Found this through kottke.org, and it’s–maybe it’s the writer in me–delightful.
Sabremetrics
It May Be Time to Acquire Power
The Hit Tracker reports that home runs are flying about five feet farther than they were last year and there is little chance this is random, which may mean the ball is juiced. Derek Carty explains how you can turn this into an advantage, maybe.
I’m not sure what to make of The Hit Tracker, Greg Rybarczyk’s trajectory simulator software. It is certainly impressive and I feel comfortable relying on it’s individual stats for the fun business of characterizing blasts, but is it really accurate enough to get granular over an average of five feet per homer? Is it accurate enough to say that the ball is juiced based on the Hit Tracker reports?Â
I’m not saying I know it isn’t, but I’m skeptical. Still, it isn’t a bad idea to look at guys who had just barely enough power last year (Jack Cust leads that list, along with Ryan Braun and Mark Reynolds in the AL) and think that they just might benefit.Â
Also, to clear up an issue in Carty’s story: If there is m ore hitting in the year, the value of the best pitchers generally goes up. And if there is more pitching, the value of the best hitters usually goes up.
The Cutter: Magic Pitch
I liked Kyle Davies going into this year because of his age, his pedigree, his good spring, and I knew he had added a cutter. What I didn’t know was how big a weapon the cutter seems to be.Â
If Alex Gershwind is right, it’s a big weapon, though there are some open questions about his study. Most pertinent is what the sample actually is. Did he only include players who threw fewer than X number of cutters in Year 1 and more than X number of cutters in Year 2? This would tend to eliminate failures from the pool, but he doesn’t say.
He also doesn’t give a demographic profile to the guys in his study. If they were mostly not Jamie Moyer, or if they were, like Jamie Moyer, all guys on the threshhold of dropping out of the league, their typical regression to their mean career stats might show a dramatic swing.
But I have Davies in one league and I need him to do well. So I’m not looking too closely.
SweetSpot by Rob Neyer
Rob used to be my editor at ESPN, and it was his blog post today that tipped me to that Alan Schwarz story about Diamond Mind (though the actual dead trees version is sitting in my living room, waiting for me to give up the computer screen). It also is the first day of Rob’s new blog format at ESPN.com.
I’m not sure what that means, in terms of the business, but I know that it means Rob is still writing about baseball (and I’m pretty sure he’s out from behind the pernicious pay wall) and is always well worth reading.
Are Catchers Brittle?
It has always been a truism that Catchers get hurt more. I’m not sure this Roto Think Tank story about a Eugene Freed study goes very far to prove anything. But it does suggest that the opposite is true.
More math needs to be done over more seasons to be convincing, but the better argument (I think) is that catchers get hurt in fits and starts. Relying on a costly catcher may increase your risk, but getting real production from a dead position has its own rewards. The strategy changes if you’re trying to win one year, or every year.
An article about Road Home Run Rates
The Hardball Times’ fantasy writer looks at which teams and players have the biggest changes in the home run rates of their road ballparks in the coming season. As he says at the end of the story, this is fun stuff, especially if you learn that one of your freezes (Josh Hamilton, let’s say) had one of the toughest road park schedules for homers last year. On the other hand, the team that gains the most this year is the Phillies, up 2.2 percent!
If they hit 105 road homers last year, this information suggests that this year they might hit 107! The last three years the Phillies have averaged 102 road home runs. Make of this what you will.
The 2008 Scouting Report
There are people with a lot of energy, and then there is Tom Tango. He not only invented the Marcel the Monkey projections, but also runs the Fans Scouting Report project, which polls real people to report on what they see when they watch players on defense.
They thought Edgar Renteria was awful last year. Some thought Michael Young, surprise Gold Glove winner, was good, but some thought he was awful. Everyone liked Erick Aybar.
The sample sizes aren’t big enough to allow much authority to the ratings, but they are another data point in the endless drive to figure out who can play defense and who can’t.
Maximum impact sluggers
This is just a solid bit of analysis of power hitters we don’t know for next year. Or, like Chris Davis, who we think we know too well.
According to former BaseballHQer Melchior, Davis should be fine but not great, and you shouldn’t overlook all those strike outs (he shows you why).
Good stuff, though you shouldn’t overrely on strikeout rates to gauge future performance. Still, youngsters who fail to make contact have a lot of things they have to improve if they’re going to be successful the second time round the league. Most, as Al points out, don’t make it.
Happy Birthday Sean Forman!
The link is an effusive thanks to Sean for creating and developing and then pretty much perfecting baseball-reference.com. All the more important because it’s all true. The HOF waits, though I suspect Sean will be in the class of 2022.
Span’s the Man
SportingNews.com – David Pinto
File this one under weird math, unless I’m missing something. David Pinto looks at Denard Span’s PECOTA projections for OBP and sees a man far outperforming expectations at the major league. Is this a sign that Span will regress?
Pinto then looks at Span’s Triple-A numbers earlier this season and sees that they match what he’s doing at the major league level, suggesting he’s taken a great leap forward. So far, so good. This is the essence of player projection, at least the verbal kind where we identify interruptions in the data.
But he then does some math showing that there is virtually no chance that PECOTA’s evaluation is correct, based on Span’s breakout numbers this year. I’m inclined to accept the verbal argument that Span has improved his approach at the plate and so is getting on base more, but have a hard time accepting as proof this year’s sample.
That’s why we compare small sample sizes to larger ones, as a reality check. And when the small sample is out of line with the bigger one, we should be very skeptical.
Notably, this year, Span is walking more than he has in the past, in Triple-A and the bigs, but he’s also getting on base much more on balls in play, and a significant part of his OBP is due to his BA. It looks to me that at the end of the day (and not necessarily this season) he should end up a .360 OBP hitter. His .399 OBP now is as much the result of good fortune as improvements in his game (though it looks like there are some of those, too).