Friday, September 27, 2013
The End of Dynasty
I have updated "The End of a Dynasty?" and removed the question mark. I am convinced that we have seen the end of Dynasty III of the New York Yankees. Go there for details.
Monday, September 3, 2012
What Makes a Winning Team?
As Eric Walker -- the real inventor of "moneyball" -- puts it, "moneyball is about seeking undervalued commodities." Perhaps baseball teams have done a better job of acquiring undervalued commodities since the publication of Michael Lewis's Moneyball in 2003. But arbitrage opportunities are fleeting, as the price of a "bargain" is driven up and it is no longer a "bargain." Moreover, as Walker explains, "moneyball" has been around since the 1980s and the concept surely had some influence on player selection even before Lewis made it famous.
At any rate, I have analyzed the performance and payrolls of American League (AL) teams for 1988-2011 to determine their effects on teams' won-lost (W-L) records. I focused on the AL because an analysis of both major leagues would have been complicated by the presence of the designated hitter in the AL and the absence of the DL in the National League. I began with 1988 because that is the first year covered by the USATODAY Salaries Database for baseball. Performance data are from Retrosheet.
What did I learn from those 24 years' worth of data? This:
1. Payroll does not drive W-L record.
2. Payroll is mainly determined by 7 aspects of performance.
3. W-L record is mainly determined by 9-11 aspects of performance, only 2-4 of which overlap with the determinants of payroll.
4. W-L record is strongly correlated with the ratio of runs scored (RS) to runs allowed (RA), with an r-squared of 0.89. There is considerable overlap between the determinants of RS-RA and the determinants of W-L record. There is not much overlap between the determinants of RS:RA and the determinants of payroll.
I begin with the relationship between payroll and W-L record:
Payroll index is the ratio of a team's payroll in a given season to the average payroll of all AL teams in the same season. A regression on W-L record with payroll index as an explanatory variable cannot include additional explanatory variables that are statistically significant (less than 1 percent chance of a random relationship with the dependent variable). As discussed below, this result indicates that payroll index is a proxy for measures of performance that are statistically significant determinants of a W-L record and RS:RA.
The following table summarizes the results of six regressions. Instead of showing the coefficients for the explanatory variables, I have converted them to elasticities, expressed as the percentage increase in a dependent variable that results from a 1-percent increase in the value of the explanatory variable.
Before I walk through the table, I should observe that the figures represent average tendencies over a 24-year span. The figures are significant, but they do not necessarily indicate the best course of action for a given team in a given situation. The best course of action for a given team in a given situation will depend on the team's options (e.g., players available on the free-agent market), on the likely payoff of those options (e.g., addition to runs scored if player A is signed and player B is traded away), and on the cost of obtaining each payoff (e.g., net addition to payroll if player A is signed and player B is traded).
The results in the payroll index column show that the payrolls of AL teams in 1988-2011 were driven mainly by batters' on-base percentage (OBP); pitchers' avoidance of giving up home runs; pitchers' ability to strike out batters (SO); fielders' avoidance of errors (ERR); batters' home runs (HR); catchers' throwing out base stealers (CS); and batters' not hitting a lot of triples (3B) -- perhaps because of the typical characteristics of triples hitters (e.g., they are not usually home-run hitters).
In the next column we see, again, that payroll spending -- in the aggregate over 24 seasons -- does not strongly influence W-L record. But because payroll is a proxy for several performance variables, no other variable show up as statistically significant when payroll index is used as an explanatory variable. It is important to note, however, that the relatively weak relationship between W-L record and payroll index for the entire AL masks considerable variation across teams. Here is a summary comparison, for the 13 teams that were in the AL in every year from 1988 through 2011:
The Oakland A's of "moneyball" fame did will for the amount of money spent on payroll, as measured by W-L record divided by payroll index (right-hand column). But, on that measure, the A's did no better than the Twins, and not a lot better than the Royals and White Sox.
The Yankees were the best team in the AL during 1988-2011, and they paid what it took to be the best. The Red Sox were the second-best team, and they paid accordingly. After that, results were mixed. The A's paid a lot less than the Yankees and Red Sox, and still won a lot -- but did not get much in return when they spent more (correlation coefficient of 0.19, as against 0.50 for the Yankees and 0.31 for the Red Sox). Though an above-average payroll index was not required for a winning record, four of the top six team in W-L and every team with a payroll index of 1.00 or grater had a winning record. All teams but one -- the Rangers -- managed to eke out some additional wins by increasing their payrolls.
The following graphs illustrate several story lines, such as success with young, low-priced players who then become higher-priced and less-productive players; overspending on once-outstanding veterans whose best seasons were behind them; overspending on promising players whose performance did not rise with their salaries. For ease of reading each team's W-L record, I have inserted in each graph a horizontal black line at the break-even mark (.500).
Seen in the context of 13 team histories, the A's look like a team that did well for a while with relatively high-priced players; pared its payroll as its fortunes faded; happened to do well for a while on low-priced players, and then faded as its payroll rose.
Let us now to the column headed W-L record (II) in the first table above. This is where the bat meets the ball, so to speak, because it tells us something about the aspects of performance that determine a team's W-L record. Except for on-base percentage (OBP), the determinants of payroll have little to do with winning. Defense (represented in fielding average) is far and away the most important determinant of winning. (The payoff of defense may be somewhat overstated, as I will come to.) After that, preventing hits (H) and getting them weigh heavily. Pitchers who save games (SV) are important to winning, as are pitchers who do not give up a lot of walks (BB). Home runs (HR) are just as important as payroll, even though they are down the list of factors that determine payroll.
As mentioned earlier, W-L record is strongly correlated with the ratio of runs scored to runs allowed (RS:RA). The next two columns of the table assess the relationships between RS and various measures of performance; the right-hand column deals with RA. On the offensive side, HR remains important, as does OBP appear, directly and in the form of key elements -- H, BA, and BB. On the defense side, fielding is key (more below). Next are H, BB, and HR allowed by pitchers, meaning that allowing relatively few H, BB, and HR are keys to the defensive side of the game. Another key, though a less important one, is striking out batters (SO).
As for fielding, a position-by-position view is more relevant, and the possibility of obtaining better fielding is overstated for some positions. The following table affords the position-by-position view and eliminates the overstatements:
What this tells us is that improved fielding, at any position, can strongly improve a team's chances of winning, even though fielding is not among the key determinants of payroll.
At any rate, I have analyzed the performance and payrolls of American League (AL) teams for 1988-2011 to determine their effects on teams' won-lost (W-L) records. I focused on the AL because an analysis of both major leagues would have been complicated by the presence of the designated hitter in the AL and the absence of the DL in the National League. I began with 1988 because that is the first year covered by the USATODAY Salaries Database for baseball. Performance data are from Retrosheet.
What did I learn from those 24 years' worth of data? This:
1. Payroll does not drive W-L record.
2. Payroll is mainly determined by 7 aspects of performance.
3. W-L record is mainly determined by 9-11 aspects of performance, only 2-4 of which overlap with the determinants of payroll.
4. W-L record is strongly correlated with the ratio of runs scored (RS) to runs allowed (RA), with an r-squared of 0.89. There is considerable overlap between the determinants of RS-RA and the determinants of W-L record. There is not much overlap between the determinants of RS:RA and the determinants of payroll.
I begin with the relationship between payroll and W-L record:
The following table summarizes the results of six regressions. Instead of showing the coefficients for the explanatory variables, I have converted them to elasticities, expressed as the percentage increase in a dependent variable that results from a 1-percent increase in the value of the explanatory variable.
Before I walk through the table, I should observe that the figures represent average tendencies over a 24-year span. The figures are significant, but they do not necessarily indicate the best course of action for a given team in a given situation. The best course of action for a given team in a given situation will depend on the team's options (e.g., players available on the free-agent market), on the likely payoff of those options (e.g., addition to runs scored if player A is signed and player B is traded away), and on the cost of obtaining each payoff (e.g., net addition to payroll if player A is signed and player B is traded).
The results in the payroll index column show that the payrolls of AL teams in 1988-2011 were driven mainly by batters' on-base percentage (OBP); pitchers' avoidance of giving up home runs; pitchers' ability to strike out batters (SO); fielders' avoidance of errors (ERR); batters' home runs (HR); catchers' throwing out base stealers (CS); and batters' not hitting a lot of triples (3B) -- perhaps because of the typical characteristics of triples hitters (e.g., they are not usually home-run hitters).
In the next column we see, again, that payroll spending -- in the aggregate over 24 seasons -- does not strongly influence W-L record. But because payroll is a proxy for several performance variables, no other variable show up as statistically significant when payroll index is used as an explanatory variable. It is important to note, however, that the relatively weak relationship between W-L record and payroll index for the entire AL masks considerable variation across teams. Here is a summary comparison, for the 13 teams that were in the AL in every year from 1988 through 2011:
The Oakland A's of "moneyball" fame did will for the amount of money spent on payroll, as measured by W-L record divided by payroll index (right-hand column). But, on that measure, the A's did no better than the Twins, and not a lot better than the Royals and White Sox.
The Yankees were the best team in the AL during 1988-2011, and they paid what it took to be the best. The Red Sox were the second-best team, and they paid accordingly. After that, results were mixed. The A's paid a lot less than the Yankees and Red Sox, and still won a lot -- but did not get much in return when they spent more (correlation coefficient of 0.19, as against 0.50 for the Yankees and 0.31 for the Red Sox). Though an above-average payroll index was not required for a winning record, four of the top six team in W-L and every team with a payroll index of 1.00 or grater had a winning record. All teams but one -- the Rangers -- managed to eke out some additional wins by increasing their payrolls.
The following graphs illustrate several story lines, such as success with young, low-priced players who then become higher-priced and less-productive players; overspending on once-outstanding veterans whose best seasons were behind them; overspending on promising players whose performance did not rise with their salaries. For ease of reading each team's W-L record, I have inserted in each graph a horizontal black line at the break-even mark (.500).
Seen in the context of 13 team histories, the A's look like a team that did well for a while with relatively high-priced players; pared its payroll as its fortunes faded; happened to do well for a while on low-priced players, and then faded as its payroll rose.
Let us now to the column headed W-L record (II) in the first table above. This is where the bat meets the ball, so to speak, because it tells us something about the aspects of performance that determine a team's W-L record. Except for on-base percentage (OBP), the determinants of payroll have little to do with winning. Defense (represented in fielding average) is far and away the most important determinant of winning. (The payoff of defense may be somewhat overstated, as I will come to.) After that, preventing hits (H) and getting them weigh heavily. Pitchers who save games (SV) are important to winning, as are pitchers who do not give up a lot of walks (BB). Home runs (HR) are just as important as payroll, even though they are down the list of factors that determine payroll.
As mentioned earlier, W-L record is strongly correlated with the ratio of runs scored to runs allowed (RS:RA). The next two columns of the table assess the relationships between RS and various measures of performance; the right-hand column deals with RA. On the offensive side, HR remains important, as does OBP appear, directly and in the form of key elements -- H, BA, and BB. On the defense side, fielding is key (more below). Next are H, BB, and HR allowed by pitchers, meaning that allowing relatively few H, BB, and HR are keys to the defensive side of the game. Another key, though a less important one, is striking out batters (SO).
As for fielding, a position-by-position view is more relevant, and the possibility of obtaining better fielding is overstated for some positions. The following table affords the position-by-position view and eliminates the overstatements:
What this tells us is that improved fielding, at any position, can strongly improve a team's chances of winning, even though fielding is not among the key determinants of payroll.
Tuesday, November 23, 2010
Is Jeter Worth It?
Rumor has it that the Yankees have offered Derek Jeter a three-year contract worth $45 million. The annual rate of $15 million would be a comedown from Jeter's 2010 pay of $22.6 million (source), but in terms of on-field performance, Jeter would be grossly overpaid. And he wants to be more grossly overpaid, of course.
Let's look at Jeter's value to the Yankees since 1996, the first year for which his salary is known:
OPS+ is a measure of offensive performance. It is on-base percentage plus slugging average (OPS) adjusted for year and ballpark. An OPS+ of 100 represents the average for the league and year.
Jeter's on-field value to the Yankees, as an offensive player, peaked in 1999, when his OPS+ reached a career-high 153. His OPS+ per $10 million of salary in that year was 0.00306. It has been all downhill since, both in terms of OPS+ (though there have been some good years since 1999) and OPS+ per $10 million of salary. The latter figure dwindled to 0.00040 in 2010, when Jeter's OPS+ fell to 90, that is, 90 percent of the league average.
It is only reasonable to assume that Jeter's productivity will decline further from its peak, even if he recovers somewhat from the 2010's unusually weak performance. Even at $15 million per season, Jeter will be an over-priced commodity, given his likely on-field performance.
So, if Jeter is worth $15 million a year, or more, it's only because of his leadership qualities (which can't be measured) and his draw as a symbol of Yankee greatness. I suspect that Jeter's leadership qualities will not be enough to reverse the Yankees' evident decline. Further, that decline will more than offset whatever value Jeter has at the box office.
I look forward, with sadness, to some relatively lean years in the Bronx, and to buyer's remorse on the part of the Yankees if they settle with Jeter for much more than $15 million a season.
Let's look at Jeter's value to the Yankees since 1996, the first year for which his salary is known:
| OPS+ per | ||||||
| Year | Age | OPS+ | Salary | $10 mn | ||
| 1996 | 22 | 101 | $130,000 | 0.07769 | ||
| 1997 | 23 | 103 | $550,000 | 0.01873 | ||
| 1998 | 24 | 127 | $750,000 | 0.01693 | ||
| 1999 | 25 | 153 | $5,000,000 | 0.00306 | ||
| 2000 | 26 | 128 | $10,000,000 | 0.00128 | ||
| 2001 | 27 | 123 | $12,600,000 | 0.00098 | ||
| 2002 | 28 | 111 | $14,600,000 | 0.00076 | ||
| 2003 | 29 | 125 | $15,600,000 | 0.00080 | ||
| 2004 | 30 | 114 | $18,600,000 | 0.00061 | ||
| 2005 | 31 | 125 | $19,600,000 | 0.00064 | ||
| 2006 | 32 | 132 | $20,600,000 | 0.00064 | ||
| 2007 | 33 | 121 | $21,600,000 | 0.00056 | ||
| 2008 | 34 | 102 | $21,600,000 | 0.00047 | ||
| 2009 | 35 | 125 | $21,600,000 | 0.00058 | ||
| 2010 | 36 | 90 | $22,600,000 | 0.00040 | ||
| 119 | $205,430,000 | 0.00087 |
OPS+ is a measure of offensive performance. It is on-base percentage plus slugging average (OPS) adjusted for year and ballpark. An OPS+ of 100 represents the average for the league and year.
Jeter's on-field value to the Yankees, as an offensive player, peaked in 1999, when his OPS+ reached a career-high 153. His OPS+ per $10 million of salary in that year was 0.00306. It has been all downhill since, both in terms of OPS+ (though there have been some good years since 1999) and OPS+ per $10 million of salary. The latter figure dwindled to 0.00040 in 2010, when Jeter's OPS+ fell to 90, that is, 90 percent of the league average.
It is only reasonable to assume that Jeter's productivity will decline further from its peak, even if he recovers somewhat from the 2010's unusually weak performance. Even at $15 million per season, Jeter will be an over-priced commodity, given his likely on-field performance.
So, if Jeter is worth $15 million a year, or more, it's only because of his leadership qualities (which can't be measured) and his draw as a symbol of Yankee greatness. I suspect that Jeter's leadership qualities will not be enough to reverse the Yankees' evident decline. Further, that decline will more than offset whatever value Jeter has at the box office.
I look forward, with sadness, to some relatively lean years in the Bronx, and to buyer's remorse on the part of the Yankees if they settle with Jeter for much more than $15 million a season.
More about the Quality of Films
In "The Quality of Films over the Decades," I compare my ratings of 1,900-plus feature films with the ratings given those same films by IMDb users:
An obvious reason for the difference is that many IMDb users, unlike me, have a strong taste for films of the 1940s through the mid-1970s. I, on the other hand, generally prefer the films of 1932-1942 (the "Golden Age") to what has been produced since. (My high marks for films of 1920-1931 are based on small samples, and should be ignored for purposes of this discussion.)
It is evident, however, that I am in step with IMDb users with regard to the average quality of films produced from 1975 to 1995. I am less enthusiastic than IMDb users about the output of the last 15 years. (The jump in my ratings for 2009-2010 reflects limited viewing.)
That I am selective in what I choose to view is born out by the following graph:
The blue bars denote the ratings given by IMDb viewers to some 113,000 feature films. The average rating assigned to all of those films is 5.8, in contrast with the 7.1 assigned by IMDb users to films I've rated (my average rating, 6.8). The distribution of the red and green bars, relative to the blue ones, attests to my selectivity in choosing films to watch.
It is the difference between the red bars and the green bars that I find most interesting. Because of my selective viewing habits, I have given ratings of 8, 9, or 10 to 13 percent of the films I've rated; whereas, IMDb users apply ratings of 8, 9, or 10 to less than 6 percent of the same features. The picture then changes. I am less generous with ratings between 5 and 7, and more willing to apply ratings below 5.
It gives me solace, for two reasons, to know that the average rating for all feature films at IMDb is only 5.8. First, it means that I haven't missed much by being selective. Second, it means that the average viewer (at least the ones who rate films at IMDb) is willing and able (at least somewhat) to tell what's good, what's bad, and what's indifferent.
Finally, there's a good reason for being selective: It prevents a sad waste of time. If the average length of the 113,000 features rated at IMDb is 105 minutes (1.75 hours), it would take about 100 years (at five hours a day, five days a week) to watch every film all the way through. That's a lot of popcorn.
An obvious reason for the difference is that many IMDb users, unlike me, have a strong taste for films of the 1940s through the mid-1970s. I, on the other hand, generally prefer the films of 1932-1942 (the "Golden Age") to what has been produced since. (My high marks for films of 1920-1931 are based on small samples, and should be ignored for purposes of this discussion.)
It is evident, however, that I am in step with IMDb users with regard to the average quality of films produced from 1975 to 1995. I am less enthusiastic than IMDb users about the output of the last 15 years. (The jump in my ratings for 2009-2010 reflects limited viewing.)
That I am selective in what I choose to view is born out by the following graph:
The blue bars denote the ratings given by IMDb viewers to some 113,000 feature films. The average rating assigned to all of those films is 5.8, in contrast with the 7.1 assigned by IMDb users to films I've rated (my average rating, 6.8). The distribution of the red and green bars, relative to the blue ones, attests to my selectivity in choosing films to watch.It is the difference between the red bars and the green bars that I find most interesting. Because of my selective viewing habits, I have given ratings of 8, 9, or 10 to 13 percent of the films I've rated; whereas, IMDb users apply ratings of 8, 9, or 10 to less than 6 percent of the same features. The picture then changes. I am less generous with ratings between 5 and 7, and more willing to apply ratings below 5.
It gives me solace, for two reasons, to know that the average rating for all feature films at IMDb is only 5.8. First, it means that I haven't missed much by being selective. Second, it means that the average viewer (at least the ones who rate films at IMDb) is willing and able (at least somewhat) to tell what's good, what's bad, and what's indifferent.
Finally, there's a good reason for being selective: It prevents a sad waste of time. If the average length of the 113,000 features rated at IMDb is 105 minutes (1.75 hours), it would take about 100 years (at five hours a day, five days a week) to watch every film all the way through. That's a lot of popcorn.
Monday, November 15, 2010
The American League's Greatest Hitters: Part II
UPDATED 12/08/11
When last seen, the best of the American League's greatest hitters were:
I left the earlier post hanging on the question of how the top hitters would compare when their batting averages were adjusted further, for age. I now have some of the answers.
To get the answers, I quantified the relationship between adjusted batting average and age for the 120 hitters considered in the earlier post. (As a reminder, those hitters attained nominal lifetime averages of .285 or better in at least 5,000 plate appearances in the American League. Their averages take into account long-term and year-to-year changes in playing conditions, as well as differences among ballparks at a give time and over time.) Here is the relationship, in graphical form:
I used the equation shown on the graph to adjust each hitter's annual batting average according to the age at which he attained the average. If the "normal" hitter peaks at 28, as the equation suggests, averages attained before and after the age of 28 are "understated." That is, if a player hits .300 at the age of 20, that's equivalent to hitting .315 at the age of 28; and if a player hits .300 at the age of 40, that's equivalent to hitting .341 at the age of 28.
My analysis of age-adjusted batting average has yielded two key findings, thus far. The first finding, which is captured in the following graph and its accompanying table, is that the top averages for ages 18-41 were accomplished by just seven different players. This graph compares the year-by-year, age-adjusted averages for each of the seven players:
For ease of viewing, I omitted the five players (Speaker, Carew, Collins, Ruth, and Gehrig) who never hold the top spot at any age, despite their impressive career averages. The top hitters at each age are as follows:
Given that information, it shouldn't surprise you to learn that Ty Cobb returns to the top of the heap when his single-season averages are age-adjusted, and weighted by his at-bats in each season, to obtain an age-adjusted lifetime average. Here is the age-adjusted list of top-12 career batting averages:
I have not extended my analysis to include the 2011 season, but it is clear that Suzuki now belongs in 3rd place. The loss of .0054 from his nominal career BA in 2011 is far greater than his age-adjusted lead (.0023) over Jackson through 2010.
When last seen, the best of the American League's greatest hitters were:
| Adjusted | Nominal | Player | Years in AL | Batting average | % change | # change | ||
| rank* | rank | (all-caps, Hall of Fame; | From | To | Nominal | Adjusted | in BA | in rank |
| * indicates active) | ||||||||
| 1 | 12 | Ichiro Suzuki* | 2001 | 2010 | .331 | .353 | 6.2% | 11 |
| 2 | 1 | TY COBB | 1905 | 1928 | .366 | .353 | -3.9% | -1 |
| 3 | 2 | Shoeless Joe Jackson | 1908 | 1920 | .356 | .351 | -1.3% | -1 |
| 4 | 10 | NAP LAJOIE | 1901 | 1916 | .336 | .333 | -0.9% | 6 |
| 5 | 3 | TRIS SPEAKER | 1907 | 1928 | .345 | .331 | -4.0% | -2 |
| 6 | 16 | ROD CAREW | 1967 | 1985 | .328 | .331 | 0.9% | 10 |
| 7 | 11 | EDDIE COLLINS | 1906 | 1930 | .333 | .326 | -2.2% | 4 |
| 8 | 6 | BABE RUTH | 1914 | 1934 | .343 | .324 | -6.1% | -2 |
| 9 | 8 | LOU GEHRIG | 1923 | 1939 | .340 | .323 | -5.4% | -1 |
| 10 | 18 | JOE DIMAGGIO | 1936 | 1951 | .325 | .322 | -0.7% | 8 |
| 11 | 4 | TED WILLIAMS | 1939 | 1960 | .344 | .319 | -7.9% | -7 |
| 12 | 15 | WADE BOGGS | 1982 | 1999 | .328 | .319 | -2.8% | 3 |
I left the earlier post hanging on the question of how the top hitters would compare when their batting averages were adjusted further, for age. I now have some of the answers.
To get the answers, I quantified the relationship between adjusted batting average and age for the 120 hitters considered in the earlier post. (As a reminder, those hitters attained nominal lifetime averages of .285 or better in at least 5,000 plate appearances in the American League. Their averages take into account long-term and year-to-year changes in playing conditions, as well as differences among ballparks at a give time and over time.) Here is the relationship, in graphical form:
I used the equation shown on the graph to adjust each hitter's annual batting average according to the age at which he attained the average. If the "normal" hitter peaks at 28, as the equation suggests, averages attained before and after the age of 28 are "understated." That is, if a player hits .300 at the age of 20, that's equivalent to hitting .315 at the age of 28; and if a player hits .300 at the age of 40, that's equivalent to hitting .341 at the age of 28.
My analysis of age-adjusted batting average has yielded two key findings, thus far. The first finding, which is captured in the following graph and its accompanying table, is that the top averages for ages 18-41 were accomplished by just seven different players. This graph compares the year-by-year, age-adjusted averages for each of the seven players:
For ease of viewing, I omitted the five players (Speaker, Carew, Collins, Ruth, and Gehrig) who never hold the top spot at any age, despite their impressive career averages. The top hitters at each age are as follows:
| Age-adjusted | ||
| Age | Player | BA |
| 18 | Cobb | .267 |
| 19 | Cobb | .336 |
| 20 | Cobb | .369 |
| 21 | Jackson | .392 |
| 22 | Cobb | .395 |
| 23 | Cobb | .399 |
| 24 | Cobb | .387 |
| 25 | Cobb | .397 |
| 26 | Cobb | .391 |
| 27 | Cobb | .380 |
| 28 | Cobb | .379 |
| 29 | Lajoie | .383 |
| 30 | Cobb | .396 |
| 31 | Cobb | .387 |
| 32 | Cobb | .369 |
| 33 | Suzuki | .377 |
| 34 | DiMaggio | .362 |
| 35 | Lajoie | .414 |
| 36 | Suzuki | .364 |
| 37 | Lajoie | .373 |
| 38 | Williams | .398 |
| 39 | Williams | .343 |
| 40 | Cobb | .357 |
| 41 | Boggs | .343 |
Given that information, it shouldn't surprise you to learn that Ty Cobb returns to the top of the heap when his single-season averages are age-adjusted, and weighted by his at-bats in each season, to obtain an age-adjusted lifetime average. Here is the age-adjusted list of top-12 career batting averages:
| Batter | Age-adjusted career BA | |
| 1 | Ty Cobb | .3639 |
| 2 | Shoeless Joe Jackson | .3559 |
| 3 | Ichiro Suzuki* | .3582 |
| 4 | Nap Lajoie | .3405 |
| 5 | Tris Speaker | .3313 |
| 6 | Rod Carew | .3307 |
| 7 | Ted Williams | .3306 |
| 8 | Eddie Collins | .3258 |
| 9 | Babe Ruth | .3236 |
| 10 | Lou Gehrig | .3228 |
| 11 | Joe DiMaggio | .3223 |
| 12 | Wade Boggs | .3190 |
| * Through 2010 season; before .272 average in 2011 reduced career BA by .0054. | ||
I have not extended my analysis to include the 2011 season, but it is clear that Suzuki now belongs in 3rd place. The loss of .0054 from his nominal career BA in 2011 is far greater than his age-adjusted lead (.0023) over Jackson through 2010.
Sunday, November 14, 2010
The Quality of Films over the Decades
I have written before about my judgment of the quality of films in various eras. In 2007, I characterized the eras from 1933 to then as follows:
I offered the following explanation for what I saw as a steady decline in quality after 1942:
I compared my ratings of individual movies with the ratings given the same movies by hundreds, thousands, and (sometimes) tens of thousands of viewers. Here's how our ratings compare, year by year and overall, from 1920 through 2010:
I'm not surprised that my ratings, on average, are lower than those of other viewers, on average. Assuming that the difference is merely a matter of tough grading on my part, I scaled up my ratings so that my overall average is the same as that of others who rated the same films. The result:
The band of vertical bars across the middle of the graph indicates the normal range of the annual ratings. Points above the vertical bands are in the upper 1/6 of my ratings; points below the vertical bands are in the bottom 1/6 of my ratings.
I find it a bit shocking to see that there is a period during the vile years with normalized ratings above 100 percent of the IMDb average, specifically, 1978 through 1997. On the other hand, the first graph shows that I considered the films of that period generally inferior to the films of earlier periods. Moreover, going back to the first graph, it is evident that there was a consensus (of which I was part) about the vileness of the Vile Years (give or take a few of them).
So, I will stick to my guns, with one amendment -- the Golden Age began in 1932:
- the Golden Age (1933-1942) -- 179 films seen, 96 favorites (54 percent)
- the Abysmal Years (1943-1965) -- 317 films seen, 98 favorites (31 percent)
- the Vile Years (1966-present) -- 1,496 films seen, 359 favorites (24 percent)
I offered the following explanation for what I saw as a steady decline in quality after 1942:
- The Golden Age had deployed all of the themes that could be used without explicit sex, graphic violence, and crude profanity -- none of which become an option for American movie-makers until the mid-1960s.
- Prejudice got significantly more play after World War II, but it's a theme that can't be used very often without boring audiences.
- Other attempts at realism (including film noir) resulted mainly in a lot of turgid trash laden with unrealistic dialogue and shrill emoting -- keynotes of the Abysmal Years.
- Hollywood productions sank to the level of TV, apparently in a misguided effort to compete with that medium. The garish technicolor productions of the 1950s often highlighted the unnatural neatness and cleanliness of settings that should have been rustic if not squalid.
- The transition from abysmal to vile coincided with the cultural "liberation" of the mid-1960s, which saw the advent of the "f" word in mainstream films. Yes, the Vile Years have brought us more more realistic plots and better acting (thanks mainly to the Brits). But none of that compensates for the anti-social rot that set in around 1966: drug-taking, drinking and smoking are glamorous; profanity proliferates to the point of annoyance; sex is all about lust and little about love; violence is gratuitous and beyond the point of nausea; corporations and white, male Americans with money are evil; the U.S. government (when Republican-controlled) is in thrall to that evil; etc., etc. etc.
I compared my ratings of individual movies with the ratings given the same movies by hundreds, thousands, and (sometimes) tens of thousands of viewers. Here's how our ratings compare, year by year and overall, from 1920 through 2010:
I'm not surprised that my ratings, on average, are lower than those of other viewers, on average. Assuming that the difference is merely a matter of tough grading on my part, I scaled up my ratings so that my overall average is the same as that of others who rated the same films. The result:
The band of vertical bars across the middle of the graph indicates the normal range of the annual ratings. Points above the vertical bands are in the upper 1/6 of my ratings; points below the vertical bands are in the bottom 1/6 of my ratings.
I find it a bit shocking to see that there is a period during the vile years with normalized ratings above 100 percent of the IMDb average, specifically, 1978 through 1997. On the other hand, the first graph shows that I considered the films of that period generally inferior to the films of earlier periods. Moreover, going back to the first graph, it is evident that there was a consensus (of which I was part) about the vileness of the Vile Years (give or take a few of them).
So, I will stick to my guns, with one amendment -- the Golden Age began in 1932:
- the Golden Age (1932-1942) -- 184 films rated, 110 favorites (60 percent)
- the Abysmal Years (1943-1965) -- 284 films rated, 107 favorites (41 percent)
- the Vile Years (1966-present) -- 1,425 films rated, 416 favorites (29 percent)
Friday, November 12, 2010
The American League's Greatest Hitters
SUPERSEDED BY "THE AMERICAN LEAGUE'S GREATEST HITTERS: III"
Through a painstaking series of adjustments for changes in playing standards and conditions, and for differences among ballparks, I have reassessed the single-season and career batting averages of the American League's top hitters. The reassessment covers 120 players whose career average in the American League is at least .285 in at least 5,000 plate appearances.
I will devote a future post to a detailed explanation of the adjustments. In this post, I give an overview of the adjustments and present a revised ranking of the 120 players. I also discuss -- but do not adjust for -- the effects of age on the revised batting averages and relative standing of players.
I make three kinds of adjustments to nominal (official) BA. One adjustment is a time constant, which captures gradual changes from 1901 to the present that have worked against batters. Such changes would be the improvement of fielding gloves (which have made it harder to get hits, while also raising fielding averages), the introduction of night baseball, and the gradual increase in proportion of games played at night.
A second adjustment is an annual factor that captures the up-and-down swings in the relative difficulty of hitting. These swings have occurred because of changes in the ball, the frequency of its replacement, the size of the strike zone, and the height of the pitching mound, and perhaps other factors.
A third adjustment -- one that is unique to each team-park combination -- reflects the relative ease or difficulty of hitting in the various parks that have been used in the American League. In many cases the adjustment factor for a given park changes during the years of its use because of significant changes in the dimensions of the field.
The following graph combines the effects of the first two adjustments into a single number for each season. A value greater than 1 means that each hitter's nominal average for that season was increased to some degree. A value less than 1 means that each hitter's average for that season was decreased to some degree.
The largest upward adjustments affect averages compiled in the "deadball" years of 1902-1909 and 1913-1916, and in the "era of the pitcher," from 1962 through 1975. The largest downward adjustments affect averages compiled in the first two years of the AL's existence and the "lively" ball era, which -- judging from the numbers -- began in 1919 and lasted through 1938.
The final adjustments -- for differences in parks -- range widely. For example, Red Sox hiiters (including Ted Williams) suffered a penalty of 5.9 percent for the 1934-2010 seasons, when Fenway Park acquired its present dimensions. By contrast, Yankees who played in the original Yankee Stadium from 1923 through 1973 earned a boost of 4 percent because the original park (despite its short foul lines) was inimical to batters (including Joe DiMaggio).
The following graph captures the total effect of the three adjustments. Each point represents one of the 120 hitters.
The pattern, which the curved line emphasizes, is consistent with the adjustments summarized in the first graph. The points don't fall neatly on the curved line for three reasons: (1) variations in the length of players' careers, (2) variations in the numbers of at-bats across seasons (and thus in the weight attached to a season in compiling a career average), and (3) the park-adjustment factor, which varies widely from park to park and (sometimes) for a particular park, if its configuration changed significantly.
How did the various adjustments affect the rankings? First, as would be expected because of the inflation of batting averages in the 1920s and 1930s, those decades are over-represented among the 120 hitters, as shown in the following table. ("Median year" refers to the decade in which a player's median year occurs. For example, Ty Cobb's career spanned 1905-1928, so he is counted as a member of the 1911-1920 decade in the following table and the one after it.)
The adjustments to nominal batting averages did a good job of rectifying the bias toward players of the 1920s and 1930s:
Until someone convinces me otherwise, I conclude that the top hitters of the "deadball" era really were great by comparison with those who came later. They are not alone at the top, however. Among the top 10 in the following table are a contemporary player (Ichiro Suzuki), a player of recent memory (Rod Carew), and three Yankees who enjoyed great years in the 1920s and 1930s (Babe Ruth, Lou Gehrig, and Joe DiMaggio). Here, then, are all 120 hitters, listed in the order of adjusted rank:
The names of Hall-of-Famers are capitalized to draw your attention to several who were enshrined mainly on the strength of grossly inflated batting averages.
There is more work to be done, especially with respect to age. Consider, for example, Shoeless Joe Jackson, whose career ended at age 30. Had Jackson continued to play until he was 40, say, his career average would have declined, and with it his position on the list.
Ichiro Suzuki didn't play in the U.S. until he was 27. Would his career average be even higher if he had crossed over the Pacific in his early 20s? He is atop the list because of his post-32 performance, relative to Ty Cobb's.
Then there is the case of Ted Williams, whose average and ranking slipped markedly because he enjoyed the friendly confines of Fenway Park. But Williams, who also hit well in his "old age," missed a lot of peak batting time during WWII and the Korean War.
I will end, for now, with this tantalizing comparison of Suzuki, Cobb, Jackson, and Williams:
Cobb's consistent brilliance from age 22 to age 32 borders on the amazing. Williams was a great "old" hitter, as Suzuki is proving to be. It is evident that Jackson, despite the closeness of his average to Cobb's, probably wouldn't have caught Cobb, unless he had finished in a Suzuki-like manner.
ADDENDUM:
Final, age-adjusted BA for the top-3 all-time AL hitters:
Go here for details.
Through a painstaking series of adjustments for changes in playing standards and conditions, and for differences among ballparks, I have reassessed the single-season and career batting averages of the American League's top hitters. The reassessment covers 120 players whose career average in the American League is at least .285 in at least 5,000 plate appearances.
I will devote a future post to a detailed explanation of the adjustments. In this post, I give an overview of the adjustments and present a revised ranking of the 120 players. I also discuss -- but do not adjust for -- the effects of age on the revised batting averages and relative standing of players.
I make three kinds of adjustments to nominal (official) BA. One adjustment is a time constant, which captures gradual changes from 1901 to the present that have worked against batters. Such changes would be the improvement of fielding gloves (which have made it harder to get hits, while also raising fielding averages), the introduction of night baseball, and the gradual increase in proportion of games played at night.
A second adjustment is an annual factor that captures the up-and-down swings in the relative difficulty of hitting. These swings have occurred because of changes in the ball, the frequency of its replacement, the size of the strike zone, and the height of the pitching mound, and perhaps other factors.
A third adjustment -- one that is unique to each team-park combination -- reflects the relative ease or difficulty of hitting in the various parks that have been used in the American League. In many cases the adjustment factor for a given park changes during the years of its use because of significant changes in the dimensions of the field.
The following graph combines the effects of the first two adjustments into a single number for each season. A value greater than 1 means that each hitter's nominal average for that season was increased to some degree. A value less than 1 means that each hitter's average for that season was decreased to some degree.
The largest upward adjustments affect averages compiled in the "deadball" years of 1902-1909 and 1913-1916, and in the "era of the pitcher," from 1962 through 1975. The largest downward adjustments affect averages compiled in the first two years of the AL's existence and the "lively" ball era, which -- judging from the numbers -- began in 1919 and lasted through 1938.
The final adjustments -- for differences in parks -- range widely. For example, Red Sox hiiters (including Ted Williams) suffered a penalty of 5.9 percent for the 1934-2010 seasons, when Fenway Park acquired its present dimensions. By contrast, Yankees who played in the original Yankee Stadium from 1923 through 1973 earned a boost of 4 percent because the original park (despite its short foul lines) was inimical to batters (including Joe DiMaggio).
The following graph captures the total effect of the three adjustments. Each point represents one of the 120 hitters.
The pattern, which the curved line emphasizes, is consistent with the adjustments summarized in the first graph. The points don't fall neatly on the curved line for three reasons: (1) variations in the length of players' careers, (2) variations in the numbers of at-bats across seasons (and thus in the weight attached to a season in compiling a career average), and (3) the park-adjustment factor, which varies widely from park to park and (sometimes) for a particular park, if its configuration changed significantly.How did the various adjustments affect the rankings? First, as would be expected because of the inflation of batting averages in the 1920s and 1930s, those decades are over-represented among the 120 hitters, as shown in the following table. ("Median year" refers to the decade in which a player's median year occurs. For example, Ty Cobb's career spanned 1905-1928, so he is counted as a member of the 1911-1920 decade in the following table and the one after it.)
| Distribution of Hitters, by Decade | ||||
| Median year | Number | Percent | ||
| 1901-1910 | 2 | 1.7% | ||
| 1911-1920 | 7 | 5.8% | ||
| 1921-1930 | 17 | 14.2% | ||
| 1931-1940 | 21 | 17.5% | ||
| 1941-1950 | 8 | 6.7% | ||
| 1951-1960 | 8 | 6.7% | ||
| 1961-1970 | 3 | 2.5% | ||
| 1971-1980 | 8 | 6.7% | ||
| 1981-1990 | 10 | 8.3% | ||
| 1991-2000 | 22 | 18.3% | ||
| 2001-2010 | 14 | 11.7% | ||
| 120 | 100% | |||
The adjustments to nominal batting averages did a good job of rectifying the bias toward players of the 1920s and 1930s:
| Average Rank, by Decade | ||||
| Median year | Nominal | Adjusted | Change* | |
| 1901-1910 | 28 | 17 | 11 | |
| 1911-1920 | 22 | 23 | -1 | |
| 1921-1930 | 29 | 65 | -36 | |
| 1931-1940 | 44 | 83 | -39 | |
| 1941-1950 | 60 | 63 | -3 | |
| 1951-1960 | 84 | 54 | 30 | |
| 1961-1970 | 83 | 43 | 40 | |
| 1971-1980 | 86 | 49 | 37 | |
| 1981-1990 | 79 | 52 | 27 | |
| 1991-2000 | 79 | 64 | 15 | |
| 2001-2010 | 58 | 79 | -21 | |
| * Positive number represents improvement (higher average rank); negative number represents slippage (lower average rank). | ||||
Until someone convinces me otherwise, I conclude that the top hitters of the "deadball" era really were great by comparison with those who came later. They are not alone at the top, however. Among the top 10 in the following table are a contemporary player (Ichiro Suzuki), a player of recent memory (Rod Carew), and three Yankees who enjoyed great years in the 1920s and 1930s (Babe Ruth, Lou Gehrig, and Joe DiMaggio). Here, then, are all 120 hitters, listed in the order of adjusted rank:
| Adjusted | Nominal | Player | Years in AL | Batting average | % change | # change | ||
| rank* | rank | (all-caps = Hall of Fame; asterisk = | From | To | Nominal | Adjusted | in BA | in rank |
| active) | ||||||||
| 1 | 12 | Ichiro Suzuki* | 2001 | 2010 | .331 | .353 | 6.2% | 11 |
| 2 | 1 | TY COBB | 1905 | 1928 | .366 | .353 | -3.9% | -1 |
| 3 | 2 | Shoeless Joe Jackson | 1908 | 1920 | .356 | .351 | -1.3% | -1 |
| 4 | 10 | NAP LAJOIE | 1901 | 1916 | .336 | .333 | -0.9% | 6 |
| 5 | 3 | TRIS SPEAKER | 1907 | 1928 | .345 | .331 | -4.0% | -2 |
| 6 | 16 | ROD CAREW | 1967 | 1985 | .328 | .331 | 0.9% | 10 |
| 7 | 11 | EDDIE COLLINS | 1906 | 1930 | .333 | .326 | -2.2% | 4 |
| 8 | 6 | BABE RUTH | 1914 | 1934 | .343 | .324 | -6.1% | -2 |
| 9 | 8 | LOU GEHRIG | 1923 | 1939 | .340 | .323 | -5.4% | -1 |
| 10 | 18 | JOE DIMAGGIO | 1936 | 1951 | .325 | .322 | -0.7% | 8 |
| 11 | 4 | TED WILLIAMS | 1939 | 1960 | .344 | .319 | -7.9% | -7 |
| 12 | 15 | WADE BOGGS | 1982 | 1999 | .328 | .319 | -2.8% | 3 |
| 13 | 47 | Don Mattingly | 1982 | 1995 | .307 | .318 | 3.3% | 34 |
| 14 | 74 | MICKEY MANTLE | 1951 | 1968 | .298 | .317 | 6.0% | 60 |
| 15 | 7 | HARRY HEILMANN | 1914 | 1929 | .342 | .315 | -8.9% | -8 |
| 16 | 30 | Derek Jeter* | 1995 | 2010 | .314 | .314 | 0.1% | 14 |
| 17 | 5 | GEORGE SISLER | 1915 | 1928 | .344 | .313 | -9.8% | -12 |
| 18 | 36 | Edgar Martinez | 1987 | 2004 | .312 | .312 | 0.1% | 18 |
| 19 | 25 | KIRBY PUCKETT | 1984 | 1995 | .318 | .311 | -2.1% | 6 |
| 20 | 89 | EDDIE MURRAY | 1977 | 1997 | .295 | .311 | 5.1% | 69 |
| 21 | 99 | Thurman Munson | 1969 | 1979 | .292 | .310 | 6.1% | 78 |
| 22 | 53 | PAUL MOLITOR | 1978 | 1998 | .306 | .310 | 1.2% | 31 |
| 23 | 35 | Magglio Ordonez* | 1997 | 2010 | .312 | .310 | -0.6% | 12 |
| 24 | 31 | Harvey Kuenn | 1952 | 1960 | .313 | .309 | -1.4% | 7 |
| 25 | 44 | Roberto Alomar | 1991 | 2004 | .309 | .308 | -0.4% | 19 |
| 26 | 9 | AL SIMMONS | 1924 | 1944 | .337 | .308 | -9.3% | -17 |
| 27 | 17 | EARLE COMBS | 1924 | 1935 | .325 | .308 | -5.6% | -10 |
| 28 | 68 | Minnie Minoso | 1949 | 1964 | .300 | .307 | 2.4% | 40 |
| 29 | 70 | Joe Judge | 1915 | 1934 | .299 | .307 | 2.7% | 41 |
| 30 | 45 | SAM CRAWFORD | 1903 | 1917 | .309 | .307 | -0.5% | 15 |
| 31 | 55 | Tony Oliva | 1962 | 1976 | .304 | .307 | 0.7% | 24 |
| 32 | 92 | Mickey Rivers | 1970 | 1984 | .295 | .306 | 3.7% | 60 |
| 33 | 38 | Baby Doll Jacobson | 1915 | 1927 | .311 | .305 | -1.9% | 5 |
| 34 | 83 | Carl Crawford* | 2002 | 2010 | .296 | .305 | 2.8% | 49 |
| 35 | 67 | Julio Franco | 1983 | 1999 | .301 | .304 | 1.3% | 32 |
| 36 | 54 | GEORGE BRETT | 1973 | 1993 | .305 | .304 | -0.3% | 18 |
| 37 | 56 | Paul O'Neill | 1993 | 2001 | .303 | .304 | 0.1% | 19 |
| 38 | 48 | HOME RUN BAKER | 1908 | 1922 | .307 | .303 | -1.2% | 10 |
| 39 | 72 | Cecil Cooper | 1971 | 1987 | .298 | .303 | 1.7% | 33 |
| 40 | 20 | SAM RICE | 1915 | 1934 | .322 | .303 | -6.2% | -20 |
| 41 | 14 | HEINIE MANUSH | 1923 | 1936 | .331 | .303 | -9.1% | -27 |
| 42 | 32 | BILL DICKEY | 1928 | 1946 | .313 | .303 | -3.3% | -10 |
| 43 | 101 | Lou Piniella | 1964 | 1984 | .291 | .302 | 3.9% | 58 |
| 44 | 29 | Cecil Travis | 1933 | 1947 | .314 | .302 | -3.9% | -15 |
| 45 | 103 | Carney Lansford | 1978 | 1992 | .290 | .302 | 4.1% | 58 |
| 46 | 41 | LUKE APPLING | 1930 | 1950 | .310 | .302 | -2.8% | -5 |
| 47 | 50 | Stuffy McInnis | 1909 | 1922 | .307 | .302 | -1.7% | 3 |
| 48 | 114 | Bill Skowron | 1954 | 1967 | .286 | .301 | 5.2% | 66 |
| 49 | 98 | Luis Polonia | 1987 | 2000 | .292 | .301 | 3.0% | 49 |
| 50 | 84 | Garret Anderson | 1994 | 2008 | .296 | .301 | 1.5% | 34 |
| 51 | 79 | AL KALINE | 1953 | 1974 | .297 | .300 | 0.9% | 28 |
| 52 | 52 | GEORGE KELL | 1943 | 1957 | .306 | .300 | -2.2% | 0 |
| 53 | 34 | Manny Ramirez* | 1993 | 2010 | .312 | .300 | -4.1% | -19 |
| 54 | 81 | Bernie Williams | 1991 | 2006 | .297 | .299 | 0.7% | 27 |
| 55 | 64 | Frank Thomas | 1990 | 2008 | .301 | .299 | -0.8% | 9 |
| 56 | 13 | JIMMIE FOXX | 1925 | 1942 | .331 | .298 | -11.1% | -43 |
| 57 | 97 | Mike Hargrove | 1974 | 1985 | .292 | .298 | 1.8% | 40 |
| 58 | 42 | Bobby Veach | 1912 | 1925 | .310 | .298 | -4.2% | -16 |
| 59 | 60 | Alex Rodriguez* | 1994 | 2010 | .303 | .297 | -2.0% | 1 |
| 60 | 91 | Kevin Seitzer | 1986 | 1997 | .295 | .297 | 0.6% | 31 |
| 61 | 105 | John Olerud | 1989 | 2005 | .289 | .297 | 2.5% | 44 |
| 62 | 102 | NELLIE FOX | 1947 | 1963 | .290 | .297 | 2.2% | 40 |
| 63 | 107 | Wally Joyner | 1986 | 2001 | .289 | .296 | 2.4% | 44 |
| 64 | 104 | Harold Baines | 1980 | 2001 | .289 | .296 | 2.2% | 40 |
| 65 | 112 | Carlos Guillen* | 1998 | 2010 | .286 | .296 | 3.2% | 47 |
| 66 | 116 | ROBIN YOUNT | 1974 | 1993 | .285 | .295 | 3.4% | 50 |
| 67 | 119 | Gene Woodling | 1946 | 1962 | .284 | .295 | 3.6% | 52 |
| 68 | 90 | LOU BOUDREAU | 1938 | 1952 | .295 | .294 | -0.3% | 22 |
| 69 | 111 | Raul Ibanez | 1996 | 2008 | .286 | .294 | 2.8% | 42 |
| 70 | 120 | YOGI BERRA | 1946 | 1963 | .284 | .294 | 3.5% | 50 |
| 71 | 86 | Kenny Lofton | 1992 | 2007 | .296 | .293 | -1.0% | 15 |
| 72 | 23 | HANK GREENBERG | 1930 | 1946 | .319 | .293 | -8.8% | -49 |
| 73 | 93 | Albert Belle | 1989 | 2000 | .295 | .293 | -0.8% | 20 |
| 74 | 94 | Pete Runnels | 1951 | 1962 | .294 | .292 | -0.7% | 20 |
| 75 | 82 | Shannon Stewart | 1995 | 2008 | .297 | .292 | -1.5% | 7 |
| 76 | 66 | Ivan Rodriguez | 1991 | 2009 | .301 | .292 | -3.0% | -10 |
| 77 | 110 | Mickey Vernon | 1939 | 1958 | .287 | .292 | 1.8% | 33 |
| 78 | 95 | Hal McRae | 1973 | 1987 | .293 | .292 | -0.4% | 17 |
| 79 | 96 | Tony Fernandez | 1983 | 2001 | .293 | .292 | -0.4% | 17 |
| 80 | 115 | Miguel Tejada* | 1997 | 2010 | .286 | .292 | 2.0% | 35 |
| 81 | 22 | MICKEY COCHRANE | 1925 | 1937 | .320 | .291 | -10.0% | -59 |
| 82 | 78 | Mike Sweeney | 1995 | 2010 | .298 | .291 | -2.4% | -4 |
| 83 | 21 | CHARLIE GEHRINGER | 1924 | 1942 | .320 | .290 | -10.5% | -62 |
| 84 | 80 | Buddy Lewis | 1935 | 1949 | .297 | .290 | -2.5% | -4 |
| 85 | 49 | George Burns | 1914 | 1929 | .307 | .289 | -6.2% | -36 |
| 86 | 26 | GOOSE GOSLIN | 1921 | 1938 | .316 | .289 | -9.4% | -60 |
| 87 | 58 | Mike Greenwell | 1985 | 1996 | .303 | .288 | -5.1% | -29 |
| 88 | 51 | Johnny Pesky | 1942 | 1954 | .307 | .287 | -6.7% | -37 |
| 89 | 24 | EARL AVERILL | 1929 | 1940 | .318 | .287 | -10.8% | -65 |
| 90 | 88 | Juan Gonzalez | 1989 | 2005 | .295 | .287 | -3.0% | -2 |
| 91 | 43 | John Stone | 1928 | 1938 | .310 | .287 | -8.0% | -48 |
| 92 | 19 | Ken Williams | 1918 | 1929 | .324 | .286 | -13.1% | -73 |
| 93 | 100 | Ken Griffey | 1989 | 2010 | .291 | .286 | -1.8% | 7 |
| 94 | 65 | Billy Goodman | 1947 | 1961 | .301 | .286 | -5.2% | -29 |
| 95 | 28 | Bibb Falk | 1920 | 1931 | .314 | .286 | -10.0% | -67 |
| 96 | 113 | Willie Wilson | 1976 | 1992 | .286 | .286 | 0.0% | 17 |
| 97 | 108 | Rafael Palmeiro | 1989 | 2005 | .288 | .285 | -0.8% | 11 |
| 98 | 59 | Buddy Myer | 1925 | 1941 | .303 | .285 | -6.1% | -39 |
| 99 | 69 | Michael Young* | 2000 | 2010 | .300 | .285 | -5.3% | -30 |
| 100 | 73 | JIM RICE | 1974 | 1989 | .298 | .285 | -4.6% | -27 |
| 101 | 39 | Bob Meusel | 1920 | 1929 | .311 | .285 | -9.2% | -62 |
| 102 | 46 | Gee Walker | 1931 | 1941 | .307 | .283 | -8.6% | -56 |
| 103 | 62 | Ben Chapman | 1930 | 1941 | .302 | .282 | -7.1% | -41 |
| 104 | 27 | Jack Tobin | 1916 | 1927 | .315 | .282 | -11.5% | -77 |
| 105 | 117 | Alan Trammell | 1977 | 1996 | .285 | .282 | -1.2% | 12 |
| 106 | 76 | Mo Vaughn | 1991 | 2000 | .298 | .281 | -5.8% | -30 |
| 107 | 106 | Chuck Knoblauch | 1991 | 2002 | .289 | .281 | -2.7% | -1 |
| 108 | 33 | JOE SEWELL | 1920 | 1933 | .312 | .281 | -11.0% | -75 |
| 109 | 37 | Bing Miller | 1921 | 1936 | .311 | .281 | -10.9% | -72 |
| 110 | 85 | Bob Johnson | 1933 | 1945 | .296 | .280 | -6.0% | -25 |
| 111 | 109 | Johnny Damon* | 1995 | 2010 | .287 | .280 | -2.8% | -2 |
| 112 | 118 | CARL YASTRZEMSKI | 1961 | 1983 | .285 | .279 | -2.2% | 6 |
| 113 | 61 | Hal Trosky | 1933 | 1946 | .302 | .278 | -8.6% | -52 |
| 114 | 40 | Joe Vosmik | 1930 | 1944 | .311 | .278 | -11.6% | -74 |
| 115 | 71 | Sam West | 1927 | 1942 | .299 | .276 | -8.2% | -44 |
| 116 | 77 | Pete Fox | 1933 | 1945 | .298 | .276 | -8.0% | -39 |
| 117 | 75 | Dom DiMaggio | 1940 | 1953 | .298 | .276 | -8.1% | -42 |
| 118 | 63 | JOE CRONIN | 1928 | 1945 | .302 | .275 | -9.7% | -55 |
| 119 | 87 | Doc Cramer | 1929 | 1948 | .296 | .274 | -7.9% | -32 |
| 120 | 57 | Charlie Jamieson | 1915 | 1932 | .303 | .274 | -10.8% | -63 |
| * The adjusted rank considers only the 120 players listed here. Players not listed could outrank some of the players near the bottom of the list. | ||||||||
The names of Hall-of-Famers are capitalized to draw your attention to several who were enshrined mainly on the strength of grossly inflated batting averages.
There is more work to be done, especially with respect to age. Consider, for example, Shoeless Joe Jackson, whose career ended at age 30. Had Jackson continued to play until he was 40, say, his career average would have declined, and with it his position on the list.
Ichiro Suzuki didn't play in the U.S. until he was 27. Would his career average be even higher if he had crossed over the Pacific in his early 20s? He is atop the list because of his post-32 performance, relative to Ty Cobb's.
Then there is the case of Ted Williams, whose average and ranking slipped markedly because he enjoyed the friendly confines of Fenway Park. But Williams, who also hit well in his "old age," missed a lot of peak batting time during WWII and the Korean War.
I will end, for now, with this tantalizing comparison of Suzuki, Cobb, Jackson, and Williams:
Cobb's consistent brilliance from age 22 to age 32 borders on the amazing. Williams was a great "old" hitter, as Suzuki is proving to be. It is evident that Jackson, despite the closeness of his average to Cobb's, probably wouldn't have caught Cobb, unless he had finished in a Suzuki-like manner.
ADDENDUM:
Final, age-adjusted BA for the top-3 all-time AL hitters:
| Cobb | 0.363919 |
| Suzuki | 0.358241 |
| Jackson | 0.355946 |
Go here for details.
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