Baseball Prospectus 1996
Think about it for a minute: what goes into a player's statistics, the results of his play? His performance, obviously. The better you play, the better your stats are, the more likely your team is to score (if you're a hitter) or keep the other team from scoring (if a pitcher). This is what we want to know.
But performance isn't the only thing in the stats. Who you're playing against makes a big difference. If Frank Thomas were to play against AAA pitchers, his stats would be better than in the majors, other things being equal. They'd be even better against AA pitchers, better still against A ball pitchers, and yet better still against high school pitchers. The quality of your opponents determines whether the exact same performance, in terms of tracking a thrown ball and bat speed, results in a batting average of .700, .400 or .100.
Where you play makes a difference as well. Different parks determine whether a fly ball is caught or goes over the fence; whether a ground ball gets through on turf or is held up by tall grass; whether a popup is caught by the third baseman or his biggest fan in the seats; whether the wind tends to blow in or out; whether the ball is easy or tough to see. All of these factors combine to create 'hitter's parks' and 'pitcher's parks': parks which tend, repeatedly, to favor or hinder offense. This is as true in the minor leagues as it is in the majors.
And when the majority of the parks in a league are either friendly to hitting, like they are in the Pacific Coast League, or to pitching, like in the Florida State League, you get hitter's leagues and pitcher's leagues. This increase in batter performance in the PCL has nothing to do with either better hitting or lousier pitching in that league compared to, say, the International League. Measuring players in those leagues only by their traditional statistics is likecomparing the heights of two players by measuring from sea level instead of the local ground. Sure, hitters in the PCL put up great numbers: but they are getting, in essence, a 20 to 30 point bonus in batting average because of where they are playing.
Unfortunately, all of these things change every year. League-wide offense goes up and down (the last three years have seen considerably more offense in the majors than the previous ten years). Park factors change somewhat over time, as the weather and other stadiums in the league change. League skill levels evolve as well: Dick Cramer published a study some 15 years ago which strongly suggested a gradual increase in average skill over baseball history (a trend which matches our experience in all other athletic events), a trend which I believe has continued to the present, although expansion has tempered it somewhat.
All of this means that it is very difficult to tell whether a player really improves from one year to the next: just because the stats get better doesn't mean the player really performed better. You have to take into account the context of each year's performance: the competition, the park, the league. Most people don't do that: they have one set of standards that they recognize as 'good', standards that were probably set by major league performances during the first ten years of their fanhood. And when they apply that standard to a league where the real standard is very different (like the 1994 PCL, where an average player hit .295) they reach an erroneous conclusion: overrating players from a league where their personal standards are too low, and underrating players when their standards are too high.
That's what the DTs are for. The effects addressed above can be measured: not perfectly, but approximately. And when you know roughly how much impact everything is having on player stats, you can take them out or put them back in at will. The DTs change the statistical lines of every single player - from any year, from any park in any league - so that they are all being held to the exact same standard. The biases have been removed.
Perfectly? Of course not. The size of the biases is based on large scale averages; the biases in a player's own statistics are based on his personal response to the conditions around him. Some players are better at taking advantage of a hitter-friendly park than others, and the translation procedure won't pick that up. But I am very confident that they are a valuable analytical tool. Translated statistics behave very much like major league statistics, for players of the same age, regardless of what league a player is currently in. There are any number of players whose major league performance is exactly what was expected from their minor league performance; many more who showed steady improvement in the minors which continued into the majors; and only a few whose performance radically changes: but radical changes in performance happen at both the minor and major league levels, at roughly the same frequency for a given age.
How is it done? I break down a player's offensive production into its component parts, asking questions like: how important are doubles to this player's total offense? Walks? What is the ratio of doubles to home runs? I make all possible comparisons between singles, doubles, triples, home runs, walks, and basestealing. And then I do the same thing again, for the league in which he actually played and for the league to which I am trying to translate the statistics. I generally choose a single target league and location and translate everybody to that it: for this book, my reference league is a neutral park in the 1995 National League.
With the information above, I also have an estimate of a player's total offensive value, adjusted for the offensive level of his league, his home park, and the level of competition in the league. I measure it through a statistic called Equivalent Average (EqA; more about that later), although you could use others. This 'total offensive value' is conserved during the translation. All that is left is to produce a line of statistics that satisfies two goals: it results in the same EqA in the new league as measured in the old league, and preserves the relative performance of each offensive component for that player. It is not a trivial task, because it is usually impossible to perfectly satisfy both goals. I treat the EqA goal as paramount, and use an iterative routine to minimize the differences in the second goal.
You may be thinking that this sounds a lot like Bill James' Major League Equivalencies, or MLEs. The concept is similar; the execution is completely different. While I happen to like DTs better, I'm hardly unbiased. As I see them, these are the main differences between my work and James':
MIKE PIAZZA 1969 C YEAR TEAM AB H DB TP HR BB SB CS BA OBA SA EQA EQH EQR 1991 BAK 451 110 13 1 18 24 0 1 .244 .282 .397 .235 106 46 1992 SAN 118 44 5 0 7 12 0 0 .373 .431 .593 .347 41 26 1992 ABQ 344 104 11 2 15 28 1 2 .302 .355 .477 .287 99 53 1992 LAD 70 18 1 0 2 5 0 0 .257 .307 .357 .234 16 7 1993 LAD 549 179 23 2 32 47 3 4 .326 .379 .550 .314 172 103 1994 LAD 408 137 16 1 22 34 1 3 .336 .387 .542 .315 128 76 1995 LAD 439 160 17 1 29 39 1 0 .364 .416 .606 .344 151 97(OK, so Mike Piazza is hardly typical.)
Top line: the player's name, baseball birth year, and primary position(s) in 1995. The 'baseball birth year' relies on the convention that a player's age on July 1 is considered his age for the season. For a player born in the first six months of the year, this will be the same as the calendar year. But for a player born in the last six months, like Mike Piazza (September 8, 1968), it will be listed as the following calendar year. Mike was considered to be 26 in 1995, and 1995 minus 1969 is 25.
YEAR: is obvious.
TEAM: A list of all the teams and their abbreviations is provided on page X. All of the stats are translated, not the real thing. Remember that!
HR: Home Runs
SB: Stolen bases
CS: Caught stealing
BA: Batting average; league average, .263.
OBA: On-base average. League, .328.
SA: Slugging average. League, .408.
EQA: Equivalent Average. See below. League average hitter: .260.
EQH: Equivalent Hits. See below.
EQR: Equivalent Runs. See below.
For space and significance purposes, seasons with less than 30 PAs have been omitted.
A Typical DT Line for Pitchers
GREG MADDUX 1966 RSP YEAR TEAM IP H ER HR BB SO ERA W L H/9 BB/9 K/9 1991 CHC 261.3 245 107 24 69 213 3.68 16 13 8.44 2.38 7.34 1992 CHC 266.7 227 76 11 77 226 2.57 21 9 7.66 2.60 7.63 1993 ATL 263.0 254 92 17 60 213 3.15 18 11 8.69 2.05 7.29 1994 ATL 201.0 155 41 4 33 161 1.84 18 4 6.94 1.48 7.21 1995 ATL 208.0 155 37 8 23 179 1.60 20 3 6.71 1.00 7.75(I know; he's not typical either.)
Pitcher's lines are in some ways similar to the hitter's lines; the first line shows the pitcher's name, 'baseball birth year', and position. The 'position' for pitchers is a three-letter code. The first letter is L or R, depending on whether the pitcher is left-or right-handed; Greg Harris could be listed as S, for switch, if he changed hands more often. The second letter is S for a pitcher who started in 80% of his games, R for a pitcher who relieved in at least 80% of his games, and B for a pitcher who started and relieved, but did neither 80% of the time. The third letter is simply P, for pitcher. RSP indicates that Maddux is a right-handed starting pitcher.
Once again, all of the listed statistics have been translated; they are not the genuine numbers.
YEAR and TEAM are once again obvious.
IP: Innings Pitched.
ER: Earned runs.
HR: Home runs.
ERA: Earned run average. Set up so that an ERA of 4.00 is perfectly average.
W: Wins, calculated from ERA, innings pitched, and average offensive support.
L: Losses, calculated with wins.
H/9: Hits per nine innings. League average is 9.06.
BB/9: Walks per nine innings. League average, 3.32.
K/9: Strikeouts per nine innings. League average, 6.63.
Seasons with less than 10 innings have been omitted.
Equivalent Averages: What Are They?
Equivalent average and equivalent runs are one of my independent creations. Equivalent runs, like runs created, are a measure of how many runs a player provided to his team. There are several reasons why we are using them here:
1) I created them.
2) Equivalent runs are slightly more accurate at projecting team run scoring from raw statistics than any other statistic I have tested, including Bill James' Runs Created and Pete Palmer's Linear Weights (although in the last ~30 years the difference between EqA and LW is virtually nil).
Table 1. Root mean square errors for predicted runs from various statistics. 1901-1992 AL NL Majors Equivalent Runs 23.99 23.00 23.51 Linear Weights 24.94 23.96 24.46 Runs Created 25.62 24.27 24.97 Onbase plus slugging 27.52 26.37 26.96 Batting Average 47.32 42.97 45.24 1960-1992 AL NL Majors Equivalent Runs 21.41 22.08 21.73 Linear Weights 21.54 21.82 21.68 Runs Created 22.85 22.68 22.79 Onbase plus slugging 23.42 23.47 23.44 Batting Average 41.77 39.85 40.873) At extremes of performance--players or teams who are either far above r far below the league average--the runs created and linear weights formulas become less accurate, a result which isn't shown by team comparisons like the above. Equivalent Runs remain on target.
4) Equivalent Average adjusts easily to corrections for park, league offensive level, and league quality, giving a uniform measure of batting skill that can be used across time. It is adjusted so that the league average is always .260. EqA measures rate of performance; EqR measures the total contribution.
5) Perhaps the most important and useful feature of EqA is that the resulting number is easy to understand. Equivalent Average comes very close to matching the historical scale of batting average. A .300 equivalent average is just as common, historically, as a .300 batting average. (see Figure 1). There have been 153 players with a .300 batting average; there have been 148 who had a .300 EQA. Since even a casual baseball fan has a good intuitive feel for the BA scale, anyone can quickly tell whether a player's EqA is good or bad. League-leading figures and alltime records are similarly close to the historical batting average record. This gives EQA a huge advantage over Runs Created or Linear Weights, because you don't have to learn a new scale. Consider a player described as having an RC/27 of 8.75 per game. How good is that? League-leading good? All-time great? What about if I help you out by saying he also rates a +64 linear weights? Still not sure where he stands? Well, I would say he had a .356 Equivalent Average, and you can think about what a .356 batting average means to decide how good it is. (It was Kevin Mitchell's league-leading 1989 season.)
As a further example, compare the leaders in Equivalent Average and Batting Average over the last ten years:
National League Equivalent Average Batting Average 1986 Tim Raines .338 Tim Raines .334 1987 Jack Clark .356 Tony Gwynn .370 1988 Darryl Strawberry .337 Tony Gwynn .313 1989 Kevin Mitchell .356 Tony Gwynn .336 1990 Barry Bonds .345 Willie McGee .335 1991 Barry Bonds .342 Terry Pendleton .319 1992 Barry Bonds .382 Gary Sheffield .330 1993 Barry Bonds .382 A. Galarraga .370 1994 Jeff Bagwell .388 Tony Gwynn .394 1995 Barry Bonds .345 Tony Gwynn .368 American League Equivalent Average Batting Average 1986 Wade Boggs .340 Wade Boggs .357 1987 Wade Boggs .358 Wade Boggs .363 1988 Wade Boggs .349 Wade Boggs .366 1989 Fred McGriff .335 Kirby Puckett .339 1990 Rickey Henderson .379 George Brett .329 1991 Frank Thomas .356 Julio Franco .341 1992 Frank Thomas .356 Edgar Martinez .343 1993 John Olerud .363 John Olerud .363 1994 Frank Thomas .390 Paul O'Neill .359 1995 Edgar Martinez .363 Edgar Martinez .356 Average NL .357 .347 Average AL .359 .352 Average Majors .358 .349Park and League Adjustments
Park adjustments are a notoriously controversial area in baseball statistics. First, there is no one park factor that can be truly accurate for every player. The park may favor left-handed hitters or right-handed, flyball hitters or groundball hitters, contact hitters or sluggers, or any combination therein. I don't even try to assess how an individual is benefitting from his park: it is too difficult to try for every hitter, and the data is usually insufficient anyway. What I do instead is determine how much the park helps the typical player who was there. If you take better than average advantage of your park, the adjustment won't remove that extra. You'll get a better ranking than you deserve, but that's OK: if you really are taking better advantage of your park, your team is getting an extra benefit. In the real world, changing parks alters the number of runs you produce; I estimate that by altering the value of a run in each park. A run in Houston is not the same as a run in Denver, any more than a dollar in the US is the same as a dollar in Canada. In Houston, fewer runs tend to score, which means that each one is more valuable: you don't need as many to win a typical game. As with the league adjustments, I am adjusting the value of the product, not the quantity.
The other argument with park factors is over how long they should be averaged. I feel comfortable using one season as my baseline, but many have argued that this is too short: that two, or even three seasons must be used to get a valid sample. I feel that there are enough fluctuations from one season to the next to legitimately cause park factors to jump around, and that this is primarily due to changes in weather. Even for an indoor stadium, this is true: a park factor is always implicitly compared to the league average, and weather effects at the outdoor parks affect everybody's baseline.
The formulas I prefer to use for a park adjustment are rather complex, because I distinguish between the effect of the park itself and the team that plays its games there. Each park receives a preliminary rating based on the total number of runs per game scored in that park, divided by the runs per game scored in road games. Both the home club and visiting club are included. The Rockies, for instance, scored 485 runs, and allowed 490, in 72 games in Coors Field: 13.54 runs per game. They scored 300 and allowed 293 in 72 away games: 8.24 runs per game. The Coors Field park factor is 13.54/8.24, or 1.643: the park seems to cause 64.3% more runs to score than average.
The adjustment given to the Colorado Rockies, however, is different from that given to Coors Field, because the Rockies don't play every game at home. I use the weighted average of the fields in which they did play, so their team rating is calculated by taking 72 times 1.643, for the games at Coors Field, plus 7 times 1.100, for the games in Atlanta, plus all of the other games they played at every other stadium in the NL, and dividing by total games played, or 144. That tends to be about half the distance between the pure park factor and the team factor; for Colorado, that comes out to 1.300.
For minor leagues, I wasn't always able to get the game-by-game data needed. There, I usually used simply (home games times home park factor) plus (road games times road park factor) divided by (total games). The road park factor is found by
N-HPF ----- N-1where HPF is the home park factor and N is the number of teams in the league.
For some years prior to 1992, I wasn't able to get some minor league data at all. Park factors for 1992 and earlier have sometimes been estimated by knowing the peak factors in later years. A full list of park factors used appears in the appendix.
League adjustment is more straightforward and less controversial, and involves two corrections: one for the average level of offense being different, and another for having different numbers of runs score from the same set of offensive statistics.
It should be obvious that different leagues, or the same league at different times, has different offensive standards. Changes in the liveliness of the ball, the size of the strike zone, the tactics of hitters and pitchers, the parks in the league, and more cause offense to fluctuate. And changes from one year to the next can very rarely be attributed to changes in the quality of pitchers or hitters. An entire league pitchers does not suddenly get worse one year or better, as apparently happened in 1992-93. Even expansion cannot explain why pitchers who were already in the league saw their ERAs increase by an average of half a run between those years. Pitchers and hitters, taken as a whole, don't get better or worse between seasons; but conditions can change that make them look better or worse.
The second adjustment is really a deficit in statistics. The EQA formula uses just eight commonly available statistics, but there is a fair amount of offense that isn't covered by those eight. Some have statistics themselves: hit by pitch, balks, sacrifices. Some don't: outs made on the basepaths, and probably the most important, reaching base on errors. These ROEs look like outs in the statistics, but act like hits in the game. The more ROEs there are, the more runs will score from the same 1set of apparent statistics. This is a very real effect when comparing across long periods of time (comparing todays major leagues to, say, the 1920s), or across leagues at different levels (between the American and Texas Leagues).
There is also a third adjustment needed: for the DH. A league using a designated hitter has a league offensive level that is higher than one without, other things being equal, simply because they replace very poor hitters with at least average hitters for about 1/18 of their plate appearances. This makes a difference of about 5% in run scoring, which equates to a difference of 2.5% in EQA.
This section looks at the top 100 performances in a variety of categories related to EQA and EQR. A number of active players play prominent roles in these lists.
Career EPEQA is the player's rate of run production per out over the course of the player's entire career. Because it is based on a how the player compares to the average player in his league, it favors old-time players, since it makes no accounting of increased player skill or the reduced difference between the standout and average player that has undoubtedly occurred. At the same time, it also has a bias in favor of active players, who have yet to finish their careers and experience the almost inevitable decline in career averages that the last few wretched seasons bring.
A minimum of 4000 plate appearances is required to appear. Otherwise, Frank Thomas would appear on this list in third place.
Player EqA Babe Ruth .3776 Ted Williams .3716 Lou Gehrig .3530 Mickey Mantle .3506 Ty Cobb .3505 Rogers Hornsby .3488 Dan Brouthers .3447 Joe Jackson .3432 Pete Browning .3383 Stan Musial .3372 Tris Speaker .3365 Jimmie Foxx .3362 Barry Bonds .3337* Billy Hamilton .3336 Willie Mays .3325 Joe DiMaggio .3311 Hank Greenberg .3307 Hank Aaron .3305 Honus Wagner .3305 Charlie Keller .3304 Dick Allen .3297 Mel Ott .3297 Johnny Mize .3287 Eddie Collins .3275 Roger Connor .3275 Frank Robinson .3271 Ed Delahanty .3259 Elmer Flick .3259 John McGraw .3245 Mike Donlin .3224 Rickey Henderson .3223* Nap Lajoie .3219 Harry Heilmann .3205 Gavvy Cravath .3196 Willie McCovey .3193 Ralph Kiner .3192 Frank Chance .3189 Fred McGriff .3188* Harry Stovey .3187 Willie Stargell .3179 Eddie Mathews .3176 King Kelly .3175 Jesse Burkett .3168 Wade Boggs .3166* Hack Wilson .3164 Bill Joyce .3160 Mike Schmidt .3160 Cap Anson .3157 Will Clark .3157* Babe Herman .3150 Sam Crawford .3149 Harmon Killebrew .3148 Kevin Mitchell .3144* Mark McGwire .3142* Joe Morgan .3142 Arky Vaughan .3140 Duke Snider .3138 Frank Howard .3136 Mike Tiernan .3136 Tip O'Neill .3131 Jackie Robinson .3131 Sam Thompson .3131 Bill Terry .3130 George Gore .3125 Pedro Guerrero .3116 Al Rosen .3111 Jake Fournier .3110 Sherry Magee .3108 Tim Raines .3106* Norm Cash .3105 Tony Gwynn .3104* Paul Waner .3103 Jeff Heath .3102 John Kruk .3101* Al Kaline .3100 Larry Doby .3099 Daryl Strawberry .3091* Roy Cullenbine .3088 Reggie Smith .3088 Jack Clark .3087 Bob Johnson .3086 Fred Clarke .3079 Frank Baker .3077 Dolph Camilli .3077 Rod Carew .3075 George Brett .3074 Jim O'Rourke .3074 Wally Berger .3073 Fred Dunlap .3073 Roberto Clemente .3072 Buck Ewing .3071 Topsy Hartsel .3071 Reggie Jackson .3071 Boog Powell .3070 Denny Lyons .3069 Ross Youngs .3069 Joe Kelley .3067 Mickey Cochrane .3065 Riggs Stephenson .3062 Chuck Klein .3057Career EPER
EPER are adjusted runs; whereas EPEQA measures a rate of production (i.e., runs per out), EPER measure how much is produced. The 2000-EPER plateau is a signal achievement in baseball history, with only 14 members, although two active players are within striking distance. It rewards both a high rate of production and longevity. It tends to favor recent hitters, who played in a 162-game season, and is strongly biased against 19th-century stars because of the short seasons in which they played.
The de facto standard for induction to the Hall of Fame would seem to be somewhere around 1675 EPER. Every eligible player above that line, regardless of position, is in. Three active players are already over that line, and several more appear likely to make it.
Player EPER Hank Aaron 2721 Ty Cobb 2685 Babe Ruth 2470 Willie Mays 2454 Stan Musial 2434 Pete Rose 2292 Tris Speaker 2225 Honus Wagner 2207 Carl Yastrzemski 2187 Frank Robinson 2185 Ted Williams 2134 Eddie Collins 2083 Mickey Mantle 2083 Mel Ott 2054 Lou Gehrig 1990 Dave Winfield 1980* Eddie Murray 1952* Reggie Jackson 1932 Al Kaline 1919 Joe Morgan 1915 George Brett 1910 Rogers Hornsby 1906 Nap Lajoie 1873 Sam Crawford 1847 Jimmie Foxx 1822 Eddie Mathews 1779 Rickey Henderson 1766* Mike Schmidt 1743 Willie McCovey 1730 Robin Yount 1730 Billy Williams 1714 Cap Anson 1709 Roberto Clemente 1700 Lou Brock 1697 Rod Carew 1691 Paul Waner 1691 Harmon Killebrew 1690 Rusty Staub 1668 Tony Perez 1642 Roger Connor 1634 Willie Stargell 1628 Andre Dawson 1610* Paul Molitor 1609* Jesse Burkett 1573 Dwight Evans 1565 Ernie Banks 1562 Zach Wheat 1560 Brooks Robinson 1553 Fred Clarke 1551 Dan Brouthers 1540 Max Carey 1515 Dave Parker 1515 Darrell Evans 1513 Harry Heilmann 1508 Tim Raines 1508* Al Simmons 1503 George Davis 1501 Goose Goslin 1500 Charlie Gehringer 1492 Ed Delahanty 1488 Vada Pinson 1487 Jake Beckley 1484 Al Oliver 1462 Joe DiMaggio 1456 Orlando Cepeda 1453 Wade Boggs 1435* Willie Keeler 1427 Ron Santo 1424 Sherry Magee 1423 Sam Rice 1422 Dick Allen 1415 Harry Hooper 1414 Frankie Frisch 1407 Duke Snider 1405 Willie Davis 1402 Steve Garvey 1394 Luke Appling 1393 Joe Torre 1390 Jim Rice 1388 Mickey Vernon 1386 Ted Simmons 1382 Lou Whitaker 1382* Johnny Mize 1373 Carlton Fisk 1370 Jose Cruz 1367 George Sisler 1362 Cal Ripken 1357* Enos Slaughter 1357 Johnny Bench 1352 Greg Nettles 1351 Reggie Smith 1351 Bill Dahlen 1348 Jack Clark 1346 Jim O'Rourke 1344 George Van Haltren 1343 Ken Singleton 1342 Joe Medwick 1334 Jimmy Ryan 1334 Keith Hernandez 1326 Jimmy Sheckard 1326Career EQH
EQH are really something of a freak-show stat; they don't quite measure EPEQA well enough to be genuinely useful, but they serve adequately as an estimator. I've included it here to highlight the equivalent 3000-hit club, and note with some despair that Pete Rose made it to the top of this hit chart, too.
Player EqA Pete Rose 4090 Hank Aaron 4078 Ty Cobb 3996 Stan Musial 3694 Willie Mays 3612 Carl Yastrzemski 3611 Honus Wagner 3433 Tris Speaker 3423 Dave Winfield 3300* Frank Robinson 3268 Eddie Collins 3246 Eddie Murray 3231* Babe Ruth 3165 George Brett 3160 Al Kaline 3124 Robin Yount 3124 Mel Ott 3096 Nap Lajoie 3077 Reggie Jackson 3014 Sam Crawford 3009 The near misses: Lou Brock 2964 Paul Waner 2932 Brooks Robinson 2912 Joe Morgan 2905 Roberto Clemente 2889 Cap Anson 2862 Rusty Staub 2862 Rod Carew 2859 Ted Williams 2858 Tony Perez 2847 Rogers Hornsby 2844 Billy Williams 2844 Andre Dawson 2837* Mickey Mantle 2835 Lou Gehrig 2819Five Year EPEQA
Five year EPEQA is one attempt to measure a player's peak. It is the highest EPEQA achieved by a player in any five consecutive years in which he had at least 2000 PA or 400 EPER. The year given indicates the start of the five year run. This list is very strongly weighted to players from before 1920, which makes the accomplishments of Barry Bonds and Frank Thomas all the more amazing.
Player EPEQA Babe Ruth .3986 1920 Ted Williams .3913 1953 Ty Cobb .3847 1909 Rogers Hornsby .3792 1921 Mickey Mantle .3719 1954 Honus Wagner .3708 1904 Joe Jackson .3702 1909 Dan Brouthers .3684 1882 Pete Browning .3669 1881 Lou Gehrig .3666 1927 Barry Bonds .3615 1991* Nap Lajoie .3615 1901 Frank Thomas .3599 1990* Jimmie Foxx .3597 1932 Stan Musial .3582 1948 Tris Speaker .3577 1912 Ed Delahanty .3570 1895 Roger Connor .3545 1882 Willie McCovey .3543 1966 Billy Hamilton .3537 1891 Eddie Collins .3527 1911 Joe DiMaggio .3509 1939 Joe Morgan .3508 1972 Dave Orr .3500 1883 John McGraw .3496 1897 Frank Robinson .3474 1966 King Kelly .3471 1884 Johnny Mize .3470 1936 Hank Aaron .3460 1959 Al Simmons .3460 1927 Harry Heilmann .3458 1921 Willie Mays .3456 1961 Mel Ott .3435 1935 Frank Chance .3430 1902 Willie Stargell .3423 1971 Charlie Keller .3422 1941 George Sisler .3415 1919 Dick Allen .3412 1963 Fred Dunlap .3407 1880 Hank Greenberg .3406 1934 Cap Anson .3401 1880 Wade Boggs .3396 1985* Joe Kelley .3390 1894 Roberto Clemente .3387 1967 Harmon Killebrew .3386 1966 Eddie Mathews .3382 1953 Ralph Kiner .3381 1947 George Stone .3377 1903 Duke Snider .3366 1952 Benny Kauff .3362 1913 Tip O'Neill .3362 1885 Mike Schmidt .3354 1979 Rickey Henderson .3353 1989* Elmer Flick .3352 1904 Rod Carew .3350 1973 Frank Baker .3347 1911 Jesse Burkett .3346 1897 Carl Yastrzemski .3338 1967 Arky Vaughan .3336 1934 Frank Howard .3335 1967 Tim Raines .3334 1983* Hack Wilson .3332 1926 Harry Stovey .3330 1882 Mike Tiernan .3327 1888 Chuck Klein .3319 1929 Al Kaline .3314 1964 Pedro Guerrero .3311 1985 Sherry Magee .3311 1906 George Gore .3310 1882 Mike Donlin .3309 1901 Jackie Robinson .3304 1949 Gavvy Cravath .3302 1913 Babe Herman .3301 1929 George Brett .3298 1979 Willie Keeler .3292 1895 Ken Singleton .3285 1975 Eddie Murray .3280 1981* Denny Lyons .3279 1887 Jake Stenzel .3278 1893 Lefty O'Doul .3274 1928 Norm Cash .3270 1959 Jeff Bagwell .3268 1990* Fred McGriff .3268 1988* Joe Medwick .3266 1935 Orlando Cepeda .3265 1963 Will Clark .3264 1988* Ken Griffey, Jr. .3263 1991* Bill Terry .3263 1930 Dolph Camilli .3261 1937 Chick Hafey .3261 1927 Ron Santo .3259 1963 Augie Galan .3257 1943 Sam Crawford .3256 1907 Eric Davis .3255 1985 Reggie Jackson .3255 1973 Don Mattingly .3250 1982* Paul Waner .3250 1925 Ken Williams .3247 1921 Al Rosen .3246 1950 Edgar Martinez .3244 1991*Five-Year EPER
Like the five year EPEQA, the five year EPER is the highest total EPER a player accumulated in any five consecutive seasons, with the year indicating the start of the run. The strike of 1994-95 almost certainly cost Bonds and Thomas spots in the very-exclusive 700 Club; the effects may be such that no new player will be able to get onto the list until 2000.
Player EPER Babe Ruth 809 1920 Lou Gehrig 789 1930 Ty Cobb 755 1908 Mickey Mantle 741 1954 Stan Musial 737 1948 Hank Aaron 736 1959 Rogers Hornsby 730 1920 Jimmie Foxx 720 1932 Willie Mays 719 1961 Honus Wagner 716 1904 Tris Speaker 699 1912 Ted Williams 696 1946 Barry Bonds 691 1989* Joe Morgan 686 1972 Frank Thomas 679 1991* Frank Robinson 663 1962 Carl Yastrzemski 662 1966 Eddie Collins 657 1909 Frank Howard 656 1967 Tim Raines 655 1983* Dick Allen 654 1964 Wade Boggs 652 1985* Willie McCovey 652 1966 Mel Ott 652 1932 Ralph Kiner 645 1947 Eddie Mathews 637 1953 Ron Santo 637 1963 Rod Carew 636 1973 Duke Snider 635 1952 Chuck Klein 634 1929 Joe DiMaggio 630 1937 Johnny Mize 629 1937 Bobby Bonds 626 1969 Don Mattingly 625 1984* Billy Williams 624 1963 Roberto Clemente 623 1963 Pete Rose 623 1969 Ed Delahanty 619 1895 Joe Medwick 617 1935 Elmer Flick 613 1903 Joe Jackson 612 1911 Harmon Killebrew 612 1966 Will Clark 608 1988* Lou Brock 607 1967 Harry Heilmann 605 1921 Dale Murphy 604 1983 Willie Stargell 603 1971 Sam Crawford 599 1907 Frank Baker 598 1910 Ernie Banks 598 1956 Mike Schmidt 598 1979 Rickey Henderson 597 1982* George Sisler 597 1918 Fred McGriff 596 1988* Tony Perez 596 1969 Ken Singleton 596 1975 Dan Brouthers 594 1885 Babe Herman 593 1929 Bill Terry 592 1928 Orlando Cepeda 591 1959 Hack Wilson 591 1926 Roger Connor 590 1885 Reggie Jackson 590 1973 Eddie Murray 590 1982* Al Rosen 589 1950 Rusty Staub 588 1967 Billy Hamilton 587 1891 Nap Lajoie 587 1906 Bobby Murcer 583 1969 Dave Parker 583 1975 Jesse Burkett 582 1897 Bill Nicholson 582 1940 Paul Waner 581 1932 Steve Garvey 580 1974 Al Simmons 579 1929 Rocky Colavito 578 1961 Sherry Magee 578 1906 George Burns 577 1913 Cesar Cedeno 577 1972 Jim Rice 577 1975 George Brett 576 1976 Charlie Gehringer 576 1934 Ken Griffey, Jr. 575 1990* Charlie Keller 575 1939 Tony Gwynn 571 1984* Vada Pinson 571 1959 Dave Winfield 570 1976* Cecil Cooper 569 1979 Paul Molitor 569 1989* Wally Berger 568 1931 George Foster 568 1976 Tony Oliva 568 1964 Arky Vaughan 566 1933 Jim Wynn 566 1965 George Stone 565 1905 Jackie Robinson 564 1948 Ryne Sandberg 564 1988* Roy White 563 1968 Harry Stovey 562 1885 Dolph Camilli 561 1938High Career EPEQA
High Career EPEQA is a different take at measuring a player's peak. It tells us how high a player's career EPEQA got, at the end of any season, after he was eligible for the list. In other words, look at the player's EPEQA after he reached 4000 PA. The highest EPEQA at the end of any season thereafter is his high career EPEQA. It essentially removes the last few years of the player's career. It has the virtue, which the career EPEQA list does not have, is that you cannot move backwards on the list; your own high-water mark is there to stay, regardless of how long you continue to play out the string.
Player EPEQA Babe Ruth .3861 Ted Williams .3773 Ty Cobb .3677 Lou Gehrig .3573 Mickey Mantle .3569 Dan Brouthers .3544 Rogers Hornsby .3540 Stan Musial .3518 Jimmie Foxx .3488 Honus Wagner .3483 Eddie Collins .3465 Tris Speaker .3457 Pete Browning .3456 Joe Jackson .3434 Johnny Mize .3431 Roger Connor .3422 Billy Hamilton .3418 Nap Lajoie .3398 Willie Mays .3396 Dick Allen .3383 Joe DiMaggio .3365 Hank Aaron .3361 Charlie Keller .3361 Cap Anson .3353 Willie McCovey .3351 Mel Ott .3345 Barry Bonds .3337* Hank Greenberg .3334 Wade Boggs .3328* George Sisler .3316 Frank Robinson .3311 Elmer Flick .3291 Eddie Mathews .3290 Chuck Klein .3281 Al Simmons .3275 Ralph Kiner .3272 King Kelly .3269 Ed Delahanty .3262 John McGraw .3262 Arky Vaughan .3262 Rickey Henderson .3256* Tim Raines .3246* Jesse Burkett .3245 Joe Morgan .3243 Harry Stovey .3239 Mike Donlin .3232 Pedro Guerrero .3231 Joe Kelley .3231 Fred McGriff .3231 Frank Baker .3229 Harmon Killebrew .3229 Frank Chance .3223 Willie Stargell .3222 Harry Heilmann .3220 Hack Wilson .3216 Jim O'Rourke .3211 Paul Waner .3211 Babe Herman .3210 Reggie Jackson .3208 Willie Keeler .3208 Will Clark .3204* Sherry Magee .3202 Duke Snider .3200 Joe Medwick .3198 Gavvy Cravath .3197 Mike Schmidt .3193 Jackie Robinson .3190 Mike Tiernan .3184 Sam Thompson .3180 Norm Cash .3178 Sam Crawford .3178 Tip O'Neill .3174 Eddie Murray .3171* Carl Yastrzemski .3170 Don Mattingly .3168* Orlando Cepeda .3162 Frank Howard .3162 Bill Joyce .3160 Larry Doby .3158 Buck Ewing .3158 George Gore .3157 Tony Oliva .3156 Ross Youngs .3154 George Brett .3153 Bill Terry .3152 Rod Carew .3151 Daryl Strawberry .3151* Al Kaline .3150 Paul Hines .3149 Jim Bottomly .3147 Hugh Duffy .3145 Ken Singleton .3145 Kevin Mitchell .3144* Jake Fournier .3142 Mark McGwire .3142* Edd Roush .3141 Goose Goslin .3139 Jim Wynn .3130 Roy Thomas .3129 Denny Lyons .3128