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Tuesday, November 23, 2004

The Thinking Fan's Guide to Baseball

I mentioned in a previous post that I had picked up a copy of Leonard Koppett's The Thinking Fan's Guide to Baseball: Revised and Updated last week. Koppett originally wrote the book in 1966 and subsequently updated it in 1991 and 2001. After his death in 2003 the book was once again updated and reissued with the help of Pat Gillick, former GM of the Toronto Blue Jays, Baltimore Orioles, and Seattle Mariners.

Koppett is generally credited with being one of the most statistically savvy media members and routinely used statistics in his columns that stretched from 1948 through 1975 while writing for The New York Times and the Oakland Tribune among others. He was also a member of SABR and appreciated much of what SABR does (SABR paid him tribute at the convention in Denver in 2003). The book moves through the various aspects of the game beginning with the activity on the field (hitting, pitching, fielding, baserunning, managing, umpires etc.) and then moving on the behind the scenes view that Koppett knew so well including the media, road trips, scouts, scoring, and the business aspects of the game. He concludes with a section titled "The Whole Ballgame" which is a series of essays on other aspects of the game including expansion, changes to the ball, spring training, and other more or less random topics.

What immediately grabbed my interest of course was chapter 15, "Statistics". Here Koppett's attitude towards statistics can be summed up in the following quote:

"Even with all these things in mind [adjustments such as contextualizing and understanding relationships between statistics], however, the fiend for statistics can be led to totally incorrect conclusions, because there is one more fundamental flaw in the way standard statistics are kept. They record how much, but not when, and in the winning and losing of ball games, the when is all-important."

Given his reputation as a bit of an innovator with statistics this surprised me somewhat. He then goes on to give several examples of particular game scenarios where the "when" is all important. Of course, what his analysis misses (although he acknowledges it a bit later in the chapter in a different context) is that statistics are by their very nature aggregate and because of this can only be interpreted when applied to the larger context in which they were created. In other words, he's absolutely correct that official baseball statistics merely record what happened and not when and are therefore an abstraction. But what they assume is that the "whens" even out across the hundreds or thousands of "whats" for a season or career. By doing so they become meaningful as a measure of a player's true ability. This assumption and the fact that studies have shown that clutch hitting is largely a myth, mean that when properly analyzed statistics can be a very useful too indeed. In other words, Koppett vastly over estimates the variability in the performance of players in different situations.

Because of this "fundamental flaw" Koppett goes on to argue that baseball statistics cannot be used to predict, they cannot be used to prove a point but only convince in an argument, and they cannot be used to compare. The reasons he gives for his third point are not controversial. He points out the different contexts in which statistics are counted including ballparks and the problem of using very small sample sizes to make decisions. He does not, however, talk about how those problems can be overcome using park factors and confidence intervals for example.

Interestingly, he then moves into a discussion of how statistics are used or interpreted by players and management. In particular, in regards to management he says:

"Also, to the professional baseball man, many implicit qualities go with the phrase '.300 hitter' or '.250 hitter' or '20-game winner,' qualities not taken into account by the fan who uses the same terms."

While I certainly agree that front office personnel takes into account things that are not reflective in the statistics alone, I'll take issue with the very categories used here as examples. Humans categorize reality in order to better understand it and the fact that the categories here include batting average and pitcher victories, two of the least effective ways to analyze the contributions of hitters and pitchers, belies the truth that the "professional baseball man" often didn't (or doesn't) understand what really goes into winning baseball games over the long haul.

And ultimately this is the problem of perspective I talked about in a recent post. By being so close to the game, the sportswriter, the scout, the manager, or the general manager can miss the bigger picture. How else can you square the fact that prior to the last few years winning strategies such as controlling the strike zone and not giving up outs were undervalued and are still controversial? You can see this in Koppett himself when throughout the book he uses batting average almost exclusively when discussing the merits of various hitters.

Finally, Koppett lays it all on the line when summing up the chapter.

"My own view is that the SABR mania (and I speak as a member of the organization) has gone out of control. The Bill James approach, of cloaking totally subjective views (to which he is entirely entitled) in some sort of asserted 'statistical evidence,' is divorced from reality. The game simply isn't played that way, and his judgments are no more nore less accurate than anyone else's. The truly mathematical manipulations - regressive analysis and all that - do not, in my opinion, add to what is already known on the working level. They are perfectly valid and even brilliant as material for conversation...But they don't tell a professional - a player, manager, or scout - anything he doesn't know anyhow by observation. And when his observation conflicts with the printout, he - and I - will trust his observation every time..."

He then goes on to give two reasons why. First, he see statistics as too blunt an instrument to tell you all of what happened which in part reflects his belief in the importance of clutch hitting discussed above, and second, that the "reality of the game" is too complex to capture statistically. He notes that "Most professionals feel the same way."

Perhaps I've been immersed in sabermetrics too long but I have hard time getting my mind around that paragraph. Taking just part of the next to last sentence, "they don't tell a professional...anything he doesn't know anyhow by observation" reveals how flawed his reasoning here is. I wonder how many players, managers, or scouts could have told you how many runs a good offensive player typically contributes in a season before the birth of sabermetrics? My guess is that the range would have been from 20 to 200. I wonder how many professionals before the popularization of OPS would have told you that a player with a .309/.321/.443 line was actually a better player than one with a .269/.389/.492? I wonder how many professionals would have told you that it is generally ok to bunt with a runner on first and nobody out (it's not - ever)? These are all examples of information not obtainable at the working level.

And further to the point, baseball, being the game of the long season with thousands of discrete events (over 193,000 in 2003) does not lend itself to making decisions based only on observation. Human observation is simply not up to the task. This point was nicely illustrated by a story that Paul DePodesta, now GM of the Dodgers but formerly the assistant to Billy Beane in Oakland, told in a speech he gave and which Rob Neyer was nice enough to send me the transcript of:

"Our manager now, Ken Macha, loves our second baseman Mark Ellis. Mark Ellis is a good player, he plays hard, and he plays every day. But he didn't have a very good offensive year this year, yet Ken Macha kept putting him in the lineup every day. It even got to the point late in the year where he started hitting him leadoff. We finally went to Ken and said, 'We like Ellis too, but he probably doesn't need to be hitting leadoff, and getting all these at-bats.' And his comment to us was, 'Ellis is a clutch hitter.'

I thought, 'OK, clutch is one of those subjective terms I'm not wild about,' so I went back and I looked at the numbers, and at that time during the year Ellis was hitting about .163 with runners in scoring position and two outs, which I think is a clutch situation. But I didn't say anything, we kept it under wraps. When we were getting close to the playoffs, though, we began talking about the way the lineup should work against the Red Sox, and at one point Macha was talking about putting Ellis leadoff. Finally Billy Beane, our General Manager, just couldn't take it any more, and he said, 'Ellis is hitting .163 with runners in scoring position and two outs. He's not clutch.' And immediately, Macha said, 'But he hit that game-winning home run off of Jason Johnson.'

'OK, that's right, but if you want to play that game I'm going to come up with a lot more instances where he failed than instances you're going to come up in which he succeeded.'"

DePodesta's point was that observation isn't always the best way of understanding something, especially where large numbers and percentages are concerned.

So while I like this book overall, the writing is of course very entertaining and the anecdotes very interesting, the perspective is one that many sabermetrically knowledgeable fans will sometimes bristle at.

On a slightly different subject I found chapter 28, "The Ball's the Same, the Bat's the Same - Or Are They?" interesting as well. Here Koppett includes a nice history of changes in the styles of bats used and how the ball has changed over time. I was a bit surprised when after discussing the offensive outburst of 1987 which he indicates may have been accounted for by weather changes, he then says "In 1993, however, it was undeniable that a truly livelier ball appeared..."

This surprised me because it doesn't appear to me that livelier balls are usually given as the culprit. I've written previously on the power surge and tests that physicist Alan Nathan did on balls from 1974 and 2004 that show no differences in their "bounciness". In addition, Koppett makes no mention of the fact that 1993 corresponds with the first year of the Colorado Rockies which certainly had an effect (I don't have the home/road breakdowns but as an example of the effect in 1993 1040 runs were scored in Denver to only 685 in Rockies road games). Further, it's evident from looking at the general trend in homeruns that the number of homeruns per game had been increasing starting in the early 1980s with a brief decline in the 1988-1989 period. Although the jump is more pronounced since 1993 the trend actually continued through 2001 before declining again.

This doesn't appear to me as the product of a livelier ball but rather the accumulation of advantages for hitters that might include increased strength, the full effect of aluminum bats, and the absence of intimidation among others. Koppett also notes, but doesn't differentiate, the possibility that the balls were not livelier starting in 1993 but instead had lower seams which makes breaking balls less effective (he assumes that seams could be lower only by winding the ball tighter which I doubt). Another trend that's obvious from watching any game from the 1970s or 80s on ESPN Classic is the frequency with which balls are thrown out of play today versus 20 or 30 years ago. This is particularly noticeable on pitches in the dirt but also of course when a new ball is introduced each half inning as the outfielder or infielder throws the previous one into the stands. Balls that stay in play longer are easier to grip and therefore easier to throw good pitches with.


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