Searching for Mr. Clutch

I'm sure many of you read the recent (SI calls it "provocative") piece by Bill James on Sports Illustrated.com titled "Mr. Clutch: Big Papi, Chipper, Pujols come through when it counts". I was reminded of the piece when I found my copy of the 2008 Hardball Times Annual patiently waiting in the mailbox over the weekend, where of course, the article is also printed.

Now, I'm certainly among those who view James as a pioneer and even a person who had an impact on my development. In fact, I submitted a snippet about how the Abstracts impacted me at just the right time that was printed in the book How Bill James Changed Our View of the Game of Baseball which said:

"When I was a teenager, I loved baseball and started dabbling in analysis of its numbers - even searching for information on platoon splits at the library at Cooperstown on a summer trip with my family. For me, reading my first Abstract in the spring of 1984, and particularly its introductory essays "Inside-Out Perspective" and "Logic and Methods in Baseball Analysis" was validation that there was an expanded way to view the game. From that point forward, my view of the game changed to one where the primary question I asked myself was not 'what happened?' but 'why did it happen?' That mindset, based on logical reasoning and inculcated by reading Bill James, has served me well in areas that transcend baseball."

So I'm always interested in what James has to say and am pre-disposed, I would guess, to be less critical than others. But when I read this piece by James I was a little disappointed for two primary reasons.

First and foremost, the article seems to promote the idea that after the now famous study titled "Do Clutch Hitters Exist?" published in the 1977 Baseball Research Journal by Dick Cramer, that little to no work has been done on the subject of clutch hitting and that what has been done has had an in-grained bias. Quite to the contrary, the topic has been the subject of almost continual debate with a variety of studies published over the years as documented on Cyril Morong's fine site. And more recently there have been several very good analyses done as I discussed in the introduction to my Schrodinger's Bat column of March 1, 2007.

The controversy was never more in evidence than in the spring and summer of 2005 when, in the wake of the Bill James piece "Underestimating the Fog" (warning: pdf) published in Volume 33 of the Baseball Research Journal, there was plenty of point-counterpoint in the analysis community.

BP's own James Click got in on the act with two interesting articles in the fall of 2005, where he used Keith Woolner's Win Expectancy (WX) framework combined with first VORP and then Marginal Lineup Value (MLV) to generate measures he termed PrjWINS and Clutch. He concluded that the correlation for Clutch from year to year, and even over halves of a career, indicated that the measure was "nearly completely random."

The issue was again resurrected after the publication of Tom M. Tango, Mitchel Lichtman, and Andrew Dolphin's The Book in early 2006, wherein the authors noted that there is indeed a small player-to-player variation in clutch skill, and measured that one in six players increase--and a comparable number of players decrease--their on base percentage by eight points or more when faced with pressure situations (defined as any situation in which the batter's team is trailing by one, two, or three runs in the eighth inning or later). The spread decreases to six points when using their weighted on base average (wOBA) metric, and when regressed to the mean the wOBA skill maxes out at around two points.

Around the same time Nate Silver (with his chapter "Is David Ortiz a Clutch Hitter?" in Baseball Between the Numbers, get your copy in paperback today) used a similar approach to Click with WX and a modified version of MLV, but also included Leverage to create a measure also termed Clutch. After crunching the numbers, he found that players with higher walk and lower strikeout rates do perform slightly better than would otherwise be expected. Overall, he concluded, clutch hitting accounts for something on the order of two percent of what it takes to produce wins at the plate.

So when James implies that the discussion has "stalled out" and that no good work (at least no work worth mentioning) has been undertaken by the sabermetric community, I'm kind of at a loss since not only have there been a variety of studies, James himself participated in the debate as recently as 2005. The tone of his article is misleading at best and intellectually dishonest at worst. Although I can understand simplifying things for the more general audience of Sports Illustrated, publishing the same version in THT's annual (where the audience is well aware of everything I've written here) makes the piece seem oddly out of place among essays by Dave Studeman, Tom Tango, John Walsh, John Beamer, and Mitchell Lichtman among others.

Secondly, as concerns the methodology James has added the opposition, standings, and calendar to the attributes accounted for already in Win Expectancy (and used by Silver in the BBTN chapter for example) to come up with the criteria used to select just what are clutch plate appearances. As far as I know that in and of itself is novel. It’s just too bad he doesn’t give us any insight into how he’s weighting these factors since without that information others in the community are not able to provide much insight into the technique. Still, I'm prepared to give him a pass on that one since he may be refining things as he moves forward.

But what I think we can assume from the article is that he's saying that by adding the three new factors, the seasonal or career correlations in these situations will have a better chance of being statistically significant thereby allowing one to measure a clutch skill beyond the small effect Silver and others have found. I’m more than a little skeptical on several grounds and wonder why he didn’t at least publish some preliminary results aside from the comment "there may be a decentralization under pressure, the good hitters getting better and the weaker hitters struggling to stay where they are."

My skepticism includes the fact that adding additional criteria reduces the sample size (as evidenced by Mike Sweeney's 29 "clutch" AB in 2005) which will make it more difficult to detect a clutch skill if it exists, and my intuition that says that opposition, standings, and schedule would seem to have the least influence of all the factors (major league baseball is fraught with intense selection pressure and so I find it hard to believe that the difference between Tampa Bay playing Texas in April and San Diego playing Los Angeles in September will be discernible). But of course on this second point I don't really know and that's the problem. Instead of doing a little analysis or waiting until he had something at least preliminary to say, he cherry-picked seven players and showed their clutch batting records for seasons since 2002. While that makes for a nice sub-head on SI.com and gets the article mentioned everywhere, it tells us...absolutely nothing. But worse, by showing these seven players only, the implication left to the general reader is that this method will show that clutch hitting is a skill with a larger magnitude than most people think. That's getting the cart ahead of the horse to say the least.

As James has said in the past:

"It is a characteristic of statisticians that they see the game by the thousands. It's a way of looking at the biggest possible picture of the game. Backing away from it a great distance and trying to see patterns that aren't apparent close up."

Maybe someday he'll get to the patterns but given the following quote from the article, I won't be holding my breath.

As to whether these data prove that David is a clutch hitter ... I ain't going there. This discussion has been messed up for 30 years because we got our shoulders way out in front of our shoelaces. From now on, I'm holding back.

And finally, there's the smaller question of why be restricted to six years of data? I realize BIS has complete data going back that far but given the parameters he mentioned in the piece I see no reason why he couldn't run his system on all of the Retrosheet data in a day. He mentions that they'll get to it but one wonders what's holding them back.

In the end though, there is another way to look at this piece and that's through the prism of James upcoming book The Bill James Gold Mine 2008 due to be published in February. Under this view the piece was little more than an ad - just not a very good one. Because I've always loved James' writing and have learned immense amounts from his historical commentary and analysis, I will of course be purchasing a copy (and in fact have pre-ordered it). I just hope the actual content far exceeds the trailer.

A Pair of GM Interviews

One of the interviewers I always make a priority to read, and I'm not just saying this because he's a colleague, is David Laurila at Baseball Prospectus. Whether it's catching up with a former player, talking to a scouting director, or even a general manager as he did last week with the Pirates new GM Neal Huntington (pictured right), Laurila is a must read.

What makes Laurila's interviews so good, and the skill most important for any interviewer I would guess, is that I find he asks the questions I would ask only in a way that never seems to offend the interviewee and cause them to shut down. And of course he has a great way of transitioning from one topic to another making the interviews pleasant to read.

That said, here's a just a brief excerpt from the Huntington interview that illustrates both skills as Laurila both asks about what can be a hot button topic in statistical analysis and does so by piggy-backing off of a series of questions on the Pirates new manager John Russell.

DL: In a recent interview you talked about how the front office will be utilizing statistical analysis to evaluate talent. To what extent do you expect John Russell and his coaching staff to utilize that type of information?

NH: From an advance scouting standpoint, to try to gain an advantage, it will be vital. John and I have talked about that extensively, and we expect him, along with his hitting and pitching coaches, to use that information objectively and creatively. During the course of the season I expect that John and I will on the phone frequently, if not daily. We’ll talk about a multitude of issues, with the use of statistics being one of them.

DL: In the same interview, you said, “we’ll rely on the more traditional objective evaluations like OPS, WHIP, Runs Created, Component ERA…” Have we reached the point where those metrics can now be considered traditional?

NH: I think so. Cleveland--led by Chris Antonetti and Mike Chernoff--does an excellent job of objectively evaluating players, and we hope to build upon that model in Pittsburgh. We look at “traditional” metrics as well as more advanced metrics any time we are breaking players down. As a matter of fact, as an organization I want us to go to the next level of metrics. I hope that we can get to the point where we’ve built an environment to create and utilize them in-house. We have a lot of work to do, but we have an outstanding statistical consultant on board, and we’re creating a computer system to help us not only store, track and access data but to also analyze the data that has been collected.

DL: Can you touch briefly on some of the more advanced metrics you alluded to, and the role you expect them to play in your evaluation and decision-making process?

NH: In terms of advanced metrics, we are trying to utilize equivalences and adjusted performance numbers as well as percentile ranks to deepen the objective evaluation of the player. An interesting development has been the objective evaluation of defensive performance and the overall impact on a player’s value. The overall role will be as a supplement to our subjective evaluations to provide us with the best information to increase our probability for success.

So here we learn that Huntington is being aggressive in pursuing analysis to the extent of commissioning the construction of a new system for the organization, that it incorporates advanced metrics, and that at least in part it deals with advanced scouting data, an area that you don't always hear about in terms of analysis where the focus is often more on player projections and translating performance across levels of competition. And so it would appear that Pittsburgh is entering the realm of the more sophisticated teams in terms of quantitative analysis. The rest of the interview is enlightening as well with thoughts on player development and a couple specific prospects.

In a second GM interview, Baseball Digest Daily's Joe Hamrahi hits the high points in an interview with the Dodger's Ned Colletti. As you might imagine the focus was on the cadre of young players the Dodgers have and what their opportunities might be in 2007 given the unfortunate decision in the 2006 off-season to load up on veterans at their expense. When asked what he might have done differently in 2007 given the chance Colletti said...

That’s a great question. I’m sure there are (some things). One thing I know I wouldn’t have done differently. I’m glad I didn’t trade 3 or 4 kids at the deadline. I think that decision will prove to be valuable for a very long time. People ask about the young players all the time. Teams need young players.

Well, yes, one would think that was obvious and of course by the trading deadline there were few people encouraging the Dodgers to give up Andre Ethier, James Loney, Matt Kemp, Andy LaRoche, or Chad Billingsley for a rent-a-player. Earlier in the interview Colletti seemed very excited about the finish of Loney in particular but there too it's not as if there weren't solid reasons to believe Loney would be a contributor long before that as indicated by his PECOTA projection and his .385/.442/.603 line before the break in limited time.

But now it appears as if either Kemp or Ethier will lose time in the wake of the signing of Andruw Jones since one doubts that Juan Pierre and his large contract will be the victim. Sometimes injuries have a way of making these kinds of problems moot as the season approaches but it'll be interesting to see what Colletti's next move is.

Finally, in relation to the signing of Jones some have wondered what the difference between Jones and Pierre might be in terms of opponent's baserunning given that Jones has among the best and Pierre the worst reputations for their throwing arms.

In an essay titled "Expanding the Cannon: Quantifying the Impact of Outfield Throwing Arms" for this year's Baseball Prospectus annual, I take a look at exactly that question and the results for Jones and Pierre confirm the common wisdom. In 2007 Jones was worth +4 runs over average in terms of throwing runners out and stopping them from advancing. The combination of his positioning and arm meant that runners advanced just 31% of the time, tops among centerfielders with 100 or more adjusted games played at the position. On the contrary Pierre found himself at the other end of the spectrum and rated a -7 for his arm as opponents advanced 47% of the time - easily the worst at his position. Suffice it to say that 2007 was not an aberration as the essay also includes 2005 and 2006 leaderboards and totals for the same time period. By simply replacing Pierre with Jones (and not considering how much Pierre might play in left where his arm will hurt the team far less), the Dodgers stand to gain around a win only considering outfield throwing. When you add the larger offensive impact, you're talking about a significant difference...

See, Math Does Pay

I hadn't seen this before but fellow SABR members posted a link to this nice video from MLB.com of Sig Mejdal, the quantitative data analyst for the St. Louis Cardinals. This is especially relevant given the recent changes in the Cardinals front office which some people don't see as positive.

The Nats and VORP

I was tipped to this article by Will Carroll. The fascinating paragraph follows:

The Nationals, too, are deploying more tools and ideas. Over the weekend, a group of the club's statistical analysts -- "I'll call them our 'Mod Squad,' " Kasten said -- made a presentation to scouts detailing trends in the draft, using such modern stats as "VORP" (Value Over Replacement Player), the kind of analysis that can make old-school scouts shudder.

It would be interesting to see just how they're applying VORP to amateur players... In any case this is just the kind of thing I was thinking about when I wrote the following in an interview with Baseball Digest Daily last year.

It seems to me that one of the ways in which a team could gain an advantage is to more tightly marry their statistical analysis to their scouting departments in a kind of Hegelian Synthesis. It's just my sense of things (being an outsider after all), but I would guess that most scouts still have little understanding of some of the core sabermetric principles and conclusions. For example, in a roundtable discussion Alan Schwarz had with Eddie Bane (Angels) and Gary Hughes (Cubs) published in 2005, it was clear that Hughes had never even heard of Defense Independent Pitching Statistics (DIPS). While I know that everybody can't know everything, and I'm not throwing Hughes under the bus, I can't imagine that it wouldn't help any team to have their staffs educated on the big picture items like this.

Selection Pressure

In my column tomorrow on Baseball Prospectus I chronicle the decline of Angel Berroa in an article titled "In the Arms of an Angel". After summarizing Berroa's tenure since coming into the Royals system in the Johnny Damon trade, I do a little analysis to document players who, like Berroa, had three straight years of decreasing productivity while in their prime and retaining their regular jobs. I did this by looking at OPS park adjusted and normalized by league for players between the ages of 24 and 30 while garnering 400 or more plate appearances.

It turns out that has happened 96 times in baseball history for 85 different players. Those 96 instances are shown in the table below sorted by primary position of the player in their first year (the second year would be the first year of the decline) and NOPS/PF in that first year. You'll then see their NOPS/PF for each subsequent season and the average decline per year calculated using the geometric mean. The list is sorted by position since a shortstop has a greater probability of keeping his job even in an offensive decline given that his defense is more important and therefore a good defender will see less selection pressure applied to his offense.

                                            NOPS/PF
Name               Yr1POS  Yr1Age   Year1   Year1   Year2   Year3   Year4    Avg
Jimmie Foxx        1B          24    1932     160     157     150     147   2.6%
Lou Gehrig         1B          27    1930     158     155     147     145   2.4%
Norm Cash          1B          26    1961     157     123     122     115   4.7%
Jimmie Foxx        1B          25    1933     157     150     147     133   4.4%
Jimmie Foxx        1B          26    1934     150     147     133     119   5.9%
Andres Galarraga   1B          27    1988     130     111     104      88  11.2%
Keith Hernandez    1B          25    1979     130     128     124     116   3.1%
Eddie Murray       1B          27    1983     129     128     126     118   2.0%
Gregg Jefferies    1B          25    1993     124     119     106     101   5.9%
Sean Casey         1B          24    1999     123     113     110      90   7.3%
Gregg Jefferies    1B          26    1994     119     106     101      98   5.3%
Willie Upshaw      1B          26    1983     118     110     106      95   6.3%
Steve Balboni      1B          27    1984     112     107      99      91   6.5%
Willie Upshaw      1B          27    1984     110     106      95      93   4.3%
--------------------------------------------------------------------------------
Eddie Collins      2B          27    1914     145     137     124     118   6.3%
Larry Doyle        2B          24    1911     133     121     111     110   4.1%
Jimmy Williams     2B          24    1901     125     122     109     107   3.6%
Jimmy Williams     2B          25    1902     122     109     107     102   4.5%
Frankie Frisch     2B          24    1923     121     120     114     107   2.9%
Bret Boone         2B          25    1994     116     104      86      85   5.9%
Carlos Baerga      2B          24    1993     113     110     105      88   5.8%
Tony Cuccinello    2B          27    1935     112     111     110     104   1.6%
Damion Easley      2B          27    1997     110     104      99      98   3.0%
Marty McManus      2B          24    1924     110     105     102     101   2.3%
Billy Goodman      2B          27    1953     108     101     100      98   2.3%
Dick Egan          2B          25    1909     107      94      92      89   4.4%
--------------------------------------------------------------------------------
Eddie Mathews      3B          27    1959     142     139     132     124   4.0%
Heinie Zimmerman   3B          25    1912     140     129     116     107   8.5%
Richie Hebner      3B          24    1972     132     120     119     103   4.7%
Tommy Leach        3B          24    1902     121     115     103     101   4.6%
Richie Hebner      3B          25    1973     120     119     103     101   2.8%
Max Alvis          3B          25    1963     115     110     103     101   3.8%
Kevin Seitzer      3B          25    1987     114     110     102     101   2.9%
Bill Mueller       3B          26    1997     109     107     104      97   3.3%
Bobby Byrne        3B          27    1912     109      98      96      84   6.4%
Eddie Foster       3B          27    1914     108     104     102      93   4.0%
Bill Coughlin      3B          26    1905     102      95      92      83   6.0%
--------------------------------------------------------------------------------
Mike Scioscia      C           26    1985     121     104      98      97   4.4%
Gus Triandos       C           27    1958     114     109     107     104   2.8%
Manny Sanguillen   C           27    1971     113     108     104     101   3.6%
Rick Ferrell       C           25    1931     109     107     106     101   2.0%
Sammy White        C           24    1953     102      99      96      82   5.1%
--------------------------------------------------------------------------------
Ken Griffey        CF          27    1997     135     127     121     118   4.1%
Raul Mondesi       CF          27    1998     114     108     107     101   3.0%
Brian Hunter       CF          25    1996      94      90      82      73   7.5%
--------------------------------------------------------------------------------
Bobby Higginson    LF          25    1996     125     118     108      94   8.5%
--------------------------------------------------------------------------------
Ty Cobb            OF          25    1912     156     154     152     147   1.8%
Ty Cobb            OF          26    1913     154     152     147     144   2.1%
Kevin Mitchell     OF          27    1989     153     131     126     108   9.2%
Tris Speaker       OF          24    1912     149     147     145     130   2.7%
Albert Belle       OF          27    1994     148     142     130     109   8.2%
Bobby Murcer       OF          25    1971     147     141     118     105   9.0%
Joe Medwick        OF          25    1937     147     125     119     118   3.9%
Tito Francona      OF          25    1959     142     118     116     103   6.8%
Duke Snider        OF          27    1954     142     140     133     126   3.3%
Edd Roush          OF          24    1917     136     131     128     125   2.7%
Tony Oliva         OF          25    1964     131     126     123     119   3.1%
Mike Greenwell     OF          24    1988     130     112     109     104   5.5%
Jim Rice           OF          25    1978     130     128     112     109   3.7%
Steve Kemp         OF          24    1979     125     116     115     111   2.8%
Kirk Gibson        OF          27    1984     123     122     117     116   1.4%
Tom Tresh          OF          27    1965     123     115     106      98   7.3%
Jim Russell        OF          25    1944     123     115     110      99   6.6%
Al Smith           OF          27    1955     121     110     104     103   3.6%
Greasy Neale       OF          25    1917     120     110     100      95   7.2%
Carl Furillo       OF          27    1949     119     110     107      94   6.3%
Amos Otis          OF          25    1972     118     115     110     101   4.5%
Tito Francona      OF          26    1960     118     116     103      93   5.7%
Phil Bradley       OF          26    1985     117     114     109     108   2.2%
Steve Kemp         OF          25    1980     116     115     111     101   3.0%
Paul Blair         OF          25    1969     116     111     104      96   5.9%
Gus Bell           OF          26    1955     116     115     101      93   4.4%
Jack Smith         OF          26    1921     115     113     106      97   4.5%
Jackie Brandt      OF          27    1961     114     111     103      97   4.8%
Luis Polonia       OF          26    1990     112     101      94      86   8.3%
Mule Haas          OF          27    1931     111     102     101      93   4.0%
Marty Cordova      OF          25    1995     110     106      96      92   5.2%
Marquis Grissom    OF          25    1992     110     107     103      93   4.6%
Don Mueller        OF          27    1954     108      98      86      83   7.3%
Joe Vosmik         OF          27    1937     107     105      96      94   3.2%
Devon White        OF          24    1987     101      97      93      90   3.7%
--------------------------------------------------------------------------------
Raul Mondesi       RF          26    1997     127     114     108     107   3.7%
--------------------------------------------------------------------------------
Alex Rodriguez     SS          24    2000     136     134     127     125   2.3%
Robin Yount        SS          26    1982     135     127     113     107   7.0%
Alex Rodriguez     SS          25    2001     134     127     125     117   3.7%
Barry Larkin       SS          27    1991     126     120     116     107   5.0%
Ray Chapman        SS          26    1917     119     111     109     108   2.2%
Travis Fryman      SS          24    1993     117     103      98      97   3.9%
Freddy Parent      SS          27    1903     117     115      95      93   4.0%
Jeff Blauser       SS          26    1992     116     115      94      89   4.4%
Jay Bell           SS          27    1993     115     106      99      96   5.4%
Jody Reed          SS          26    1989     106     104      99      87   4.8%
Chick Galloway     SS          25    1922     106      94      87      79   9.2%
Rafael Ramirez     SS          24    1982      99      98      87      85   3.0%
Angel Berroa       SS          25    2003      98      92      90      73   6.3%
Bud Harrelson      SS          25    1969      93      92      91      86   1.9%

What you'll notice of course is that all but four of these declines started with the hitter having an NOPS/PF greater than 100. As you might expect, players can retain their jobs through performance declines typically only if that decline starts from a fairly high plateau. Overall, the average of the first year is 123 and the fourth 104. You'll find Berroa second from the bottom as only Bud Harrelson began from a lower NOPS and retained his job through three years of decline. But you'll also notice that Berroa's decline averaged 6.3% while Harrelson was fairly steady at 1.9%.

The bottom line is that Berroa's ability to keep his job was pretty much unprecedented given his offensive performance and even more incredible given that under John Dewan's Plus/Minus system and the data shared by The Hardball Times he fielded 84 fewer balls than would have been expected.

Major League players are under a great deal of selection pressure akin to what organisms feel ala natural selection. Berroa didn't feel it nearly as acutely as he probably should have.

Protectionism and Its Effects

Interesting article by Ken Rosenthal on lineup protection. One quote that caught my eye was this...

Bill James, godfather of the statistical movement, called such protection [the idea that the Phillies wanted to sign Alfonso Soriano to protect Ryan Howard] "overrated" and told the Philadelphia Inquirer that he would project Howard's numbers to be the same whether Albert Pujols or Corey Patterson were hitting behind him.

He then goes on to say:

With all due respect to James, Pujols and Patterson are not equal, never will be equal and do not merit equal consideration. "Do not let this guy beat you," is a mantra at pitchers' meetings. Previous matchups, left-right concerns and ballpark effects all figure into a pitcher's approach. Still, according to statistical research, the difference in the Phillies' No. 5 spot between relatively comparable batters such as Burrell and Jimmy Rollins would be marginal.

Of course James was not saying that Pujols and Patterson are equals, only that their effect on the performance of Howard would essentially be equal.

Rosenthal goes on to cite J.C. Bradbury's study on protection where he concludes that contrary to a protection effect, a good hitter actually has a negative effect on the previous hitter. Bradbury calls this the "effort externality", which occurs when a good on-deck batter causes the pitcher to use more effort to pitch to the current batter, thereby lowering the hit probability of the current batter. In addition, this means the pitcher will throw more pitches in the strike zone which decreases the walk rate. Bradbury sums up the study in this way.

Using MLB play-by-play data from 1984–1992, we are able to control for many factors, including the quality of the on-deck hitter, that influence the probability that a player reaches base. We find that the quality of the on-deck hitter negatively impacts the preceding hitter, which is consistent with pitchers using extra effort to minimize the damage of the batter who follows. This finding contradicts traditional baseball thinking that a good on-deck hitter protects the hitter who precedes him in the batting order. Though we find that a spillover does exist, its magnitude is very small. It takes a very large difference in the quality of the on-deck hitter to have only a minor impact on the current batter.

New-Age Stats

Just a follow-up to the Murray Chass piece posted a couple days ago by Carl Bialik and Jason Frey...

VORP is Absurd

Was tipped off to this little ditty from Murray Chass of the New York Times.

Statistics mongers promoting VORP and other new-age baseball statistics.

I receive a daily e-mail message from Baseball Prospectus, an electronic publication filled with articles and information about statistics, mostly statistics that only stats mongers can love.

To me, VORP epitomized the new-age nonsense. For the longest time, I had no idea what VORP meant and didn’t care enough to go to any great lengths to find out. I asked some colleagues whose work I respect, and they didn’t know what it meant either.

Finally, not long ago, I came across VORP spelled out. It stands for value over replacement player. How thrilling. How absurd. Value over replacement player. Don’t ask what it means. I don’t know.

I suppose that if stats mongers want to sit at their computers and play with these things all day long, that's their prerogative. But their attempt to introduce these new-age statistics into the game threatens to undermine most fans' enjoyment of baseball and the human factor therein.

People play baseball. Numbers don’t.

Talk about a lack of intellectual curiosity. Whither Chass' colleague at the Times Alan Schwarz?

To me the idea that attempting to appreciate the game on a different level somehow diminishes the enjoyment one receives from the other levels is what is truly absurd. People aren't that one-dimensional and one could certainly argue that quantitative analysis in many respects enhances our appreciation for the human element of the game by helping us see in a different way the range of human abilities that come into play and how those abilities interact with, among other things, random chance.

While one would hope that new sources of information and new ways of knowing would extend in some way to everyone, this piece reinforces the notion articulated by Max Planck and quoted in Thomas Kuhn's The Structure of Scientific Revolutions:

"a new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generations grows up that is familiar with it."

Mid-Course Corrections

So here is something I don't understand (I know that will come as no surprise to many). In researching the prevalence of in-season managerial changes for teams that made it to the postseason I noticed that for playoff teams and other teams alike there is an interesting curve that looks as follows:



In-season managerial changes hovered at around 10% of teams in the first three decades (1900s-1920s) of the 20th century before increasing to around 15% over the next three decades (1930s-1950s) and then exploding to over 20% for the next three decades (1960s-1980s). Since then the frequency of changes has dropped again to around 13%.

Why the upward trend over much of baseball history? I thought at first it might be because it seemed to work but outside of the 1932 and 1938 Cubs who changed managers midstream and caught fire on their way to the pennant, there aren't similar cases until the summer of 1978 when Yankees manager Billy Martin resigned and was replaced by Bob Lemon. And then why the downward trend over the past two decades? Is a new found realization that the influence of managers is circumscribed responsible for the downward trend? Is it the more substantial financial investment in managers by front offices that makes them less willing to pull the plug during the season? Is there something else going on? It's a mystery to me.

Incidentally, over the course of history 15.6% of teams changes manager during the season - 11.9% for teams that reach the post season. I would have thought the latter percentage would have been less but then again it is sometimes the case that a team going nowhere installs a new manager (the 1989 Blue Jays who fired Jimmy Williams after a 12-24 start and hired Cito Gaston who led them to a 77-49 finish comes to mind) and takes off for whatever reason.

Numb3rs

I assume some of you caught last Friday night's episode wherein a 25 year-old high school dropout and fantasy league champion sabermetrician detects steroid use. Well. I've tried to be a fan of the show but haven't been able and this episode was no exception. I did write about it and The Science Channel program "Baseball's Secret Formula" this week on BP so you can check that out.

As far Numb3rs was concerned, did appreciate our hero the math professor noting that sabermetrics "really is a powerful form of analysis," and, in a voice-over companied by some nice graphics, he continued:

The physical nature of the game involves chance. So the difference between a hit and out could be millimeters or milliseconds. So when you have athletic situations involving chance repeated over and over again, a statistical analysis can isolate and reveal human performance.

Nice. Much of the rest wasn't however.

Information Revolution

My column this week on BP discusses aspects of the information revolution in the availabiltiy of data about the game. The most recent addition to that is the pitch velocities, break, and release point data used by the enhanced version of MLB.com's Gameday application. Taking a cue from a friend I wrote a little .NET Framework application to do basic pitch charting showing location, release velocity, and pitch outcome, and ran the app against Kenny Roger's Game 2 performance. For those interested, this is what the basic XML data looks like and those who've read Baseball Hacks will of course find this familiar.