The Seven-Game World Series

The Astros and Dodgers have just concluded a seven-game Word Series, the 37th to date. Courtesy of Baseball-Reference.com, here are the scores of the deciding games of every seven-game Series:

1909 – Pittsburgh (NL) 8 – Detroit (AL) 0

1912 – Boston (AL) 3 – New York (NL) 2 (10 innings)

1924 – Washington (AL) 4 – New York (NL) 3 (12 innings)

1925 – Pittsburgh (NL) 9 – Washington (AL) 7

1926 – St. Louis (NL) 3 – New York (AL) 2

1931 – St. Louis (NL) 4 – Philadelphia (AL) 2

1934 – St. Louis (NL) 11 – Detroit (AL) 0

1940 – Cincinnati (NL) 2 – Detroit (AL) 1

1945 – Detroit (AL) 9 – Chicago (NL) 3

1946 – St. Louis (NL) 4 – Boston (AL) 3

1947 – New York (AL) 5 – Brooklyn (NL) 2

1955 – Brooklyn (NL) 2 – New York (AL) 0

1956 – New York (AL) 9 – Brooklyn (NL) 0

1957 – Milwaukee (NL) 5 – New York (AL) 0

1958 – New York (AL) 6 – Milwaukee (NL) 2

1960 – Pittsburgh (NL) 10 – New York (AL) 9 (decided by Bill Mazeroski’s home run in the bottom of the 9th)

1964 – St. Louis (NL) 7 – New York (AL) 5

1965 – Los Angeles (NL) 2 – Minnesota (AL) 0

1967 – St. Louis (NL) 7 – Boston (AL) 2

1968 – Detroit (AL) 4 – St. Louis (NL) 1

1971 – Pittsburgh (NL) 2 – Baltimore (AL) 1

1972 – Oakland (AL) 3 – Cincinnati (NL) 2

1973 – Oakland (AL) 5 – New York (NL) 2

1975 – Cincinnati (AL) 4 – Boston (AL) 3

1979 – Pittsburgh (NL) 4 – Baltimore (AL) 1

1982 – St. Louis (NL) 6 – Milwaukee (AL) 3

1985 – Kansas City (AL) 11 – St. Louis (NL) 0

1986 – New York (NL) 8 – Boston (AL) 5

1987 – Minnesota (AL) 4 – St. Louis (NL) 2

1991 – Minnesota (AL) 1 – Atlanta (NL) 0 (10 innings)

1997 – Florida (NL) 3 – Cleveland (AL) 2 (11 innings)

2001 – Arizona (NL) 3 – New York (AL) 2 (decided in the bottom of the 9th)

2002 – Anaheim (AL) 4 – San Francisco (NL) 1

2011 – St. Louis (NL) 6 – Texas (AL) 2

2014 – San Francisco (NL) 3 – Kansas City (AL) 2

2016 – Chicago (NL) 8 – Cleveland (AL) 7 (10 innings)

2017 – Houston (AL) 5 – Los Angeles (AL) 1

Summary statistics:

34 percent (37) of 109 Series have gone to the limit of seven games (another four Series were in a best-of-nine format, but none went to nine games)

20 of the 37 Series were decided by 1 or 2 runs

14 of those Series decided by 1 run (7 times in extra innings or the winning team’s last at-bat)

19 of the 37 Series were won by the team that was behind after five games

six of the 37 Series were won by the team that was behind after four games.

Does the World Series deliver high drama? It depends on your definition of high drama. If a seven-game series is high drama, the World Series has delivered about one-third of the time. If high drama means a seven-game series where the final game was decided by only 1 run in extra innings or the winning team’s final at-bat, the World Series has delivered only 6 percent of the time. There are other ways to define high drama — take your pick.

Competitiveness in Major-League Baseball (III)

UPDATED 10/04/17

I first looked at this 10 years ago. I took a second look 3 years ago. This is an updated version of the 3-year-old post, which draws on the 10-year-old post.

Yesterday marked the final regular-season games of the 2014 season of major league baseball (MLB), In observance of that event, I’m shifting from politics to competitiveness in MLB. What follows is merely trivia and speculation. If you like baseball, you might enjoy it. If you don’t like baseball, I hope that you don’t think there’s a better team sport. There isn’t one.

Here’s how I compute competitiveness for each league and each season:

INDEX OF COMPETITIVENESS = AVEDEV/AVERAGE; where

AVEDEV = the average of the absolute value of deviations from the average number of games won by a league’s teams in a given season, and

AVERAGE =  the average number of games won by a league’s teams in a given season.

For example, if the average number of wins is 81, and the average of the absolute value of deviations from 81 is 8, the index of competitiveness is 0.1 (rounded to the nearest 0.1). If the average number of wins is 81 and the average of the absolute value of deviations from 81 is 16, the index of competitiveness is 0.2.  The lower the number, the more competitive the league.

With some smoothing, here’s how the numbers look over the long haul:


Based on statistics for the National League and American League compiled at Baseball-Reference.com.

The National League grew steadily more competitive from 1940 to 1987, and has slipped only a bit since then. The American League’s sharp climb began in 1951, peaked in 1989, slipped until 2006, and has since risen to the NL’s level. In any event, there’s no doubt that both leagues are — and in recent decades have been — more competitive than they were in the early to middle decades of the 20th century. Why?

My hypothesis: integration compounded by expansion, with an admixture of free agency and limits on the size of rosters.

Let’s start with integration. The rising competitiveness of the NL after 1940 might have been a temporary thing, but it continued when NL teams (led by the Brooklyn Dodgers) began to integrate by adding Jackie Robinson in 1947. The Cleveland Indians of the AL followed suit, by adding Larry Doby later in the same season. By the late 1950s, all major league teams (then 16) had integrated, though the NL seems to have integrated faster. The more rapid integration of the NL could explain its earlier ascent to competitiveness. Integration was followed in short order by expansion: The AL began to expand in 1961 and the NL began to expand in 1962.

How did expansion and integration combine to make the leagues more competitive? Several years ago, I opined:

[G]iven the additional competition for talent [following] expansion, teams [became] more willing to recruit players from among the black and Hispanic populations of the U.S. and Latin America. That is to say, teams [came] to draw more heavily on sources of talent that they had (to a large extent) neglected before expansion.

Further, free agency, which began in the mid-1970s,

made baseball more competitive by enabling less successful teams to attract high-quality players by offering them more money than other, more successful, teams. Money can, in some (many?) cases, compensate a player for the loss of psychic satisfaction of playing on a team that, on its record, is likely to be successful.

Finally,

[t]he competitive ramifications of expansion and free agency [are] reinforced by the limited size of team rosters (e.g., each team may carry only 25 players until September 1). No matter how much money an owner has, the limit on the size of his team’s roster constrains his ability to sign all (even a small fraction) of the best players.

It’s not an elegant hypothesis, but it’s my own (as far as I know). I offer it for discussion.

UPDATE

Another way of looking at the degree of competitiveness is to look at the percentage of teams in W-L brackets. I chose these seven: .700+, .600-.699, .500-.599, .400-.499, .300-.399, and <.300. The following graphs give season-by-season percentages for the two leagues:

Here’s how to interpret the graphs, taking the right-hand bar (2017) in the American League graph as an example:

  • No team had a W-L record of .700 or better.
  • About 13 percent (2 teams) had records of .600-.699; the same percentage, of course, had records of .600 or better because there were no teams in the top bracket.
  • Only one-third (5 teams) had records of .500 or better, including one-fifth (3 teams) with records of .500-.599.
  • Fully 93 percent of teams (14) had records of .400 or better, including 9 teams with records of .400-.499.
  • One team (7 percent) had a record of .300-.399.
  • No teams went below .300.

If your idea of competitiveness is balance — with half the teams at .500 or better — you will be glad to see that in a majority of years half the teams have had records of .500 or better. However, the National League has failed to meet that standard in most seasons since 1983. The American League, by contrast, met or exceeded that standard in every season from 2000 through 2016, before decisively breaking the streak in 2017.

Below are the same two graphs, overlaid with annual indices of competitiveness. (Reminder: lower numbers = greater competitiveness.)

I prefer the index of competitiveness, which integrates the rather jumbled impression made by the bar graphs. What does it all mean? I’ve offered my thoughts. Please add yours.

A Baseball Note: The 2017 Astros vs. the 1951 Dodgers

If you were following baseball in 1951 (as I was), you’ll remember how that season’s Brooklyn Dodgers blew a big lead, wound up tied with the New York Giants at the end of the regular season, and lost a 3-game playoff to the Giants on Bobby Thomson’s “shot heard ’round the world” in the bottom of the 9th inning of the final playoff game.

On August 11, 1951, the Dodgers took a doubleheader from the Boston Braves and gained their largest lead over the Giants — 13 games. The Dodgers at that point had a W-L record of 70-36 (.660), and would top out at .667 two games later. But their W-L record for the rest of the regular season was only .522. So the Giants caught them and went on to win what is arguably the most dramatic playoff in the history of professional sports.

The 2017 Astros peaked earlier than the 1951 Dodgers, attaining a season-high W-L record of .682 on July 5, and leading the second-place team in the AL West by 18 games on July 28. The Astros’ lead has dropped to 12 games, and the team’s W-L record since the July 5 peak is only .438.

The Los Angeles Angels might be this year’s version of the 1951 Giants. The Angels have come from 19 games behind the Astros on July 28, to trail by 12. In that span, the Angels have gone 11-4 (.733).

Hold onto your hats.

The American League’s Greatest Hitters: III

This post supersedes “The American League’s Greatest Hitters: Part II” and “The American League’s Greatest Hitters.” Here, I build on “Bigger, Stronger, and Faster — but Not Quicker?” which assesses the long-term trend (or lack thereof) in batting skill.

Specifically, I derived ballpark factors (BF) for each AL team for each season from 1901 through 2016. For example, the fabled New York Yankees of 1927 hit 1.03 times as well at home as on the road. Given a schedule evenly divided between home and road games, this means that batting averages for the Yankees of 1927 were inflated by 1.015 relative to batting averages for players on other teams.

The BA of a 1927 Yankee — as adjusted by the method described in “Bigger, Stronger…” — should therefore be multiplied by a BF of 0.985 (1/1.015) to obtain that Yankee’s “true” BA for that season. (This is a player-season-specific adjustment, in addition the long-term trend adjustment applied in “Bigger, Stronger…,” which captures a gradual and general decline in home-park advantage.)

I made those adjustments for 147 players who had at least 5,000 plate appearances in the AL and an official batting average (BA) of at least .285 in those plate appearances. Here are the adjustments, plotted against the middle year of each player’s AL career:

batting-average-analysis-top-147-al-hitters-unadjusted-graph

When all is said and done, there are only 43 qualifying players with an adjusted career BA of .300 or higher:

batting-average-analysis-greatest-hitters-top-43-table

Here’s a graph of the relationship between adjusted career BA and middle-of-career year:

batting-average-analysis-top-43-al-hitters-graph

The curved line approximates the trend, which is downward until about the mid-1970s, then slightly upward. But there’s a lot of variation around that trend, and one player — Ty Cobb at .360 — clearly stands alone as the dominant AL hitter of all time.

Michael Schell, in Baseball’s All-Time Best Hitters, ranks Cobb second behind Tony Gwynn, who spent his career (1982-2001) in the National League (NL), and much closer to Rod Carew, who played only in the AL (1967-1985). Schell’s adjusted BA for Cobb is .340, as opposed to .332 for Carew, an advantage of .008 for Cobb. I have Cobb at .360 and Carew at .338, an advantage of .022 for Cobb. The difference in our relative assessments of Cobb and Carew is typical; Schell’s analysis is biased (intentionally or not) toward recent and contemporary players and against players of the pre-World War II era.

Here’s how Schell’s adjusted BAs stack up against mine, for 32 leading hitters rated by both of us:

batting-average-analysis-schell-vs-pandp

Schell’s bias toward recent and contemporary players is most evident in his standard-deviation (SD) adjustment:

In his book Full House, Stephen Jay Gould, an evolutionary biologist [who imported his ideological biases into his work]…. Gould imagines [emphasis added] that there is a “wall” of human ability. The best players at the turn of the [20th] century may have been close to the “wall,” many of their peers were not. Over time, progressively better hitters replace the weakest hitters. As a result, the best current hitters do not stand out as much from their peers.

Gould and I believe that the reduction in the standard deviation [of BA within a season] demonstrates that there has been an improvement in the overall quality of major league baseball today compared to nineteenth-century and early twentieth-century play. [pp. 94-95]

Thus Schell’s SD adjustment, which slashes the BA of the better hitters of the early part of the 20th century because the SDs of that era were higher than the SDs after World War II. The SD adjustment is seriously flawed for several reasons:

1. There may be a “wall” of human ability, or it may truly be imaginary. Even if there is such a wall, we have no idea how close Ty Cobb, Tony Gwynn, and other great hitters have been to it. That is to say, there’s no a priori reason (contra Schell’s implicit assumption) that Cobb couldn’t have been closer to the wall than Gwynn.

2. It can’t be assumed that reaction time — an important component of human ability, and certainly of hitting ability — has improved with time. In fact, there’s a plausible hypothesis to the contrary, which is stated in “Bigger, Stronger…” and examined there, albeit inconclusively.

3. Schell’s discussion of relative hitting skill implies, wrongly, that one player’s higher BA comes at the expense of other players. Not so. BA is a measure of the ability of a hitter to hit safely given the quality of pitching and other conditions (examined in detail in “Bigger, Stronger…”). It may be the case that weaker hitters were gradually replaced by better ones, but that doesn’t detract from the achievements of the better hitter, like Ty Cobb, who racked up his hits at the expense of opposing pitchers, not other batters.

4. Schell asserts that early AL hitters were inferior to their NL counterparts, thus further justifying an SD adjustment that is especially punitive toward early AL hitters (e.g., Cobb). However, early AL hitters were demonstrably inferior to their NL counterparts only in the first two years of the AL’s existence, and well before the arrival of Cobb, Joe Jackson, Tris Speaker, Harry Heilmann, Babe Ruth, George Sisler, Lou Gehrig, and other AL greats of the pre-World War II era. Thus:

batting-average-analysis-single-season-change-in-ba-following-league-switch

There seems to have been a bit of backsliding between 1905 and 1910, but the sample size for those years is too small to be meaningful. On the other hand, after 1910, hitters enjoyed no clear advantage by moving from NL to AL (or vice versa). The data for 1903 through 1940, taken altogether, suggest parity between the two leagues during that span.

One more bit of admittedly sketchy evidence:

  • Cobb hit as well as Heilmann during Cobb’s final nine seasons as a regular player (1919-1927), which span includes the years in which the younger Heilmann won batting titles with average of .394, .403, 398, and .393.
  • In that same span, Heilmann outhit Ruth, who was the same age as Heilmann.
  • Ruth kept pace with the younger Gehrig during 1925-1932.
  • In 1936-1938, Gehrig kept pace with the younger Joe DiMaggio, even though Gehrig’s BA dropped markedly in 1938 with the onset of the disease that was to kill him.
  • The DiMaggio of 1938-1941 was the equal of the younger Ted Williams, even though the final year of the span saw Williams hit .406.
  • Williams’s final three years as a regular, 1956-1958, overlapped some of the prime seasons of Mickey Mantle, who was 13 years Williams’s junior. Williams easily outhit Mantle during those years, and claimed two batting titles to Mantle’s one.

I see nothing in the preceding recitation to suggest that the great hitters of the years 1901-1940 were inferior to the great hitters of the post-WWII era. In fact, it points in the opposite direction. This might be taken as indirect confirmation of the hypothesis that reaction times have slowed. Or it might have something to do with the emergence of football and basketball as “serious” professional sports after WWII, an emergence that could well have led potentially great hitters to forsake baseball for another sport. Yet another possibility is that post-war prosperity and educational opportunities drew some potentially great hitters into non-athletic trades and professions. In other words, unlike Schell, I remain open to the possibility that there may have been a real, if slight, decline in hitting talent after WWII — a decline that was gradually reversed because of the eventual effectiveness of integration (especially of Latin American players) and the explosion of salaries with the onset of free agency.

Finally, in “Bigger, Stronger…” I account for the cross-temporal variation in BA by applying a general algorithm and then accounting for 14 discrete factors, including the ones applied by Schell. As a result, I practically eliminate the effects of the peculiar conditions that cause BA to be inflated in some eras relative to other eras. (See figure 7 of “Bigger, Stronger…” and the accompanying discussion.) Even after taking all of those factors into account, Cobb still stands out as the best AL hitter of all time — by a wide margin.

And given Cobb’s statistical dominance over his contemporaries in the NL, he still stands as the greatest hitter in the history of the major leagues.

Back to Baseball

In “Does Velocity Matter?” I diagnosed the factors that account for defensive success or failure, as measured by runs allowed per nine innings of play. There’s a long list of significant variables: hits, home runs, walks, errors, wild pitches, hit batsmen, and pitchers’ ages. (Follow the link for the whole story.)

What about offensive success or failure? It turns out that it depends on fewer key variables, though there is a distinct difference between the “dead ball” era of 1901-1919 and the subsequent years of 1920-2015. Drawing on statistics available at Baseball-Reference.com. I developed several regression equations and found three of particular interest:

  • Equation 1 covers the entire span from 1901 through 2015. It’s fairly good for 1920-2015, but poor for 1901-1919.
  • Equation 2 covers 1920-2015, and is better than Equation 1 for those years. I also used it for backcast scoring in 1901-1919 — and it’s worse than equation 1.
  • Equation 5 gives the best results for 1901-1919. I also used it to forecast scoring in 1920-2015, and it’s terrible for those years.

This graph shows the accuracy of each equation:

Estimation errors as a percentage of runs scored

Unsurprising conclusion: Offense was a much different thing in 1901-1919 than in subsequent years. And it was a simpler thing. Here’s Equation 5, for 1901-1919:

RS9 = -5.94 + BA(29.39) + E9(0.96) + BB9(0.27)

Where 9 stands for “per 9 innings” and
RS = runs scored
BA = batting average
E9 = errors committed
BB = walks

The adjusted r-squared of the equation is 0.971; the f-value is 2.19E-12 (a very small probability that the equation arises from chance). The p-values of the constant and the first two explanatory variables are well below 0.001; the p-value of the third explanatory variable is 0.01.

In short, the name of the offensive game in 1901-1919 was getting on base. Not so the game in subsequent years. Here’s Equation 2, for 1920-2015:

RS9 = -4.47 + BA(25.81) + XBH(0.82) + BB9(0.30) + SB9(-0.21) + SH9(-0.13)

Where 9, RS, BA, and BB are defined as above and
XBH = extra-base hits
SB = stolen bases
SH = sacrifice hits (i.e., sacrifice bunts)

The adjusted r-squared of the equation is 0.974; the f-value is 4.73E-71 (an exceedingly small probability that the equation arises from chance). The p-values of the constant and the first four explanatory variables are well below 0.001; the p-value of the fifth explanatory variable is 0.03.

In other words, get on base, wait for the long ball, and don’t make outs by trying to steal or bunt the runner(s) along,.

A Rather Normal Distribution

I found a rather normal distribution from the real world — if you consider major-league baseball to be part of the real world. In a recent post I explained how I normalized batting statistics for the 1901-2015 seasons, and displayed the top-25 single-season batting averages, slugging percentages, and on-base-plus-slugging percentages after normalization.

I have since discovered that the normalized single-season batting averages for 14,067 player-seasons bear a strong resemblance to a textbook normal distribution:

Distribution of normalized single-season batting averrages

How close is this to a textbook normal distribution? Rather close, as measured by the percentage of observations that are within 1, 2, 3, and 4 standard deviations from the mean:

Distribution of normalized single-season batting averrages_table

Ty Cobb not only compiled the highest single-season average (4.53 SD above the mean) but 5 of the 12 single-season averages more than 4 SD above the mean:

Ty Cobb's normalized single-season batting_SD from mean

Cobb’s superlative performances in the 13-season span from 1907 through 1919 resulted in 12 American League batting championships. (The unofficial number has been reduced to 11 because it was later found that Cobb actually lost the 1910 title by a whisker — .3834 to Napoleon Lajoie’s .3841.)

Cobb’s normalized batting average for his worst full season (1924) is better than 70 percent of the 14,067 batting averages compiled by full-time players in the 115 years from 1901 through 2015. And getting on base was only part of what made Cobb the greatest player of all time.

Baseball’s Greatest and Worst Teams

When talk turns to the greatest baseball team of all time, most baseball fans will nominate the 1927 New York Yankees. Not only did that team post a won-lost record of 110-44, for a W-L percentage of .714, but its roster featured several future Hall-of-Famers: Babe Ruth, Lou Gehrig, Herb Pennock, Waite Hoyt, Earl Combs, and Tony Lazzeri. As it turns out, the 1927 Yankees didn’t have the best record in “modern” baseball, that is, since the formation of the American League in 1901. Here are the ten best seasons (all above .700), ranked by W-L percentage:

Team Year G W L W-L%
Cubs 1906 155 116 36 .763
Pirates 1902 142 103 36 .741
Pirates 1909 154 110 42 .724
Indians 1954 156 111 43 .721
Mariners 2001 162 116 46 .716
Yankees 1927 155 110 44 .714
Yankees 1998 162 114 48 .704
Cubs 1907 155 107 45 .704
Athletics 1931 153 107 45 .704
Yankees 1939 152 106 45 .702

And here are the 20 worst seasons, all below .300:

Team Year G W L W-L%
Phillies 1945 154 46 108 .299
Brown 1937 156 46 108 .299
Phillies 1939 152 45 106 .298
Browns 1911 152 45 107 .296
Braves 1909 155 45 108 .294
Braves 1911 156 44 107 .291
Athletics 1915 154 43 109 .283
Phlllies 1928 152 43 109 .283
Red Sox 1932 154 43 111 .279
Browns 1939 156 43 111 .279
Phillies 1941 155 43 111 .279
Phillies 1942 151 42 109 .278
Senators 1909 156 42 110 .276
Pirates 1952 155 42 112 .273
Tigers 2003 162 43 119 .265
Athletics 1919 140 36 104 .257
Senators 1904 157 38 113 .252
Mets 1962 161 40 120 .250
Braves 1935 153 38 115 .248
Athletics 1916 154 36 117 .235

But it takes more than a season, or even a few of them, to prove a team’s worth. The following graphs depict the best records in the American and National Leagues over nine-year spans:

Centered nine-year W-L record, best AL

Centered nine-year W-L record, best NL

For sustained excellence over a long span of years, the Yankees are the clear winners. Moreover, the Yankees’ best nine-year records are centered on 1935 and 1939. In the nine seasons centered on 1935 — namely 1931-1939 — the Yankees compiled a W-L percentage of .645. In those nine seasons, the Yankees won five American League championships and as many World Series. The Yankees compiled a barely higher W-L percentage of .646 in the nine seasons centered on 1939 — 1935-1943. But in those nine seasons, the Yankees won the American League championship seven times — 1936, 1937, 1938, 1939, 1941, 1942, and 1943 — and the World Series six times (losing to the Cardinals in 1942).

Measured by league championships, the Yankees compiled better nine-year streaks, winning eight pennants in 1949-1957, 1950-1958, and 1955-1963. But for sheer, overall greatness, I’ll vote for the Yankees of the 1930s and early 1940s. Babe Ruth graced the Yankees through 1934, and the 1939 team (to pick one) included future Hall-of-Famers Bill Dickey, Joe Gordon, Joe DiMaggio, Lou Gehring (in his truncated final season), Red Ruffing, and Lefty Gomez.

Here are the corresponding worst nine-year records in the two leagues:

Centered nine-year W-L record, worst AL

Centered nine-year W-L record, worst NL

The Phillies — what a team! The Phillies, Pirates, and Cubs should have been demoted to Class D leagues.

What’s most interesting about the four graphs is the general decline in the records of the best teams and the general rise in the records of the worst teams. That’s a subject for another post.

Great (Batting) Performances

The normal values of batting average (BA), slugging percentage (SLG), and on-base plus slugging (OPS) have fluctuated over time:

Average major league batting statistics_1901-2015

In sum, no two seasons are alike, and some are vastly different from others. To level the playing field (pun intended), I did the following:

  • Compiled single-season BA, SLG, and OPS data for all full-time batters (those with enough times at bat in a season to qualify for the batting title) from 1901 through 2015 — a total of 14,067 player-seasons. (Source: the Play Index at Baseball-Reference.com.)
  • Normalized (“normed”) each season’s batting statistics to account for inter-seasonal differences. For example, a batter whose BA in 1901 was .272 — the overall average for that year — is credited with the same average as a batter whose BA in 1902 was .267 — the overall average for that year.
  • Ranked the normed values of BA, SLG, and OPS for those 14,067 player-seasons.

I then sorted the rankings to find the top 25 player-seasons in each category:

Top-25 single-season offensive records

I present all three statistics because they represent different aspects of offensive prowess. BA was the most important of the three statistics until the advent of the “lively ball” era in 1919. Accordingly, the BA list is dominated by seasons played before that era, when the name of the game was “small ball.” The SLG and OPS lists are of course dominated by seasons played in the lively ball era.

Several seasons compiled by Barry Bonds and Mark McGwire showed up in the top-25 lists that I presented in an earlier post. I have expunged those seasons because of the dubious nature of Bonds’s and McGwire’s achievements.

The preceding two paragraphs lead to the question of the commensurability (or lack thereof) of cross-temporal statistics. This is from the earlier post:

There are many variations in the conditions of play that have resulted in significant changes in offensive statistics. Among those changes are the use of cleaner and more tightly wound baseballs, the advent of night baseball, better lighting for night games, bigger gloves, lighter bats, bigger and stronger players, the expansion of the major leagues in fits and starts, the size of the strike zone, the height of the pitching mound, and — last but far from least in this list — the integration of black and Hispanic players into major league baseball. In addition to these structural variations, there are others that mitigate against the commensurability of statistics over time; for example, the rise and decline of each player’s skills, the skills of teammates (which can boost or depress a player’s performance), the characteristics of a player’s home ballpark (where players generally play half their games), and the skills of the opposing players who are encountered over the course of a career.

Despite all of these obstacles to commensurability, the urge to evaluate the relative performance of players from different teams, leagues, seasons, and eras is irrepressible. Baseball-Reference.com is rife with such evaluations; the Society for American Baseball Research (SABR) revels in them; many books offer them (e.g., this one); and I have succumbed to the urge more than once.

It is one thing to have fun with numbers. It is quite another thing to ascribe meanings to them that they cannot support.

And yet, it seems right that the top 25 seasons should include so many of Ty Cobb’s, Babe Ruth’s, and of their great contemporaries Jimmie Foxx, Lou Gehrig, Rogers Hornsby, Shoeless Joe Jackson, Nap Lajoie, Tris Speaker, George Sisler, and Honus Wagner. It signifies the greatness of the later players who join them on the lists: Hank Aaron, George Brett, Rod Carew, Roberto Clemente, Mickey Mantle, Willie McCovey, Stan Musial, Frank Thomas, and Ted Williams.

Cobb’s dominance of the BA leader-board merits special attention. Cobb holds 9 of the top 19 slots on the BA list. That’s an artifact of his reign as the American League’s leading hitter in 12 of the 13 seasons from 1907 through 1919. But there was more to Cobb than just “hitting it where they ain’t.” Cobb probably was the most exciting ball player of all time, because he was much more than a hitting machine.

Charles Leershen offers chapter and verse about Cobb’s prowess in his book Ty Cobb: A Terrible Beauty. Here are excerpts of Leershen’s speech “Who Was Ty Cobb? The History We Know That’s Wrong,” which is based on his book:

When Cobb made it to first—which he did more often than anyone else; he had three seasons in which he batted over .400—the fun had just begun. He understood the rhythms of the game and he constantly fooled around with them, keeping everyone nervous and off balance. The sportswriters called it “psychological baseball.” His stated intention was to be a “mental hazard for the opposition,” and he did this by hopping around in the batter’s box—constantly changing his stance as the pitcher released the ball—and then, when he got on base, hopping around some more, chattering, making false starts, limping around and feigning injury, and running when it was least expected. He still holds the record for stealing home, doing so 54 times. He once stole second, third, and home on three consecutive pitches, and another time turned a tap back to the pitcher into an inside-the-park home run.

“The greatness of Ty Cobb was something that had to be seen,” George Sisler said, “and to see him was to remember him forever.” Cobb often admitted that he was not a natural, the way Shoeless Joe Jackson was; he worked hard to turn himself into a ballplayer. He had nine styles of slides in his repertoire: the hook, the fade-away, the straight-ahead, the short or swoop slide (“which I invented because of my small ankles”), the head-first, the Chicago slide (referred to by him but never explained), the first-base slide, the home-plate slide, and the cuttle-fish slide—so named because he purposely sprayed dirt with his spikes the way squid-like creatures squirt ink. Coming in, he would watch the infielder’s eyes to determine which slide to employ.

There’s a lot more in the book, which I urge you to read — especially if you’re a baseball fan who appreciates snappy prose and documented statements (as opposed to the myths that have grown up around Cobb).

Cobb’s unparalleled greatness was still fresh in the minds of baseball people in 1936, when the first inductees to baseball’s Hall of Fame were elected. It was Cobb — not Babe Ruth — who received the most votes among the five players selected for membership in the Hall.

The Hall of Fame Reconsidered

Several years ago I wrote some posts (e.g., here and here) about the criteria for membership in baseball’s Hall of Fame, and named some players who should and shouldn’t be in the Hall. A few days ago I published an updated version of my picks. I’ve since deleted that post because, on reflection, I find my criteria too narrow. I offer instead:

  • broad standards of accomplishment that sweep up most members of the Hall who have been elected as players
  • ranked lists of players who qualify for consideration as Hall of Famers, based on those standards.

These are the broad standards of accomplishment for batters:

  • at least 8,000 plate appearances (PA) — a number large enough to indicate that a player was good enough to have attained a long career in the majors, and
  • a batting average of at least .250 — a low cutuff point that allows the consideration of mediocre hitters who might have other outstanding attributes (e.g., base-stealing, fielding).

I rank retired batters who meet those criteria by career wins above average (WAA) per career PA. WAA for a season is a measure of a player’s total offensive and defensive contribution, relative to other players in the same season. (WAA therefore normalizes cross-temporal differences in batting averages, the frequency of home runs, the emphasis on base-stealing, and the quality of fielders’ gloves, for example.) Because career WAA is partly a measure of longevity rather than skill, I divide by career PA to arrive at a normalized measure of average performance over the span of a player’s career.

These are the broad standards of accomplishment for pitchers:

  • at least 3,000 innings pitched, or
  • appearances least 1,000 games (to accommodate short-inning relievers with long careers).

I rank retired pitchers who meet these criteria by career ERA+,. This is an adjusted earned run average (ERA) that accounts for differences in ballparks and cross-temporal differences in pitching conditions (the resilience of the baseball, batters’ skill, field conditions, etc.). Some points to bear in mind:

  • My criteria are broad but nevertheless slanted toward players who enjoyed long careers. Some present Hall of Famers with short careers are excluded (e.g., Ralph Kiner, Sandy Koufax). However great their careers might have been, they didn’t prove themselves over the long haul, so I’m disinclined to include them in my Hall of Fame.
  • I drew on the Play Index at Baseball-Reference.com for the statistics on which the lists are based. The Play Index doesn’t cover years before 1900. That doesn’t bother me because the “modern game” really began in the early 1900s (see here, here, and here). The high batting averages and numbers of games won in the late 1800s can’t be compared with performances in the 20th and 21st centuries.
  • Similarly, players whose careers were spent mainly or entirely in the Negro Leagues are excluded because their accomplishments — however great — can’t be calibrated with the accomplishments of players in the major leagues.

In the following lists of rankings, each eligible player is assigned an ordinal rank, which is based on the adjacent index number. For batters, the index number represents career WAA/PA, where the highest value (Babe Ruth’s) is equal to 100. For pitchers, the index number represents career ERA+, where the highest value (Mariano Rivera’s) is equal to 100. The lists are coded as follows:

  • Blue — elected to the Hall of Fame. (N.B. Joe Torre is a member of the Hall of Fame, but he was elected as a manager, not as a player.)
  • Red — retired more than 5 seasons but not yet elected
  • Bold (with asterisk) — retired less than 5 seasons.

Now, at last, the lists (commentary follows):

Hall of fame candidates_batters

If Bill Mazeroski is in the Hall of Fame, why not everyone who outranks him ? (Barry Bonds, Sammy Sosa, and some others excepted, of course. Note that Mark McGwire didn’t make the list; he had 7,660 PA.) There are plenty of players with more impressive credentials than Mazeroski, whose main claim to fame is a World-Series-winning home run in 1960. Mazeroski is reputed to have been an excellent second-baseman, but WAA accounts for fielding prowess — and other things. Maz’s excellence as a fielder still leaves him at number 194 on my list of 234 eligible batters.

Here’s the list of eligible pitchers:

Hall of fame candidates_pitchers

If Rube Marquard — 111th-ranked of 122 eligible pitchers — is worthy of the Hall, why not all of those pitchers who outrank him? (Roger Clemens excepted, of course.) Where would I draw the line? My Hall of Fame would include the first 100 on the list of batters and the first 33 on the list of pitchers (abusers of PEDs excepted) — and never more than 100 batters and 33 pitchers. Open-ended membership means low standards. I’ll have none of it.

As of today, the top-100 batters would include everyone from Babe Ruth through Joe Sewell (number 103 on the list in the first table). I exclude Barry Bonds (number 3), Manny Ramirez (number 61), and Sammy Sosa (number 99). The top-33 pitchers would include everyone from Mariano Rivera through Eddie Plank (number 34 on the list in the second table). I exclude Roger Clemens (number 5).

My purge would eliminate 109 of the players who are now official members of the Hall of Fame, and many more players who are likely to be elected. The following tables list the current members whom I would purge (blue), and the current non-members (red and bold)  who would miss the cut:

Hall of fame batters not in top 100

Hall of fame pitchers not in top 33

Sic transit gloria mundi.

Signature

Baseball Trivia for the 4th of July

It was a “fact” — back in the 1950s when I became a serious fan of baseball — that the team that led its league on the 4th of July usually won the league championship. (That was in the days before divisional play made it possible for less-than-best teams to win league championships and World Series.)

How true was the truism? I consulted the Play Index at Baseball-Reference.com to find out. Here’s a season-by-season list of teams that had the best record on the 4th of July and at season’s end:

Teams with best record on 4th of July and end of season

It’s obvious that the team with the best record on the 4th of July hasn’t “usually” had the best record at the end of the season — if “usually” means “almost all of the time.”   In fact, for 1901-1950, the truism was true only 64 percent of the time in the American League and 60 percent of the time in the National League. The numbers for 1901-2014: American League, 60 percent; National League, 55 percent.

There are, however, two eras in which the team with the best record on the 4th of July “usually” had the best record at season’s  end — where “usually” is defined by a statistical test.* Applying that test, I found that

  • from 1901 through 1928 the best National League team on the 4th of July usually led the league at the end of the season (i.e., 75 percent of the time); and
  • from 1923 through 1958 the best American League team on the 4th of July usually led the league at the end of the season (i.e., 83 percent of the time).

I was a fan of the Detroit Tigers in the 1950s, and therefore more interested in the American League than the National League. So, when I became a fan it was true (of the American League) that the best team on the 4th of July usually led the league at the end of the season.

It’s no longer true. And even if it has happened 55 to 60 percent of the time in the past 114 years, don’t bet your shirt that it will happen in a particular season.

*     *     *

Related post: May the Best Team Lose

__________
* The event E occurs when a team has the league’s best record on the 4th of July and at the end of the season. E “usually” occurs during a defined set of years if the difference between the frequency of occurrence during that set of years is significantly different than the frequency of occurrence in other years. Significantly, in this case, means that a t-test yields a probability of less than 0.01 that the difference in frequencies occurs by chance.

Signature

May the Best Team Lose

This is an update of a six-season-old post. It includes 2016 post-season play to date. I will update it again after the 2016 World Series.

The first 65 World Series (1903 and 1905-1968) were contests between the best teams in the National and American Leagues. The winner of a season-ending Series was therefore widely regarded as the best team in baseball for that season (except by the fans of the losing team and other soreheads). The advent of divisional play in 1969 meant that the Series could include a team that wasn’t the best in its league. From 1969 through 1993, when participation in the Series was decided by a single postseason playoff between division winners (1981 excepted), the leagues’ best teams met in only 10 of 24 series. The advent of three-tiered postseason play in 1995 and four-tiered postseason play in 2012, has only made matters worse.*

By the numbers:

  • Postseason play originally consisted of a World Series (period) involving 1/8 of major-league teams — the best in each league. Postseason play now involves 1/3 of major-league teams and 7 postseason series (3 in each league plus the inter-league World Series).
  • Only 3 of the 22 Series from 1995 through 2016 have featured the best teams of both leagues, as measured by W-L record.
  • Of the 21 Series from 1995 through 2015, only 6 were won by the best team in a league.
  • Of the same 21 Series, 10 (48 percent) were won by the better of the two teams, as measured by W-L record. Of the 65 Series played before 1969, 35 were won by the team with the better W-L record and 2 involved teams with the same W-L record. So before 1969 the team with the better W-L record won 35/63 of the time for an overall average of 56 percent. That’s not significantly different from the result for the 21 Series played in 1995-2015, but the teams in the earlier era were each league’s best, which is no longer true. . .
  • From 1995 through 2016, a league’s best team (based on W-L record) appeared in a Series only 15 of 44 possible times — 6 times for the NL (pure luck), 9 times for the AL (little better than pure luck). (A random draw among teams qualifying for post-season play would have resulted in the selection of each league’s best team about 6 times out of 22.)
  • Division winners have opposed each other in only 11 of the 22 Series from 1995 through 2016.
  • Wild-card teams have appeared in 10 of those Series, with all-wild-card Series in 2002 and 2014.
  • Wild-card teams have occupied more than one-fourth of the slots in the 1995-2016 Series — 12 slots out of 44.

The winner of the World Series used to be a league’s best team over the course of the entire season, and the winner had to beat the best team in the other league. Now, the winner of the World Series usually can claim nothing more than having won the most postseason games — 11 or 12 out of as many as 19 or 20. Why not eliminate the 162-game regular season, select the postseason contestants at random, and go straight to postseason play?

__________
* Here are the World Series pairings for 1994-2016 (National League teams listed first; + indicates winner of World Series):

1995 –
Atlanta Braves (division winner; .625 W-L, best record in NL)+
Cleveland Indians (division winner; .694 W-L, best record in AL)

1996 –
Atlanta Braves (division winner; .593, best in NL)
New York Yankees (division winner; .568, second-best in AL)+

1997 –
Florida Marlins (wild-card team; .568, second-best in NL)+
Cleveland Indians (division winner; .534, fourth-best in AL)

1998 –
San Diego Padres (division winner; .605 third-best in NL)
New York Yankees (division winner, .704, best in AL)+

1999 –
Atlanta Braves (division winner; .636, best in NL)
New York Yankees (division winner; .605, best in AL)+

2000 –
New York Mets (wild-card team; .580, fourth-best in NL)
New York Yankees (division winner; .540, fifth-best in AL)+

2001 –
Arizona Diamondbacks (division winner; .568, fourth-best in NL)+
New York Yankees (division winner; .594, third-best in AL)

2002 –
San Francisco Giants (wild-card team; .590, fourth-best in NL)
Anaheim Angels (wild-card team; .611, third-best in AL)+

2003 –
Florida Marlines (wild-card team; .562, third-best in NL)+
New York Yankees (division winner; .623, best in AL)

2004 –
St. Louis Cardinals (division winner; .648, best in NL)
Boston Red Sox (wild-card team; .605, second-best in AL)+

2005 –
Houston Astros (wild-card team; .549, third-best in NL)
Chicago White Sox (division winner; .611, best in AL)*

2006 –
St. Louis Cardinals (division winner; .516, fifth-best in NL)+
Detroit Tigers (wild-card team; .586, third-best in AL)

2007 –
Colorado Rockies (wild-card team; .552, second-best in NL)
Boston Red Sox (division winner; .593, tied for best in AL)+

2008 –
Philadelphia Phillies (division winner; .568, second-best in NL)+
Tampa Bay Rays (division winner; .599, second-best in AL)

2009 –
Philadelphia Phillies (division winner; .574, second-best in NL)
New York Yankees (division winner; .636, best in AL)+

2010 —
San Francisco Giants (division winner; .568, second-best in NL)+
Texas Rangers (division winner; .556, fourth-best in AL)

2011 —
St. Louis Cardinals (wild-card team; .556, fourth-best in NL)+
Texas Rangers (division winner; .593, second-best in AL)

2012 —
San Francisco Giants (division winner; .580, third-best in AL)+
Detroit Tigers (division winner; .543, seventh-best in AL)

2013 —
St. Louis Cardinals (division winner; .599, best in NL)
Boston Red Sox (division winner; .599, best in AL)+

2014 —
San Francisco Giants (wild-card team; .543, 4th-best in NL)+
Kansas City Royals (wild-card team; .549, 4th-best in AL)

2015 —
New York Mets (division winner; .556, 5th best in NL)
Kansas City Royals (division winner; .586, best in AL)+

2016 —
Chicago Cubs (division winner; .640, best in NL)
Cleveland Indians (division winner; .584, 2nd best in AL)

Signature

Competitiveness in Major League Baseball

Yesterday marked the final regular-season games of the 2014 season of major league baseball (MLB), In observance of that event, I’m shifting from politics to competitiveness in MLB. What follows is merely trivia and speculation. If you like baseball, you might enjoy it. If you don’t like baseball, I hope that you don’t think there’s a better team sport. There isn’t one.

Here’s how I compute competitiveness for each league and each season:

INDEX OF COMPETITIVENESS = AVEDEV/AVERAGE; where

AVEDEV = the average of the absolute value of deviations from the average number of games won by a league’s teams in a given season, and

AVERAGE =  the average number of games won by a league’s teams in a given season.

For example, if the average number of wins is 81, and the average of the absolute value of deviations from 81 is 8, the index of competitiveness is 0.1 (rounded to the nearest 0.1). If the average number of wins is 81 and the average of the absolute value of deviations from 81 is 16, the index of competitiveness is 0.2.  The lower the number, the more competitive the league.

With some smoothing, here’s how the numbers look over the long haul:

Index of competitiveness
Based on numbers of wins by season and by team, for the National League and American League, as compiled at Baseball-Reference.com.

I drew a separate line for the American League without the Yankees, to show the effect of the Yankees’ dominance from the early 1920s to the early 1960s, and the 10 years or so beginning around 1995.

The National League grew steadily more competitive from 1940 to 1987, and has slipped only a bit since then. The American League’s climb began in 1951, and peaked in 1989; the AL has since slipped a bit more than the NL, but seems to be rebounding. In any event, there’s no doubt that both leagues are — and in recent decades have been — more competitive than they were in the early to middle decades of the 20th century. Why?

My hypothesis: integration compounded by expansion, with an admixture of free agency and limits on the size of rosters.

Let’s start with integration. The rising competitiveness of the NL after 1940 might have been a temporary thing, but it continued when NL teams (led by the Brooklyn Dodgers) began to integrate, by adding Jackie Robinson in 1947. The Cleveland Indians of the AL followed suit, by adding Larry Doby later in the same season. By the late 1950s, all major league teams (then 16) had integrated, though the NL seems to have integrated faster. The more rapid integration of the NL could explain its earlier ascent to competitiveness. Integration was followed in short order by expansion: The AL began to expand in 1961 and the NL began to expand in 1962.

How did expansion and integration combine to make the leagues more competitive? Several years ago, I opined:

[G]iven the additional competition for talent [following] expansion, teams [became] more willing to recruit players from among the black and Hispanic populations of the U.S. and Latin America. That is to say, teams [came] to draw more heavily on sources of talent that they had (to a large extent) neglected before expansion.

Further, free agency, which began in the mid-1970s,

made baseball more competitive by enabling less successful teams to attract high-quality players by offering them more money than other, more successful, teams. Money can, in some (many?) cases, compensate a player for the loss of psychic satisfaction of playing on a team that, on its record, is likely to be successful.

Finally,

[t]he competitive ramifications of expansion and free agency [are] reinforced by the limited size of team rosters (e.g., each team may carry only 25 players from May through August). No matter how much money an owner has, the limit on the size of his team’s roster constrains his ability to sign all (even a small fraction) of the best players.

It’s not an elegant hypothesis, but it’s my own (as far as I know). I offer it for discussion.

Signature

*     *     *

Other related posts:
The End of a Dynasty
What Makes a Winning Team
More Lessons from Baseball
Not Over the Hill

Decline

Although I’ve declared baseball the “king of team sports,” I would agree with anyone who says that baseball is past its prime. When was that prime? Arguably, it was the original lively ball era, which by my reckoning extended from 1920 to 1941. The home run had become much more prevalent than in earlier dead-ball era, but not so prevalent that it dominated offensive strategy. Thus batting averages were high and scoring proceeded at a higher pace than in any of the other eras that I’ve identified.

In 1930, for example, the entire National League batted .303. The Chicago Cubs of that season finished in second place and batted .309 (not the highest team average in the league). The average number of runs scored in a Cubs’ game was 12.0 — a number surpassed only by the lowly Philadelphia Phillies, whose games yielded an average of 13.8 runs, most of them scored by the Phillies’ opponents. Despite the high scoring, the average Cubs game of the 1930 season lasted only 2 hours and 5 minutes. (An estimate that I derived from the sample of 67 Cubs’ games for which times are available, here.)

In sum, baseball’s first lively ball era produced what fans love to see: scoring. A great pitching duel is fine, but a great pitching duel is a rare thing. Too many low-scoring games are the result of failed offensive opportunities, which are marked by a high count of runners left of base. Once runners get on base, what fans want (or at least one team’s fans want) is to see them score.

The game in the first lively ball era was, as I say, dynamic because scoring depended less on the home run than it did in later eras. And the game unfolded at a smart pace. That pace, by the way, was about the same as it had been in the middle of the dead-ball era. (For example, the times recorded for the Cubs’ two games against the Cincinnati Reds on July 4, 1911, are 2:05 and 2:00.)

Baseball has declined since the first lively ball era, not just because the game has become more static but also because it now unfolds at a much slower pace. The average length of a game in 2014 is 3:08 (for games through 07/17/14) — more than an hour longer than the games played by the Cubs in 1930.

Baseball is far from the only cultural phenomenon that has declined from its peak. I have written several times about the decline of art and music, movies, language, and morals and mores: here, here, here, and here. (Each of the foregoing links leads to a post that includes links to related items.)

Baseball is sometimes called a metaphor for life. (It’s a better metaphor than soccer, to be sure.) I now venture to say that the decline of baseball is a metaphor for the decline of art, music, movies, language, and morals and mores.

Indeed, the decline of baseball is a metaphor for the decline of liberty in America, which began in earnest — and perhaps inexorably — during the New Deal, even as the first lively ball era was on the wane.

*     *     *

See also “The Fall and Rise of American Empire.”

Baseball: The King of Team Sports

There are five major team sports: baseball, basketball, football (American style), ice hockey, and soccer (European football). The skills and abilities required to play these sports at the top professional level are several and varied. But, in my opinion — based on experience and spectating — the skills can be ranked hierarchically and across sports. When the ordinal rankings are added, baseball comes out on top by a wide margin; hockey is in the middle; basketball, football, and soccer are effectively tied for least-demanding of skill and ability.

Ranking of sports by skill and ability

Baseball or Soccer? David Brooks Misunderstands Life

David Brooks — who is what passes for a conservative at The New York Times — once again plays useful idiot to the left. Brooks’s latest offering to the collectivist cause is “Baseball or Soccer?” Here are the opening paragraphs of Brooks’s blathering, accompanied by my comments (underlined, in brackets):

Baseball is a team sport, but it is basically an accumulation of individual activities. [So is soccer, and so is any team sport. For example, at any moment the ball is kicked by only one member of a team, not by the team as a whole.] Throwing a strike, hitting a line drive or fielding a grounder is primarily an individual achievement. [This short list omits the many ways in which baseball involves teamwork; for example: every pitch, involves coordination between pitcher and catcher, and fielders either position themselves according to the pitch that’s coming or are able to anticipate the likely direction of a batted ball; the double play is an excellent and more obvious example of teamwork; so is the pickoff play, from pitcher to baseman or catcher to baseman; the hit and run play is another obvious example of teamwork; on a fly to the outfield, where two fielders are in position to make the catch, the catch is made by the fielder in better position for a throw or with the better throwing arm.] The team that performs the most individual tasks well will probably win the game. [Teamwork consists of the performance of individual tasks, in soccer as well as in baseball.]

Soccer is not like that. [False; see above.] In soccer, almost no task, except the penalty kick and a few others, is intrinsically individual. [False; see above.] Soccer, as Simon Critchley pointed out recently in The New York Review of Books, is a game about occupying and controlling space. [So is American football. And so what?] ….

As Critchley writes, “Soccer is a collective game, a team game, and everyone has to play the part which has been assigned to them, which means they have to understand it spatially, positionally and intelligently and make it effective.” [Hmm… Sounds like every other team sport, except that none of them — soccer included, is “collective.” All of them — soccer included — involve cooperative endeavors of various kinds. The success of those cooperative endeavors depends very much on the skills that individuals bring to them. The real difference between soccer and baseball is that baseball demands a greater range of individual skills, and is played in such a way that some of those skills are on prominent display.] ….

Most of us spend our days thinking we are playing baseball, but we are really playing soccer. [To the extent that any of us think such things, those who think they are playing baseball, rather than soccer, are correct. See the preceding comment.]

At this point, Brooks shifts gears. I’ll quote some relevant passages, then comment at length:

We think we individually choose what career path to take, whom to socialize with, what views to hold. But, in fact, those decisions are shaped by the networks of people around us more than we dare recognize.

This influence happens through at least three avenues. First there is contagion. People absorb memes, ideas and behaviors from each other the way they catch a cold…. The overall environment influences what we think of as normal behavior without being much aware of it. Then there is the structure of your network. There is by now a vast body of research on how differently people behave depending on the structure of the social networks. People with vast numbers of acquaintances have more job opportunities than people with fewer but deeper friendships. Most organizations have structural holes, gaps between two departments or disciplines. If you happen to be in an undeveloped structural hole where you can link two departments, your career is likely to take off.

Innovation is hugely shaped by the structure of an industry at any moment. Individuals in Silicon Valley are creative now because of the fluid structure of failure and recovery….

Finally, there is the power of the extended mind. There is also a developed body of research on how much our very consciousness is shaped by the people around us. Let me simplify it with a classic observation: Each close friend you have brings out a version of yourself that you could not bring out on your own. When your close friend dies, you are not only losing the friend, you are losing the version of your personality that he or she elicited.

Brooks has gone from teamwork — which he gets wrong — to socialization and luck. As with Brooks’s (failed) baseball-soccer analogy, the point is to belittle individual effort by making it seem inconsequential, or less consequential than the “masses” believe it to be.

You may have noticed that Brooks is re-running Obama’s big lie: “If you’ve got a business — you didn’t build that.  Somebody else made that happen.” As I wrote here,

… Obama is trying, not so subtly, to denigrate those who are successful in business (e.g., Mitt Romney) and to make a case for redistributionism. The latter rests on Obama’s (barely concealed) premise that the fruits of a collective enterprise should be shared on some basis other than market valuations of individual contributions….

It is (or should be) obvious that Obama’s agenda is the advancement of collectivist statism. I will credit Obama for the sincerity of his belief in collectivist statism, but his sincerity only underscores and how dangerous he is….

Well, yes, everyone is strongly influenced by what has gone before, and by the social and economic milieu in which one finds oneself. Where does that leave us? Here:

  • Social and economic milieu are products of individual acts, including acts that occur in the context of cooperative efforts.
  • It is up to the individual to make the most (or least) of his social and economic inheritance and milieu.
  • Those who make the most (or least) of their background and situation are rightly revered or despised for their individual efforts. Consider, for example, Washington and Lincoln, on the one hand, and Hitler and Stalin, on the other hand.
  • Beneficial cooperation arises from the voluntary choices of individuals. Destructive “cooperation” (collectivism)  — the imposition of rules through superior force (usually government) — usually thwarts the individual initiative and ingenuity that underlie scientific and economic progress.

Brooks ends with this:

Once we acknowledge that, in life, we are playing soccer, not baseball, a few things become clear. First, awareness of the landscape of reality is the highest form of wisdom. It’s not raw computational power that matters most; it’s having a sensitive attunement to the widest environment, feeling where the flow of events is going. Genius is in practice perceiving more than the conscious reasoning. [A false distinction between baseball and soccer, followed by false dichotomies.]

Second, predictive models [of what?] will be less useful [than what?]. Baseball is wonderful for sabermetricians. In each at bat there is a limited [but huge] range of possible outcomes. Activities like soccer are not as easily renderable statistically, because the relevant spatial structures are harder to quantify. [B.S. “Sabermetrics” is coming to soccer.] Even the estimable statistician Nate Silver of FiveThirtyEight gave Brazil a 65 percent chance of beating Germany. [An “estimable statistician” would know that such a statement is meaningless; see the discussion of probability here.]

Finally, Critchley notes that soccer is like a 90-minute anxiety dream — one of those frustrating dreams when you’re trying to get somewhere but something is always in the way. This is yet another way soccer is like life. [If you seek a metaphor for life, try blowing a fastball past a fastball hitter; try punching the ball to right when you’re behind in the count; try stealing second, only to have the batter walked intentionally; try to preserve your team’s win with a leaping catch and a throw to home plate; etc., etc., etc.]

The foregoing parade of non sequitur, psychobabble, and outright error simply proves that Brooks doesn’t know what he’s talking about. I hereby demote him from “useful idiot” to plain old “idiot.”

*     *     *

Related posts:
He’s Right, Don’t Listen to Him
Killing Conservatism in Order to Save It
Ten Commandments of Economics
More Commandments of Economics
Three Truths for Central Planners
Columnist, Heal Thyself
Our Miss Brooks
Miss Brooks’s “Grand Bargain”
More Fool He
Dispatches from the Front
David Brooks, Useful Idiot for the Left
“We the People” and Big Government
“Liberalism” and Personal Responsibility

More Lessons from Baseball

Regular readers of this blog will know that I sometimes draw on the game of baseball and its statistics to make points about various subjects — longevity, probability, politics, management, and cosmology, for example. (See the links at the bottom of this post.)

Today’s sermon is about the proper relationship between owners and management. I will address two sets of graphs giving the won-lost (W-L) records of the “old 16” major-league franchises. The “old 16” refers to the 8 franchises in the National League (NL) and the 8 franchises in American League (AL) in 1901, the first year of the AL’s existence as a major league. Focusing on the “old 16” affords the long view that’s essential in thinking about success in an endeavor, whether it is baseball, business, or empire-building.

The first graph in each set gives the centered 11-year average W-L record for each of the old teams in each league, and for the league’s expansion teams taken as a group. The 11-year averages are based on annual W-L records for 1901-2013. The subsequent graphs in each set give, for each team and group of expansion teams, 11-year averages and annual W-L records. Franchise moves from one city to another are indicated by vertical black lines. The titles of each graph indicates the city or cities in which the team has been located and the team’s nickname or nicknames.

Here are the two sets of graphs:

W-L records of old-8 NL franchises

W-L records of old-8 AL franchises

What strikes me about the first graph in each set is the convergence of W-L records around 1990. My conjecture: The advent of free agency in the 1970s must have enabled convergence. Stability probably helped, too. The AL had been stable since 1977, when it expanded to 14 teams; the NL had been stable since 1969, when it expanded to 12 teams. As the expansion teams matured, some of them became more successful, at the expense of the older teams. This explanation is consistent with the divergence after 1993, with the next round of expansion (there was another in 1998). To be sure, all of this conjecture warrants further analysis. (Here’s an analysis from several years ago that I still like.)

Let’s now dispose of franchise shifts as an explanation for a better record. I observe the following:

The Braves were probably on the upswing when they moved from Boston to Milwaukee in 1953. They were on the downswing at the time of their second move, from Milwaukee to Atlanta in 1966. It took many years and the acquisition of astute front office and a good farm system to turn the Braves around.

The Dodgers’ move to LA in 1958 didn’t help the team, just the owners’ bank accounts. Ditto the Giants’ move to San Francisco in 1958.

Turning to the AL, the St. Louis Browns became the latter-day Baltimore Orioles in 1954. That move was accompanied by a change in ownership. The team’s later successes seem to have been triggered by the hiring of Paul Richards and Lee McPhail to guide the team and build its farm system. The Orioles thence became a good-to-great from the mid-1960 to early 1980s, with a resurgence in the late 1980s and early 1990s. The team’s subsequent decline seems due to the meddlesome Peter Angelos, who became CEO in 1993.

The Athletics, like the Braves, moved twice. First, in 1955 from Philadelphia to Kansas City, and again in 1968 from Kansas City to Oakland. The first move had no effect until Charles O. Finley took over the team. His ownership carried over to Oakland. Finley may have been the exceptional owner whose personal involvement in the team’s operations helped to make it successful. But the team’s post-Finely record (1981-present) under less-involved owners suggests otherwise. The team’s pre-Kansas City record reflects Connie Mack’s tight-fisted ways. Mack — owner-manager of the A’s from 1901 until 1950 — was evidently a good judge of talent and a skilled field manager, but as an owner he had a penchant for breaking up great teams to rid himself of high-priced talent — with disastrous consequences for the A’s W-L record from the latter 1910s to late 1920s, and from the early 1930s to the end of Mack’s reign.

The Washington Senators were already resurgent under owner Calvin Griffith when the franchise was moved to Minnesota for the 1961 season. The Twins simply won more consistently than they had under the tight-fisted ownership of Clark Griffith, Calvin’s father.

Bottom line: There’s no magic in a move. A team’s success depends on the willingness of owners to spend bucks and to hire good management — and then to get out of the way. (Yes, George Steinbrenner bankrolled a lot of pennant-winning teams during his ownership years, from 1973 to 2010, but the Yankees’ record improved as “The Boss” became a less-intrusive owner from the mid-1990s until his death.)

There are many other stories behind the graphs — just begging to be told, but I’ll leave it at that.

Except to say this: The “owners” of America aren’t “the people,” romantic political pronouncements to the contrary notwithstanding. As government has become more deeply entrenched in the personal and business affairs of Americans, there has emerged a ruling class which effectively “owns” America. It is composed of professional politicians and bureaucrats, who find ample aid and comfort in the arms of left-wing academicians and the media. The “owners’ grip on power is sustained by the votes of the constituencies to which they pander.

Yes, the constituencies include “crony capitalists,” who benefit from regulatory barriers to competition and tax breaks. Though it must be said that they produce things, and would probably do well without the benefits they reap from professional politicians and bureaucrats. Far more powerful are the non-producers, who are granted favors based on their color, gender, age, etc., in return for the tens of millions of votes that they cast to keep the “owners” in power.

Far too many Americans are whiners who grovel at the feet of their “owners,” begging for handouts. Far too few Americans are self-managed winners.

*     *     *

Related posts:

The Hall of Fame and Morality

Jonathan Mahler, in the course of an incoherent article about baseball, makes this observation:

This year, not a single contemporary player was voted into the Hall of Fame because so many eligible players were suspected of steroid use. Never mind that Cooperstown has its share of racists, wife beaters and even a drug dealer. (To say nothing of the spitballers.)

Those few sentences typify the confusion rampant in Mahler’s offering. The use of steroids and other performance-enhancing drugs calls into question the legitimacy of the users’ accomplishments on the field. Racism, wife-beating, and drug-dealing — deplorable as they are — do not cast a shadow on the perpetrators’ performance as baseball players. As for the spitball, it was legal in baseball until 1920, and when it was outlawed its avowed practitioners were allowed to continue using it. (Some modern pitchers have been accused of using it from time to time, but I can’t think of one who used it so much that his career is considered a sham.)

Election to the Hall of Fame isn’t (or shouldn’t be) a moral judgment. If it were, I suspect that the Hall of Fame would be a rather empty place, especially if serial adultery and alcohol abuse were grounds for disqualification.

At the risk of being called a moral agnostic, which I am not, I say this: Election to the Hall of Fame (as a player) should reflect the integrity and excellence of on-field performance. Period.

I do have strong views about the proper qualifications for election to the Hall of Fame (as a player). You can read them here, here, and here. I’ve also analyzed the statistical evidence for indications of the use of performance-enhancing drugs by a few notable players: Barry Bonds and Mark McGwire (both guilty) and Roger Clemens (unproved).

Do Managers Make a Difference?

INTRODUCTION

The activity of managing ranges from the supervision of one other person in the performance of a menial task to the supervision of the executive branch of the government of the United States. (The latter is a fair description of a president’s constitutional responsibility.) And there are many criteria for judging managers, not all of which are unambiguous or conducive to precise quantification. It may be easy, for example, to determine whether a ditch was dug on time and within budget. But what if the manager’s methods alienated workers, causing some of them to quit when the job was done and requiring the company to recruit and train new workers at some expense?

Or consider the presidency. What determines whether an incumbent is doing a good job? Polls? They are mere opinions, mostly based on impressions and political preferences, not hard facts. The passage by Congress of legislation proposed by the president? By that measure, Obama earns points for the passage of the Affordable Care Act, which if not repealed will make health care less affordable and less available.

Given the impossibility of arriving at a general answer to the tittle question, I will turn — as is my wont — to the game of baseball. You might think that the plethora of baseball statistics would yield an unambiguous answer with respect to major-league managers. As you’ll see, that’s not so.

WHAT BASEBALL STATISTICS REVEAL (OR DON’T)

Data Source

According to this page at Baseball-Reference.com, 680 different men have managed teams in the history of major-league baseball, which is considered to have begun in 1871 with the founding of the National Association. Instead of reaching that far back into the past, when the game was primitive by comparison with today’s game, I focus on men whose managing careers began in 1920 or later. It was 1920 that marked the beginning of the truly modern era of baseball, with its emphasis on power hitting. (This modern era actually consists of six sub-eras. See this and this.) In this modern era, which now spans 1920 through 2013, 399 different men have managed major-league teams. That is a sizable sample from which I had hoped to draw firm judgments about whether baseball managers, or some of them, make a difference.

Won-Lost Record

The “difference” in question is a manager’s effect — or lack thereof — on the success of his team, as measured by its won-lost (W-L) record. For the benefit of non-fans, W-L record, usually denoted W-L%, is determined by the following simple equation: W/(W + L), that is, games won divided by games won plus games lost. (The divisor isn’t number of games played because sometimes, though rarely, a baseball game is played to a tie.) Thus a team that wins 81 of its 162 games in a season has a W-L record of .500 for that season. (In baseball statistics, it is customary to omit the “0” before the decimal point, contrary to mathematical convention.)

Quantifying Effectiveness

I’m about to throw some numbers at you. But I must say more about the samples that I used in my analysis. The aggregate-level analysis described in the next section draws on the records of a subset of the 399 men whose managerial careers are encompassed in the 1920-2013 period. The subset consists of the 281 men who managed at least 162 games, which (perhaps not coincidentally) has been the number of games in a regulation season since the early 1960s. I truncated the sample where I did because the W-L records of mangers with 162 or more games are statistically better (significance level of 0.05) than the W-L records of managers with fewer than 162 games. In other words, a manager who makes it through a full season is likely to have passed a basic test of management ability: not losing “too many” games. (I address this subjective assessment later in the post.)

Following the aggregate-level analysis, I turn to an individual-level analysis of the records of those managers who led a team for at least five consecutive seasons. (I allowed into the sample some managers whose fifth full season consisted of a partial season in year 1 and a partial season in year 6, as long as the number of games in the two partial seasons added to the number of games in a full season, or nearly so. I also included a few managers whose service with a particular team was broken by three years or less.) Some managers led more than one team for at least five consecutive seasons, and each such occurrence is counted separately. For reasons that will become evident, the five seasons had to begin no earlier than 1923 and end no later than 2010.  The sample size for this analysis is 63 management tours accomplished by 47 different managers.

Results and Inferences: Aggregate Level

“Just the facts” about the sub-sample of 281 managers:

Number of games managed vs W-L record

The exponential equation, though statistically significant, tells us that W-L record explains only about 21 percent of the variation in number of games managed, which spans 162 to 5,097.

Looking closer, I found that the 28 managers in the top decile of games managed (2,368 to 5,097) have a combined W-L record of .526. But their individual W-L records range from .477 to .615, and eight of the managers compiled a career W-L record below .500. Perhaps the losers did the best they could with the teams they had. Perhaps, but it’s also quite possible that the winners were blessed with teams that made them look good. In any event, the length of a manager’s career may have little to do with his effectiveness as a manager.

Which brings me to the next topic.

Results and Inferences: Individual Level

This view is more complicated.  As mentioned above, I focused on those 47 managers who on 63 separate occasions led their respective teams for at least five consecutive seasons (with minor variations). To get at each manager’s success (or failure) during each management tour, I compared his W-L record during a tour with the W-L record of the same team in the preceding and following three seasons.

My aim in choosing five years for the minimum span of a manager’s tenure with a team was to avoid judging a manager’s performance on the basis of an atypical year or two. My aim in looking three years back and three years ahead was to establish a baseline against which to compare the manager’s performance. I could have chosen on time spans, of course, but a plausible story ensues from the choices that I made.

First, here is a graphical view of the relationship between each of the 63 managerial stints and the respective before-and-after records of the teams involved:

Manager's W-L record vs. baseline

A clue to deciphering the graph: Look at the data point toward the upper-left corner labeled “Sewell SLB 41-46.” The label gives the manager’s last name (Sewell for Luke Sewell, in this case), the team he managed (SLB = St. Louis Browns), and the years of his tenure (1941-46). (In the table below, all names, teams, and dates are spelled out, for all 63 observations.) During Sewell’s tenure, the Browns’ W-L record was .134 points above the average of .378 attained by the Browns in 1938-40 and 1947-49. That’s an impressive performance, and it stands well above the 68-percent confidence interval. (Confidence intervals represent the range within which certain percentages of observations are expected to fall.)

The linear fit (equation in lower-left corner) indicates a statistically significant negative relationship between the change in a team’s fortunes during a manager’s tenure and the team’s baseline performance. The negative relationship means that there is a strong tendency to “regress toward the mean,” that is toward a record that is consistent with the quality of a team’s players. In other words, the negative relationship indicates that a team’s outstanding or abysmal record my owe nothing (or very little) to a manager’s efforts.

In fact, relatively few managers succeeded in leading their teams significantly far (up or down) from baseline performance. Those managers are indicated by green (good) and red (bad) in the preceding graph.

The following table gives a rank-ordering of all 47 managers in their 63 management stints. The color-coding indicates the standing of a particular performance with respect to the trend (green = above trend, red = below trend). The shading indicates the standing of a particular performance with respect to the confidence intervals: darkest shading = above and below the 95-percent confidence interval; medium shading = between the 68-percent and 95-percent confidence intervals; lightest shading = between the 68-percent confidence intervals.

Ranking of manager's performances

Of the 63 performances, 4 of them (6.3 percent) lie outside the 95-percent confidence interval; 13 of them (20.6 percent) are between the 68-percent and 95-percent confidence intervals; the other 46 (73.0) percent are in the middle, and statistically indistinguishable.

Billy Southworth’s tour as manager of the St. Louis Cardinals in 1940-45 (#1) stands alone above the 95-percent confidence interval. Two of Bucky Harris’s four stints rank near the bottom (#61 and #62) just above Ralph Houk’s truly abysmal performance as manager of the Detroit Tigers in 1974-78 (#63).

Southworth’s tenure with the Cardinals is of a piece with his career W-L record (.597), and with his above-average performance as manager of the Boston Braves in 1946-51 (# 18). Harris had a mixed career, as indicated by his overall W-L record of .493 and two above-average tours as manager (#22 and #26). Houk’s abysmal record with the Tigers was foretold by his below-average tour as manager of the Yankees, a broken tenure that spanned 1961-73 (#47).

Speaking of the Yankees, will the real Casey Stengel please stand up? Is he the “genius” with an above-average record as Yankees manager in 1949-60, (#13) or the “bum” with a dismal record as skipper of the Boston Bees/Braves in 1938-42 (#56)? (Stengel’s ludicrous three-and-a-half-year tour as manager of the hapless New York Mets of 1962-65 isn’t on the list because of its brevity. It should be noted, however, that the Mets improved gradually after Stengel’s departure, and won the World Series in 1969.)

Stengel is one of seven managers with a single-season performance below the 68-percent confidence level. Four of the seven — Harris, Houk, Stengel, and Tom Kelly (late of the Minnesota Twins) — are among the top decile on the games-managed list. The top decile also includes seven managers who turned in performances that rank above the 68-percent confidence interval: Earl Weaver, Bobby Cox, Al Lopez, Joe Torre, Sparky Anderson, Joe McCarthy, and Charlie Grimm (#s 2-4 and 6-9).

I could go on and on about games managed vs. performance, but it boils down to this: If there were a strong correlation between the rank-order of managers’ performances in the preceding table and the number of games they managed in their careers, it would approach -1.00. (Minus because the the best performance is ranked #1 and the worst is ranked #68.) But the correlation between between rank and number of games managed in a career is only -0.196, a “very weak” correlation in the parlance of statistics.

In summary, when it comes to specific management stints, Southworth’s performance in 1940-45 was clearly superlative; the performances of Harris (1929-33, 1935-42) and Houk (1974-78) were clearly awful. In between those great and ghastly performance lie a baker’s dozen that probably merit cheers or Bronx cheers. A super-majority of the performances (the 73 percent in the middle) probably have little to do with management skills and a lot to do with other factors, to which I will come.

The Bottom Line

It’s safe to say that the number of games managed is, at best, a poor reflection of managerial ability. What this means is that (a) few managers exert a marked influence on the performance of their teams and (b) managers, for the most part, are dismissed or kept around for reasons other than their actual influence on performance. Both points are supported by the two preceding sections.

More tellingly, both points are consistent with the time-tested observation that “they” couldn’t fire the team, so “they” fired the manager.

CLOSING THOUGHTS

The numbers confirm what I saw in 30 years of being managed and 22 (overlapping) years of managing: The selection of managers is at least as random as their influence on what they manage. This is true not only in baseball but wherever there are managers, that is, throughout the world of commerce (including its entertainment sectors), the academy, and government.

The is randomness for several reasons. First, there is the difficulty of specifying managerial objectives that are measurable and consistent. A manager’s basic task might be to attain a specific result (e.g., winning more games than the previous manager, winning at least a certain number of games, turning a loss into a profit). But a manager might also be expected to bring peace and harmony to a fractious workplace. And the manager might also be charged with maintainng a”diverse” workplace and avoiding charges of discrimination? Whatever the tasks, their specification is often arbitrary and, in large organizations, impossible to relate the objective to an overarching organization goal (e.g., attaining a profit target).

Who knows if it’s possible to win more games or turn a loss into a profit, given the competition, the quality of the workforce, etc.? Is a harmonious workplace more productive than a fractious one if a fractious one is a sign of productive competitiveness?  How does one square “diversity” and forbearance toward the failings of the “diverse” (to avoid discrimination charges), while also turning a profit?

Given the complexity of management, at which I’ve only hinted, and the difficulty of judging managers, even when their “output” is well-defined (e.g., W-L record), it’s unsurprising that the ranks of managers are riddled with the ineffective and the incompetent. And such traits are often tolerated and even rewarded (e.g., raise, promotion, contract extension). Why? Here are some of the reasons:

  • Unwillingness to admit that it was a mistake to hire or promote a manager
  • A manager’s likeability or popularity
  • A manager’s connections to higher-ups
  • The cost and difficulty of firing a manager (e.g., severance pay, contract termination clauses, possibility of discrimination charges)
  • Inertia — Things seem to be going well enough, and no one has an idea of how well they should be going).

The good news is that relatively few managers make a big difference. The bad news is that the big difference is just as likely to be negative as it is to be positive. And for the reasons listed above, abysmal managers will not be rooted out until they have done a lot of damage.

So, yes, some managers — though relatively few — make a difference. But that difference is likely to prove disastrous. Just look at the course of the United States over the past 80 years.

Pseudoscience, “Moneyball,” and Luck

Orin Kerr of The Volokh Conspiracy endorses the following clap-trap, uttered by Michael Lewis (author of Liar’s Poker and Moneyball) in the course of a commencement speech at Princeton University:

A few years ago, just a few blocks from my home, a pair of researchers in the Cal psychology department staged an experiment. They began by grabbing students, as lab rats. Then they broke the students into teams, segregated by sex. Three men, or three women, per team. Then they put these teams of three into a room, and arbitrarily assigned one of the three to act as leader. Then they gave them some complicated moral problem to solve: say what should be done about academic cheating, or how to regulate drinking on campus.

Exactly 30 minutes into the problem-solving the researchers interrupted each group. They entered the room bearing a plate of cookies. Four cookies. The team consisted of three people, but there were these four cookies. Every team member obviously got one cookie, but that left a fourth cookie, just sitting there. It should have been awkward. But it wasn’t. With incredible consistency the person arbitrarily appointed leader of the group grabbed the fourth cookie, and ate it. Not only ate it, but ate it with gusto: lips smacking, mouth open, drool at the corners of their mouths. In the end all that was left of the extra cookie were crumbs on the leader’s shirt.

This leader had performed no special task. He had no special virtue. He’d been chosen at random, 30 minutes earlier. His status was nothing but luck. But it still left him with the sense that the cookie should be his.

So far, sort of okay. But then:

This experiment helps to explain Wall Street bonuses and CEO pay, and I’m sure lots of other human behavior. But it also is relevant to new graduates of Princeton University. In a general sort of way you have been appointed the leader of the group. Your appointment may not be entirely arbitrary. But you must sense its arbitrary aspect: you are the lucky few. Lucky in your parents, lucky in your country, lucky that a place like Princeton exists that can take in lucky people, introduce them to other lucky people, and increase their chances of becoming even luckier. Lucky that you live in the richest society the world has ever seen, in a time when no one actually expects you to sacrifice your interests to anything.

All of you have been faced with the extra cookie. All of you will be faced with many more of them. In time you will find it easy to assume that you deserve the extra cookie. For all I know, you may. But you’ll be happier, and the world will be better off, if you at least pretend that you don’t.

Never forget: In the nation’s service. In the service of all nations.

Thank you.

And good luck.

I am unsurprised by Kerr’s endorsement of Lewis’s loose logic, given Kerr’s rather lackadaisical attitude toward the Constitution (e.g., this post).

Well, what could be wrong with the experiment or Lewis’s interpretation of it? The cookie experiment does not mean what Lewis thinks it means. It is like the Candle Problem in that Lewis  draws conclusions that are unwarranted by the particular conditions of the experiment. And those conditions are so artificial as to be inapplicable to real situations. Thus:

1. The  teams and their leaders were chosen randomly. Businesses, governments, universities, and other voluntary organizations do not operate that way. Members choose themselves. Leaders (in business, at least) are either self-chosen (if they are owners) or chosen by higher-ups on the basis of past performance and what it says (imperfectly) about future performance.

2. Because managers of businesses are not arbitrarily chosen, there is no analogy to the team leaders in the experiment, who were arbitrarily chosen and who arbitrarily consumed the fourth cookie. For one thing, if a manager reaps a greater reward than his employees, that is because the higher-ups value the manager’s contributions more than those of his employees. That is an unsurprising relationship, when you think about it, but it bears no resemblance to the case of a randomly chosen team with a randomly chosen leader.

3. Being the beneficiary of some amount of luck in one’s genetic and environmental inheritance does not negate the fact that one must do something with that luck to reap material rewards. The “extra cookie,” as I have said, is generally produced and earned, not simply put on a plate to be gobbled. If a person earns more cookies because he is more productive, and if he is more productive (in part) because of his genetic and environmental inheritance, that person’s great earning power (over the long haul) is based on the value of what he produces. He does not take from others (as Lewis implies), nor does he owe to others a share of what he earns (as Lewis implies).

Just to drive home the point about Lewis’s cluelessness, I will address his book Moneyball, from which a popular film of the same name was derived. This is Amazon.com‘s review of the book:

Billy Beane, general manager of MLB’s Oakland A’s and protagonist of Michael Lewis’s Moneyball, had a problem: how to win in the Major Leagues with a budget that’s smaller than that of nearly every other team. Conventional wisdom long held that big name, highly athletic hitters and young pitchers with rocket arms were the ticket to success. But Beane and his staff, buoyed by massive amounts of carefully interpreted statistical data, believed that wins could be had by more affordable methods such as hitters with high on-base percentage and pitchers who get lots of ground outs. Given this information and a tight budget, Beane defied tradition and his own scouting department to build winning teams of young affordable players and inexpensive castoff veterans.

Lewis was in the room with the A’s top management as they spent the summer of 2002 adding and subtracting players and he provides outstanding play-by-play…. Lewis, one of the top nonfiction writers of his era (Liar’s Poker, The New New Thing), offers highly accessible explanations of baseball stats and his roadmap of Beane’s economic approach makes Moneyball an appealing reading experience for business people and sports fans alike.

The only problems with Moneyball are (a) its essential inaccuracy and (b) its incompleteness as an analysis of success in baseball.

On the first point, “moneyball” did not start with Billy Beane and the Oakland A’s, and it is not what it is made out to be. Enter Eric Walker, the subject and author of “The Forgotten Man of Moneyball, Part 1,” and “The Forgotten Man of Moneyball, Part 2,” published October 7, 2009, on a site at deadspin.com. (On the site’s home page, the title bar displays the following: Deadspin, Sports News without Access, Favor, or Discretion.) Walker’s recollections merit extensive quotation:

…[W]ho am I, and why would I be considered some sort of expert on moneyball? Perhaps you recognized my name; more likely, though, you didn’t. Though it is hard to say this without an appearance of personal petulance, I find it sad that the popular history of what can only be called a revolution in the game leaves out quite a few of the people, the outsiders, who actually drove that revolution.

Anyway, the short-form answer to the question is that I am the fellow who first taught Billy Beane the principles that Lewis later dubbed “moneyball.” For the long-form answer, we ripple-dissolve back in time …

. . . to San Francisco in 1975, where the news media are reporting, often and at length, on the supposed near-certainty that the Giants will be sold and moved. There sit I, a man no longer young but not yet middle-aged, a man who has not been to a baseball game — or followed the sport — for probably over two decades….

With my lady, also a baseball fan of old, I go to a game. We have a great time; we go to more games, have more great times. I am becoming enthused. But I am considering and wondering — wondering about the mechanisms of run scoring, things like the relative value of average versus power…. I go to the San Francisco main library, looking for books that in some way actually analyze baseball. I find one. One. But what a one.

If this were instead Reader’s Digest, my opening of that book would be “The Moment That Changed My Life!” The book was Percentage Baseball, by one Earnshaw Cook, a Johns Hopkins professor who had consulted on the development of the atomic bomb….

…Bill James and some others, who were in high school when Cook was conceiving the many sorts of formulae they would later get famous publicizing in their own works, have had harsh things to say about Cook and his work. James, for example, wrote in 1981, “Cook knew everything about statistics and nothing at all about baseball — and for that reason, all of his answers are wrong, all of his methods useless.” That is breathtakingly wrong, and arrogant. Bill James has done an awful lot for analysis, both in promoting the concepts and in original work (most notably a methodology for converting minor-league stats to major-league equivalents). But, as Chili Davis once remarked about Nolan Ryan, “He ain’t God, man.” A modicum of humility and respect is in order…. Cook’s further work, using computer simulations of games to test theory (recorded in his second book, Percentage Baseball and the Computer), was ground-breaking, and it came long before anyone thought to describe what Cook was up to as “sabermetrics” and longer still before anyone emulated it.

…I wanted to get a lot closer to the game than box seats. I had, some years before, been a radio newscaster and telephone-talk host, and I decided to trade on that background. But in a market like the Bay Area, one does not just walk into a major radio station and ask for a job if it has been years since one’s last position; so, I walked into a minor radio station, a little off-the-wall FM outfit, and instantly became their “sports reporter”; unsalaried, but eligible for press credentials from the Giants….

Meanwhile, however, I was constantly working on expanding Cook’s work in various ways, trying to develop more-practical methods of applying his, and in time my, ideas….

When I felt I had my principles in a practical, usable condition, I started nagging the Giants about their using the techniques. At first, it was a very tough slog; in those days — this would be 1979 or so, well before Bill James’ Abstracts were more than a few hundred mimeographed copies -– even the basic concepts were unknown, and, to old baseball men, they were very, very weird ideas….

In early 1981, as a demonstration, I gave the Giants an extensive analysis of their organization; taking a great risk, I included predictions for the coming season. I have that very document beside me now as I type…. I was, despite the relative crudeness of the methodology in those days, a winner: 440 runs projected, 427 scored; ERA projected, 3.35, ERA achieved, 3.28; errors projected, 103, actual errors committed, 102; and, bottom line, projected wins, 57, actual wins 56….

By this time, I had taken a big step up as a broadcaster, moving from that inconsequential little station to KQED, the NPR outlet in San Francisco, whence I would eventually be syndicated by satellite to 20 NPR affiliates across the country, about half in major markets.

As a first consequence of that move, a book editor who had heard the daily module while driving to work and thought it interesting approached me with a proposal that I write a book in the general style of my broadcasts. I began work in the fall of 1981, and the book, The Sinister First Baseman and Other Observations, was published in 1982, to excellent reviews and nearly no sales. Frank Robinson, then the Giants’ manager and a man I had come to know tolerably well, was kind enough to provide the Foreword for the book, which was a diverse collection of baseball essays….

At any rate, there I was, finally on contract with a major-league ball club, the Giants, but in a dubious situation…. I did persuade them to trade Gary Lavelle to the Blue Jays, but instead of names like John Cerutti and Jimmy Key, whom I had suggested, Haller got Jim Gott, who gave the Giants one good year as a starter and two forgettable years in the pen, plus two guys who never made the majors. But deals for Ken Oberkfell and especially for John Tudor, which I lobbied for intensely, didn’t get made (Haller called 20 minutes too late to get Oberkfell). I still remember then-Giants owner Bob Lurie, when I was actually admitted to the Brain Trust sanctum on trade-deadline day, saying around his cigar, “What’s all this about John Tudor?” (Tudor, then openly available, had a high AL ERA because he was a lefty in Fenway — this was well before “splits” and “park effects” were commonplace concepts — and I tried to explain all that, but no dice; Tudor went on to an NL ERA of 2.66 over seven seasons.)

When Robinson was fired by the Giants, I knew that owing to guilt by association (remember, Robby wrote the Foreword to my book) I would soon be gone, and so I was. My term as a consultant with the Giants was about half a season. In that brief term, I had had some input into a few decisions, but most of what I advocated, while listened to, was never acted on.

But having once crossed the major-league threshold, I was not about to sink back into oblivion. Across the Bay was an organization with a famously more forward-looking front office, with which I had already had contact. I asked, they answered, and so my career with the A’s began.

Modern analysis has shown a whole treasure chest of interesting and often useful performance metrics, but it remains so that the bedrock principle of classic analysis is simple: out-making controls scoring. What I call “classic” analysis is the principles that I presented to the Oakland Athletics in the early 1980s, which governed their thinking through 20 or so successful seasons, and which were dubbed “moneyball” by Michael Lewis in his book of that title. Because of that book, there has arisen a belief that whatever the A’s do is, by definition, “moneyball”; with the decline in their fortunes in recent years has come a corresponding belief that “moneyball” is in decline — dead, some would say [1] — because the A’s and moneyball are seen as essentially one thing.

That is simply wrong…. “Moneyball,” as the name says, is about seeking undervalued commodities [emphasis added]. In my day, what I regard as the crucial aspects of run-generation, notably on-base percentage, were seriously undervalued, so “moneyball” consisted in finding batters with those skills.

A team that today sustains one of the lowest on-base percentages in baseball, and actively acquires players with drastically low career on-base numbers, is very obviously practicing a different “moneyball” than that for which it became famed. Today’s A’s, it seems, see the undervalued commodities as “defense and athletic players drafted out of high school” (as a recent article on the organization put it). These are not your father’s A’s. What success their new tack will have remains to be seen (their present fortunes are a transition state); but “moneyball” as practiced today by the A’s seems no longer to have at its core the same analytic principles that then-GM Sandy Alderson and I worked with a quarter-century ago, and that I presented to Billy Beane in that now semi-famous paper [“Winning Baseball”]….

In 1994, Sandy promoted Billy Beane to assistant GM. At the same time, he asked me to prepare an overview of the general principles of analysis for Billy, so that Billy could get in one sitting an idea of the way the organization was looking at talent. In the end, I delivered a report titled “Winning Baseball,” with the subtitle: “An objective, numerical, analytic analysis of the principles and practices involved in the design of a winning baseball team.” The report was 66 pages long; I still grit my teeth whenever I remember that Michael Lewis described it as a “pamphlet [on page 58 of this edition of Moneyball].”…

My goal in that report, which I seem to have met, was to put the ideas — not the detailed principles, just the ideas — forward in simple, clear language and logical order, so that they would be comprehensible by and reasonable to a working front-office executive. Sandy Alderson didn’t need a document like this, then or at the outset, but he was a Harvard-trained attorney; I considered myself to be writing not just to Billy Beane but to any veteran baseball man (which, as it turned out, was just as well)….

Lewis not only demotes “Winning Baseball” to a pamphlet, but also demotes Walker to passing mention on three pages of Moneyball: 58, 62, and 63 (in the paperback edition linked above). Why would Lewis slight and distort Walker’s contributions to “moneyball”? Remember that Lewis is not a scientist, mathematician, or statistician. He is a journalist with a B.A. in art history who happened to work at Salomon Brothers for a few years. I have read his first book, Liar’s Poker. It is obviously the work of a young man with a grievance and a flair for dramatization. Moneyball is obviously the work of a somewhat older man who has honed his flair for dramatization. Do not mistake it for a rigorous analysis of the origins and effectiveness of “moneyball.”

Just how effective was “moneyball,” as it was practiced by the Oakland Athletics? There is evidence to suggest that it was quite effective. For example:


Sources and notes: Team won-lost records are from Baseball-Reference.com. Estimates of team payrolls are from USA Today’s database of salaries for professional sports teams, which begins in 1988 for major-league baseball (here). The payroll index measures the ratio of each team’s payroll in a given year to the major-league average for the same year.

The more that a team spends on player salaries, the better the team’s record. But payroll accounts for only about 18 percent of the variation in the records of major-league teams during the period 1988-2011. Which means that other factors, taken together, largely determine a team’s record. Among those factors is “moneyball” — the ability to identify, obtain, effectively use, and retain players who are “underpriced” relative to their potential. But the contribution of “moneyball” cannot be teased out of the data because, for one thing, it would be impossible to quantify the extent to which a team actually practices “moneyball.” That said, it is evident that during 1988-2011 the A’s did better than the average team, by the measure of wins per dollar of payroll: Compare the dark green regression line, representing the A’s, with the black regression line, representing all teams.

That is all well and good, but the purpose of a baseball team is not to win a high number of games per dollar of payroll; it is to win — period. By that measure, the A’s of the Alderson-Beane “moneyball” era have been successful, at times, but not uniquely so:


Source: Derived from Baseball-Reference.com.

The sometimes brilliant record of the Athletics franchise during 1901-1950 is owed to one man: Cornelius McGillicuddy (1862-1956). And the often dismal record of the franchise during 1901-1950 is owed to one man: the same Cornelius McGillicuddy. True fans of baseball (and collectors of trivia) know Cornelius McGillicuddy as Connie Mack, or more commonly as Mr. Mack. The latter is an honorific bestowed on Mack because of his dignified mien and distinguished career in baseball: catcher from 1886 to 1896; manager of the Pittsburgh Pirates from 1894 to 1896; manager of the Philadelphia Athletics from 1901 to 1950; part owner and then sole owner of the Athletics from 1901 to 1954.  (He is also an ancestor of two political figures who bear his real name and alias: Connie Mack III and Connie Mack IV.)

Mack’s long leadership and ownership of the A’s is important because it points to the reasons for the A’s successes and failures during the fifty years that he led the team from the bench. Here, from Wikipedia, is a story that is familiar to persons who know their baseball history:

[Mack] was widely praised in the newspapers for his intelligent and innovative managing, which earned him the nickname “the Tall Tactician”. He valued intelligence and “baseball smarts”, always looking for educated players. (He traded away Shoeless Joe Jackson despite his talent because of his bad attitude and unintelligent play.[9]) “Better than any other manager, Mack understood and promoted intelligence as an element of excellence.”[10] He wanted men who were self-directed, self-disciplined and self-motivated; his ideal player was Eddie Collins.[11]

“Mack looked for seven things in a young player: physical ability, intelligence, courage, disposition, will power, general alertness and personal habits.”[12]

He also looked for players with quiet and disciplined personal lives, having seen many players destroy themselves and their teams through heavy drinking in his playing days. Mack himself never drank; before the 1910 World Series he asked all his players to “take the pledge” not to drink during the Series. When Topsy Hartsel told Mack he needed a drink the night before the final game, Mack told him to do what he thought best, but in these circumstances “if it was me, I’d die before I took a drink.”[13]

In any event, his managerial style was not tyrannical but easygoing.[14] He never imposed curfews or bed checks, and made the best of what he had; Rube Waddell was the best pitcher and biggest gate attraction of his first decade as A’s manager, so he put up with his drinking and general unreliability for years until it began to bring the team down and the other players asked Mack to get rid of him.[15]

Mack’s strength as a manager was finding the best players, teaching them well and letting them play. “He did not believe that baseball revolved around managerial strategy.”[10] He was “one of the first managers to work on repositioning his fielders” during the game, often directing the outfielders to move left or right, play shallow or deep, by waving his rolled-up scorecard from the bench.[12] After he became well known for doing this, he often passed his instructions to the fielders by way of other players, and simply waved his scorecard as a feint.[16]

*   *   *

Mack saw baseball as a business, and recognized that economic necessity drove the game. He explained to his cousin, Art Dempsey, that “The best thing for a team financially is to be in the running and finish second. If you win, the players all expect raises.” This was one reason he was constantly collecting players, signing almost anyone to a ten-day contract to assess his talent; he was looking ahead to future seasons when his veterans would either retire or hold out for bigger salaries than Mack could give them.

Unlike most baseball owners, Mack had almost no income apart from the A’s, so he was often in financial difficulties. Money problems – the escalation of his best players’ salaries (due both to their success and to competition from the new, well-financed Federal League), combined with a steep drop in attendance due to World War I — led to the gradual dispersal of his second championship team, the 19101914 team, who [sic] he sold, traded, or released over the years 1915–1917. The war hurt the team badly, leaving Mack without the resources to sign valuable players….

All told, the A’s finished dead last in the AL seven years in a row from 1915 to 1921, and would not reach .500 again until 1926. The rebuilt team won back-to-back championships in 1929–1930 over the Cubs and Cardinals, and then lost a rematch with the latter in 1931. As it turned out, these were the last WS titles and pennants the Athletics would win in Philadelphia or for another four decades.

With the onset of the Great Depression, Mack struggled financially again, and was forced to sell the best players from his second great championship team, such as Lefty Grove and Jimmie Foxx, to stay in business. Although Mack wanted to rebuild again and win more championships, he was never able to do so owing to a lack of funds.

Had an earlier Michael Lewis written Moneyball in the 1950s, as a retrospective on Mack’s career as a manager-owner, that Lewis would have said (correctly) that the A’s successes and failures were directly related to (a) the amount of money spent on the team’s payroll, (b) Connie Mack’s character-based criteria for selecting players, and (c) his particular approach to managing players.  That is quite a different story than the one conveyed by the Moneyball written by the real Lewis.

Which version of Moneyball is correct? No one can say for sure. But the powerful evidence of Connie Mack’s long tenure suggests that it takes a combination of the two versions of Moneyball to be truly successful, that is, to post a winning record year after year. It seems that Lewis (inadvertently) jumped to a conclusion about what makes for a successful baseball team — probably because he was struck by the A’s then-recent success and did not look to the A’s history.

In any event, success through luck is not the moral of Moneyball; the moral is success through deliberate effort. But Michael Lewis ignored the moral of his own “masterwork” when he stood before an audience of Princeton graduates and told them that they are merely (or mainly) lucky. How does one graduate from Princeton merely (or mainly) by being lucky? Does it not require the application of one’s genetic talents? Did not most of the graduates of Princeton arrive there, in the first place, because they had applied their genetic talents well during their years in high school or prep school (and even before that)? Is one’s genetic inheritance merely a matter of luck, or is it the somewhat predictable result of the mating of two persons who were not thrown together randomly, but who had a lot in common — including (most likely) high intelligence?

Just as the cookie experiment invoked by Lewis is a load of pseudoscientific hogwash, the left-wing habit of finding luck at the bottom of every achievement is a load of politically correct hogwash. Worse, it is an excuse for punishing success.

Lewis’s peroration on luck is just a variation on a common left-wing theme: Success is merely a matter of luck, so it is the state’s right and duty to redistribute the spoils of luck.

Related posts:
Moral Luck
The Residue of Choice
Can Money Buy Excellence in Baseball?
Inventing “Liberalism”
Randomness Is Over-Rated
Fooled by Non-Randomness
Accountants of the Soul
Rawls Meets Bentham
Social Justice
Positive Liberty vs. Liberty
More Social Justice
Luck-Egalitarianism and Moral Luck
Nature Is Unfair
Elizabeth Warren Is All Wet
Luck and Baseball, One More Time
The Candle Problem: Balderdash Masquerading as Science
More about Luck and Baseball
Barack Channels Princess SummerFall WinterSpring
Obama’s Big Lie

Conducting, Baseball, and Longevity

It seems to be a matter of conventional wisdom that conductors (of musical performances) live longer than most mortals, and that that their above-average longevity something to the fact that the occupation of conducting involves vigorous arm motions. Various writers have looked into the matter of conductors’ longevity, and have come to various conclusions about it. In the late 1970s, for example, a medical doctor named Joseph Atlas published an article on the subject, a news account of which is available here. According to the news story,

Atlas selected a random sampling of 35 major decades symphony leaders and computed their longevity at 73.4 years, compared with 68.5 for the average American male.

Then he arrived at a series of conclusions.

Among them:

– Gratifying, or happy, stress promotes longevity.

– Driving motivation and the sense of fulfillment that comes with world recognition help forestall the ravages of age.

Atlas defines gratifying stress as the opposite of frustrating stress, which is the kind that can lead to coronaries….

…As yet there is no scientific documentation to back him up. His conclusions are hypothetical, he says, “more anecdotal than statistical.”…

…Among the prime examples of longevity, Atlas cites, is Leopold Stokowski, who was active and vital until his death at 95.

“Arturo Toscanini lived an active life to the age of 89, Bruno Walter to 85, Ernest Ansermet to 86, Walter Damrosch to 88, Arthur Fiedler is 84.”

There are problems with Atlas’s analysis, but — at bottom — Atlas is right about the longevity of conductors, and probably right that “gratifying stress” enhances longevity. Whether vigorous arm movement has anything to do with longevity is another matter, to which I will come.

As for the problems with Atlas’s analysis,consider this passage from Robert P. Abelson’s Statistics as Principled Argument (1995):

 The longevity datum on famous orchestral conductors (Atlas, 1978) provides a good example [of a spurious attribution of causality]. With what should the mean age at their deaths, 73.4 years, be compared? With orchestral players? With nonfamous conductors? With the general public?

All of the conductors studied were men, and almost all of them lived in the United States (though born in Europe). The author used the mean life expectancy of males in the U.S. population as the standard of comparison. This was 68.5 years at the time the study was done, so it appears that the conductors enjoyed about a 5-year extension of life and indeed, the author of the study jumped to the conclusion that involvement in the activity of conducting causes longer life. Since the study appeared, others have seized upon it and even elaborated reasons for a causal connection (e.g., as health columnist Brody, 1991, wrote, “it is believed that arm exercise plays a role in the longevity of conductors.”

However, as Carroll (1979) pointed out in a critique of the study, there is a subtle flaw in life-expectancy comparisons: The calculation of average life expectancy includes infant deaths along with those of adults who survive for many years. Because no infant has ever conducted an orchestra, the data from infant mortalities should be excluded from the comparison standard. Well, then, what about teenagers? They also are much too young to take over a major orchestra, so their deaths should also be excluded from the general average. Carroll argued that an appropriate cutoff age for the comparison group is at least 32 years old, an estimate of the average age of appointment to a first orchestral conducting post. The mean life expectancy among U.S. males who have already reached the age of 32 is 72.0 years, so the relative advantage, if any, of being in the famous conductor category is much smaller than suggested by the previous, flawed comparison. (p.4, quoted here)

But the comparison is not as flawed as Abelson makes it out to be. Consider the excerpts of an talk given in 2005 by Jeremiah A. Barondess, M.D., then president of the New York Academy of Medicine:

I have had more than a glancing interest in this subject [longevity] for a long time. I was first attracted to it many years ago when I came across a squib in the newspaper to the effect that Leopold Stokowski, then about 90 years old, had been the subject of a complaint to the authorities by a young woman whom he had pinched. Morals aside, I thought the act reflected a certain energy on Stokowski’s part, and I found myself led into a rumination about the apparent vigor, and then the differential longevity of symphonic conductors. Stokowski, as it turned out, lived for 95 years, and gave his last concert at the age of 93 at the Vence Festival in France. Toscanini lived to be 90, Sir Thomas Beecham 83, and Eugene Ormandy 86. The more general question that emerged for me had to do with who, in any frame of life, lives a long time, and why. And, if the posit about symphonic conductors was correct, what was it about them or their activities that was operational?

Was it the music? There is some evidence that the right side of the brain is more involved in processing music than the left, and blood flow studies have shown that the same areas of the brain that respond to euphoria-inducing stimuli like food, sex and some drugs also respond to stimulating music. How this might have to do with longevity is admittedly obscure; connections between pleasure and longevity have not been clearly established….

In any case, to return to symphonic conductors, the fact that that the sample was small and hardly random didn’t deter me much. Maybe it was just the successful ones who lived a long time. Maybe it was the music that did it. Maybe, if symphonic conductors really had preternatural longevity, it had something to do with waving their arms so much. That idea really intrigued me…. First of all, it’s plain that when people run they also move their arms a lot, so even if running is good for you, you may be able to get the same effect a lot more efficiently. And notice that arm waving is a form of upper body aerobic exercise, so the arms have a claim along that line as well, and, in any case, I found the idea that it might be better to play a little Mozart or Shostakovich and wave your arms in time with it much more congenial. Finally, in the case of symphonic conducting, an enormous amount of cognitive activity is involved, another element that has been linked to longevity.

Ultimately I felt more or less requited when I discovered a paper by Leonard Hayflick citing a MetLife study that involved 437 active and former conductors of major regional and community symphonies. The study started in 1956 and ended in 1975 when 118 of them had died, more than 20% at age 80 or older. The death rate for the entire group was 38% below that of the general population, and for conductors aged 50 to 59, a decade when stress and responsibilities are at their peak, the death rate was 56% less than that of the general population. I was somewhat disconcerted by a nearly simultaneous MetLife study that showed that corporate executives enjoyed longevity similar to that of orchestra conductors, punching a hole in the arm waving theory, though possibly not a definitive hole, since the study did not control for arm waving among the executives.

In any case, the conducting and arm waving thing had me hooked. The next thought, if you’ll forgive the expression, was that it might be interesting to compare longevity among baseball players who spent years in positions that involved a lot of throwing, and to compare them with those whose positions called for infrequent throwing. I tried to recruit to this question a bright young man who was taking a fellowship in general medicine with me, and he seemed interested. Accordingly (this was before every statistic in the world was available online, in fact before anything was available online), I provided him with the Encyclopedia of Baseball, thinking he could do the necessary with it. It contained data on everyone who had ever played professional baseball, the teams, the years and the positions played. The task proved too daunting for my young colleague, and it fell by the wayside under the pressure of other responsibilities, but, as evidence the idea wasn’t uniquely quirky, in 1988 a group at the University of Alabama published an article on the mortality experience of major league baseball players, in the New England Journal of Medicine. They assembled a cohort that included all players who had played their first games for a major league team in the United States between 1911 and 1915 and who survived at least until 1925. They had a cohort of 985 players to analyze, and successfully acquired follow-up information on 958 of them. Their average age at death was 70.7 years, the average year 1960. Infielders had the lowest overall mortality rate and catchers the highest; the differences were not statistically significant. Grouping all infielders may have blunted the study; it might have been better to compare first basemen, say, who throw relatively little, with pitchers or short stops. But there was an inverse association between standardized mortality ratios for the groups and the length of the player’s career; and being a baseball player in fact conferred a slight protective effect against death, with the cohort having only 94% of the deaths expected. It was most interesting to me that the data suggested that players who performed the best lived the longest, a fact that should bring some comfort to the accomplished people in this room. But my arm waving theory was not supported, at least by the gross categories established within the cohort. (“How to Live a Long Time: Facts, Factoids, and Descants,” Transactions of the American Clinical and Climatological Association, 116: 77–89)

It seems indisputable, based on the statistics cited by Barondess, that conductors and baseball players tend to outlive their peers. This leads to two questions: By how much do conductors and baseball players outlive their peers, and why do they and others, like corporate executives, outlive them? As Barondess suggests, the answer is not vigorous arm movement. If it were the answer, one would expect pitchers be longer-lived than other baseball players, and that is not the case.

But I am getting ahead of myself. Before considering what factors might yield a long life-span, I will present some statistics about conductors and baseball players.

I compiled a list of 152 conductors born after 1800 and before 1930 who are prominent enough to have Wikipedia entries. I obtained names of candidates for the list from this page at a site known as knowledgerush (which displays obnoxious banner ads), and from two lists at Wikipedia (one of 19th century conductors, the other of 20th century conductors). Of the 152 conductors, 18 are still living. The earliest year of birth of a living conductor is 1919. I therefore focused on the 112 conductors who were born in 1918 or earlier,* inasmuch as the inclusion of conductors born after 1918 (among them 18 living ones) would bias the analysis by understating the longevity of conductors born after 1918. Here is a plot of the 112 conductors’ ages at death, by year of birth:

The linear relationship between age at death and year of birth (dashed line) is statistically insignificant, but it roughly parallels the rising trend of life expectancies for white males aged 40 (the green line). (I used life expectancies for white, American males, given here, because there are no non-whites or females in the sample of 112 conductors.) In words, a person who — like almost everyone in the sample — had become a conductor by the age of 40 was very likely to outlive the general run of 40-year old white males, and to do so by a wide margin. By 1918, that margin had shrunk to about 6 years, but it was still large enough to say that conductors enjoyed unusually long lives. The trend line, however weak statistically, suggests that conductors will continue to enjoy unusually long lives.

But, as I have said, arm-waving probably is not the key to conductors’ long lives. The evidence for that assertion is found in an analysis of the longevity of baseball players. Using the Play Index (subscription) tool at Baseball-Reference.com, I compiled lists of deceased major-league players who either pitched at least 1,000 innings or played in at least 1,000 games in one of the following positions or groups of positions: pitcher, catcher, second base-third base-shortstop, first base-outfiield. I chose those groupings because pitchers use their arms intensely and often every few games; catchers use their arms somewhat less intensely than pitchers, but more often than other players; the second base-third base-shortstop positions involve less intense and frequent arm motions than pitching and catching, but more frequent (if not more intense) than the first base-outfield positions.

The total number of players in the sample is 1,039, broken down as follows: 592 pitchers; 41 catchers; 178 players at second, third, or short; and 228 players at first or outfield. A regression on age at death yields the following:

Age at death = 57.1 + 1.02 x number of seasons in major-league baseball – 0.004 x number of games played + 6.05 if played primarily (at least 90% of games) at 2b, 3b, or SS + 5.17 if played primarily at 1b or OF + 2.90 if played primarily at catcher.

The P-values on the intercept and coefficients are 1.7E-178,  7.89E-12, 0.05, 0.02, 0.05, and 0.33, respectively

What about pitchers? The positive coefficients on the non-pitching positions imply a negative coefficient on “pitcher.” The correlation between “pitcher” and “age at death” is negative and significant at better than the 1-percent level. The difference between the average age of pitchers at death (68.4 years) and the average age of other players at death (71.3 years) is statistically significant at the 1-percent level.

In sum, pitchers do not live as long as other players. And catchers, though they live longer than pitchers, do not live as long as other non-pitchers. So much for the idea that longevity is positively related to and perhaps abetted by vigorous and frequent arm motion.

What about the longevity of baseball players in relation to that of the population of white males? I derived the following graphs from the Play Index and the table of life expectancies (both linked above):

I chose 1918 as the cutoff point for ballplayers because that is the last year in the sample of 112 conductors. (As of today, only 15 of the thousands of players born in 1918 or earlier survive** — not enough to affect the comparison.) Before I bring in the conductors, I want to point out the positive trend for longevity among ballplayers (indicated by the heavy black line), especially in relation to the trend for white, 20-year old males. The linear fit, though weak, is statistically robust, and it reflects the long, upward rise in ballplayers’ longevity that is evident in the scatter plot.

I now add conductors to the mix:

For the period covered by the statistics (birth years from 1825 through 1918), conductors enjoyed a modest and significantly insignificant increase in longevity, indicated by the dashed black line. By 1918, ballplayers had almost caught up with conductors. The trends suggest that, on average, today’s MLB players can expect to live longer than today’s conductors. Conductors, nevertheless, seem destined to live longer than their contemporaries in the population at large, but not because they (conductors) wave their arms a lot.

If the secret of a long life is not a lot of arm-waving, what is it? I return to Dr. Barondess:

[W]e’ve heard for years that the best way to live a long time is to pick long-lived parents, and there is increasing evidence that the pace of aging is to a significant degree genetically determined, but environmental influences and personal behaviors are clearly also of great importance. Scandinavian studies have calculated the heritability of average life expectancy to be 20 to 30%, with environmental differences accounting for at least 70% of variation in age at death among twins. And studies of 7th Day Adventists suggest that optimizing health related behaviors could yield up to 25 years of good health beyond age 60 with a compression of morbidity toward the end of life. The authors of that study suggested that when it comes to aging well there is no such thing as the anti-aging industry’s free lunch. I think a better suggestion might be that a really good anti-aging maneuver is no lunch, in light of other studies connecting undereating with extension of life expectancy….

There are some data connecting a specific region on chromosome 4 to the longevity of centenarians and nonagenerians, and a number of longevity genes have been discovered in yeasts, worms and fruit flies. So apparently there are gerontogenes, or longevity-enabling genes, and the genetic contribution to longevity is being investigated with increasing enthusiasm….

There’s been a good deal of research activity, and perhaps even more in the public prints in recent years, with relation to diet and longevity, especially caloric restriction. These effects were first demonstrated in the 1930s, when it was shown that laboratory rats on limited diets live about 40% longer than normal and are resistant to many chronic diseases typical of aging. These studies have been replicated in yeasts, fruit flies, nematodes, fish, spiders and mice, and there are hints that the effect may also hold true for primates. Recent research on the mechanisms underlying these phenomena has shown that the effect of caloric restriction is tied to genetic factors….

Numerous mechanisms have been suggested without great clarification to this point, but it does appear that life lengthening through caloric restriction is not primarily related to retardation of disease processes, but rather to slowing of primary aging processes, and this is related to restriction of calories rather than specific nutrients….

On the other hand, specific nutrients may impact disease processes themselves…. One study suggested that pizza intake had the potential to reduce cardiovascular risk, presumably because of the tomato sauce component, and despite the cheese.

A number of other foodstuffs have been thought to enhance health prospects, including nuts, for their resveratrol content, organo-sulfur compounds in garlic and onions, and various carotenes. Cocoa, flavanol rich, is thought to be good for you; the makers of Mars Bars are working hard on this. So are blueberries, high in antioxidants, as are raspberries, cranberries and strawberries….

One study from Rome considered alcohol consumption and its effect on longevity. The study suggested that drinking 4 to 7 drinks per day, roughly 63 grams of alcohol, a dose some might think heroic, led to a two-year edge in life expectancy, but drinking more than 10 drinks was negatively associated with longevity. These drinks were 97% wine, primarily red, high in resveratrol content. Other studies have suggested that 250 to 500 cc. of red wine a day is associated with a diminished risk of macular degeneration, Alzheimer’s disease and cognitive deficits….

Several studies … have found that social networks among humans are important predictors of longevity, including participation in formal organizations, contact with friends or relatives, and so forth. In one study of African American women aged 55 to 96, those who were extremely isolated in a social sense were more than three times as likely to die within a five-year period of observation, an impact unaffected by the use of community senior services. A search for the effects on longevity of living as a recluse or a hermit produced no results, I imagine because follow-up would be difficult, but on the other hand a number of additional papers about socialization in humans turned up. One suggested that providing social support may be more beneficial than receiving it. Mortality was significantly reduced for individuals reported to be providers of support to friends, relatives and neighbors, and emotional support for spouses.

In a study from Columbia University, the impact of marital closeness on survival was examined in 305 older couples. Closeness was defined as naming one’s spouse as a confidant or as a source of emotional support, versus not naming, or being named by the spouse on at least one of the two dimensions, versus not being named. Husbands who were named by their wives as confidants or supports, but did not name them, were least likely to have died after six years. Compared with them, husbands who were not named by their wives as a confidant or source of social support, or did not name their wives, were from 3.3 to 4.7 times more likely to be dead. The results among wives showed a similar pattern, but a weaker one….

Studies of personal histories have illuminated some personality factors that may bear on longevity. One important investigation is the Nun Study, organized by David Snowden in 1986. In this longitudinal study of aging and dementia, he was looking at a convent community of nuns aged 74 to 106, retired from careers in a variety of sites, many of them as teachers. They tended not to drink or smoke, had similar diets, income, and quality of health care and had an active social network. Snowden examined short biographical notes written at an average age of 22, on entry into the order. These suggested that positive emotions in early life were associated with longevity, with a difference of nearly 7 years between the highest and lowest quartiles of positive emotion sentences. That is, positive emotional content in early life autobiographies was strongly associated with longevity six decades later. There was some sense that positive response patterns, or more rapid return to a positive outlook after negative events, serves to dampen the physiologic sequelae of emotional arousal, such as heart rate and blood pressure changes, and presumably also hormonal responses. In a word it’s best to be cheery, or at least positive.

Further to the effect of optimism and pessimism as risk factors for disease, Peterson and his group studied questionnaires filled out by 99 Harvard graduates in the classes of 1942 to ’44, when they were about 25 years of age, and then determined physical health from ages 30 to 60 as measured by examination by physicians. Pessimistic explanatory style, the belief that bad events are caused by stable, global or internal personal factors, predicted poor health at ages 45 through 60 even when physical and mental health at age 25 were controlled for, across an array of diseases ranging from gout to diabetes, kidney stones to hypertension. The correlations increased across the life span, from age 30 to 60.

With regard to the impact of cognitive activity on optimism, health, and possibly life expectancy, there is good reason to believe, as Guy McKhann and Marilyn Albert have pointed out, that the phrase “use it or lose it” applies. Maintaining one’s mental abilities is made easier through a variety of activities, including reading, doing crossword puzzles, learning ballroom dancing, using a computer and going to lectures or concerts. Studies have shown that in rats an enriched environment that includes exercise, toys, mirrors, tunnels and interaction with other rats strengthens connections between cells in the hippocampus and even increases the rate at which new cells are born. The idea of rat fraternization may be counterintuitive, but somewhere here there may be a link with the academic parable expressed in prior talks by Dick Johns on how to swim with sharks. Fraternization with rats, has, I think, a weaker set of academic projections, but I pass it along for what it’s worth.

Related to the last point is evidence of

[a] strong inverse correlation between early life intelligence and mortality … across different populations, in different countries, and in different epochs.”[3][4][5] Various explanations for these findings have been proposed:

“First, …intelligence is associated with more education, and thereafter with more professional occupations that might place the person in healthier environments. …Second, people with higher intelligence might engage in more healthy behaviours. …Third, mental test scores from early life might act as a record of insults to the brain that have occurred before that date. …Fourth, mental test scores obtained in youth might be an indicator of a well-put-together system. It is hypothesized that a well-wired body is more able to respond effectively to environmental insults…”[5]

A study of one million Swedish men found showed “a strong link between cognitive ability and the risk of death.”[6][7][8][9]

People with higher IQ test scores tend to be less likely to smoke or drink alcohol heavily. They also eat better diets, and they are more physically active. So they have a range of better behaviours that may partly explain their lower mortality risk.—-Dr. David Batty[7]

A similar study of 4,289 former US soldiers showed a similar relationship between IQ and mortality.[7][8][10]

The strong correlation between intelligence and mortality has raised questions as to how better public education could delay mortality.[11]

There is a known inverse correlation between socioeconomic position and health. A 2006 study found that controlling for IQ caused a marked reduction in this association.[12]

Research in Scotland has shown that a 15-point lower IQ meant people had a fifth less chance of seeing their 76th birthday, while those with a 30-point disadvantage were 37% less likely than those with a higher IQ to live that long.[13]

Here is my take on all of this: Conductors, baseball players, and corporate executives (among members of other identifiable groups) tend to be long-lived because they tend to be physically and mentally vigorous, to begin with. Conductors must possess stamina and intelligence to do what they do.To rise in the corporate world, one must be capable of working long hours, putting up with a lot of stress, and coping with many complex issues. And, contrary to the popular view of athletes as “dumb,” they are not (as a group); in fact, intelligence and good health (a key component of athleticism) are are tightly bound.

Moreover, conductors (who make music), ballplayers (who play a game) and corporate executives (who attain high status and high incomes) are engaged in occupations that yield what Robert Atlas calls “gratifying stress.” And, as persons who usually enjoy above-average incomes, they are likely to enjoy better diets and better health-care than most of their contemporaries.

The finding that pitchers do not live as long as other ballplayers supports the view that “gratifying stress” fosters longevity, whereas “frustrating stress” may shorten a person’s life. Pitchers, uniquely among ballplayers, are credited or charged with the wins and losses of their teams. And pitchers, as a group, win only half the time — an ungratifying outcome. Further, pitchers with consistently bad records do not last long in the big leagues, and end their careers having won less than 50 percent of the games for which they were held responsible (i.e., with a won-lost record below .500). Accordingly, more pitchers end their careers with losing records than with winning records: In the history of the major leagues, from 1871 through 2011, there have been 6,744 pitchers with a career record of at least one loss; only 29 percent of them (1,935) had a career won-lost record better than .500.

I conclude that occupation — conducting, playing professional baseball, etc. — is a function of the main influences on longevity — mental and physical robustness — and not the other way around. Occupation influences longevity only to the extent that increases it (at the margin) by bestowing “gratifying stress” and/or material rewards, or reduces it (at the margin) by bestowing “frustrating stress” and/or exposure to health-or life-threatening conditions.

*   *   *

The footnotes are below the fold.
Continue reading