. That’s always been the theory of park effects as I understood. If you had the road stats of all of the league’s shorstops, that would be a good, although not perfect way, of judging them.

I think the point that Petco suppresses offense for all players is right on (not that this is surprising). There may be a rare player or two who would benefit from Petco, but by and large, it hurts offense of all forms. However, my point was that Greene is hurt above and beyond the average player (who is still hurt) because of how he puts the ball in play (or something else, perhaps). Let’s look at some numbers. I don’t have shortstop data, but I loaded every team’s stats into a speadsheet (from baseball reference) and then took out the Padres.

NL players not in Petco (in 07): .268/.336/.427
NL players in Petco: ………….. .232/.294/.354

So, on the road they get a 15.5% boost in average/14% boost in OBP/21% boost in SLG.

Now let’s look at Khalil’s career (note there are a few PA’s from Qualcomm that I didn’t take out):

Greene on the road (career): .280/.335/.515
Greene in Petco (career): .228/.288/.370

On the road: 23% boost in BA/16% boost in OBP/39% boost in SLG.

Greene is hurt more by Petco (or helped more on the road) than the average player, especially in slugging percentage. I am not really trying to prove anything by these numbers. It’s only a 1 year sample for the league. For Greene, it’s his career, but it’s split in half. There’s still a large margin for error and it’s not like there are any tests for significance or anything here. Consider this just for illustration and bear with me if you can.

From this small exercise we can conclude, at least by these numbers, that (with the note from above in mind):

• Hitters are, on average, a lot worse in Petco than in other parks (Yeah, I think we knew that)
• Greene is worse at home, relative to his road stats, than other players (the margin is wider).

The question then becomes: in what ways is Greene hurt?

Enter Didi in comment #6:

He’s still able to hit HR at Petco, with far fewer doubles than on the road. Including 3B, he had 30/63 hits are XBH at Petco, 44/92 on the road. That difference of about 30 hits is what killing his OBP at home since he drew about the same number of walks.

I wonder if he is approaching his ABs differently at home than on the road. And it’s possible that what would have turned into 2Bs on the road were turning into outs at home due to the size of the OF and the better OF defenses (maybe).

That prompted me to look up Greene’s career numbers on the road and in Petco … but this time we’ll look at singles per ball in play (singles/AB-SO-HR), doubles per BIP, etc.

In Petco:
1b/BIP: .190
2b/BIP: .056
3b/BIP: .010
hr/BIP: .035

On the road
1b/BIP: .184
2b/BIP: .117
3b/BIP: .005
hr/BIP: .060

To make that a little more clear, let’s do it per 550 balls in play, which is about how many Greene had last year.

In Petco
1b/550: 104.5
2b/550: 30.8
3b/550: 5.5
hr/550: 19.25

On the road
1b/550: 101.2
2b/550: 64.35
3b/550: 2.75
hr/550: 33

Plain and simply, he’s been an extra base machine on the road. And that’s the same area where he’s getting hurt in Petco. I don’t think that’s surprising, really. I mean, you don’t hear many people talking about Petco hurting singles. It may increase triples a little, but they just don’t happen enough to be that significant. Again, remember this is just sample data, so don’t make any conclusions based on it. I’m just pointing out what’s happened so far for Greene. Like everyone else, he’s hurt by Petco. It just so happens that he’s hurt a lot more than the average joe.

The next question, I guess, becomes: why is he hurt more than the average player? I’m not sure, but I guess I have a few ideas. He hits a lot of balls that are caught in Petco that would be extra base hits in most other parks. In a more technical sense, Greene could hit balls at a certain angle, bat speed, etc. that work fine on the road, but get caught up in the heavy air in Petco Park. I don’t know how to save it and paste it here, so here’s his hitting chart from mlb.com (you have to customize it … it also won’t let me link it to a customized version). Anyway, I’m really not sure. It’s an issue for another day — or an issue for someone far brighter than I am.

More park factor fun

I’m in park factor mode here lately, as you can see. Geoff Young wrote an article for the Hardball Times on PF’s and, specifically, Petco in 2006. Good read.

The topic of taking advantage of your own park (as a team), which Geoff gets into a little,  is probably yet another topic for another day (among the long list of topics I’ll never get to).

Anyway, here’s my question (that I already asked in the comment section of the last post): If you have unlimited resources (i.e., any kind of data you want), how would you construct park factors? Well, here’s my take. Why can’t we take each ball that a player hits in play and attempt to strip it completely from it’s context.  That is, if we know the angle off the bat, the speed off the bat, and things like that, we can say, to a certain extent, what would happen to this ball in a completely neutral environment (weather, fences, air density, etc.). Then we could take each ball that a hitter put in play and give it some kind of percentage of a hit, based on what usually happens to balls with those parameters. So, for instance, if you hit a ball at an angle, speed, etc. that usually produces a 400 foot fly ball, you could call that a home run, no matter what actually happened to that ball. Of course, I’m just thinking out loud here — this would take tons of work and may not even be practical (or valuable). If there’s anyone who will do it, though, it’s probably Greg Rybarczyk. From the article:

Hit Tracker in its usual form uses observations of hit outcomes (landing point, time of flight) to derive the hit’s initial parameters (Horizontal Launch Angle or HLA, Vertical Launch Angle or VLA, and Speed off Bat or SOB, with spin assumed to be a function of these factors). But, with a few lines of code added, it becomes “Hit Whacker,” using HLA, VLA, SOB and atmospheric inputs to generate a hit’s outcome. With this capability, we can create a procedure for assessing how easy or hard it is to hit homers in any park.

To cover the range of possible batted balls that could become homers, I created a “test set” of trajectories, representing 45 different HLA’s (every two degrees from foul line to foul line), 41 different VLA’s (15 to 55 degrees) and 26 different SOB’s (95 to 120 mph). That’s 47,970 different fly ball paths! I ran this complete test set in each park, in that park’s actual altitude, in the park’s average game time temperature from 2002-06, with no wind (I’ll describe how to account for different winds shortly). The trajectories were evaluated as “home run” or “not home run”, and the results were compiled.

That is essentially along the same lines as what I’m trying to say here. Of course, this is all for component factors, rather than runs based ones.

Anyway, my point of this mini-series, if there is one, is to know what question you’re asking before you go looking for the answer. You may not get there initially (we surely haven’t), but the ride will probably be more valuable.

Or it will just chew up 2,000 words.