Sabremetrics

Rays of Light!

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Nate Silver Rocks

On this day, a couple of days past midseason, Nate Silver talks about how PECOTA only projected the Rays to win 88-90 games this year, an audacious projection that now seems a little lily livered. That’s how good the Rays have been.

My goal here is to shout out about Nate’s analysis, which is excellent, but I have another objective. In Joe Sheehan’s column today he also takes on the Rays. Tucked behind the BP pay wall I can only guess at his overall point, but the tease is apologetic for not getting the Rays analysis right in the preseason.

But, and maybe Joe is being coy here, it was his analysis of the deal for Garza and Bartlett (oh so many months ago) that turned me on to what seemed like the key preseason Rays story. This was a defense that was moving from crap to good, and there would be some pitchers who would benefit. Based on that analysis, of Sheehan’s,  I made a bet on Edwin Jackson, a post-hype starter who finished fairly strongly last year, but whose historical ineptitude made him a $2 pickup in the endgame.

There is a ton we don’t know about defense and how it helps and hurts teams, but when a team like the Rays moves from the bottom of the defensive efficiency ratings to the top, and at the same time dramatically reduces the number of runs it allows, it may be time to say that we at least know what works.

Without shorting the shrift of the excellent Nate Silver, I think this defensive shift has been Sheehan’s baby the last few years (at least) and he deserves a lot of credit for seeing what was happening.

 

 

Industry Top 100 Prospect Analysis

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Project Prospect

Adam Forster looks at the Top 100 Prospect  lists from Baseball America, ESPN, Mound Talk and his own website and compares them, looking for analytical trends. No real conclusions can be drawn, I think you’d have to look at a few years worth of lists to get a fair appraisal of tendencies, but he shines a very bright light on the issues that go into the making of one of these lists.

He’ll share data, too, if you ask.

Talking to Brian Bannister

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Baseball Digest Daily :: Voros

I’m a big Brian Bannister fan, because it looks to me like he gets better results out of his stuff than other pitchers do. It turns out he’s a bit of a stathead and knows who Voros is, and can maybe actually even explain DIPS (though he doesn’t in this story).

Now I’m a bigger Brian Bannister fan, and I think maybe he gets better results out of his stuff because he’s thoughtful. That would be so excellent.

Fantasy Advice From the Front Office to Your Office

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MLB Front Office

These guys sent me an email today and asked me (and I’m sure everyone else they could think of) to write about them. The site is brand new and a little thin. The two big news stories are the launch of the new design, which is more exciting for them than us, and their season preview, which isn’t yet available. It’s also one of those widgeted together CMS jobs, not unattractive (at all) but distractingly overwhelming with information on the front page. And, I would suggest, not terribly helpful steering me toward what I should be reading.

But I looked around some more and found a nice simple story in the articles section about contact rates and batting average that will be very useful if true for fantasy players, and made me want to put together my own study.

There’s also a piece, linked to here, about the very varying ADP for some fellas with similar stats last year. I like this sort of thing and thought it might be of interest for me to post my projections for the five guys (rather than the writer’s use of last year’s stats).

Player A   550   116   45    133   2    .306

Player B   600    91   32   121     5   .323

Player C   550     91  35    110    5    .296

Player D   550    83    33   109   1   .300

Player E   450    89   26     88   5    .321

I think my projections explain why Player E is being drafted 66th (I have him rated a little lower, as the 50th best hitter) this year. He’s old and he’s been hurt recently. But as the writer says in this useful piece, when the perception runs ahead of the player’s decline (one hopes) there’s a chance to find some real value. (Actually, he ends the story with a rather wishy-washy conclusion, which suggests he thinks taking Player E with the 66th pick is pushing it a little, too. Good for him!)

Good luck to MLB Front Office. Or as my intellectual property attorney, Confucius, might say, May the legal team at MLB and MLBAM not hear about their site for a long time.

Changes in home run rates during the Retrosheet years

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The Hardball Times

Tom Tango methodically and revealingly demonstrates, using information gleaned from Retrosheet and MLB’s ball-testing lab, that there is real evidence that the home run boom that began in 1993 was a product of a juiced ball. Don’t believe me? Read the story.

Which isn’t to say that this is the final word. Tom’s data relates to balls put into play as they relate to home run rate, which is the best way to figure out the effect of hitting the ball farther, but not so good for determining changes that might stem from the umpires’ calling of the strike zone (in which case the ball might be hit less often).

Plus, I find it hard to believe that given the potency of Mile High in Denver, that the control group of players had a similar increase in home runs to those who didn’t play in Colorado. That’s something to think about while reading Tom’s story.

David Pinto, of baseballmusings.com, says that manufacturing standards tightened up for the ball manufacturers in 1993, and that balls were tested more often. His theory is that the manufacturers established a more tightly wound ball (but still within the official specs) as the de facto standard. Unlike times past, when the equipment would slide and the balls would loosen up and a range of tightnesses were created, the modern ball is uniform and tightly wound.

In no way does this argument rule out the possibility that other factors played a part in the recent power boom (Tom doesn’t publish the numbers after 1998 for one thing), but it does establish that only modest changes to the ball could readily explain much if not all of the changes. That’s worth remembering when it is tempting to overreact.

The running of the monkeys —

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Sal Baxamusa — The Hardball Times

Sal looks at the way the Marcel the Monkey projections change based on a ballplayers’ (in this case Torii Hunter and Andruw Jones) recent hot and cold streaks. His charts do a particularly good job of showing how short-term changes shape our overall picture of a player’s skills and future value.

His conclusion is pretty dull, considering how much fun the charts are (if you like charts), but that’s probably right, too.

Would anyone like to see more of these?

Web based PITCHf/x tool

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The Hardball Times

Josh Kalk has taken the first big step toward taming the PITCHf/x data that MLB collects and allows researchers access to. MLB’s freeness with the data promises to be a boon for sabermetrics and Kalk’s database front end, which allows you to compare how pitchers throw to different hitters and vice versa, with results displayed graphically is an inspiring beginning.

Kalk is talking about having splits ready by Christmas, and non-graphical data sometime soon, too.

I don’t have time right now to sift through all of this, but it’s potential importance makes me give thanks.

Thanks, Josh. Keep up the good work.

The Bill James Handbook

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Baseball Info Solutions

Every year I get a package from my friend Steve Moyer. Sometimes it comes when we’re together in the beginning of November at Ron Shandler and Rick Wilton’s First Pitch Arizona conference (which is a blast, a chance to see many of the next year’s rookies up close, and did I mention it was fun?) and sometimes it comes in the mail at home. What I know is that if it’s the first week of November it’s the Bill James Handbook.

What I remember, back in the day, was the Red Book from Stats, which also had Bill James’ name attached and which, for a while, Steve worked on, too. But Stats was sold to Fox and niceties like really useful baseball reference books became too small scale for them.

Steve has made a business off of the opportunities Fox threw away when it bought Stats, which isn’t to say that Fox was wrong, just that as a baseball fan I really much prefer what comes from Steve’s company, Baseball Info Solutions.

The Bill James Handbook, under the BIS aegis, has become a comprehensive statistical review of the previous baseball season, and it comes out less than 30 days after the season is over. It now has fielding rankings, managerial tendencies, home-road splits, batter and pitcher splits, projections for hitters and pitchers, and an assortment of other really interesting baseball data.

You can support this site by buying the Bill James Handbook from Amazon through the link below, or you can buy it somewhere else. My point is that there isn’t another baseball book that is more useful all season long.

Unfiltered Nate Silver

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Baseball Prospectus

Nate appears to have uncovered something really interesting. Platoon differentials are based on the pitcher, not the hitter (mostly).

This is a reason to pay attention to platoon splits again, if they’re based on lefty hitters (righties don’t show no bias).

Nate finds some specific uses for this data in his article, but I’m not sure it’s going to matter as much in the real world as it might in roto. Any time it’s better not to play than to play, fantasy owners take notice.