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As some of you may have noticed, there have been some changes in the Minor League EqA page.

Let’s start with the simple. When you go there now, you’ll get a short, simple, fast download, with what is essentially a page of links. The long list of every player in the minors? Not gone, but moved under its own link–so that only the people who really want it have to wait for it to download.

The main feature on the page is a list of all the leagues, along with their stats, sorted by offensive level. I’m always trying to remind people of the context of minor league statistics, and this is one more heavy-handed way to remind people that some leagues (near the top) favor the hitters, while others (near the bottom) favor the pitchers. Click on the league, and you’ll get the information that was on the old minor league page: a top-10 list for each league, a breakdown of league statistics by position (approximated by games played at each position), and a list of all players in that league, sorted by team.

Completely new to the page are the links below the list of leagues–the Future DTs and PDTs. These are the projections that one would reasonably make about how players (DTs) and pitchers (PDTs) will perform at their peak in the majors. These numbers are based only on 2004, and as the link says it uses the player’s performance, age, and level to generate the projection.

First of all, what do I mean by “their peak”? It does not mean their best season; I would expect most players to do better than what is shown for their best season. I am talking about the expected level of performance we would get from this player when he is between 27 and 31. This is a new projection scheme, developed by comparing players’ entire minor and major league careers (not just individual seasons). The resulting routine is substantially different from the ‘MjEqA’ found on the league pages. That value is pretty simple: equivalent average in minor league, times difficulty adjustment for league, equals major league eqa. But the Future DTs calculate the future EqA by using:

1. Age. As most BP readers surely know, the age of a prospect can be as important as his actual performance. The more time a player has until he reaches 27, the more he is likely to improve. Moreover, the improvement is not linear; the average amount of improvement goes down as you get closer to age 27. A successful 20-year-old, on average, improves by more in one year than a successful 21-year-old does, and so on. There is an important caveat to that, however: while the youngest players, on average, improve by more than the older ones, the spread of their performance is also wider. The “peak” we are searching for is a combination of established ability plus expected improvement: the bigger the share of established ability (i.e., the closer to 27), the more certain the results.

2. Age compared to league. Players who are old for their league–a 25-year-old in A-ball, for instance–have a strong tendency to do worse than expected when and if they get promoted. There appears to be something–I am guessing experience, something knowledge-based rather than physical, what you might call “guile”–that gives them an advantage when playing against younger players, beyond what the normal improvements from being older would create. More to the point, the advantage disappears when they face more age-appropriate competition. Surprisingly, though, I haven’t been able to document any effect from being unusually young for one’s league; the average improvement for them tends to always be based simply on their age.

3. Differential changes in statistics. EqA essentially breaks “offense” down into four components: hitting for average, hitting for power, drawing walks, and stealing bases. What we call “normal aging” does not affect these components equally, and in fact most of what we call “normal aging” comes from improvement in power. The Future DTs treat the components separately; as a result, some players that we have gone a little too crazy over in the past based on higher minor league walk rates (ahem, Jackie Rexrode), don’t project nearly so favorably under this system.

4. Size matters. This ties in with point #3, that the change in power is the single biggest driver in age-related improvements. Short players, under 6’0″, rarely develop as much power as their taller teammates (the Giles brothers being a major, current exception), and so a height adjustment is built into the projection. There is another issue related to size that is not yet included, because I haven’t yet studied it properly, but the logic goes like this: If improvement is largely driven by power, which in turn comes from the fairly common increase in strength (“filling out”) of players in their 20s, then it stands to reason that players who, at age 20, have already filled out–players like Calvin Pickering, Jack Cust, Prince Fielder–are not going to see the same sort of gains as their skinnier cohorts. Like I said, I haven’t built this factor in, since I first need to build a reliable database of player weights at different stages of their careers, assuming that “reliable database of player weights” isn’t a complete oxymoron.

5. Strikeouts matter. Players whose translations suggest that they will strike out 130+ times tend not to develop well, although the data here can be misleading. If you look at players who had 650 plate appearances in the minors and majors who had a lot of strikeouts in the minors, you’ll find that they actually did better than expected, which would make you think that strikeouts don’t matter. The problem is that by selecting only those who have had 650-PA major league careers, you’ve created a selection bias–mainly, the ones who learned to cut down on their strikeouts and make it to the majors. Most strikeout-prone players don’t. This is true even though, as in the majors, a strikeout isn’t any more damaging than any other out. In a projection setting like the future DTs, it isn’t about the value; strikeouts are a proxy for a skill set, the skill of “making contact,” which is a very valuable skill for a successful major league career.

6. Extreme performances tend not to be repeated. While it might, in fact, be skill, it is more likely to be the combination of a good skill level plus some random chance in the player’s favor. That is why, in addition to the regular adjustments for league and park effects, the future DTs incorporate a regression to the mean on the player’s rate statistics. For some performances, like Jeremy Reed‘s 2003 singles rate, this creates a substantial tempering of enthusiasm.

The effect of all these changes is, I believe, a better set of completely objective projections than I’ve had before, although still not without its share of misses. I’ve supplied lists of the top 20 minor league players in runs above replacement for each season from 1996-2003 (I’m missing strikeout and height data on a lot of players in the database before 1996). For a set of 654 players with at least 650 PA in the minors and majors since 1970–yes, I’m introducing a selection bias, and no, this isn’t every player, just every player in my database who meets these criteria–and only applying the regression to the mean adjustments to minor league data (or else I could regress completely to the mean and generate perfect results), the improvement between the test I would have used six months ago and today is remarkable (errors comparing minor league numbers per 650 PA with major league performance per 650 PA)

```
Old             New
RMS Error       RMS Error
Batting average         .024            .017
Onbase average          .029            .022
Slugging average        .050            .040
Equivalent average      .020            .017
Hits                    14.6            10.5
Doubles                 8.1             5.0
Triples                 2.4             1.9
Home runs               6.9             5.4
Walks                   11.1            10.5
Steals                  6.4             5.8

```

Of the 654 players, 272 of them, 42%, had an EqA difference of 10 points or less. A total of 495 of them, 76%, were within 20 points. (The pitching DTs are a completely different story, again using performance, level, and age in combination, but we’ll cover that in a separate article.)

Note: Players with XX’s in their names are players who have not had their biographical information entered into the database, or who have changed organizations and haven’t been updated. The start of short-season play creates quite the hammer for data entry. Note that these players will all show an age of “23” because of a default entry in the program. Player positions and games played have been entered, but because we don’t have defensive information yet, all players will show a fielding level of “0.”

```
Top prospects, 2003              Projected future peak performance
BA   OBA  SLG   EQA EQR  RAR
1  B.J. UPTON               .302 .388 .528  .307  96  49
2  JEREMY REED              .325 .396 .532  .314  92  49
3  JOSE LOPEZ               .292 .352 .566  .303  98  48
4  PRINCE FIELDER           .286 .373 .511  .299  91  43
5  ANDY MARTE               .278 .367 .528  .300  87  42
6  JOE MAUER                .322 .385 .486  .299  87  41
7  JOSH BARFIELD            .296 .359 .511  .293  91  41
8  GRADY SIZEMORE           .294 .363 .521  .297  85  40
9  JEFF FRANCOEUR           .291 .341 .530  .290  87  38
10  ALEXIS RIOS              .309 .361 .489  .290  81  35
11  MIGUEL CABRERA           .321 .395 .609  .329  58  34
12  DAVID WRIGHT             .259 .350 .493  .286  80  33
13  J.J. HARDY               .280 .365 .505  .294  73  33
14  BRIAN MCCANN             .292 .342 .545  .293  72  32
15  ERICK AYBAR              .301 .346 .497  .286  77  32
16  VICTOR DIAZ              .291 .346 .503  .285  77  32
17  CHASE UTLEY              .289 .362 .489  .290  71  31
18  AARON BALDIRIS           .295 .367 .450  .282  76  30
19  BOBBY CROSBY             .267 .353 .473  .284  75  30
20  JEFF MATHIS              .282 .351 .487  .284  73  30

2002
1  JOSE LOPEZ               .337 .381 .591  .320 103  58
2  ANDY MARTE               .278 .349 .552  .298  88  41
3  TRAVIS HAFNER            .298 .407 .499  .311  78  41
4  MIGUEL CABRERA           .284 .349 .539  .296  86  40
5  WILL SMITH               .296 .342 .533  .292  91  40
6  BRENDAN HARRIS           .295 .355 .525  .295  82  38
7  JEFF MATHIS              .288 .351 .527  .294  84  38
8  JASON STOKES             .290 .374 .564  .311  71  37
9  JOSE REYES               .289 .347 .495  .285  91  37
10  JUAN TEJEDA              .286 .354 .489  .286  86  36
11  ROCCO BALDELLI           .309 .355 .528  .295  80  36
12  BRAD NELSON              .269 .337 .514  .285  84  34
13  JUSTIN HUBER             .278 .371 .487  .293  75  34
14  VICTOR MARTINEZ          .286 .363 .508  .294  76  34
15  JASON KUBEL              .290 .352 .528  .293  72  33
16  SHAUN BOYD               .286 .348 .478  .282  82  33
17  HEE CHOI                 .258 .367 .458  .285  79  32
18  SHIN-SOO CHOO            .280 .376 .445  .286  78  32
19  GRADY SIZEMORE           .290 .378 .452  .288  72  31
20  MARK TEIXEIRA            .280 .371 .539  .305  62  31

2001
1  HANK BLALOCK             .315 .388 .559  .315 101  54
2  ADAM DUNN                .292 .403 .562  .322  77  43
3  ADRIAN GONZALEZ          .294 .365 .524  .298  92  43
4  WILSON BETEMIT           .304 .360 .522  .296  87  40
5  MIKE CUDDYER             .269 .360 .498  .290  89  39
6  JUAN RIVERA              .296 .349 .530  .292  86  38
7  JUSTIN MORNEAU           .289 .364 .500  .294  81  36
8  KELLY JOHNSON            .269 .368 .498  .295  78  36
9  MARCUS THAMES            .269 .352 .486  .285  87  36
10  SEAN BURROUGHS           .315 .384 .510  .304  70  35
11  CHRIS SNELLING           .304 .377 .468  .291  74  32
12  JASON LANE               .263 .341 .486  .281  83  32
13  JOSE REYES               .307 .352 .532  .295  70  32
14  GARRETT ATKINS           .286 .372 .447  .284  77  31
15  CARLOS PENA              .250 .362 .468  .286  73  30
16  JASON BOTTS              .283 .372 .464  .288  70  30
17  WILL SMITH               .284 .335 .492  .278  79  30
18  BRANDON PHILLIPS         .280 .355 .465  .282  73  29
19  MARLON BYRD              .276 .345 .464  .279  79  29
20  JESUS COTA               .291 .386 .543  .311  53  28

2000
1  ALBERT PUJOLS            .300 .361 .579  .309  94  48
2  HANK BLALOCK             .301 .372 .533  .304  96  48
3  AUSTIN KEARNS            .269 .363 .518  .296  89  41
4  KEVIN MENCH              .278 .362 .521  .297  89  41
5  CARLOS PENA              .261 .359 .466  .285  86  35
6  CARL CRAWFORD            .302 .348 .479  .283  87  35
7  JOSE ORTIZ               .303 .354 .503  .290  82  35
8  JOE CREDE                .283 .350 .482  .283  85  34
9  TONY TORCATO             .310 .357 .498  .290  79  34
10  AUBREY HUFF              .282 .357 .522  .294  70  32
11  JOSE CASTILLO            .284 .336 .505  .281  81  31
12  SEAN BURROUGHS           .300 .384 .478  .295  68  31
13  JASON HART               .268 .338 .474  .277  82  30
14  COREY PATTERSON          .260 .335 .504  .282  73  29
15  HEE CHOI                 .259 .343 .490  .282  75  29
16  KEITH GINTER             .269 .368 .444  .283  73  29
17  VAL PASCUCCI             .259 .354 .455  .279  76  29
18  ADAM DUNN                .252 .365 .447  .283  72  28
19  BRIAN COLE               .272 .325 .472  .273  80  28
20  BRAD WILKERSON           .253 .359 .451  .280  70  27

1999
1  NICK JOHNSON             .307 .456 .523  .338 102  63
2  SEAN BURROUGHS           .330 .418 .517  .319  90  50
3  ARAMIS RAMIREZ           .301 .396 .536  .314  89  48
4  D'ANGELO JIMENEZ         .308 .373 .499  .296  89  41
5  MIKE CUDDYER             .283 .376 .499  .298  84  40
6  VERNON WELLS             .299 .360 .518  .297  85  40
7  JASON ROMANO             .292 .359 .539  .299  82  39
8  STEVE COX                .291 .363 .498  .292  87  39
9  DEE BROWN                .286 .377 .488  .296  81  37
10  ADAM PIATT               .256 .360 .475  .286  81  33
11  MIKE RESTOVICH           .276 .359 .482  .286  80  33
12  TONY MOTA                .297 .369 .557  .308  65  33
13  DAVID ECKSTEIN           .286 .387 .409  .284  77  31
14  PAT BURRELL              .267 .362 .479  .286  74  31
15  ANGEL SANTOS             .267 .348 .481  .282  76  30
16  AUBREY HUFF              .270 .344 .490  .282  77  30
17  MIKE LAMB                .278 .341 .483  .279  81  30
18  COREY PATTERSON          .278 .323 .530  .283  73  29
19  LUKE ALLEN               .285 .341 .478  .278  79  29
20  RICO WASHINGTON          .277 .361 .445  .279  77  29

1998
1  CALVIN PICKERING         .278 .391 .508  .305  95  47
2  ERUBIEL DURAZO           .300 .398 .566  .321  84  47
3  ERIC CHAVEZ              .290 .356 .556  .302  95  46
4  MITCH MELUSKEY           .295 .402 .502  .311  76  40
5  JOE CREDE                .287 .363 .508  .294  85  39
6  DOUG MIENTKIEWICZ        .279 .375 .470  .291  86  38
7  GABE KAPLER              .278 .345 .515  .288  88  38
8  MIKE CUDDYER             .271 .352 .509  .290  85  37
9  PETER BERGERON           .293 .373 .450  .287  89  37
10  RUBEN MATEO              .295 .359 .538  .301  76  37
11  NICK JOHNSON             .289 .420 .517  .321  65  36
12  TROY GLAUS               .270 .366 .538  .302  75  36
13  ALEX ESCOBAR             .272 .355 .522  .296  75  35
14  SHAWN GALLAGHER          .269 .347 .492  .284  84  34
15  DERNELL STENSON          .259 .365 .452  .283  82  33
16  LANCE BERKMAN            .257 .362 .471  .286  80  33
17  MICHAEL BARRETT          .296 .342 .529  .290  73  32
18  TROT NIXON               .278 .362 .461  .283  80  32
19  WILTON VERAS             .297 .336 .535  .289  75  32
20  CARLOS FEBLES            .277 .377 .444  .289  72  31

1997
1  BEN GRIEVE               .293 .400 .554  .319  97  54
2  ARAMIS RAMIREZ           .271 .370 .514  .298  89  42
3  JUAN ENCARNACION         .294 .365 .532  .302  87  42
4  NICK JOHNSON             .275 .386 .501  .303  85  42
5  BRENT BUTLER             .289 .366 .523  .299  85  40
6  ADRIAN BELTRE            .283 .372 .517  .301  80  39
7  DERNELL STENSON          .275 .373 .494  .295  84  38
8  MIKE LOWELL              .283 .361 .504  .292  84  37
9  PAUL KONERKO             .279 .366 .508  .295  79  36
10  RUBEN MATEO              .305 .362 .567  .307  72  36
11  CHAD HERMANSEN           .267 .361 .488  .290  81  35
12  DAVID ORTIZ              .281 .340 .512  .285  86  35
13  ROBERTO PETAGINE         .266 .372 .485  .293  76  34
14  RICHARD HIDALGO          .291 .347 .496  .284  82  33
15  MIKE DARR                .293 .354 .469  .282  78  31
16  ADAM JOHNSON             .265 .330 .508  .280  79  30
17  CALVIN PICKERING         .268 .349 .492  .285  74  30
18  DARYLE WARD              .294 .359 .479  .286  72  30
19  MARK KOTSAY              .274 .360 .477  .287  71  30
20  SEAN CASEY               .324 .389 .538  .313  58  30

1996
1  VLADIMIR GUERRERO        .323 .397 .601  .327 106  62
2  PAUL KONERKO             .295 .394 .558  .317  99  54
3  GABE KAPLER              .279 .354 .557  .302 100  49
4  ADRIAN BELTRE            .281 .359 .556  .302  91  44
5  ANDRUW JONES             .290 .370 .539  .304  83  41
6  CHAD HERMANSEN           .270 .363 .513  .296  87  40
7  RICHARD HIDALGO          .297 .354 .526  .294  85  39
8  DERREK LEE               .255 .343 .530  .291  87  38
9  RUBEN MATEO              .289 .342 .539  .293  85  38
10  TODD WALKER              .281 .348 .499  .286  86  36
11  BEN GRIEVE               .282 .358 .490  .288  83  35
12  EDGARD CLEMENTE          .279 .352 .498  .288  80  34
13  SCOTT ROLEN              .292 .384 .500  .301  71  34
14  MIKE RENNHACK            .291 .352 .518  .291  74  33
15  RICKY LEDEE              .267 .345 .496  .284  79  32
16  FRANK CATALANOTTO        .273 .356 .464  .281  78  31
17  MARIO VALDEZ             .286 .383 .482  .297  66  31
18  BRENT BREDE              .282 .379 .430  .284  74  30
19  DANTE POWELL             .267 .347 .465  .279  81  30
20  DARIN ERSTAD             .304 .380 .501  .301  62  30

```

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