Prospectus Idol Entry

"To use an Idol metaphor, Brian doesn't have that low range that we were looking for, but he's got a voice."

Brian Cartwright has one heck of a voice!

Every Week One article was good, but Brian tackled an issue currently consuming the baseball writers' attention and he offered unique value to the readers of BP. Perhaps he wrote at a level that could have been misunderstood by some baseball fans, but he wrote at a level easily understood by the readers of BP and he offered that for which we seek, insight missing from sites such as ESPN.com, MLB.com, or our local Sunday newspapers.

I gave just one thumbs up this week. Congratulations, Brian: you got my vote. Superb analysis for working on so short a deadline!

I like Brian's voice, but the two pieces I've read so far by him seem to be more suited to an academia journal, textbook or research paper at a SABR convention. Still, this came across to me as more reader-friendly than the Initial Entry.

I think the first half of the article did a good job at explaining the basics to a new reader... I like succinct lines like: "71 homers at Yankee Stadium divided by 49 in the Yankees road games gives a factor of 1.45" and "A stadium having a factor of 1.45 tells us that plays in that park will be increased by 45% over normal rates." I do feel that, besides a change in schedule, there should've been a mention about park factors changing possibly because of stadium renovations or a humidor. Overall, I liked the article.

In light of Will's comments, some of the challenge here could be usefully eliminated by using simpler language and/or by clarifying the terms that you do use (e.g., what does "true value" mean? And you could very quickly define "standard deviation" for those of us who haven't taken a statistics course in more than a decade or at all. And you could note that "SD" refers to standard deviation).

But as far as the complexity of your analysis goes, don't change a thing. Personally, I like reading BP articles that challenge me; that's what attracted me to the site in the first place. This is Baseball Prospectus, not the FOX game of the week.

Your article seems to be less about park factors per se and more about how and when park factors can be used as a reliable statistic. Couldn't all that be simplified by referring to the "larger sample sizes are needed" mantra? Does it really matter (to the reader) exactly how large, and exactly what kind, of a sample size is needed to properly evaluate park factors? Moreover, is a new BP reader going to care about the reliability of one park factors figure versus another? That might be a petty critique, but you might want to think about not just what your topic is, but why you're writing about it (as opposed to other things).

Nonetheless, you have fantastically impressive statistical analysis skills and I'd LOVE to read several more articles by you.

Thumbs up!

I think he's going to end up as this year's Adam Lambert. A bit over the top, which will make him popular with the BP intellectual crowd, but I'm not sure if he broke down everything enough for the average reader.

*but I'm not sure that he did a great job of breaking down the topic of park factors sufficiently for the average reader.

That moustache deserves a thumbs up

On Standard Deviation, I did not attempt to explain how it's calculated, but instead how it's used to show a range of possible answers, a measure of uncertainty in the calculation. 'SD' was on the second reference, as I chose to vary my wording.

"The chart below shows the standard deviation...If Yankee Stadium still has a homerun factor of 1.45 at the end of the year, with a SD of .149, that means there's a 70% chance the 'true' value is between 1.30 and 1.60, and a 95% chance of it being between 1.15 and 1.75"

'true' being the real underlying value we are estimating, the result we would get with an infinite amount of data.

Richard said:
there should've been a mention about park factors changing possibly because of stadium renovations

"Three Rivers Stadium opened in Pittsburgh in 1970...In 1975, an inner wooden fence was constructed, about 6 feet shorter, creating version 2"

He's wrong.
Fenway Park *did* change between 1978 and 1999. Around 1989, (I might be off by a year) they built what was then called the "600 club" a second deck behind home plate that added about 600 (get it?) new seats set, bizarrely, completely behind plexiglass. Mike Greenwell and other Red Sox outfielders were all quite clear that this addition to the park caused balls to not carry as well as they had before it was constructed. It cut off the prevailing west-southwesterly wind that pushed balls out toward center and left center field in warm weather.

Fenway Park did change.

roughcarrigan - I was not aware of the '600 Club' - it's one of the things that doesn't chow up when looking at a list of park dimensions.

To be fair and objective, I just went back to my database and created a new version for Fenway from 1988 on.

Base hits (babip) was unchanged at 1.07, but all the extra base hits dropped - it does appear that the ball did not carry as well. Doubles went from 1.30 to 1.22, triples from 1.03 to 0.97, and homers from 1.07 to 0.92.

I thought it was a good example, and although now struck down, the point stays the same - if the park hasn't changed, the park factor shouldn't. It's the team factor, the weighted mean of all the parks each team plays in, that can change from year to year as the schedule or any one park changes.

If this is how Brian presents the basics, we're going to need Wolfram|Alpha to get through the competition. Thumbs up if it were just about the analysis, but I don't think the writing is there for a newbie.

I really like all of Brian's work, but this was another example of not sticking to The Basics. That being said, I'd rather read and use Brian's non-basic research than most others' non-basic research.

This might just be a factual question for Brian (or anyone else who's calculated park factors before). To adjust for the unbalanced schedule, it looks like you are doing the following steps: (1) Calculate park factors assuming balanced schedule; (2) Adjust the team's road stats based on park factors found in step 1; (3) Recalculate park factors; (4) Iterate for as long as you want (though it stops mattering very quickly). Why do you do it this way? Is this just the traditional way to do it? (I really have no idea.)

My bigger point is that, unless I'm missing something, this method gets the wrong answer, doesn't it? Say every stadium is exactly the same, except for Coors which dramatically increases HRs. Team A plays a disproportionate number of games in Coors so their stadium ends up with a park factor of 0.9. Team B ends up with 0.97 (#s are illustrative only - not sure they really make sense). Team A and Team B _should_ end up with the same park factor. But this would never happen. Because A and B are exactly the same, they (on average) have the same numbers of HRs hit in them. But, after your adjustment, it looks like Stadium A has 0.9/0.97 as many HRs as Stadium B. The park factors should definitely converge quickly, but they're converging to the wrong numbers. Am I off-base?

I can think of some straightforward ways to get the right numbers (which, honestly, I've always just assumed were used to get PFs) so I'm wondering why this method was used. Thanks!

Yeah, the swipe at Olney bugged me for the same reason it bugged KG. Then Brian proceeded to tackle park factors the way I would hope they are tackled.

This wasn’t so artful as some of the more glib writers, nor was this as basic as requested, but I’m heavily rooting to see more articles from Brian Cartwright.

I disagree about all of the newbie talk. Why "dumb" down analysis? If something needs explanation, make a post in the comments section. As a newbie to Brian's writing about a year ago on Fangraphs (it seems like a year, but who knows), I would do that. He answered. I learned. The idea is to challenge the baseball "norms" and get to the true. Brian is a forward thinker, you don't want to bore your vets for the the sake of newbies. The reason the newbies come is to learn. This is a great guy to learn from.

"If the park doesn't change, then the park factor shouldn't change." But if HRpf = Home HR / Road HR, wouldn't the denominator (and therefore the pf) potentially change every time *any* park changed? Granted, the change wouldn't be large, but ...

There's something weird to me about ending the piece with a table.

This is a good piece, of the sort that you might find in ESPN's "BP Daily" section.

I didn't think Brian was particularly mean to Olney. Olney wrote something and Brian politely pointed out that it was false. BP writers shouldn't trash mainstream writers. But they probably should be pointing out inaccuracies.

Despite really liking this piece, I'm left with a nagging question.

Why shouldn't the park factor change if the park doesn't change? As long as the park changes relative to the average major-league park, that should be enough to change the park factor, shouldn't it?

For me, this piece fell off track as a Basics article with this phrase: "so let's instead find the ratio of the home HR% (hr/(ab-so)) of .064 to their road game rate of .043, which is 1.48—slightly higher, but basically the same."

I get it, I get it, but it's too much for Basics. You also introduce park factor numbers and show us a chart of them before giving us the nut graf about how the stat actually works: "A stadium having a factor of 1.45 tells us that plays in that park will be increased by 45% over normal rates..." -- that's the wrong order.

As with many of these articles, this is actually a pretty good piece. Its failings are that, in my opinion, it's just not Basic enough, and makes too many assumptions about a reader's knowledge (standard deviations).

Try imagining your mom when you write these Basics articles. Or, if you prefer, some dude sitting next to you at the ballpark who has been watching baseball for years but knows nothing about sabermetrics and things that Batting Average is the bee's knees.

So I just finished reading all of the articles, and this article got my first vote. I will probably vote for 2 or 3 more, although I thought most of the articles were at least two or three wins above replacement.

In any case, I thought that this article actually told me something new, and in a convincing way. However, I voted for this article because I thought that it was the best article, in a vacuum. At the same time, it is probably a few levels above a "Basics" article. One of my main problems with American Idol is that people vote based on each week, forgetting past results, and future upside. I would rather vote on potential, at least this early in the competition, rather than how closely the week's prompt was followed. I think that Brian is capable of having a Hanley Ramirez-type breakout season, as soon as his manager stops asking him to bunt the runner over. At this point, I'd rather vote for that then the solid singles hitter with limited upside.

I like this article a lot. I think there is a lot of good content there.

Constructive criticism:

1. Don't use an acronym without introducing it first (at least not an acronym for something that wouldn't be found in the standard box scores). I wasn't so concerned with SD for standard deviation as I was with something like SDT in the table. I'm guessing it is singles+doubles+triples, but I'm not 100% sure. One tip to make it clearer too so that people don't miss your abbreviations is to use full words the first time and also include your abbreviation there. So something like "The chart below shows the standard deviation (SD) of the results for each category at each sample size". Now it is crystal clear in the next paragraph that SD is standard deviation.

2. I know a lot of people have moved into component park factors instead of aggregate park factors. However, for the guy in the ballpark slightly more refined in statistics then a true basic reader, but not yet a full, Brian/Tango/MGL type they may have thought about park factors only in terms of runs. Or hitters park versus pitchers park. It would be nice to either include the run factor of each park and/or explain why components are more important than runs. But touching on it in the tables would be good (unless I missed it and that is what SDT is).

3. I think using Buster's quote as an opening is fair, but one way to keep the tone from seeming to arrogant or snarky would be to credit Olney with intentionally exaggerating for effect and then have you come along to refine the argument to show how it is done for real. I think Olney knows that Yankee stadium is unlikely to have 400 HR hit there this season, even if he maybe couldn't run a bunch of multiyear regressions.

Overall though, the piece is easily better than the average BP article (which I think is the proper strict bar for BP Idol).

I liked the piece overall; it gets a definite thumb from me. But it could have stayed on course better - it ends up being at least as much about small sample size as about park factors, which muddies the main message. I don't think that was quite on target for this assignment. If one is in fact reading about park factors for the first time, it would be enough just to digest the fact that there are different park factors for different quantities, and get some idea of what the ranges are. Looking at the differences between the highest and lowest park factors might have sufficed; I wouldn't have used standard deviation at all.

"Home run" is two words; getting that wrong in your first sentence is not a great start. Getting it wrong again later in the article is worse; you can't possibly sound like an expert at that point. One hopes that these would get caught by the editor, but BP is not the gold standard in that regard.

Overall... odd mix of detail and generality. Nothing grabbed me in the prose, much as I love making fun of Buster Olney. C+

I had no idea who this writer was before BP Idol. I gather from some of the comments that Brian has a fine reputation in the baseball analysis community. He sure knows more about math and statistical mumbo jumbo than do I. What he does not seem to possess, based on this and his initial entry, is an ability to write in a manner that appeals to anyone without a degree in math.

Putting aside one's feelings with regard to math-heavy analysis, Brian seems to have ignored the rules for this week's entries. The most basic requirement is to "craft an article around one statistic or concept and explain it." If the concept of "Park Factors" is explained in this article, the explanation eludes me.

While reading all the original submissions and the first week entries for all the contestants, I have wondered if this contest shouldn't have been called America's Next Great Sabermetrician. I originally subscribed to BP because i'm sucker for Napaleonic War references in transaction analyis. Having said that, i think the criticisms leveled at this article as being not basic enough are misplaced. It seems to me that this is a cautionary tale. For the average baseball fan, first impressions seem to linger and with every network throwing up the graph of the home runs hit in new yankee stadium vs. old yankee stadium, the danger of new yankee stadium being labeled a hitters park is pretty great. The author, at least for me, helped with clear and easy to understand statisticall analysis put the brakes on that first impression.

More important to me was this clear misinterpretation of math in the article itself:

"Yankees hitters would be normalized by having their homerun percentage reduced by 22%, and the pitchers increased by 22%."

If the new Yankee Stadium increases home runs by 22% relative to other parks, in order to normalize the hitters' home run percentages, you should reduce them by 22%. However, to penalize pitchers by increasing their home runs allowed by 22% because they pitch in a home run-inflating ballpark is clearly adjusting the numbers in the wrong direction. Why should we penalize the Kevin Millwoods of the world for pitching in Texas? Isn't it bad enough being a Kevin Millwood?

I liked parts of this article a lot, but I am a stickler for accuracy on the basics. At first glance (and only having read 2 or 3 others), it doesn't get a vote from me.

Whoops. That was a misstatement. You are absolutely correct. I'm surprised no one (including myself) saw that yet.

The piece started off well but lost my attention. Nevertheless, I have a serious problem with Brian's analysis:

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"As of this writing on May 20, the Yankees have played 19 games at home, which have seen a total of 71 homeruns, 37 by Yankees hitters, 34 by their opposition. They've played 21 games on the road, with 49 homeruns, 27 by Yankees hitters and 22 by their opposition. 71 homers at Yankee Stadium divided by 49 in the Yankees road games gives a factor of 1.45—indicating the new Yankee Stadium inflates homerun rates 45%. The Yankees have played two more games on the road than at home, so let's instead find the ratio of the home HR% (hr/(ab-so)) of .064 to their road game rate of .043, which is 1.48—slightly higher, but basically the same.
Is 20 games, a quarter of a season, enough of a sample size to get a reliable factor?"
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This is the wrong question. The problem with the 1.48 number is not that the sample size may be too small, BUT THAT IT IS WRONG. Park factors are calculated using the SAME SET OF OPPONENTS at home and on the road. The Yankees have not played the same set of opponents at home and on the road. Therefore, the calculation is simply wrong.

Also, I'm all for attacking mainstream writers (though BP doesn't do that as a general rule), but Olney doesn't deserve that criticism here. He was simply pointing out that a lot of home runs have been hit at the Stadium this year. He presented the home run "pace" over a full year only to put that pace in context (comparing it to last year) and demonstrate how high it was; he was not presenting an "analysis" of the park, or alleging that the park would give up that many home runs this year. He used words like "might" and "appears." He said AT THIS PACE there would be 400 homers.

As for Brian not quite being in tune with a "Basics" column, well, perhaps he went a little overboard. But I don't think it's a great idea for BP to dictate the theme of the piece each contestant must write, either. Speaking generally here (i.e., this has nothing to do with Brian), writers have certain skills; they're good at some things and bad at others. Presumably BP would hire the winner to write to his strengths -- e.g., a "notes" column such as Gammons might present; an analysis column such as Nate might present; or commentary such as Sheehan might present. Sure, you want writers who can handle various assignments, but I thought the goal here was to find a talented, interesting writer to add to BP's staff, and I don't see how shoehorning the writers into topic areas they may not be comfortable with furthers that goal.

I was hoping to read entries that were chosen exclusively by the contestant according to his or her strengths and interests, instead of forced-theme columns.

I thought it was good. Nice intro, and didn't think it was too "advanced" for a newbie...good job.