BABIP Park Factors and the Batted Ball Connection
Some of you may recall that before being promoted from a FanGraphs Community Research writer to an actual FanGraphs writer, my primary focus was on the relationship between batted ball types (infield fly balls, in particular) and BABIP for pitchers. At the time, I’d been leaving park factors out of the equation in a [vain] attempt to keep things simple, but now I want to give them a bit of attention.
Now, Guts! is a great resource on FanGraphs, but it does leave out BABIP, HR/FB, and — I believe — something else I’d like to talk to you about for a second. If you’re a big fan of the batted ball stats here, this bit of information might be completely earth-shattering, leaving you sobbing in a heap on the floor, pondering how your life will never be the same again: IFFB% may not mean what you think it does. Now, FB%, for example — that’s defined as fly balls divided by batted balls, right? Many of us might therefore assume that IFFB% equals infield fly balls divided by batted balls… but it doesn’t. IFFB% is actually infield fly balls divided by fly balls. This means that IFFB% doesn’t tell you much about a player unless you have the context of his FB% to go with it. It also means that IFFB% * FB% equals what you probably thought IFFB% was, which is IFFB/(Batted Balls).
Hopefully you’ll be able to read this clearly through your tears: I’m going to introduce a new, not-officially-FanGraphs-sanctioned term here: IFFB% * FB% = PU%. PU%, or popup percentage — again, it’s what you probably thought IFFB% meant — is the percentage of batted balls that are infield flies. This leaves OFFB%, or outfield fly balls, as the remainder of FB% (i.e., FB% = PU%+OFFB%).
So, without further ado, here’s a sortable list of the park factors I came up with for the 2009-2012 seasons, with the exceptions noted at the bottom:
| Team | BABIP | GB/FB | LD% | GB% | FB% | IFFB% | PU% | OFFB% | HR/FB |
|---|---|---|---|---|---|---|---|---|---|
| Angels | 98.4 | 98.0 | 99.3 | 99.2 | 101.3 | 95.4 | 96.5 | 101.8 | 93.8 |
| Astros | 99.4 | 99.8 | 98.7 | 100.3 | 100.4 | 98.4 | 98.9 | 100.6 | 103.2 |
| Athletics | 98.0 | 98.9 | 101.2 | 99.0 | 100.5 | 102.7 | 103.2 | 100.2 | 91.5 |
| Blue Jays | 99.7 | 99.6 | 100.0 | 99.7 | 100.3 | 98.6 | 98.9 | 100.4 | 105.9 |
| Braves | 100.6 | 100.7 | 101.8 | 99.9 | 99.2 | 95.8 | 95.0 | 99.6 | 97.7 |
| Brewers | 99.5 | 96.0 | 98.7 | 98.4 | 102.8 | 98.2 | 100.8 | 103.0 | 109.3 |
| Cardinals | 99.3 | 102.8 | 101.4 | 100.7 | 98.1 | 102.1 | 100.1 | 98.0 | 91.8 |
| Cubs | 100.9 | 98.4 | 99.6 | 99.4 | 101.0 | 99.2 | 100.2 | 101.1 | 99.5 |
| Diamondbacks | 102.3 | 100.4 | 101.5 | 99.8 | 99.4 | 97.5 | 96.9 | 99.7 | 104.1 |
| Dodgers | 98.8 | 100.4 | 97.8 | 100.8 | 100.2 | 109.5 | 109.4 | 99.3 | 98.9 |
| Giants | 99.8 | 106.6 | 100.4 | 102.7 | 96.6 | 101.3 | 97.9 | 96.5 | 90.5 |
| Indians | 98.9 | 104.5 | 101.0 | 101.7 | 97.5 | 101.9 | 99.3 | 97.3 | 96.3 |
| Mariners | 98.2 | 99.4 | 101.1 | 99.4 | 100.2 | 103.9 | 104.1 | 99.8 | 90.4 |
| Marlins ** | |||||||||
| Mets *** | 98.3 | 96.6 | 97.2 | 99.2 | 102.5 | 107.0 | 110.0 | 101.8 | 92.6 |
| Nationals | 99.5 | 99.4 | 99.3 | 99.9 | 100.5 | 98.9 | 99.4 | 100.6 | 99.3 |
| Orioles | 101.4 | 99.8 | 98.6 | 100.1 | 100.6 | 95.2 | 95.7 | 101.1 | 108.9 |
| Padres | 96.6 | 102.4 | 97.2 | 101.7 | 99.4 | 96.4 | 95.9 | 99.8 | 89.6 |
| Phillies | 99.5 | 100.4 | 100.8 | 100.1 | 99.6 | 97.7 | 97.3 | 99.8 | 103.4 |
| Pirates | 98.2 | 103.4 | 100.4 | 101.3 | 98.2 | 96.2 | 94.5 | 98.6 | 90.6 |
| Rangers | 102.2 | 97.7 | 103.8 | 98.0 | 100.3 | 96.6 | 96.7 | 100.6 | 109.9 |
| Rays | 98.3 | 96.5 | 99.1 | 98.4 | 102.3 | 112.2 | 115.1 | 101.0 | 93.6 |
| Red Sox | 104.3 | 100.7 | 101.1 | 100.1 | 99.4 | 103.7 | 103.1 | 99.0 | 97.0 |
| Reds | 100.2 | 99.6 | 100.5 | 99.7 | 100.1 | 105.3 | 105.5 | 99.6 | 115.5 |
| Rockies | 105.5 | 103.8 | 104.1 | 100.5 | 97.2 | 90.6 | 88.3 | 98.2 | 115.5 |
| Royals | 101.8 | 103.4 | 97.7 | 102.1 | 98.7 | 91.1 | 90.0 | 99.7 | 91.3 |
| Tigers | 99.8 | 98.7 | 97.9 | 99.9 | 101.2 | 104.2 | 105.5 | 100.8 | 96.3 |
| Twins * | 101.1 | 103.4 | 102.9 | 100.9 | 97.4 | 102.8 | 100.0 | 101.0 | 94.5 |
| White Sox | 99.7 | 95.2 | 99.7 | 97.8 | 102.8 | 97.3 | 99.9 | 103.2 | 113.6 |
| Yankees | 98.9 | 99.0 | 98.2 | 99.9 | 101.0 | 102.9 | 103.9 | 100.7 | 112.7 |
* Twins’ factors based on 2010-2012 data only
** Marlins Park excluded due to 2012 being first year (insufficient sample size)
*** Citi Field’s walls were moved closer in 2012
Park factors are halved (based on the assumption that a player will play half of their games there).
If you hadn’t heard, The Padres and Mariners will be moving the fences in a bit this year, by the way. My apologies if I neglected to mention any significant park dimension changes that happened between 2009 and 2012.
If you’re like me, you might find this table interesting; if you’re a normal person, skip right ahead:
Correlations Between Park Factors
| BABIP | GB/FB | LD% | GB% | FB% | IFFB% | PU% | OFFB% | |
| GB/FB | 0.198 | |||||||
| LD% | 0.544 | 0.296 | ||||||
| GB% | 0.003 | 0.922 | -0.087 | |||||
| FB% | -0.322 | -0.966 | -0.527 | -0.799 | ||||
| IFFB% | -0.389 | -0.249 | -0.228 | -0.165 | 0.274 | |||
| PU% | -0.432 | -0.501 | -0.357 | -0.378 | 0.534 | 0.959 | ||
| OFFB% | -0.167 | -0.867 | -0.366 | -0.753 | 0.863 | 0.009 | 0.258 | |
| HR/FB | 0.450 | -0.342 | 0.205 | -0.435 | 0.257 | -0.239 | -0.137 | 0.306 |
An obligatory refresher for those who haven’t taken statistics in a while (or ever): correlation coefficients (“r”) range between -1 and 1. A correlation of “0” means the two factors being compared have no apparent connection, whereas “1” indicates the two factors move together in a perfectly linear way, and “-1” means they move perfectly linearly in opposite directions.
I bolded the connections I thought were the most interesting. Now, to discuss them more in-depth:
High LD% factor = high BABIP factor
This should come as no surprise to those of you who read my first Community article, in which I pointed out LD% and [what I’m now calling] PU% as the two main factors for explaining pitcher BABIPs. LD% for a pitcher is hard to predict from year-to-year, and park factors aren’t entirely consistent on a yearly basis either, but many of the line drive park factors do make a lot of sense, and you can reasonably expect the factors behind them to exert their influence yearly.
Specifically, let’s look at the top two parks in terms of high LD% — Colorado and Texas. What do they have in common? Well, the most obvious is thin air; Colorado due to its altitude, and Texas presumably due to heat and perhaps dryness. Thin air, of course, offers less resistance to a batted ball, but it also should theoretically allow for less break on pitches. Most of the stadia (that’s fancy talk for “stadiums”) at the low end of the list also make sense, having thick marine air. KC is an exception… but then again, its BABIP factor isn’t in-line with those of its surrounding teams on the LD% list. This could have something to do with scorer’s bias issues, such as the one discussed here. Another possible contributor to LD% differences is the batter’s eye in each stadium.
High PU% = low BABIP
This shouldn’t be a shocker to those of you who’ve read my previous work. Popups are pretty close to automatic outs. Let’s talk about how stadium characteristics might influence PU%. The first thing that comes to mind is that a greater amount of foul territory should lead to a higher PU%; that’s because a foul IFFB is only recorded if caught.
Another possible factor, judging by the Rays’ home field being firmly at the top of the list, is the dome factor. You might think the whitish background of the dome against a popup might not be so conducive to catching it, but perhaps the lack of sun and wind helps to make up for that. And it’s not like fielders in non-domed parks never have to deal with whitish backgrounds — clouds and haze are a thing, after all.
High HR/FB equals high BABIP, high OFFB%, and low PU%?
It’s worth reminding you at this point that home runs are excluded from consideration in BABIP, but not in batted ball stats. That’s one reason why fly ball pitchers tend to have lower BABIPs — they may allow more HR, but those don’t count as a knock against their BABIPs. The other reason is that fly balls, especially popups, make for easier putouts.
So, if HR aren’t part of BABIP, why would HR/FB have an apparent strong-ish connection to BABIP? The most obvious is that a high HR/FB is a sign of harder contact being made, for whatever reason, which you might expect to lead to a higher BABIP. Of course, you would also expect a higher HR/FB in small stadia, where perhaps more balls are bouncing uncatchably off of outfield walls and dropping for hits.
Now, PU% and OFFB% generally move together, both moving against BABIP, whereas HR/FB moves together with BABIP. That’s why I found it interesting that HR/FB divides PU% and OFFB%. I think that’s easy to explain in the context of an individual pitcher, but maybe not so much in the context of park factors. I’d like to hear your theories on it.
Oh, but before we make too much of this, I should tell you that the HR/FB factor appears to be the most prone to fluctuation of the bunch.
Putting it all together, kind of
As I am wont to do, I’ve regressed some of the various park factors to see how they might be able to explain each park’s BABIP factor:
BABIP = 0.48*LD% + 0.37*GB% – 0.05*PU% + 0.11*OFFB% + 0.09*HR/FB
The formula itself is, since it only applies to park factors, as useful as a poopie-flavored lollipop (Patches O’Houlihan) but it does have a 0.696 correlation to a stadium’s BABIP factor, meaning it can explain nearly half of the differences in BABIP factors (with a 0.484 R-squared). The park it has the hardest time explaining — by far — is Fenway, no doubt largely thanks to The Green Monster’s extreme BABIP-boosting ways. Take Boston out of the mix, and the correlation shoots to 0.772 (0.596 R-squared). Remove the second-biggest outlier, Kauffman Stadium in KC (with its suspiciously-low LD% factor) and the correlation goes to 0.816, explaining 2/3 of the differences. The exclusion of these outliers lends itself to the creation of a formula not tainted by them, which you probably don’t care about, yet here it is anyway:
BABIP = 0.52*LD% + 0.28*GB% – 0.03*PU% + 0.12*OFFB% + 0.11*HR/FB
That one achieves a 0.829 correlation to the remaining BABIP park factors (0.687 R-squared).
That can be whittled down to:
BABIP = 0.552*LD% + 0.320*GB% + 0.124*HR/FB
…which has a 0.821 correlation to BABIP factor, but if you remove any of those three factors, the correlation takes a major hit (though PU% and OFFB% together can mostly compensate for the loss of GB%).
I haven’t talked about what might contribute to a park’s GB% factor… well, groundskeeping might have a bit to do with it, but my guess is that it’s mainly due to less foul territory, and therefore fewer easy foul ball outs.
Methodology
For those who are curious, the formula I used to calculate each factor was:
(Home Pitching + Home Batting) / (Away Pitching + Away Batting) * 100
… which is a pretty standard park factor formula. I then halved it like so: 0.5 + Factor/2 … this is based on the assumption that the player plays half their games away at a neutral-factor stadium. When you consider that some teams play in divisions full of non-neutral opponent stadiums (e.g., Texas faces a bunch of pitcher’s parks), that’s probably not such a safe assumption to make, buuut it’s how park factors are done, and it’s a topic for a different conversation.
Steve is a robot created for the purpose of writing about baseball statistics. One day, he may become self-aware, and...attempt to make money or something?
Does anyone know why the Wrigley’s HR/FB is so much lower than Comiskey’s? Are the fences that different? They get more wind on the South Side?
Yeah, that’s an odd one. Wind direction could have something to do with it — the two parks are oriented differently.
Another possibility is that the Cubs play more games in HR/FB-happy parks than the White Sox do, making their park look less homer-friendly by comparison. It’s perhaps the biggest potential flaw of park factors in general.
No magic. It’s the fences. See:
http://www.hardballtimes.com/main/article/home-run-park-factor-a-new-approach/
Thanks, Studes. Hmm, well, the parks are similar in size (though Wrigley is deeper at the corners, shallower at the power alleys). Wall heights are different, though (maybe 12′ at Wrigley, including the fence, vs. 8′ at US Cellular, from the pictures I’m seeing). http://www.andrewclem.com/Baseball/Dimensions.html
I’m definitely a fan of Greg Rybarczyk’s idea of trying out a “calibrated hitting machine” in all the parks.
Are pitcher PAs excluded from this analysis? I don’t know how big of an impact they would make, but I’d feel pretty confident that HR/FB will be lower for pitchers hitting than for non-pitchers hitting.
They’re not excluded, but since the formula to calculate the park factor is all relative (Home Pitching HR/FB + Home Batting HR/FB)/(Away Pitching HR/FB + Away Batting HR/FB), it cancels out.
Actually for the Rays, I’d say that the size of the foul territory has more influence. IIRC, the Trop has the largest infield foul territory in the majors.
OK, maybe the dome factor is just a non-factor overall when it comes to popups. Well, if you went as far as to break things down by day and night games, you might see the Trop is better than most parks in the day, but a little worse at night?
Uhhh… how would background (sky vs clouds/haze) have anything to do with the number of infield flies hit? We are assuming ALL IFFs are nearly automatic outs, no matter what the conditions… right? So the visual background the fielder must contend with is therefore (essentially) irrelevant.
Knew I should’ve elaborated on that more…
Well, as I said, a foul IFFB is only recorded if it’s caught. There are plenty of foul IFFBs that are uncatchable due to being in the stands, and some that are not caught because the fielders have a hard time tracking them. Those IFFBs — ones which might have been caught in a different stadium — don’t show up in the records.
Yes, I understood that only caught foul IFFBs are counted. No problem with that.
You are saying the number of “missed” foul potential IFFBs due to poor visibility / weather effects could significantly affect these numbers?
I have no data, and I’m very willing to be proven wrong, but I have real difficulty believing that.
I just don’t think that happens nearly enough to be significant.
Well, first of all, IFFBs happen on less than 4% of balls in play — less than 300 times per season for a team’s offense and pitching combined, on average — so it wouldn’t be hard to make a noticeable impact on them. I don’t have proof either, but it just seems to me that it happens pretty often in day games that a ball gets lost in the sun. Anecdotally: http://www.fangraphs.com/blogs/index.php/when-the-pop-ups-fell-in/
Anyway, the foul territory is a much more significant factor, I’m sure.
Steve, this is awesome… and I really wish you’d done this a few weeks ago.
So, let’s say (just for the sake of argument) that I imagine that HR’s and HR/FB will go up 8% for Mariners pitchers and hitters (16% in home games) and I’m trying to figure out how to adjust the other park factors. I assumed that extra base hits would be down but by less than HR’s are up (say 2%) but I also assumed that BABIP would be down a little. If it’s harder contact, as you say, leading to both high BABIP and high HR/FB than that wouldn’t apply to moving fences, of course. Is it possible that teams with larger OFs (low HR/FB) tend to have better defensive outfielders (low BABIP)? Is there something I’m missing here? Also, do you have some rough confidence ranges on those correlations?
Thanks, J!
Well, this is speculation on my part, but I think if a *high* wall is moved closer to the plate, it might actually raise BABIP and even doubles rates, seeing as how some previous warning-track flies could instead bounce high off of the wall. Or at least it might cancel out the effect you describe (which I agree with).
Yeah, great point about the defensive compositions of teams possibly affecting their park factors. The HR/FB – BABIP connection you point out makes a ton of sense to me. When the great outfielders play away in a small stadium, their BABIP-reducing effect is then minimized.
Confidence intervals… yeah, there’s a pretty broad range there, I have to assume. What’s the appropriate n to use here — I don’t know. Is it 29 teams? 115 seasons? Something even larger? Anyway, what I can tell you is the order of how variable the factors are from year-to-year:
HR/FB>>PU%>IFFB%>>>GB/FB>>BABIP>LD%>FB%>GB%
The wall idea is interesting. I had this twisted around backwards in my head and I think Chad Young has this right (below).
Steve, this need you have to be the smartest guy in the room is… off-putting
I guess that’s why I’m still single…
(Kidding, girlfriend who is probably going to yell at me — going along with a line)
Seriously, though, I’m fairly new to this writing thing, so if my style sucks, I’m open to suggestions.
I don’t agree with the previous writer’s criticism at all, if that’s any help. I don’t get that impression from your writing style. I always look forward to your FG articles.
Thanks!
Well, anybody who thought this article was too esoteric is going to HATE something I’ve got up my sleeve (hint: a bigger, better Markov)…
That’s not hard to do in this room, and he is.
interesting article! you mention some of the parks being higher and some lower, but so many of these are so close to 100. Which of these values actual statistical outliers? or are they all (except maybe Rox, Sox, and Pads) essentially 100%?
Yeah, you’re right — the differences are pretty small, overall, and there are only 3 major outliers, as far as BABIP is concerned.
Still, you have to go over BABIP with a pretty fine-toothed comb, if you want to pick anything out of it. The plus or minus 2% to BABIP that separates the main pack might mean a difference of a not-insignificant number of hits (80+ for a team over a season, batting and pitching combined, off the top of my head?).
Might be a small detail, but Re: HR/FB and BABIP – the other factor which might be pretty meaningful, is that every FB that turns into a HR does NOT turn into an out. BABIP on fly balls is low (I want to say it is like .150 or lower) which means that if a FB is hit, and it stays in play, it will lower BABIP, on average, but by becoming a HR, it does not impact BABIP. So each additional FB that becomes a HR takes 1 out of the BABIP denominator, but 0 (or .150 or something) out of the numerator, which increases BABIP.
Also (and this should have come first) – excellent article; really fascinating research!
Thank you very much!
Great point. So everybody can follow along, the formula for BABIP is:
BABIP = (H-HR)/(AB-K-HR+SF)
How do you like this illustration: hitting a HR that barely clears the fence doesn’t directly affect the batter’s BABIP, but it does save him from having his BABIP negatively impacted by a ball that likely would’ve been an out in a larger stadium.
I wasn’t aware that HR’s are not included in BABIP. I can’t figure out why. Can anybody tell me the rationale?
I think the reason for excluding HR is to only consider catchable balls (assuming HR are uncatchable). Maybe its original intent was to be a measure of defensive efficiency — which it kind of is, but it measures other things as well.
A comment on the content:
In stadiums with thin air, one would expect balls to carry farther, leading to more homeruns, but one would also expects balls to slow down less as they carry. If a ball faces less air resistance, it will maintain more of its speed, meaning that it gives the fielder less time to get to the ball before it drops. This factor could increase HR/FB and BABIP at the same time. This also doesn’t depend on variation in the contact that hitters make.
Also, holding OFFB%, PU%, LD% and GB% constant, BABIP should still positively correlate with HR/FB since an OFFB that goes for a homer reduces the denominator of BABIP, whereas one that isn’t a homer is likely to be caught, increasing the denominator and not the numerator.
As for BABIP, PU%, OFFB% and HR/FB, I don’t see what requires explanation. Since no combination of those factors 100% explain the others, there is room for confounding factors to cause the relationships to be unclear.
An observation (I’m using ~ to mean correlates and !~ to mean negative correlation for convenience):
BABIP ~ HR/FB for reasons above.
HR/FB ~ OFFB% since more OFFBs probably means balls carry farther in general
therefore
BABIP !~ OFFB% seems to violate some transitivity rule. However, consider more OFFB% means slightly more homers and way more outs. The extra outs from extra catchable fly balls lowers BABIP, and the extra homers slightly increase BABIP, if you consider that they would be outs, given OFFB%. However, even though the outs to homers ratio is still very high in terms of BABIP (batting average on OFFB is probably around .120), it is very low on the scale of HR/FB. HR/FB is typically in the range of 11%. If we increase OFFB%, then we are adding batted balls with a HR/FB rate probably closer to 20%. This will greatly increase HR/FB ratio, but since OFFB% are still outs over 80% of the time, it will lower BABIP.
So, the issue is increasing OFFBs raises HR/FB and lowers BABIP. However, when you compare HR/FB and BABIP, you hold OFFB% constant, so we notice that increasing homers without increasing fly balls actually increases BABIP.
A comment on the writing:
It was too self-referential. We don’t need to be reminded all the time that you are weird, and that statistics is boring for normal people, or that we’re busy. If we’re reading your article, you can assumed we’re interested and probably know something about statistics. You’re not giving a speech on a street corner, this is fangraphs.
My math is a little weird. Batting average of OFFB is probably closer to .200 than I said.
Thanks for the thoughtful commentary, Bip.
Yeah, I thought the HR/FB and air resistance connection was so obvious that I neglected to talk about it. I should have mentioned it, at least, though — sorry.
Now, I understand a batter hitting more OFFB, or even a pitcher inducing more of them… but what I’m really wondering is how a *park* can create more of them (other than by having more foul ground). Could the OFFB to HR/FB connection be about hitters changing their approach and swinging for the fences more in HR-prone parks?
Note to self: Bip does *NOT* appreciate my sense of humor. Comedy is hard.
How much of the correlation between HR/FB and PU% is driven by Coors field? It’s the most extreme for both. Perhaps removing Coors would change that relationship. On the other hand, places like Texas and Arizona that have thinner air and less pitch movement might also produce more solid contact, leading to fewer popups and more home runs.
Also, I personally think that park factors should be calculated with an implicit method. It seems to me that there are to many factors which affect scoring to separate them out. In other words, park factors go along with the strength of an offense and the opposing pitching staff to affect the final score. All of these need to solved simultaneously, relative to each other. Not that the current method is bad, but I think an implicit approach would be better.
Ah, you nailed it, Jeremiah — good thinkin’. When I remove Coors (well, as much as I can — I’m not removing the fact that some teams play away games there), the PU% to HR/FB connection becomes non-existent (0.006 correlation). On the other hand, the OFFB% to HR/FB correlation jumps to 0.436. All of the correlations to BABIP drop significantly (except GB%, which was already nonexistent).
Yeah, I agree that something ought to be done about park factors, and your idea definitely sounds like it’s worth exploration.
I’ve actually made an attempt, but so far my approach doesn’t seem to be correct. If I ever get it figured out I’ll share it somewhere.
Good luck — sounds extremely complicated.
I think it might even take something like a really, really good simulator (better than what currently exists, I’m guessing) to truly get it right.
first of all: very nice article.. ver informative..
one minor mistake, though:
“thick” marine air, as you called it is actually lighter air. Dry air is actually thicker, heavier air.
confusing?
intuitively this seems to be false… but since we are humans and we breathe oxygen, we feel like non-humid air is light, whereas the air with high amounts of humidity seems thick and heavy. this air is obviously filled with water vapor i.e. water molecules which makes it tougher to breathe for us humans. but humid air is lighter because water molecules are lighter than the usual “stuff” in our air.
therefore, a ball flying through humid, sticky, “thick” air experiences less drag than in the heavier, dry air, leading to a greater distance in humid air.
Thank you!
Hmm, well I have heard that before, but I guess I refused to completely believe it. Has this been actually been studied in the context of a projectile moving through the air?
I realize that the molecular weight of H2O is lower than that of air’s main components (N2 and O2), but I imagine it’s only true that water lowers the density of air if the present water is purely vapor.
What if there are actual tiny liquid water particles suspended in the air in these marine environments? I think especially very close to the ocean, this is true. Or how about the ball traveling rapidly through humid air, compressing it and forming condensation? Liquid water is obviously a lot denser than air, and I think this could more than counteract what you’re talking about.
If there are any experts around, I’d like to hear about whether hydrogen bonding between water molecules in the air could possibly lead to a greater viscosity, despite lower density.
Plus there’s a bunch of anecdotal evidence about the “marine layer” killing home runs (though colder night air probably has a lot to do with it).
Anyway, if I’m wrong, I have no idea how to explain the marine stadiums (other than having denser air due to low altitude).
The balls absorb moisture and become heavier and harder to hit as far (this is the purpose of the humidor in Coors). That comes pretty close to offsetting the effect of the lower air density.
Maybe it’s your phrasing, but it sounds like you’re saying that the balls absorbing moisture offsets the lower air density to the point that the effect is neutralized.
But we know that the West Coast parks (especially those very close to the water, like Petco, AT&T, and Safeco) are anything but neutral. Is it merely a coincidence?
AC is correct that humid air is less dense, thus it creates less drag on the ball (more distance and speed). He is also correct that the ball becomes heavier and larger, which has the opposite effect. As well, pitchers can probably throw more effectively with a “wet” ball than a dry one. The net effect of humidity in the air is probably around zero.
The reason that coastal parks tend to be pitcher’s parks is that they are generally at sea level, typically cool, and often with the wind blowing off the water into the park from the outfield.
Thanks guys. Yeah, the humidity’s effect on the ball itself is important — I forgot about that.
MGL (I’m a fan, btw), looking at the west coast parks on Google Maps, they seem to be angled such that the onshore breezes that are predominant along the coast would generally help blow the balls out of the stadium, not keep them in. I’d break them down like so:
Wind pushes balls out to center: SF
…pushes out towards right-center: OAK
…pushes out towards right field: LA, ANA, SEA
… kind of pushes balls in, but mainly towards RF: SD
So, overall, a righty’s power numbers might suffer a tiny bit, but a lefty’s would benefit more, seems to me.
Somebody correct me if I’m wrong if you’re very familiar with the fields.
Although humid air is indeed less dense than dry air, the effect is pretty small. In the past I’ve compared Coors Field to a sea level stadium with ~80% humidity, and the decrease in density due to elevation is about 18%, while the increase due to lower humidity is around 1%.
With respect to the ball compressing the air and condensing water, baseballs do not move fast enough for compressibility to be an issue. Unless the relative humidity was already close to 100%, I don’t think this could happen. Also, the viscosity of water vapor is lower than the viscosity of dry air.
I think it might be interesting to look at the effect of game time temperature on park factors. I think there has probably been some work on this, and I wouldn’t be surprised if it explained part of the offensive explosion last year at Coors.
Great info, Jeremiah, thanks. Any thoughts on mist potentially having an effect, though?
Yeah, good idea on the temperature study. I’m inclined to say it should only look at night games, so as not to introduce too many other variables. Of course, even that introduces the variable of different times of the season, where there may be some performance trends that have nothing to do directly with temperature… it’s tricky.
I appreciate the research, but I have to question the inferences made. You are essentially inferring that the ballpark is the cause of PU%, etc. But this could very well be a case of spurious causation. The data are only over three years; in my opinion there’s no reason to believe the cause of these data could lie somewhere else, perhaps in the pitching staff and/or the defense behind the pitchers. Without controlling for these possible “lurking” variables, enthusiasm about these assumed ballpark factors should be tempered.
I generally agree, except it was actually 4 years (2009, 2010, 2011, and 2012), with the exception of Minnesota. I would’ve liked more years, but there were 2 new stadiums in 2009, and since what park factors are doing is comparing stadiums to each other… well, I felt I couldn’t go back any further without compromising the integrity of it all.
Yeah, park factors are very imperfect, but still relevant. You just have to assume bit of a margin of error. Regarding differences in pitching staffs… true, and that’s part of why I did this — you can use it to go a little deeper into how a park’s overall factors might be affected by whether it takes advantage of its unusual traits through its team construction. That’s just one layer of the onion peeled back. It gets a lot more complicated past this, though.
I may have missed it, but I haven’t seen tall outfield walls (such as Fenway’s Green Monster)discussed as a possible contributor to a park’s higher BABIP.
HRs are excluded from BABIP because they’re not playable. But neither are balls hit high off the wall at Fenway (and to a lesser extent, at other parks). A large percentage of wall ball doubles and singles are generally NOT playable balls. Since it’s not practical to exclude these from BABIP because of the difficulty in gathering accurate data on these hits from conventional sources, it makes sense that they’ll show up as increased BABIP in ballparks where these kinds of hits are frequent.