Justin Upton Is Not a Park Effect Mirage

Big news everyone – Justin Upton is still on the trade block. He has been for about three years now, and he will continue to be until he is mercifully traded to a team that wants him more than Arizona seems to. With yesterday’s reports about Upton’s availability, more than a few people on Twitter asked me about Upton’s home/road splits, and whether or not we should expect him to regress significantly if he’s traded to a less hitter friendly ballpark. In case you haven’t seen them, Upton has enormous home/road splits, and it’s a career trend, not just a one year blip.

Split PA BB% K% ISO BABIP AVG OBP SLG wOBA wRC+
Home 1496 11% 22% 0.241 0.361 0.307 0.389 0.548 0.399 138
Away 1534 9% 24% 0.157 0.310 0.250 0.325 0.406 0.320 96

Over 3,000 plate appearances, Upton’s been one of the game’s best hitters while playing in Arizona, but a slightly below average hitter when playing anywhere else. Chase Field is one of the best hitters parks in all of baseball, and the numbers suggest that Upton has taken full advantage of the hitter’s paradise that he has called home for the last five years. In fact, if you look at the biggest home/road splits since 2008, Upton features prominently on the list.

Name wOBA – Home wOBA – Road wOBADIF
Carlos Gonzalez 0.424 0.317 0.107
Aramis Ramirez 0.417 0.328 0.089
Nelson Cruz 0.407 0.324 0.083
Ian Kinsler 0.395 0.315 0.080
Justin Upton 0.404 0.324 0.079
Paul Konerko 0.416 0.339 0.077
Luke Scott 0.384 0.308 0.076
Jay Bruce 0.385 0.311 0.074
Dexter Fowler 0.384 0.312 0.073
Kevin Youkilis 0.423 0.352 0.071

This all makes sense. A Colorado hitter as at the top, a couple of Texas guys, right-handed doubles machine who played in Fenway, a a couple of power hitters in Chicago, and Upton are all among the 10 who have the biggest splits between their home and road performance. If you scroll to the bottom, you find Buster Posey, Chase Headley, Will Venable, and Adrian Gonzalez all among those who were hurt the most – a Giant and three Padres. Again, nothing surprising here. San Francisco and San Diego are notoriously pitcher friendly. While split data is often unreliable in small samples, over a five year period, you’re going to see things start to make sense. Justin Upton derives a benefit from playing in Arizona. Buster Posey is hurt by playing in San Francisco. None of this is news.

However, there can be a temptation to take split data like this at face value. After all, we’re dealing with over 1,000 plate appearances in both home and road data for most of these guys, so it doesn’t seem like small sample problems should exist. But they do, and while the lists above are interesting, you shouldn’t read too much into the specific numbers for the individual players, and you definitely shouldn’t treat a player’s road numbers as if they represent his park neutral true talent levels.

For starters, home field advantage is a real thing, and most players hit better at home than they do on the road. Last year, non-pitchers posted an aggregate .327 wOBA in their home parks and a .314 wOBA on the road. In 2011, it was .326/.315. In 2010, it was .335/.317. For the 714 players who have garnered at least 100 PA at both home and road over the last five years, the weighted average comes out to a 14 point wOBA advantage at home. Pretty much every player is better than his road performance alone suggests. Home field advantage is not solely an effect of the dimensions and weather, and hitters derive some benefit from playing in their home park even if it is not a hitter friendly park. It is entirely possible for the dimensions and weather to wipe out that effect, and then some, so that hitting at home is a net negative in some parks, but the negatives are smaller than the positives in large part due to the non-park related aspects of home field advantage.

If you’re more of a graphical person, here’s a visual representation of hitters home/road wOBAs over the last five years.

wOBAsplit

Second, we cannot pretend that “away” is the same thing for every hitter, nor is “away” an even playing time distribution in neutral parks. Upton plays in the NL West. Because of unbalanced schedule, his career road games have skewed heavily towards San Francisco, San Diego, Los Angeles, and Colorado; 45% of his career “away” plate appearances have come in those four parks. Maybe Colorado and San Diego cancel each other out to some degree, but that still leaves a big chunk of games in cooler weather west coast cities, and not surprisingly, Upton hasn’t hit well in either LA or San Francisco.

In fact, when you look at a hitter who plays in an extreme hitters park at home, and then you only look at his road stats, you’re almost certainly going to be looking at a collection of parks that skew to the pitcher side, because you’ve automatically removed one of the few remaining hitters parks from the sample. Buster Posey’s road numbers include both Colorado and Arizona, but not San Francisco. We should not be surprised that these numbers are better than those published by a guy whose road numbers swap out out a hitter’s paradise for a pitchers haven.

Pretty much any west coast hitter is going to be at a disadvantage in road stats compared to an east coast hitter, due to the unbalanced schedule and the summer climate of the two sides of the U.S. The west coast is much cooler, much less humid, and is home to many of the most extreme pitchers parks in baseball. A guy who plays in the AL or NL West is not going to play half his games in a collection of parks that grade out as average run environments. And, because MLB has put Colorado, Texas, and Arizona — teams that are not actually on the west coast, and play in very different environments than the teams near the water — in the western divisions, the drastic differences in parks within the western divisions helps drive even larger splits. For guys in Texas, Colorado, and Arizona, not only is their home park a great place to hit, but their collection of road parks are heavily slanted towards extreme pitchers parks.

Finally, there’s the simple reality of necessary regression. Even over multiple years, we’re still dealing with noisy data, and noisy data has to heavily regressed if it’s going to be used in a projection. We know the left/right platoon split is real, but we still regress left/right platoon splits more towards league average than a player’s individual split up to the 1,000/2,200 PA levels for left-handers and right-handers. Regression is just a fact of life when it comes to split data, and if you’re not heavily regressing splits, you’re probably using them incorrectly.

If you want to see regression as it relates to home/road splits, Tom Tango did a study on individual player park effects about 10 years ago, but we’ll dig it up here, because it’s still relevant today. He was studying the idea of “reverse platoon splits”, looking for guys who hit well in pitchers parks or pitched well in hitters parks, but the concept of necessary regression for home/road data remains the same. Read the whole thing, but I’ll quote a couple of the more pertinent paragraphs below.

As usual, I am going to hypothesize that a player’s historical splits are not very predictive of his future splits – therefore our best tool for predicting a player’s splits is his average home park factor applied to his home stats. In other words, I am suggesting that the regression rate for a player’s home/road splits is near 100% for a small sample and 80 or 90% (maybe more) for even a large sample. If I am right, then it is correct to simply park adjust a player’s home stats in the traditional way if we want to compare players on a level playing field, without worrying about the fact that any given player might be uniquely affected by his home park in ways that are not captured by that park’s average park factor.

When you do a study like this, the most telling statistics are the aggregate results of each group. If you look at each individual player’s OPS ratio in one year and then the other, you will be tempted to make conclusions one way or another about each individual player. That is what you were trying to avoid in the first place and why you want to look at as many “extreme” players as possible combined in order to get a large sample. Here are the composite results:

In 2002, the players in the hitter’s parks who originally all had a “reverse” OPS ratio of a combined .91, had a combined OPS ratio of 1.02 the following year. The average OPS park factor for these parks was 1.04. The players in the pitchers parks who had a “reverse” combined OPS ratio of 1.14 in 2001, ended up with a combined OPS ratio of .89 in 2002. The average OPS park factor for these parks was .96.

While further (and better) study, especially establishing a larger sample size, is needed to address this issue, my preliminary conclusion is that a player’s sample home/road ratio, at least for one year, is not at all a reliable predictor of his future home/road splits, and that in fact, the best predictor of a player’s home/road splits is the average multi-year park factor of his home park.

He was testing single year data, which is noisier than multi-year data, but the need to understand the fact that home/road data contains noise is still important. If you don’t, you’re forced to draw some really weird conclusions. For instance, did you know that Andre Ethier has a 58 point wOBA gap between his home and road numbers over the last five years? Dodger Stadium isn’t the absurd pitchers park that it used to be, but we still don’t think that Andre Ethier is a product of his fantastic home hitting environment, right? But, here we are, with over 3,000 plate appearances, and he has a .393 wOBA at home and a .335 wOBA on the road. Given what we know about their home parks, Ethier’s gap is actually bigger than Upton’s, as it translates into 47 points of wRC+. That’s the fourth largest wRC+ gap of any hitter over the last five years. For a hitter in Dodger Stadium, who gets to go to both Colorado and Arizona when he’s on the road.

You know who shows up near the bottom of the list when sorting by wRC+? Poor Adam Dunn, who has had to toil in the pitchers parks of Cincinnati, Washington, and Chicago. Oh, wait, none of those are pitchers parks. And yet, he’s posted a .347 wOBA at home and a .371 wOBA on the road over the last five seasons.

This is noise. This is why you regress, even large samples. And this is why you’re better off using something like wRC+, which takes known park factors into account, then you are using a player’s individual home/road splits. Or, better yet, use a projection system that also accounts for aging curves and park adjustments.

Whatever you do, though, don’t just look at a player’s road stats and assume that it’s a window into his real talent level, with the difference between his home and road stats being a mirage of the park he played in. That’s simply not how home/road splits work.



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Dave is the Managing Editor of FanGraphs.


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James
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James
3 years 8 months ago

Thank you for this. For everyone who quotes park effects as signs of regression, (Mostly for Coors guys) I’ve always wondered whether there was a set baseline that showed that an average season for home/away splits dictated that splits had to be identical for it to be average.

Any way you could look at this for platoon splits (or point me to an already written article) because it’s always been annoying when a player (such a Carl Crawford) is signed and it is attacked for being “basically a platoon player” when that baseline for how a player should hit against same-handed pitchers isn’t defined.

Mike
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Mike
3 years 8 months ago

I have Justin Upton as a potential keeper in a small but deep league. What are your thoughts after the down year?

Travis L
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Travis L
3 years 8 months ago

I honestly can’t think of a format where Upton isn’t a top 40 player. So as long as the top 40 guys fall within your keeper regulations (10 teams 4 keepers, etc.), JUpton is def a keeper.

ODawg
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ODawg
3 years 8 months ago

I’m reading this to say not “disregard Upton’s home/road splits completely,” but “Upton’s home/road splits should not be simply taken at face value.” I would like to see a follow-up where Tango’s methods are applied and Upton’s value is further analyzed.

jim
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jim
3 years 8 months ago

the people who simply look at home/road splits and do nothing else are the kind of people who aren’t going to be persuaded by any amount of regression, so they’ll just keep on thinking every upton and gonzalez is just a home park creation

Tomcat
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Tomcat
3 years 8 months ago

I think that it might also be important to look at the role a hitting environment plays in the way a hitter hits.

Eric Young Sr play in COL during his physical peak age 26-29 his overall line was .295/.378/.412 which as nice as it looks by today’s standard was good for a 93 OPS+(96 wRC+) in those heady days. Young had hit .349/.433/.504 during that stretch at home, and .246/.326/.317 on the road.

From the age of 31-32 as a Dodger Young hit .283/.363/.376 which wasn’t his Colorado numbers but much better than his pure road numbers especially considering he now played half of his games at a pitchers park.

So from 26-30 Eric Young hit .295/.378/.412 playing half his games in the most extreme hitters environment in the history of MLB from 31-38 with LAD, CHC, MIL, TEX, SDP he hit .280/.353/.384 his OPS+ in his prime years was 93 in his 30s it was 94.

Phantom Stranger
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Phantom Stranger
3 years 8 months ago

I think the type of a swing a player consistently uses will affect the home/road splits for certain players in certain parks. Extreme pull hitters really shouldn’t care how deep the opposite power alley is and so forth. It would be interesting to see if a consistent spray hitter’s results are more uniform from park to park.

Jon
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Jon
3 years 8 months ago

Nice article. Everything that you say about why Upton’s splits should be regressed heavily makes a lot of sense, but isn’t it possible to do a more rigorous adjustment? For instance, it’s true that Upton plays more games than the average player in Colorado and also in San Diego, why do we have to simply say “those may cancel out.” Aren’t there reasonable ways of park-adjusting his stats, and correcting for the home/road split that even average players face? Of course park adjustments would come with all the usual caveats of statistical analysis but shouldn’t they be reliable enough to get an idea of *how much* to regress the home/road split, rather than just acknowledging that it should be adjusted?

Travis L
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Travis L
3 years 8 months ago

I would guess the reason this wasn’t done is twofold. One, it’s a lot of work to run the actual analysis rather than just discussing it from a high level. Two, the noise from park factors would probably obscure the effect of these adjustments.

But it would be neat to see someone calculate exactly how many games each player played in each park and average it for those days (since this is imaginary, why not try to include the weather for each day since we know that will affect PF?)

Is that kind of what you were suggesting?

Matthias
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3 years 8 months ago

wRC+ adjusts for the distribution of ballparks, I believe, but obviously needs regession for any future projections.

Baltar
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Baltar
3 years 8 months ago

Thank you, Dave, thank you, thank you. I am so tired of reading analyses where a player’s home/road, righty/lefty and maybe even other splits are taken as gospel.
This is my favorite kind of FanGraphs article.

Alex
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Alex
3 years 8 months ago

Wow. Very well done. As a fan of a team in a hitter’s park, I’m sick of hearing about how my team’s offensive stats are aberrations, park adjusted stats be damned. This may be my favorite Fangraphs article to date

Dr. Chaleeko
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Dr. Chaleeko
3 years 8 months ago

I’d love to see how this applies to Larry Walker. It’s easy to take his Colorado years and discount like crazy by looking at his road stats. In fact, it’s one way to argue that he’s just below the HOF line (the other is probably playing time).

jpg
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jpg
3 years 8 months ago

Yeah this was great Dave. You and FG as a whole continue to teach me things I would never have learned had I not discovered this site a few years ago. Thanks again.

James G
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James G
3 years 8 months ago

He’s actually hit well at PETCO for his career, 291/354/547, over 148 at bats.

http://sports.yahoo.com/mlb/players/8080/splits;_ylt=AoIw53wxCcwcVkd._bcs4OyFCLcF?year=career&type=Batting

ron
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ron
3 years 8 months ago

So I guess you can turn this around in a discussion about Kyle Seager. Of course his career sample size is too small but still his splits are absurd in the opposite manner. On the other hand it could say that Safeco was so crazy that it really negated his true talent. Splits are so confusing.

Phil D
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Phil D
3 years 8 months ago

Excellent piece.

Nathaniel Dawson
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Nathaniel Dawson
3 years 8 months ago

Yes, yes, excellent.

We learn a lot more about a player by looking at everything he’s done rather than looking at only half of what he’s done. Perhaps there are players whose individual skills translate to huge differences in his home/away performances, but I suspect those players are few and far between. Much better to do as Tango suggested, and take a generic adjustment with his park factors to get an idea of what his neutral park hitting would be.

pft
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pft
3 years 8 months ago

This was an example of an article trying to support a pre-determined conclusion when the facts presented in the article all point to the fact that Uptons performance is driven by his home park, much like Jim Rice.

You have almost 1500 PA for H and A so SSS is not a factor, like it is with noisy Y2Y numbers. Uptons H-A wOBA differential is about 5 times the average. Both of which scream the obvious conclusion which is ignored.

As for Coors cancelling out Petco, he actually hit better in Petco and has performed poorly (sub 800 OPS) in every other NL park except ATL and MIA .Noise by individual park is a factor due to SSS even if the collective data is not.

Buyer beware with an Upton.

Alex
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Alex
3 years 8 months ago

Wow, it’s like you ignored the entire article aside from the incorrect “not a good hitter in Petco” bit

pft
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pft
3 years 8 months ago

He danced around it a bit but everything in the article from the headline on is meant to suggest Uptons H-A splits are a mirage without any real data to support it. OTOH, he writes an article on ESPN which makes a big deal about Hamiltons H-A splits which in terms of wOBA are only half as large a differential as Uptons and skewed by 2010.

Hank
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Hank
3 years 8 months ago

+1

The article might have some merit if some actual regression was done. Simply saying noise and pointing out a few outliers doesn’t validate Upton being an outlier.

There is noise in every sample; the key is to identify how much and if it is significant, simply saying it exists and pointing out an outlier or two is shoddy analysis

The obvious data point missing is a look at AZ as a whole when asserting the away parks in the NL West dragging down Upton’s #’s. From 2008 on:

AZ home (all players) wRC+ 98
AZ away (all players) wRC+ 86

League wide the wRC+ split is….. ~10 points. But surely players in AZ are being punished a lot more because of the heavy West Coast unbalanced schedule, the weather and the away parks involved? If it is a real impact it is tiny compared to the split Upton is showing. This is what happens when you don’t do the analysis – wave your hands with an intuitive argument but don’t attempt to actually measure the impact/determine if it is really significant.

While I’m sure there is some noise component in the data, sadly this article avoids the most basic question… how much? All it says is “there is some”, so be careful using splits.

Also are the park factors clean enough such that wRC+ stats aren’t also “noisy”. The Yankees as a team from 2008 on have a road wRC+ 10-12 points higher on the road. If you consider a ~10point home benefit, that’s a 20+ point swing over a fairly large sample. Outlier? Noise? Issue with park adjustments? Real?

Hank
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Hank
3 years 8 months ago

EDIT – I juxtaposed the home/road splits on the NY #’s; it’s a +11point home split so that is inline with expected.

MGL
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MGL
3 years 8 months ago

“If you want to see regression as it relates to home/road splits, Tom Tango did a study on individual player park effects about 10 years ago, but we’ll dig it up here, because it’s still relevant today.”

First of all, that is MY (MGL) study/article and not Tango’s, as in:

“MGL takes a quick and dirty look at hitters who have Home/Road splits that are in reverse of the HFA of his home park.”

The very first paragraph of that article on Tango’s web site (yes, occasionally people put OTHER people’s work on THEIR web sites).

I have done much work on this. There is very little spread of “talent” as far as a player having his own unique park factor for a given park. Not none, but little. Probably similar to platoon “talent” for a RHB.

There is virtually no “talent” for a player’s unique home/road splits (HFA) irrespective of their home park. Probably similar to a pitcher’s BABIP (excluding certain classes of pitchers, like knuckleballers).

Yes, it is true that not all of a player’s road parks combined are created equal. However, even, for example, players in the NL West, you will not find huge anomalies/outliers in the average PF of their road parks – maybe .97 and 1.02, for players that play in extreme hitter’s and pitcher’s parks, respectively (just a guess).

Basically, this:

Simply park adjust a player’s home stats using his home park multi-year, regressed PF (either using individual component PF’s or a total run factor or OPS or wOBA factor). Then combine that with either a player’s raw, unadjusted road stats, or if you want to be more rigorous, adjust those road stats by the average PF of a player’s road parks (again, in most cases that is going to be very near 1.0, and at worst, .97 or 1.02 I think).

That gives you his neutral stats from which you can assess his value. If he moves to a new home park, don’t assume or speculate that his value will change. It likely will not, and if it does, it will likely be a very small change. So basically ignore his unique home/road ratios. Assume that everyone is affected more or less equally by each park and that everyone has more or less the same home field advantage (hitting better at home, in all categories, but especially triples, SO, and BB). The latter is almost 100% true, and the former is not nearly true but close enough for government work and very difficult to figure out who is is affected more or less in each particular park. IOW, simply park adjust Upton’s home stats and combine them with his road stats. If you want to assume that his road parks have an average PF of .98, fine. Simply bump his road stats up a little. When combining home and road stats there is no need to do any HFA adjusting.

Also, don’t make the mistake of thinking or assuming that, for example, fly ball batters and pitchers create less value in small or hitter’s parks and more value in large or pitcher’s parks, and all the other permutations. There is no evidence that that is true, yet everyone seems to think it is whenever a fly ball or ground ball (or high or low HR) pitcher or batter (particularly a pitcher) moves to or from a small or large park (i.e., such and such extreme fly ball pitcher will have greater value in a park like Petco and less value in a park like Coors). I have done extensive research in this area and I have NO evidence that extreme fly (or ground) ball batters or pitchers do better or worse in extreme pitcher’s or hitter’s parks with respect to overall run scoring or home runs. It is one of those things that seems intuitively true but is not (apparently).

See this thread for data and a discussion on that:

http://www.insidethebook.com/ee/index.php/site/article/are_fly_ball_pitchers_at_a_disadvantage_in_a_hitters_park_especially_one_wi/

MGL

Wah Wah
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Wah Wah
3 years 8 months ago

Very educated response, by someone who clearly knows this.

I’d be interested in your thoughts about Melky Cabrera, who appears to have extreme negative home/road splits. Unlike Upton, who “might” go to a different team, Melky will definitely be on a different team. PEDs aside, some stats (change in parks at face value, career PAs at his age) suggest a surprisingly strong season is coming.

MGL
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MGL
3 years 8 months ago

From the article that Dave references on Tango’s site, in the comments section, I had added some data. Not the conclusion:

Because of the realtively small sample size of the original study, I did the exact same thing for 1999 and 2000. Here are the abbreviated results using the same parks:

There were 27 players (4499 PA’s) in the 1999 sample with “reverse” splits. The average of the players’ sample splits in the pitcher’s parks was 1.13 (remember it “should” be .96). In the hitter’s parks, whereas the splits of all players “should” be 1.04, the players with “revrese” splits had a composite split ratio of .89.

In 2000, 19 of the 27 players “survived.” The players in the hitter’s parks who had a “reverse” composite split of .89 regressed to a composite split of 1.11 and the players in the pitcher’s parks regressed from a “revrese” split of 1.13 to a split of .96.

The conclusion is now stronger that, without knowing anything else about a player other than his sample one-year home/road splits, in order to estimate his “true” splits or predict his future splits (again, they are basically one and the same), one should ignore those sample splits and simply assume that his future or true split ratio will be approximately the same as the average player in the league.

John Smith
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John Smith
3 years 8 months ago

Argument For Park Effects:

Seth Smith 2007-2011(COL): .275/.348/.485
Seth Smith 2012(OAK): .240/.333/.420

Argument Against Park Effects:

Jonny Gomes 2008-2011(CIN/WSN): .250/.329/.447
Jonny Gomes 2012(OAK): .262/.377/.491

Alex
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Alex
3 years 8 months ago

One example isn’t an argument.

an android
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an android
3 years 8 months ago

Smith also DHed a lot in Oakland, something he didn’t do in Colorado.

Also his A’s wRC+ is 107, which is right in line with his career 109 wRC+

tufte
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tufte
3 years 8 months ago

Nice piece. But… for a site that has the word “graph” in its title, the wOBA figure is a truly terrible display of data.

Ryan S
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Ryan S
3 years 8 months ago

I thought it was a rather elegant way to show each major league hitters wOBA gap, it also conveyed the point that the majority of players tend to hit better at home regardless of park.

SouthPawRyno
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SouthPawRyno
3 years 8 months ago

Do you think that can be attributed to playing in SD and SF more regularly than players in other divisions?

Nathaniel Dawson
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Nathaniel Dawson
3 years 8 months ago

Read MGL’s comments above. He plays in all NL parks, albiet a bit unevenly. Still, the overall difference between the road parks he plays in and the road parks the average player plays in isn’t going to be a whole lot, maybe a few percentage points, as MGL suggested. Not enough to assume a huge disadvantage to his numbers on the road.

Eric R
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Eric R
3 years 8 months ago

I grabbed the road park splits from bb-ref and did a weighted average. I got 99.75…

truffleshuffle
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truffleshuffle
3 years 8 months ago

Interesting stuff here, Dave. I’ve literally never thought about park effects like that.

By the way, the new design and layout looks great and reads well… Keep it up!

MGL
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MGL
3 years 8 months ago

It took me all of about 5 minutes to go through Upton’s game log on ESPN.com and apply OPS park factors to them in order to get his average road OPS park factor. Keep in mind that a park run factor (which is usually what a “park factor” is) is equal to around a OPS park factor squared.

For example, the OPS park factor for SD is .92 and for COL, it is 1.06. The run factors for those parks are .83 and 1.25. I am using my own multi-year regressed run and OPS factors. Yours may differ.

Anyway, Upton’s average road OPS park factor in 2012 was .994. So, no, he did not play in pitcher’s parks on the road. While he did in fact play 9 games in SD, SF and LA, he also played 9 games in COL, 3 games in TEX, 3 games in CHN, 4 games in CIN, 3 games in PHI, and 3 games in HOU, all of these being moderate to extreme hitter’s parks.

As I said, it is unlikely that any player has an extreme average road park factor, and if there are any players with moderately extreme road factors, it is more likely that they play their home games in COL or SD, the most extreme parks in MLB.

Hurtlockertwo
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Hurtlockertwo
3 years 8 months ago

San Diego moved their fence in for 2013, I’ll be interested to see if there will be a difference.

MGL
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MGL
3 years 8 months ago

Of course there will be a difference. How much depends on how much they moved the fences in and in what location.

You will not be able to “tell” the impact of the change based on the numbers this year, or even 2 or 3 years. There is too much noise.

What I do is this:

Let’s say that the run factor for Petco is estimated at .83 (depressed run scoring by 17%) based on its history (and regression). If the fences are moved in, say 10 feet in one or both alleys (that is where most HR are hit – moving fences in CF or the lines do not change anything much), the new run factor might be .88 (just a guess) or something like that. And of course the HR factor will increase as well.

For one year, looking at home run scoring (per 27 outs) divided by road run scoring (for both teams of course), and adjusting for the road parks, if you want to be more rigorous, you will likely see anything from .75 to 1.01 (I am guessing the 2 SD interval), so, again, it is likely not going to “confirm” the fact that the park became more hitter friendly, and because of the noise in those numbers, you are not going to know how much more hitter friendly it has become for many, many years, and even then, it will just be an estimate.

You are better off estimating the change based solely on the fact that the fences were moved in X number of feet (as I did above in assuming that the run factor changed .05, which was just a wild guess). You can do that by looking historically at parks that have moved their fences in or out X number of feet (and in what location), where you have many years of before and after data.

CFJ250
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CFJ250
3 years 8 months ago

Great article. Upton played hurt all year in 2012 and still finished as a top 50 hitter. When he was healthy in 2011, he was a stud at age 23 and his average HR distance was among the league leaders; so I see a big bounce back in HRs regardless of where he plays.

smocon
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smocon
3 years 8 months ago

I wonder what the odds are of Justin Upton matching his WAR total from last year now that he is moving positions and playing in a pitcher friendly park.

I dont buy this argument, and see Upton putting up about 2.5 to 3 WAR this season. He is still a nice player, but Dave, you are going to great lengths trying to justify overpayment for, misleading bad numbers for, and overall lack of perofmance for this guy.

I like Upton, I believe him to be a solid player with huge potential, I just do not feel that he is the elite player he was in 2010 nor will he consistently maintain elite types of production.

G
Member
G
3 years 8 months ago

nice article. hopefully some of the announcers read this so we don’t have to listen to them raburn over home/road stats incorrectly during broadcasts.

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