Wonderful article by Ben, on the Molina v Lawrie episode (though Molina was invisible to everyone except Ben).

This writer gives us some examples of what happened following changes in pitch selection frequencies.  Obviously, it is both anecdotal, and shows no specific correlation=causation effect.

But this is one of those areas of PITCHf/x that could potentially lead to dramatic results.

It’s bound to happen.  It’ll start with the NHL, then the NBA, the NFL, and finally MLB.  But, it might not hit all the MLB teams right away.  The Yankees for example won’t corporate-name their stadium.  That’s because the Yankees IS a corporate-name.

Eventually, teams will realize that just like they can sell their stadium names (and get a money stream unavailable to the Yankees), they will do the same with jersey rights.  I think the Pirates and Marlins and A’s would dearly love to sell rights on their jerseys.  Eventually, the other teams will follow.  But, the Yankees?  It’s possible they won’t.

And, how would it work?  Will it be a team-by-team negotiation, or will all teams split the money equally (but only if they participate)?  Or, will MLB actually force the Yankees to participate, thereby depleting (to a small extent) their corporate-value name of Yankees?

***

Grantland has an article about the Goldman Sachs Yankees.  I haven’t seen it, because their RSS feed is blocked at the office, except for the headline.  Feel free to post clips of that article below.

A nice little interview with Hawk.

Big Lead Sports gives us a preview, starting with host Poland.

David Ortiz shows that he’s man enough to bunt.  Surprisingly, this is an extremely rare occurrence, as Ortiz, in his career, has only five bunt hits on ten bunt attempts entering today, and now 6 for 11 for a 55% success rate. 

The question is how often can a hitter bunt to make it more effective for him to produce runs, than to work the count and/or swing away.  We know that Ortiz has a career wOBA of close to .400 when he doesn’t bunt, which is where you will find great hitters. 

With the bases empty, the wOBA equation gives a weight of almost 0.9 for a single with 0 outs and under 0.8 with 2 outs.  So, in order to get a .400 wOBA (and breakeven for a great hitter), a batter would need to successfully lay one down about 45% of the time with 0 outs, and 50% of the time with 2 outs.

Ortiz as I said, is a career 55% success rate.  That is of course based on only a sample of 11 attempts, so we really don’t know how good a bunter he is.  Any hitter who can lay one down over 50% of the time against the shift should simply keep bunting.  As Jeff notes, it’s the batter’s equivalent of the IBB.

The Mariners, like most baseball teams or every baseball team, shift the infield for David Ortiz. In the fifth inning today, Ortiz dropped a perfect bunt down the third-base line for a single. Fans eat it up when players do this, even though it takes the bat out of their hands. When players get intentionally walked, they get the bat taken out of their hands, and fans can’t stand it. Fans are so weird.

Anyway, a player once told me that he could lay one down 100% of the time if the pitch were over the plate, and 50% of the time if the pitch was off the plate.  That would mean that roughly 75% of the time, a great hitter should be able to lay one down.  Even if this player is exaggerating, let’s say it’s 80% of the time he can lay one down if it’s over the plate and 40% if it’s not.  That still sets the success rate at 60%, and that’s if the hitter bunts equally if the pitch is a strike or not. 

What if we’re trying to be realistic and more complex?  How about if it’s a strike, he can lay one down 70% of the time.  If it’s off the plate, he can lay it down 35% of the time.  And let’s say that he’ll attempt the bunt on 90% of the strikes and 30% of the balls.  And let’s say pitchers throw an equal number of balls and strikes. That gives us a success rate of .9*.7*.5 + .3*.35*.5 all divided by .9*.5 + .3*.5 equals 61%.

So, we should be setting our expectation that a great hitter would lay one down and be successful 60% of the time, which would give them a wOBA of .500 to .550, and turn them into Barry Bonds.

That we don’t see this happening is a huge inefficiency among great hitters who are shifted.  These batters, when shifted with no runners on, should bunt, bunt, and then bunt some more. 

Among average to poor hitters, the breakeven point is that much lower.  Whereas the breakeven point for a great hitter is 45% to 50% success rate on bunts, for an average hitter, it’s all the way down to close to 40%, and for a bad hitter, it’s around 35%.  And, we’d expect average hitters to be able to bunt better than great hitters (because of experience), and similarly, the bad hitters may be the best bunters (because they need to learn whatever to survive as hitters).

So, to shift against an average or worse hitter is about the worst defensive alignment you can imagine, and the average or worse batter needs to bunt any chance he gets, when the bases are empty.

Just fantastic work from Poz:

This is a very good example of how regression toward the mean works.  A better one would be to add pro-rate his current 2012 stats for one more week, and then repeat what Poz did starting May 22 to end of year for each season.

And the best way is to do the pro-rating for one week, then May 22 to end of month give him some random player’s stats for May 22-31 (any random player), and then take Pujols’ June 1-onward like Poz did.

As you can see, just going with the Poz approach gets you most of the way there.

Really, a very good way that Poz is doing it.

This is an update from last year, comparing Kershaw since July 26, 2011, to Strasburg’s career:

IP: 140, 140
K: 172, 128 <--
BB: 31, 28
HR: 8, 10
H: 108, 100
R: 43, 31 <--

Strangely enough, all those extra Ks are being counteracted by outs on ball in play for Kershaw.  They are matching on walks, hits, and HR.  Strasburg’s career BABIP is close to league average, which must mean that Kershaw’s BABIP since July 26 is among the league lows.  (Now that I think about it, I should have used Pinto’s Day-by-Daytabase to do this.)

On top of which, with the much lower runs scored, despite matching component numbers, means that Kershaw has favorable splits with men on base and/or Strasburg has unfavorable ones (relatively speaking to their own greatnesses).

It’ll be interesting to see how long Kershaw can continue to keep pace, since Kershaw is winning in the two things that sabermetrics would argue is filled with random variation (BABIP and performance with men on base).

And since we can presume that Strasburg’s “raw stuff” is superior to Kershaw’s, that must mean Kershaw beats him on location and/or sequencing and/or having more good luck go his way in order for him to match him overall.  Compared to Strasburg, Kershaw is the “crafty lefty”!

For those who can’t get enough, Mark has you covered.

NOTE: Thread originally posted Nov 17, 2008, but it’s always relevant.

I get asked every now and then “if you know some guy...” who wants to work for some major league team (baseball or otherwise).  I figure I should create a rolodex, so that when the time comes, I can be a good matchmaker.  So, send me an email (tom~tangotiger~net), type at least the word Rolodex in the subject line, and with numbered answers, to the following questions:
1. your preferred sport(s)
2. where you live
3. where you’d consider relocation (or answer “no")
4. general skillset

This blogger didn’t read the fine print!

As MGL pointed out yesterday, the shift plays that Lawrie (or anyone) makes are excluded from the calculations.  I’d respond directly to the blogger, but AN is blocked at the office.

Other than the over-shifting, then, yes, UZR is a measure of range and positioning.  Indeed, EVERY fielding metric is a measure of range and positioning.  Why is that?  Because other than the over-shifting, we aren’t being told where the fielders are being positioned.  So, while you as the observer can tell where the fielders are roughly positioned, no one is actually recording this information for us to use!  You can’t then fault a metric for not using data that it’s not being given.  Indeed, if we had the data, WE WOULD USE THE DATA.

Any time there’s a new concept introduced, there’s bound to have hiccups.  Sean recently rolled out the new WAR, and the two big problems were quickly identified by the community, and corrected by Sean.  Fangraphs has actually rolled out a ton of features over the years, and I think the biggest glaring issue they had was when they went with my “quick” park factors over the more standard ones (like Patriot has).  David corrected that fairly quickly. 

I should also point out that I love everything David does with Fangraphs, a true steward of sabermetrics.

Anyway, now we come back to the power rankings they are posting over at SI.  While I ignore all such power rankings, and wouldn’t bother commenting on them if they were just squirreled away in some corner of the internet, they get feedback by the saber-followers.  And anytime saber work gets a black eye, I like to whisper something, and hope it has a little impact.

The latest example is the Blue Jays, who are all the way down at 28th.  The rankings are based only on performance in 2012, which while not a great idea, we’re going to at least evaluate the rankings based on the constraint they impose on themselves.

The Blue Jays have scored 16 more runs than they’ve allowed.  But if we break it up by component, we see that the offense and defense has been an almost perfect match.  They’ve hit 44 HR and allowed 45.  The hitters have 14 fewer 2B+3B and 23 fewer BB+HB, but 24 more singles and 6 more reached on errors.  They’ve stolen 5 more and grounded into 8 fewer bases.  When you add it all up, the wOBA for the offense and the defense are identical.  So, a power ranking should at worst call them .500, and at best use their runs totals and make them above-average.

The key component is that the offense has a BABIP of only .264, while the defense has a BABIP of an equally low .259.  In fWAR-speak, the offense BABIP “counts”, while the defense BABIP is ignored.  In its place is only UZR on the fielding, but not on the hitting.  The end result is that any luck associated with the hitting BABIP is included in WAR, but any luck associated with pitching BABIP is excluded in WAR.  We have a mis-match, as I talked about last week, in that offense is treated under one set of assumptions while defense is treated under another set of assumptions.

(There’s also a typo that shows 16 actual wins and a .543 win%.  The Jays were 19-16, so they showed losses, not wins.)

Anyway, going back to the issue at hand, and that’s basically BABIP for offense and defense.  If someone wants to run an even/odd correlation of BABIP for offense and one for defense, I’d like to see if there’s any meaningful difference.  My guess is that the correlation is going to mimic that of the other components (BB, HR, SO).  That is, we’ll get a slightly higher correlation for offense BABIP than for defense BABIP, and they’ll be close enough that they should be treated in the same manner.

Someone can do the work to prove me right, or prove me wrong.  I don’t know the answer, but the above is the bet I’m making.  And if I need to be more specific, I think the difference in r is going to be about .05, and I’d be shocked if the difference would be more than .10.

I get a little kick when a player says anything sabermetric related, and in a good way.

There are stats now like defensive UZR, and all that, but they don’t show the placement of the player. Everything nowadays is so statistical. Like wOBA. We have a wOBA this year –weighted on-base average. It’s pretty cool to look at that, even though we joke about it.