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Sabermetric reading list: 5 March 2010

Ergogenic Aids: A Review of Basic Science, Performance, Side Effects, and Status in Sports | American Journal of Sports Medicine

This past week, MLB announced that they will institute a blood test for hGH in the minor leagues. There's a lot of hype about hGH right now, but the science on the subject is fairly clear: there are minimal if any positive effects of using hGH as an adult. Here, I'm linking a very readable review paper looking at most of the common PED's that I assigned to students in my baseball class. It turns out that perhaps the best demonstration of hGH's effects occurs in a condition called acromegaly, which is caused by elevated growth hormone titers in adults. In this case, affected individuals actually experience a weakening of muscles. MLB is wasting both time and money pursuing a test for what is apparently an expensive placebo.

Sabermetrics 101: Positional Adjustment - Lookout Landing

Good piece by Graham describing he logic behind the positional adjustments used in WAR. One small quibble: the other flawed assumption behind offense-based position adjustments is that position talent (offense+fielding) is equal across positions. It's not. 2B's and 3B's are equivalent as fielders (on average), but 3B's hit better.

Matt on DIPS | THE BOOK--Playing The Percentages In Baseball

The debate rages on DIPS statistics. Essentially, we have three more or less equally accurate (at the population level) approaches at this point. xFIP, Tango's new bbFIP, and SIERA. They all differ substantially in their construction, but there's a sense in which they all do the same thing and come to very similar estimates most of the time. The other player is tRA*, which has yet to be empirically tested, at least publicly. On top of this is a hardcore community vs. BPro social divide, which seems to be driving some hostile feelings. It's an interesting thing to watch...but the payoff is that we're seeing more advances in component-ERA estimators this past week than we've seen in the year+ since tRA was published.

Star-divide

Tango Introduces bbFIP | THE BOOK--Playing The Percentages In Baseball

Tom Tango, inspired by SIERA and the conversation around it, derived yet another component ERA estimator: bbFIP. Very simple little construction, but uses batted ball data instead of HR/FB data. And it works as well as SIERA or xFIP. And then, in the same thread, there's also an older stat, kwERA, which is even more simple, looking only at walks and strikeouts...and it performs just as well in terms of prediction! Amazing stuff.

Panas' sabermetric primer book | THE BOOK--Playing The Percentages In Baseball

Lee Panas published a terrific sabermetric primer. It's extremely current, with great scope, and will be an awesome resource for those interested in learning more about sabermetrics--especially player valuation statistics. I'm linking to Tango's review of it, but you can find the book on Lulu. If I do my baseball class again next year, I'll probably assign Lee's book.

The Baseball Analysts: Spring Training, PECOTA, and the Regular Season

More testing of PECOTA, this time at its team-level projections. Sky Andrecheck finds that it probably needs to be regressed a fair bit toward the mean, at least over the last two years (2008 & 2009). Whether the same will carry forward in 2010, I don't know--BPro seems to be working hard to get PECOTA back into shape in a variety of ways.

Minor league run environments | The Hardball Times

I'd like to throw out a link to a little study that I published at Hardball Times this week. It breaks down the run scoring environment across the minor leagues, and finds (not surprisingly) major differences. What I like about this piece is a) it looks at component statistics in each league and finds, for example, that not all high run environments are constructed equally. And b), that it allowed me to develop a base runs equation that works a bit better, at least for this purpose, than others I've used.

Minor issues in the air | The Hardball Times

Neat study by Harry showing massive stringer bias in some individual minor league parks, especially Huntsville, where they just stopped using LD!

Quantifying Carroll (Steamer Projections)

Jared Cross takes a look at Will Carroll's team health reports. He finds that, at least for players with the "red light," we'd be right to expect players to appear on the DL 10-20 more days than "green light" players. Yellow light results are mixed. To me, this is a pretty favorable result for Carroll's work, as injuries are not easy things to forecast.

Improving pitcher projections

John looks at how differences in fastball velocity affect the degree to which CHONE (and, apparently also, PECOTA) misses high or low on pitchers, and finds a real effect--at least for soft tossers. The inclusion of more and more batted ball/velocity/pitchf/x data into projections is the way forward, as John shows here.

Introducing Oliver

After a long period of development, Brian Cartwright's Oliver projection system finally makes its debut. It's a quality system and with a few tweaks should be competitive with the other heavyweights: CHONE, ZiPS, Marcels, etc. (and yes, by including Marcels, I'm saying that the most basic system is one of the best--its solid showing in every year's projection roundup can't be denied).

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Wow, reading through the thread the DIPS article links to hurt my head.

My question is, why are all these checks they’re doing based against ERA?

Presumably, if you’re trying to predict future performance using an advanced stat because of the unreliability of the basic stat, why then check it against the same basic stat in coming years? Basically, why base the success or failure of a DIPS on a DDPS? Wouldn’t the prediction be applicable only (or, at least, most closely) to a team with a perfectly neutral defense with a 0 UZR (on second thought, let’s not get into UZR)?

They even measure out 3 years down the line when the defense and even park around a player could and likely has changed significantly. Do we accept that this sort of thing balances out somehow? For every pitcher who goes from a standard defensive team to a good one and has his ERA go below his DIPS numbers, there must be one pitcher going from a good to a standard? Would that work?

USG

by Ben Buchanan on Mar 6, 2010 6:50 PM EST reply actions  

Definitely see your point

If I remember right, when Eric and Matt did their SIERA testing, they were comparing it to park-adjusted ERA. That might be the better way to go, for the reasons you mentioned. You certainly don’t want park effects to be influencing which mostly-park-independent measures you are choosing, because the bias would be to favor more park-dependent stats. That said, my guess is that it won’t change the answer very much…
-j

by JinAZ on Mar 6, 2010 8:42 PM EST up reply actions  

Probably not. Still, a bit odd if you ask me.

Anyways, there’s also the nagging idea that batted ball profile HAS to be somewhat important. LD% seems to move around a lot, but there are undoubtedly ground ball pitchers. Unless the 7% difference between groundballs and flyballs (including home runs) turned into outs is EXACTLY equal to the generally increased production of flyballs (doubles, triples, home runs vs. singles) then there’s got to be some effect, even if it’s small.

USG

by Ben Buchanan on Mar 7, 2010 12:33 AM EST up reply actions  

I'm not following

Both SIERA and bbFIP include GB and FB, and bbFIP also includes LD. I know there are subjectivity issues associated with LD%, but it’s also a very stable statistic based on Pizza Cutter’s past work:
http://web.archive.org/web/20080112135748/mvn.com/mlb-stats/2008/01/06/on-the-reliability-of-pitching-stats/
Really not sure what to make of it right now. It certainly doesn’t seem to hurt bbFIP to include it.

Furthermore, it’s worth nothing again that the best performing statistic in Brian Cartwright’s study, at least, was kwERA, which only includes strikeout-walk differential. I’m still not ready to use it (I just can’t accept it yet), but it probably at least indicates that walk and strikeout rates are more important than anything having to do with batted balls.
-j

by JinAZ on Mar 7, 2010 10:25 AM EST up reply actions  

Ah, yes, I see kwERA is what they renamed it too.

Anyways, that’s what I was referring to. It seems hard to imagine that one which only includes BB-K is really the most accurate. You’d think that GB% would be at least somewhat important.

Perhaps I just haven’t looked into the math re: GB vs. FB enough.

USG

by Ben Buchanan on Mar 7, 2010 1:13 PM EST up reply actions  

I agree

I’m shocked that it came out ahead. I don’t see a substantial problem with the test, but even with those data I’m not ready to embrace it. I’m liking bbFIP a lot, though.
-j

by JinAZ on Mar 7, 2010 8:02 PM EST up reply actions  

LD numbers really do seem to fluctuate a bit much, so I expect there's still some tweaking to be done.

But yeah, can’t argue with the accuracy.

I just checked out SIERA, and it seems ridiculously complicated, yet only provides small benefits over bbFIP.

USG

by Ben Buchanan on Mar 8, 2010 12:34 PM EST up reply actions  

I think the fact that they're coefficients to the 10-thousandth place makes it look worse than it is

The interaction terms are interesting, as they are significant coefficients. But I sometimes wonder if it’s more interesting for research than for practical application.
-j

by JinAZ on Mar 9, 2010 11:02 AM EST up reply actions  

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