Who is the best base stealer in
MLB history? Most “who da best?”
questions don’t provoke answers as
seemingly obvious as this one. But…
I’ll post a quickie with my take
that the obvious answer isn’t the
right one. Maybe in a week or two.
For now, y’all can think about it.
Who is the best base stealer in
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I have never seen such a motion in all my life. The man is amazing at what he does.
Tim Lincecum is a God at pitching.
Industrial fasteners are made worldwide; the major producer for the USA except for the military is Taiwan. Nuts from small sizes ¼” to about 1” are made on cold header machines. These machines literally punch a hole through the wire (steel). The quality is very consistent. The variation from one respect maker to another does not affect functionality.
However the start of the wire is not as consistent as the drawn wire is after the beginning of the coil.Nor is the end of the coil. The machine takes some time to start up and produce 99.5 % quality nuts, the other .04% are rejected by the machine perhaps the balance get passed QC.
The humidity in the factory, the age of the oil, the age of the machine, the speed of the machine, the type of steel wire, the skill of the mechanics who set the machine up and the shape of the dies used all are factors that reflect upon the finished product. (Note the nuts next process is threading).
What is the point of this? Whitey Ford, Billy Pierce, Sandy Koufax. And Don Newcomb, Johnny Antonelli and Roger Clemens.
Humans are not developed to produce the same results on a consistent basis as are machines. Many of the same factors that affect machines affect ball players and emotions. Yes emotions play a key role in how anyone performs and no amount of charts or graphs can dissude me of that.
We all have known peers who may have done great in law school but never could pass the bar. Doctor stories where one doctor consistently performs better under certain conditions than other doctors. The type that handles the tough cases.
Co-workers who when the tough situation arises runs and hides and kids in soccer who run from kicking the ball at goal.
I understand that the current vogue is to dispute “clutch.” And while it may not prove out over an entire career any long time student of the game knows that certain players at certain times are more apt to produce in pressure situations.
Perhaps the effort to prove statically that “clutch” does not exist is simply the inability to prove that it does.
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One of the great missing adjustments to even our most advanced stats is the adjustment for the quality of the opposition. A given stat line put up in the AL East means something rather different from the NL Central. Compounding the problem is that the adjustment needs to be iterative; once you have calculated the opposition quality for everyone and adjusted all the stats, you have to paste the adjusted stats over the originals, recalculate the opposition quality, and so on, again and again until the values stabilize. I do that with my adjusted standings at SoSH, but no one, AFAIK, has ever done that with individual hitting and pitching lines.
How exactly would you do this? You could just take opponent overall quality, which would in fact be your best adjustment for value. But an interesting and in some ways better alternative would be to include handedness. For instance, each LHB would get an adjustment based on the numbers versus LHB of the LHP pitchers he faced, and a separate adjustment for the RHP.
(As an aside, if you’re studying whether some hitters have persistent quality-of-opposition splits, you have to do it this way. AFAIK, no one ever has — the few studies showing no such persistence have used opponent ERA, which adds a huge amount of noise. Jon Lieber in his prime was a great pitcher vs. RHB and a lousy one vs. LHB; why count him as average vs. everyone?)
This notion of adjusting for opposition quality by handedness immediately suggests a value adjustment I’ve never heard mentioned. As a rule, elite LHB face a better quality of LHP than do average LHB. Not only are they more likely to not get benched against a C. C. Sabathia, they are hugely more likely to face a nasty LHR in the late innings. The differences among LHB are mitigated (slightly? more than that? I don’t think anyone knows) by this. The quality-of-opposition adjustment I just outlined would put the proper distance between the Alex Coras and Adrian Gonzalezes of the world. And this would be hugely desirable and interesting — when you’re assessing talent, that is.
Now, the funny thing is this: when assessing value, this can probably be safely overlooked. It’s built into the way the game is played now that this will happen. That we are underestimating how much better Gonzalez is than Cora is pretty much negated by the better pitching Gonzalez faces as a result.
However, there is probably a small but very interesting class of exceptions to this rule. You would expect there to be some hitters who get too much or not enough respect from opposing managers, and thus face more or fewer LHR than they ought to based on their own platoon splits. You would adjust for this by finding the correlative relationship between LHB platoon splits and the percentage of time they are at the platoon disadvantage, and then calculate the expected number and quality of LHR they faced versus the actual. The players with the biggest differences, in both directions, would make for very interesting lists. It’s possible that some of the “noise” in platoon splits is actual signal; as LHB establish reputation, managers begin to match them up with their lefty-killers. But reputations lag behind reality, both at the start and end of careers (David Ortiz may now be seeing tougher LHP than other LHB of his quality).
(As an aside, I know that Trot Nixon’s career path of splits vs. LHB was made completely nonsensical by the genius of Jimy Williams, who benched Nixon against even the easiest LH starters but never pinch hit for him against even the toughest LHR. So he was probably leading the league in toughest quality of LHP faced despite being nowhere near the top of the list for overall LHB quality. That’s the sort of guy it would be neat to identify and adjust.)
Two things we all know (or is that “know”?) about pitching:
— There’s no such thing as a pitcher with the skill of pitching out of jams. If a pitcher has much better numbers with runners on (or RISP) than with the bases empty, that’s luck and it will normalize, sooner than later.
— There’s no such thing as a pitcher with the skill of getting lots of easy outs on balls in play. While there are real differences in BABIP skill (with knuckleballers leading the pack), if a pitcher has a low enough BABIP, that’s luck and it will normalize, sooner than later.
What, then, do we make of a pitcher who consistently pitches out of jams by improving his rate of easy outs on balls in play?
I think we all knew that Dice-K has both a crazy bases empty / runners on split (and hence strand rate) and a crazy BABIP. What I didn’t know until I ran the numbers is that the crazy BABIP happens only with runners on:
The improvement in strike zone command seems modest, but it’s crucial: even without the change in BABIP it would be enough to reduce his component ERA with runners on from 6.02 to 4.58. But that guy would still be lousy; he needs the low BABIP with runners on to succeed. And the improvement in strike zone command with runners on shows that the notion that he nibbles more to avoid harder contact is just wrong; when runners get on he comes after hitters more — and with more success.
It’s not merely that he’s doing it with smoke and mirrors, it’s like the smoke is in front of the mirrors and nowhere else.
One might begin to think that he simply pitches better out of the stretch. But then what do we make of this already legendary split, even with its eenie-weenie-teenie-tiny sample size?
Or this more obscure and even more puzzling one?
Let’s just combine the last two for easy scanning:
Pure science fiction.
I definitely intend to attack this question with pitch/fx data over the winter. Right now, I’m open to any possibility.
Taken from: Sons of Sam Horn
I have friends who are complete non-baseball fans, but big-time math-science geeks, i.e., they’re folks who understand the normal distribution of natural phenomena and can appreciate exceptions. I have entertained them greatly by reading the leader boards from those two years in descending order. Especially 2000, where the 2-5 finisher in most stat categories were tightly clustered.
Let’s play “what’s the next term in this sequence?”!
ERA: 4.17, 4.14, 4.13, 4.12, 4.11, 3.88, 3.79, 3.79, 3.70 . . .
How many people had 1.74?
WHIP: 11.52, 11.48, 11.18, 10.79 …. 7.22
Opposition OBP: .306, .303, .298, .291 … .213.
Opposition SLG: .392, .384, .374, .371 … .259.
This (as well as 1999, of course) is an essentially superhuman performance. If there had been a league as much better than MLB as MLB was to AAA, and then another league with the same performance differential, Pedro would have still been the best pitcher of that league.