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SABR 301 - PZR - Blueprint (June 17, 2003)

Posted 11:51 a.m., June 19, 2003 (#1) - Mike Emeigh(e-mail)
  Minor point:

We don't know - for sure - that knuckleballers are different. The knuckleballers in Voros's analysis have all been fly ball pitchers as well, and we do know that fly ball pitchers as a group have a lower $H than do ground ball pitchers. So what Voros has posited as a "knuckleballer's advantage" might be nothing more than a reflection of the known fly ball pitchers' advantage.

More comments on the thoughts expressed in the linked thread as/when I get time...

-- MWE

UZR inter-positional linear correlations (July 6, 2003)

Posted 11:36 a.m., July 8, 2003 (#6) - Mike Emeigh(e-mail)
  The interesting pair of observations in #4 are those for Cameron and Griffey. Cameron lost 30 runs of UZR moving from Cincy to Seattle, and Griffey gained 31 runs moving the other way. That suggests the possibility of a park or pitching staff effect that might be worth investigating.

-- MWE

Retrosheet Game Logs - Most Wanted (July 9, 2003)

Posted 6:45 p.m., July 10, 2003 (#3) - Mike Emeigh(e-mail)
  The data sets generated by the Baseball Workshop covering 1992 through the present *might* be available through Retrosheet sometime in the next few years.

-- MWE

DIPS year-to-year correlations, 1972-1992 (August 5, 2003)

Posted 11:40 a.m., August 15, 2003 (#113) - Mike Emeigh(e-mail)
  I do know that the BABIP for GB and FB are more or less similar (about .030 off, with the value lower against OF). But the Levitt numbers show a BABIP of under .100 for IF and over .500 for OF. Do some balls hit into the OF count as GB? Is this what I'm missing?

Yes.

The Levitt numbers are based on the identity of the player who initially fielded the ball. A ground ball through the SS hole will be initially fielded by the left fielder, so in the Levitt study that ball will count in the LF's totals.

-- MWE

Factors that affect the chances of scoring (September 24, 2003)

Posted 9:30 p.m., September 26, 2003 (#13) - Mike Emeigh(e-mail)
  (eraOuts is League ERA by definition.)

I don't think this is quite correct; because there are innings in which runs are scored without an out being recorded (leadoff walkoff HRs and the like), I would think the eraOuts would be slightly less than the league ERA. If you're calculating eraEvent on a per-game basis, then it would be true, but if it's being done on a per-inning basis or a per-pitcher basis it would not be.

-- MWE

Anatomy of a Collapse (October 15, 2003)

Posted 11:10 a.m., October 17, 2003 (#34) - Mike Emeigh(e-mail)
  So either the two data sets are very different or there are a lot of long at bats where the count never reaches 3-2.

It's actually the other way around - there are a lot of 3-2 counts that never reach 10+ pitches. Most 3-2 counts are resolved within a pitch or two.

-- MWE

Cities with best players (October 23, 2003)

Posted 11:54 p.m., October 25, 2003 (#30) - Mike Emeigh(e-mail)
  The Steelers had Mean Joe Greene, Jack Lambert, Jack Ham, LC Greenwood, Lynn Swann, and John Stallworth as top-10 candidates at their positions. Unfortunately, Pittsburgh has never been a pro basketball town. Probably the best player to play pro basketball in Pittsburgh was Connie Hawkins, who was the best player in the league in the inaugural season of the ABA. The Hawk was barred from the NBA as a result of the early-60s gambling scandals, after which he became one of the best playground players around before becoming an ABA star. By the time he finally got to the NBA, his knees were shot (and I think he was heavily into drugs, as well).

-- MWE

Cities with best players (October 23, 2003)

Posted 11:56 p.m., October 25, 2003 (#31) - Mike Emeigh(e-mail)
  I left out Mike Webster of the Steelers, arguably the best center ever.

-- MWE

ALCS Game 7 - MGL on Pedro and Little (November 5, 2003)

Posted 10:04 a.m., November 7, 2003 (#21) - Mike Emeigh(e-mail)
  There are a lot of pitchers who have had years with BABIP over .400 - they just didn't get to pitch very many innings.

Exactly. I'm not sure exactly where the breakpoint is (it's at least .350, and might even be lower than that), but pitchers whose BA/BIP exceeds a certain threshold aren't *hit-unlucky* - they don't have the stuff to get major league hitters out on a consistent basis, they get hammered when the ball is put into play (allowing a high percentage of line drives), and they get pulled from the rotation very quickly most of the time when that happens.

-- MWE

ALCS Game 7 - MGL on Pedro and Little (November 5, 2003)

Posted 10:40 a.m., November 7, 2003 (#22) - Mike Emeigh(e-mail)
  There were 18 pitchers in the NL in 2003 who had a BA/BIP of .400 or higher. The most innings thrown by any one of them was 21, by Blaine Neal. As a group, they threw a total of 120 innings, 6 2/3 IP per pitcher.

The highest BA/BIP against any pitcher with 100 or more innings was .381, for Glendon Rusch. He was the only pitcher with 100 or more innings who was over .350. There were 32 other NL pitchers between .350 and .399, and those 32 threw a total of 584 1/3 innings, about 18 1/3 IP per pitcher.

-- MWE

ALCS Game 7 - MGL on Pedro and Little (November 5, 2003)

Posted 10:49 p.m., November 8, 2003 (#38) - Mike Emeigh(e-mail)
  I maintain that at the MLB level, the true talent level for $H is probably around .330 at worst-case.

Probably not much different than that.

It's possible that a hard hit ball against Pedro-tired would simply become a hit rather than a HR against other types of pitchers. That is, we may *expect* Pedro-tired to have a $H far higher than a schlub, simple because he gets to keep the ball in the park, even tired.

I think that what happens is that, against Pedro-tired, balls that would ordinarily be either ground balls or perhaps soft line drives become solidly-hit line drives. There are not that many line-drive home runs; one usually has to get some lift on a ball in order for it to be a home run. But line drives in play become hits *far* more often than either GBIP or FBIP. It'd be interesting to see the G/F/L breakdown against Pedro-tired vs. Pedro-normal.

-- MWE

ALCS Game 7 - MGL on Pedro and Little (November 5, 2003)

Posted 2:25 p.m., November 9, 2003 (#41) - Mike Emeigh(e-mail)
  I'm saying that .330 is about as bad as bad should get, that .400 is really quite inconceivable from a true talent persective (but obviously quite expected from a sample of 63 BIP).

If Pedro-tired gives up more line drives as a percentage of BIP than either Pedro-normal or a league-average hurler, then it's conceivable that Pedro-tired could have a $H higher than .330.

National League pitchers, in 2003, had a $H of .290. 49% of their BIP were ground balls ($H on GB was .232), 32% were fly balls ($H on FB was .122), 19% were line drives ($H on line drives was .719). If you take an NL pitcher with 450 BIP, an average distribution of BIP, and an average rate of converting the various types of BIP into outs, convert 10% of the BIP against him into LD while keeping his GB/FB ratio on the other BIP constant and not changing the rate of conversion of BIP into outs, his $H would go from .290 to .343 just because he was being hit harder.

I think that for any tired pitcher, this is exactly what happens - more LD in play, and probably harder-hit ground balls as well, all of which are less likely to be converted into outs. One needs to look at the BIP distribution of Pedro-tired's BIP; my guess is that the LDIP% for Pedro-tired is significantly higher that Pedro-normal or most ML pitchers.

-- MWE

ALCS Game 7 - MGL on Pedro and Little (November 5, 2003)

Posted 10:42 p.m., November 9, 2003 (#42) - Mike Emeigh(e-mail)
  I took a look at the pitching lines of all NL starting pitchers in games in which they went four innings or fewer (in most of which you can safely assume that they were getting hammered), and compared them to the starting lines for that same group of pitchers in games where they went more than four innings. There were 138 NL pitchers who had at least one start of four innings or fewer, a total of 331 starts in which the average IP was just under 3 per start. In the games in which they went four innings or fewer, the starters had a $H of .433; in the games in which they went more than four innings (2010 starts, averaging just under 6 1/3 IP per start), the same starters had a $H of .278. The starters allowed line drives on 26.5% of their balls in play when they didn't make it past the fourth, as compared to 18.2% of balls in play when they did. Finally, the defense converted fewer of *all* of the ball types in play when the starters weren't getting through four innings. When the starters lasted more than four innings, the $H values were .224 on GBIP, .113 on FBIP, and .699 on LDIP. When the starters lasted four or fewer innings, the $H values were .347 on GBIP, .205 on FBIP, and .838 on LDIP. If the balls had been fielded behind the starters at the same rate in the short starts as they were in the longer starts, the $H would have been .316.

Looking at Glendon Rusch's 19 starts, in which he posted a $H of .393 and an LDIP% of 25.1%: His $H on GBIP was .301, on FBIP was .179, and on LDIP was .818 - also all well above the league average.

What appears to be happening here is that, the harder that the pitching is being hit, the more difficult it is for the fielders to field *any* ball put into play behind him. That's not really a surprising conclusion, at least to me, and it suggests that pitchers who are being smoked - for whatever reason - could easily have a very high $H.

-- MWE

ALCS Game 7 - MGL on Pedro and Little (November 5, 2003)

Posted 8:32 a.m., November 10, 2003 (#45) - Mike Emeigh(e-mail)
  So, by selectively sampling those games where Torre took him out after 2, you are not giving Clemens a chance to show his true stuff.

True - although Clemens would probably get left in longer to prove himself than, say, Jeff Weaver.

But the *true stuff* isn't really the issue here, from my vantage point. I was trying to find a set of games where it was likely that the pitcher in question was being hit hard, to see what the impact on $H was likely to be - and what I see is that, when pitchers are being hammered, not only does their hard-hit ball percentage go up, but any given BIP in play is more likely to be a hit, whether it's a ground ball, fly ball, or line drive. So while his *true stuff* might be in the .320-.330 range, his actual $H is likely to be quite a bit higher than that - not because of bad luck, but because the balls he's giving up are harder to field.

-- MWE

Offensive Performance, Omitted Variables, and the Value of Speed in Baseball (November 6, 2003)

Posted 1:02 p.m., November 7, 2003 (#6) - Mike Emeigh(e-mail)
  I recall (though can't cite) work (I think by MGL) that SB attempts are randomly distributed throughout game situations.

The last time I looked at this, it was my recollection that (a) more SB attempts occurred early in games rather than late in games and (b) it was usually the team that was ahead that attempted the SB. There are more data points available now, and maybe it's time to take another look.

-- MWE

METS SEARCHING FOR STATS ANALYST (November 7, 2003)

Posted 9:50 a.m., November 7, 2003 (#1) - Mike Emeigh(e-mail)
  So did you and MGL send in your applications yet??

-- MWE

METS SEARCHING FOR STATS ANALYST (November 7, 2003)

Posted 10:52 a.m., November 7, 2003 (#5) - Mike Emeigh(e-mail)
  MLB would have to pay me ARod money to get me to move to NY :)

-- MWE

David Pinto and fielding (November 10, 2003)

Posted 1:21 p.m., November 15, 2003 (#18) - Mike Emeigh(e-mail)
  With the parameters I've used so far, the A's pitchers look like they set up easy chances for their fielders.

I wonder how much of this is due to the immense amount of foul territory at the NAC. When I was parsing the PBP data, it seemed to me that there were a *lot* of foulouts in Oakland this year.

-- MWE

David Pinto and fielding (November 10, 2003)

Posted 10:15 p.m., November 16, 2003 (#25) - Mike Emeigh(e-mail)
  I think Mike Emeigh wrote in his "Jeter" series that lefties face more righties, and righties of course have a longer distance to run to beat out a throw on a ground ball, so you'd sorta expect the *average* out-conversion on ground balls hit against lefties *not* controlled for batter handedness to be higher.

In the NL, in 2003, it actually worked the other way around. LHP got a lower percentage of outs on GBIP than their right-handed counterparts, and LHB were retired more often on GBIP than RHB. There are several competing factors at work here:

1. RHB have to run further than LHB when they put a ball into play, and thus should (all else being equal) be retired more often on a GBIP than a LHB, all else being equal.
2. RHB tend to hit more balls to the left side of the infield than do LHB, and GBIP hit to the left side are turned into outs less often than balls hit to the right side.
3. Balls hit the other way are turned into outs less often than balls that are pulled.

In 2003, the middle factor was the largest one. RHB hit four times as many GB to the left side of the infield as did their LH counterparts, so that even though they were retired more often on those balls (76.2% of the time, to 73.0% for LHB), the sheer numbers of BIP outweighed the higher conversion rate on those balls. As a result, 52% of GBIP against NL LHP were hit to the left side of the infield, vs only 46% of GBIP vs NL RHP. On balance, the chances for NL infielders were easier when a right-hander was on the mound.

I don't know the extent to which MGL makes these adjustments, but it seems to me to be pretty clear looking at the PBP data that there are distinct differences in expected results when a LHB hits a ball into a zone vs when a RHB hits a ball into the same zone. Batter handedness seems to have a greater impact than pitcher handedness on the results from BIP, and while pitcher handedness is a generally useful proxy for batter handedness in the platoon era, I think we need to remember that it is just a proxy.

-- MWE

MGL - Component Regression Values (PDF) (January 8, 2004)

Posted 10:24 p.m., January 9, 2004 (#14) - Mike Emeigh(e-mail)
  But the pitcher apparently has more influence than the batter over doubles and triples.

Most doubles and triples are hit in the air. From the PBP evidence, the pitcher has more impact as to whether a particular BIP is a ground ball or a fly ball than does the batter. Thus the pitcher should influence 2B+3B more, and singles (which are much closer to having the same distribution as the overall ratio of fly balls to ground balls) less.

-- MWE

Futility Infielder - 2003 DIPS (January 27, 2004)

Posted 2:28 p.m., February 5, 2004 (#44) - Mike Emeigh(e-mail)
  One possible explanation for the decline in correlation over 162 IP could be that the range of variation among pitchers ERA'S is a lot less.

Right. Correlations over a narrow range of performance tend to mask true performance differences; you need to broaden the comparision enough to realistically cover as much of the actual range of performance as you can, without making it so broad as to allow players with small sample sizes to skew the results. An approach based on residuals would help us expand the sample size here; we have an article that has been submitted for the Visitor's Dugout which I'm hoping that Dan S will publish in a day or two.

I prefer to use balls in play as the basis for my comparisons, rather than innings pitched. It's a personal preference, but since we're trying to look at a measure of skill on balls in play it makes more sense to me to evaluate the group based on that, rather than on IP which is only indirectly related to BIP.

-- MWE

The genius of Paul DePodesta (February 4, 2004)

Posted 2:09 p.m., February 9, 2004 (#22) - Mike Emeigh(e-mail)
  I wholeheartedly believe in a combination of scouting and obejective analysis, however, to some extent objective analysis ALWAYS trumps scouting.

A player with a .250 level of *real* talent derived from past performance performs at a .270 level this year; real improvement or just a fluke? The objective analysts would suggest that his likely level of performance would be around .252, or .255, or something else closer to .250 than .270 next year. But the scout watches game tape of the hitter last year and this, and picks up something he's doing differently, whether it's laying off the hard slider out of the strike zone, driving pitches the other way rather than trying to pull everything, opening up his stance and hitting line drives rather than weak popups and shallow flyouts. Or maybe he's seeing that there's nothing really different, and that he just had a couple of extra good weeks where those little loopers were dropping in.

Objective analysis shouldn't trump scouting, nor should scouting trump objective analysis. They complement and supplement each other. Objective analysis is derived from aggregate data about players with similar performance levels, and provides a *likely performance* estimate for the player; scouting gives you information that can tell you whether a *particular* player has a good chance of being different from the aggregate. The GM needs to evaluate both sets of information.

-- MWE

The genius of Paul DePodesta (February 4, 2004)

Posted 5:40 p.m., February 10, 2004 (#29) - Mike Emeigh(e-mail) (homepage)
  When I say that objective analysis "trumps" scouting what I mean is that while objective analysis has it's limitations (even in a perfect form), it makes no mistakes!

This assertion is dead wrong. I'm not aware of *any* objective analysis technique that we use that is *not* based on the assumption that individual performance can be inferred from aggregate performance, and the paper I've linked in the homepage discusses some of the problems with that inference. Walt Davis has discussed this at some length in past threads.

-- MWE

Clutch Hits - Tango's 11 points to think about --- to understand why we regress towards the mean (February 12, 2004)

Posted 8:01 p.m., February 23, 2004 (#16) - Mike Emeigh(e-mail)
  If the distribution of true talent IS normal, then we know for a fact that the true correlation, "r", between any 2 set of independent samples drawn from that population tells us EXACTLY how much to regress to the mean.

The distribution of true talent across the entire population of baseball-playing individuals may be normal, but in professional baseball it's some form of a truncated normal (there's a minimum level of sustainable performance below which a player cannot succeed in pro baseball). At the major league level, the distribution of true talent is probably closer to a gamma distribution than anything else; it resembles the tail of a normal distribution (this is supported by AED's observation that there are many more mediocre players than great players, and the general observation that there are no players who are as far below the mean as the best players are above the mean). For that reason, I'm not convinced that the normal distribution is an appropriate model of the prior distribution of true talent at the major league level.

-- MWE

Baseball Prospectus - : Evaluating Defense (March 1, 2004)

Posted 8:48 a.m., March 3, 2004 (#33) - Mike Emeigh(e-mail)
  and I was credited for it, making me I think the only BBBA contributer ever mentioned in BP.

Not quite. Michael Wolverton mentioned something I did on "bequeathed runners" in BPros a few years ago.

-- MWE