Peter Kreutzer’s Crystal Ball (1996)

One of the early book series about fantasy baseball was Peter Golenbock’s How-to-Win at Rotisserie Baseball. I was invited by lead writer Les Leopold to contribute the first set of baseball projections for the 1995 edition of the book, but the 2004 baseball lockout was still going on at presstime and the publisher cancelled that year’s book. I sold that set of projections to the brand spanking new ESPN Sportszone, on the internet, so all was not lost. The following year I did another set of projections and adapted the original book chapter for the 2006 edition of How to Win. Here is that mostly unedited story. (Any changes are noted in the text.)


I predict…you don’t know me.

Am I right? I thought so. Okay, I’m doing pretty good so far. But it wasn’t supposed to be this way. We should be old buddies by now. Or sworn enemies. Depending on whether you used the predictions from my chapter in last year’s book. Or not. And whether they helped you win. Or not. I wrote the all-new prediction chapter in the ill-starred How-to-Win at Rotisserie Baseball 95, but then the strike dragged on and on and last year’s publisher got cold feet. When the season began there was no book. So we start over.

I’m not a mathmatician, nor a statistician. I’m a writer, a storyteller, and one of the ongoing stories in my life, since 1982, has been rotisserie baseball. Back then there were no roto analysts. Nobody was telling us what a power hitting SS was worth. Nobody could. We knew about Okrent and the original Rotisserie League, we’d read his story about roto’s invention in the (then) great Inside Sports magazine. But when my partner and I were invited into a league (which had started at Inside Sports, coincidentally) we had no idea what we were getting into. We felt incredibly lucky to be able to play in what we thought was one of the two or three leagues anywhere to be found in the whole wide world. We were baseball fans back then, we believed we knew the game, and so we marched into our first draft with confidence. And left it with one of the worst teams of all time. But we had an excuse. We’d only been asked to play the day before the draft. We had no time to prepare. We knew baseball, but not all these guys! Or whether or not they could play. We’d get ‘em next time, we said.

The next year we studied hard. We now knew we needed to know not only the starters on all the teams, but also the platoon guys and the scrubs and the rookies. We decided the key to roto was figuring out who was going to play, and how much. I remember devouring Bill James, whose Abstract (which rarely acknowledged roto at all) we’d been reading for years, and culling lists from Street and Smith’s. There weren’t any guidebooks to roto yet, at least none that we knew of, and so we crammed ourselves full of raw, pure baseball. We went to our second draft once again full of confidence, and left with another terrible team.

As the season dragged on and on and on, our Bo Belinskies languishing far off the pace, we stopped calling for the standings. We expected to be kicked out of the league. Everybody hates a loser, we thought. But of course, that isn’t true. Everybody loves a loser, especially when there’s money at stake, so in spite of our bad manners we were asked back the next year. And it was that winter that it finally occurred to us that roto wasn’t baseball at all, but rather something else, and if we were going to win we were going to have to figure out what that something else was.

The something else, of course, is math. Roto is a game of numbers, but unlike baseball, where the numbers give an indication of what’s going on but are secondary to the actual results (which means winning), in roto who wins or loses is determined by the cold black and white of the calculations. In roto no points are scored for heart, no team has chemistry. If your roto team doesn’t put up the numbers, you aren’t going to win. And so, while roto is built on a foundation of baseball knowledge, the edge that seperates the winners from the losers is mathmatical. Winning roto is played by making the best estimates of what players are going to do, and knowing what those performances are worth. That is, evaluation and strategy.

For years, Les and Peter and a coterie of other contributors have provided you, the readers of this book, with the tools, anecdotes, strategies and all else, to play roto baseball well. What they’ve never done is offer projections of what the players are going to do in the upcoming year. I think they were a little less interested in predictions than they were in exploring the other issues they’ve covered (and continue to cover). I think, also, they didn’t want to confront the biggest problem of putting projections in the book, which is predicting in December what guys are going to do four months later, especially in these times when players more and more frequently move every year from team to team. As I write this there are still more than 200 players who could conceivably change teams this year. And while many won’t, enough will that the predictions that follow have to be considered a work-in-progress. It isn’t until opening day that we can best guess which 168 hitters and 108 pitchers are most worthy of being drafted, and even then, what with injuries and trades and whatever other fortunes good and bad come to scores of players, we won’t know until the last day of the season what these guys are really going to do for the year. There’s no better prediction than hindsight.

So until now (well, last year) How-to-Win at Rotisserie Baseball has not had a prediction chapter. And now it does. I like to think they asked me to do this because after I joined the American Dreams League, four years ago, they saw the brilliance of my play, the acuity and boldness of my moves, and liked my big ideas. It’s more likely I’m doing this because I wanted to, badly, and I’m working real cheap. Like most players, you see, I’m competitive. I think I know more than the next guy. There is a special charge in going public with your ideas, putting them on the line for judgement. Last year, my projections ended up on the ESPNet SportZone, to be had by anyone with a modem and internet access. Uploading the files with the projections, the moment when you know you’re committed to an idea be it brilliant or foolish, my knees got kind of rubbery and my heart pounded a little faster. I like that, and with Peter and Les’s grace, I’m looking for the same little thrill now.

You’ll find here predictions for all the players we think will be worth drafting in 1996, compiled using the formulas I’ve developed over the last 11 years. And you’ll find a preliminary attmept to adjust those predictions to reflect the continually changing baseball scene. I have to say, I’m excited about Phil Plantier in Tiger Stadium, but I also have to ask you not to hold me too strongly to the prediction you find here. I’ll feel much more responsible come March, when we might have a better view of what his role is going to be and how healthy he is. And the same goes for Rick Aguilera (reliever or starter?) and Craig Biggio (Rocky or Yankee or somewhere else) and….the list goes on and on.

So in addition to the actual predictions, we’ll explore some of the issues that make predicting players’s stats from year to year so hard. And we’ll try and figure out what we can know about a player and how much we can rely on that to tell us about his future. There’s a bunch of musing and theorizing here, with the thought that you’ll find some nuggets that enchance your appreciation of what goes on on the field, which in turn may help you improve your team. But the bottom line is the predictions, which is where we end up. I hope you enjoy.

The Baseline Projections

After those first two miserable years the Bo Belinskies (my first roto team[ADD, comanaged and owned with Jon Glascoe)/] became perpetual contenders. We didn’t win in our third year, we fought through some bad luck and came in third, but it became clear to my partner and me that we’d made great strides. The next year we did win and over the subsequent seven years we were always in the money, with four more first place finishes. Success came not because we knew more about baseball (although undoubtedly we did get more inside the game) but because we knew more about roto. And what we learned about roto can be categorized in two ways: values and predictions.

Les takes care of values elsewhere, even introducing a fascinating new two-value system that goes a long way toward showing why winning teams win. Suffice it to say that the valuation system I developed for the Bo Belinskies back then in roto’s primitive days used a different method but comes up with numbers similar to Les’s Draft Prices. As does Patton’s system and Benson’s and Shandler’s and all the other roto analysts who derive values for roto. There are differences, of course, and those can be important, but the fact is we’re all measuring the same stuff and spending $3120 to buy it. So it makes sense there aren’t going to be radical deviations.

The same can be said for predictions. When each season ends I add that year’s stats to my database and calculate baseline projections for the next year for all the players who appeared. I can do this the day after the season ends because these projections don’t take into account anything but the numbers on the page. My assumption is that the best, most objective starting place to see what a player’s talents are is to see what he’s done in the past. The formulas I use to calculate the baseline projections were derived using multiple regression, which is a statistician’s tool for predicting future events based on past events. They are the best method I have right now for determining, objectively–uncolored by September hot streaks and the enthusiasm of scouts and sportswriters–what a player can be expected to accomplish the next season.


Which is the system’s strength, and its weakness. As we all know, players are surprising. Who would’ve thought Jim Edmonds would become a power hitter? Or Mo Vaughn a speed guy? Murray Chass, in the NY Times’s baseball preview last year, reported that Jose Mesa was the closer in Cleveland, but I don’t know anyone who thought he’d save twenty games, much less 46. Why? Because he’d never done it before. The formulas we’ve derived from multiple regression are good, we had Chuck Knoblauch projected for 7 homers last year, more than he’d ever hit before, the only problem being he went out and hit 11. No statistical formula could’ve foreseen that.

But a human did. I’ve included the Knoblauch example here because Alex Patton predicted Knoblauch, whose previous high had been 5, would hit exactly 11 homers. It is a mystery how Patton came to make this projection. When I asked him he said: “I haven’t any idea why I moved Knoblauch from 7 to 11 HR between the book and the update,” and went on to list the other players he had projected for 11 homers in 1995, including Nokes and Gaetti, who didn’t exactly do it exactly. We are only able to conjecture about Alex’s inspiration here, maybe he read somewhere that Knoblauch was going to go for the downs more. There is evidence in his strikeout to walk ration the past few years (striking out more) that he is changing his approach to hitting. But whatever the inspiration, we can conclude that Patton did what no machine could’ve done: That is, predict something for which there was no historical justification.

Which is what tweaking is about. Tweaking is incorporating the wayward information and inspiration we pick up as baseball fans, the stuff that isn’t necessarily mathematically consistent or even quantifiable, and using it to adjust the baselines. Tweaking is terribly important. I know that the tweaked numbers, the spring training projections, of Benson and Patton and everyone else, are better than the baselines. They reflect team changes, league changes, evaluations of playing time and injuries, that are impossible to calculate mechanically. I also know that the adjusted predictions are better than they otherwise might be because I started with an understanding of what the player can, objectively, be expected to do. That is, the player’s baseline.

What follows are the baselines for some of the top hitters and pitchers in each league. While these lists most obviously tell you who the top baseball players for roto are today, that’s not likely news to you. At the end of this section you’ll find a complete, tweaked list of projections, arranged by position, that I hope will help you at your draft. But here we start with the straight baselines and a little discussion about what the our regression tells us we can know from a player’s past performance. Prices are according to Les, with his new Point Price followed by the projected Draft Price.

“BONDS, BARRY”,536,0.304,38,105,30,$29 ,$41
“BELLE, ALBERT”,569,0.313,42,125,7,$26 ,$40
“BICHETTE, DANTE”,606,0.309,35,124,15,$26 ,$39
“SOSA, SAMMY”,584,0.278,34,114,32,$28 ,$38
“THOMAS, FRANK”,527,0.319,38,114,4,$23 ,$36
“GALARRAGA, ANDRES”,577,0.305,32,106,12,$23 ,$34
“VAUGHN, MO”,569,0.299,32,122,11,$23 ,$34
“PALMEIRO, RAFAEL”,579,0.3,35,104,6,$21 ,$32
“LOFTON, KENNY”,528,0.319,11,59,54,$27 ,$32
“BAGWELL, JEFF”,493,0.317,28,95,13,$21 ,$31
“SANDERS, REGGIE”,520,0.284,22,97,33,$24 ,$31
“WALKER, LARRY”,527,0.294,27,102,17,$21 ,$30
“BIGGIO, CRAIG”,578,0.296,20,79,32,$23 ,$30
“MONDESI, RAUL”,564,0.296,23,88,24,$22 ,$29
“MCGRIFF, FRED”,558,0.287,35,98,5,$19 ,$29
“SALMON, TIM”,555,0.302,29,104,6,$19 ,$29
“PIAZZA, MIKE”,484,0.324,28,98,2,$18 ,$29
“KNOBLAUCH, CHUCK”,568,0.307,12,66,43,$24 ,$29
“LARKIN, BARRY”,534,0.298,12,69,45,$24 ,$28
“GWYNN, TONY”,563,0.348,10,90,16,$20 ,$27
“BAERGA, CARLOS”,582,0.313,20,94,12,$19 ,$27
“RAMIREZ, MANNY”,501,0.294,27,103,7,$18 ,$27
“BUHNER, JAY”,503,0.268,32,116,1,$17 ,$26
“MARTINEZ, EDGAR”,529,0.308,21,105,6,$18 ,$26
“BELL, DEREK”,502,0.303,14,86,26,$20 ,$26
“CORDOVA, MARTY”,548,0.281,22,88,20,$19 ,$26
“VALENTIN, JOHN”,531,0.295,18,97,17,$19 ,$25
“ALOMAR, ROBERTO”,544,0.307,12,69,29,$20 ,$25
“MCGWIRE, MARK”,385,0.271,33,94,3,$16 ,$25
“WILLIAMS, MATT”,378,0.297,32,77,3,$15 ,$25
“PUCKETT, KIRBY”,568,0.296,21,104,5,$17 ,$24
“O’NEILL, PAUL”,498,0.309,22,97,3,$16 ,$24
“GRIFFEY, KEN JR”,358,0.296,33,58,7,$15 ,$24
“COLBRUNN, GREG”,562,0.28,22,93,12,$17 ,$24
“MESA, JOSE”,77,2.45,5,8.95,39,$47 ,$39
“MADDUX, GREG”,203,1.88,19,8.25,0,$54 ,$35
“SMITH, LEE”,78,3.14,3,10.73,39,$40 ,$35
“HENKE, TOM”,83,2.42,5,9.81,33,$40 ,$33
“FRANCO, JOHN”,71,3.21,5,12.48,34,$35 ,$32
“HOFFMAN, TREVOR”,63,3.61,6,10.67,33,$35 ,$31
“AGUILERA, RICK”,75,2.93,4,10.99,33,$36 ,$31
“WETTELAND, JOHN”,71,2.44,4,8.99,31,$38 ,$31
“BECK, ROD”,61,3.33,4,11.38,34,$34 ,$30
“MONTGOMERY, JEFF”,81,3.12,4,11.45,31,$34 ,$30
“MYERS, RANDY”,73,3.49,2,12.09,35,$33 ,$30
“BRANTLEY, JEFF”,88,3.11,5,10.41,29,$36 ,$30
“WORRELL, TODD”,79,3.87,6,10.66,30,$34 ,$29
“ECKERSLEY, DENNIS”,88,3.88,7,11.17,28,$33 ,$28
“JOHNSON, RANDY”,207,2.89,19,10.06,0,$40 ,$28
“ROJAS, MEL”,78,3.58,3,11.55,30,$30 ,$26
“HERNANDEZ, ROBERTO”,72,3.74,4,12.2,29,$29 ,$26
“SLOCUMB, HEATH”,70,3.31,5,13.38,26,$27 ,$25
“CONE, DAVID”,215,3.21,18,10.55,0,$37 ,$25
“JONES, DOUG”,83,3.49,3,11.7,26,$28 ,$24
“MARTINEZ, DENNIS”,207,2.96,15,10.17,0,$37 ,$24
“WAKEFIELD, TIM”,150,1.5,11,5.43,0,$45 ,$24
“HENNEMAN, MIKE”,76,3.18,3,12.96,25,$25 ,$22
“MUSSINA, MIKE”,188,3.84,18,10.66,0,$30 ,$21
“WOHLERS, MARK”,56,4.11,7,12.93,21,$22 ,$21
“GLAVINE, TOM”,187,3.54,17,12.55,0,$25 ,$20
“NOMO, HIDEO”,165,2.55,13,9.64,0,$32 ,$20
“HILL, KEN”,195,3.79,17,12.25,0,$24 ,$19
“AYALA, BOBBY”,70,4.61,6,12.78,21,$21 ,$19
“HERSHISER, OREL”,181,3.42,15,11.34,0,$27 ,$19
“MCDOWELL, JACK”,203,3.8,16,11.85,0,$25 ,$19
“FETTERS, MIKE”,54,3.06,2,14.06,24,$20 ,$19
“WELLS, DAVID”,198,3.76,15,10.99,0,$27 ,$18
“ROGERS, KEN”,194,4.02,17,11.95,0,$23 ,$18
“FINLEY, CHUCK”,204,3.81,16,12.22,0,$23 ,$18
“APPIER, KEVIN”,185,3.61,14,11.27,0,$25 ,$17

I’ve included players and pitchers from both leagues in one list each because there are still a lot of players who will be changing teams. Some players on these lists, like Tom Henke and Mike Henneman, said they were going to retire after last season. And still may well. But then, so did Orel Hershiser, and he recently signed a two-year deal with the Indians. So I leave them here because it’s easier to delete them from the list come spring than it is to add them.

By including both leagues in one list, some interesting comparisons emerge. Mo Vaughn ranks just under Andres Galarraga, even though he shows 16 more RBI, with just one fewer steal and six fewer batting average points. Rick Aguilera’s baseline seems to be significantly better than John Franco’s, yet he ranks behind him. The reason, of course, is league context. Because there is more offense in the AL (the DH effect) it takes a better performance for an American League hitter to equal the impact of a NL hitter, in his league. And vice versa for pitchers.

These lists are made up of the best players, so there isn’t a lot of tweaking that needs to be done. But some of it is significant. Ken Griffey Jr. appears down at the bottom of this list, projected for just 33 homers. It isn’t until we look at the number of at bats that we see why. Griffey missed about half of the 1995 season. The regression doesn’t know this, it’s just a stupid formula, so it projects Junior for 358 at bats. If we scale up his projection to 540 AB, which in this context is about right, Griffey ends up with 50 homers, which seems about right. You don’t want to scale up all players who have missed time due to injuries. For some, like Mark McGwire, who has recurrent foot and back problems, the projection reflects the fact that they miss time every year. Until he proves he can stay healthy a full year, I’m happier projecting him for part of a season. It’s safer. But for Griffey and Matt Williams, whose scaled up homer projection is 46, there is no reason to think that last year’s freakish injuries are going to come back.

Of course, there is a vast gray area here. Williams might have hit 60 homers in 1994, if a full season had been played, and was on a similar pace in 1995 until he got hurt. After he came back he hit one homer every 15 at bats, as opposed to one every 10 at bats before. Can the falloff be attributed to problems caused by the injury, or did he simply revert to his previously established level of skill (his homer rate the last five years is 1 homer every 15.4 AB)? My guess is the injury affected him, but I’m not going to pump up his projection any more than what happens when I restore his lost at bats, because I’m not sure and I like to be conservative. Forty six homers is what his history shows Williams will hit if healthy, so that’s what I’m willing to pay for.

Players who had surprising 1995 seasons after modest 1994s, like Edmonds and Castilla, don’t show up on the short list, in spite of hitting 33 and 32 homers respectively, while Marty Cordova, who hit 19 homers, is shown projected for 22. Why, you might ask, is this? Well, it’s all in the numbers. Part of it is Cordova’s speed, which give him a higher ranking than the other guys, but the fact is even after their stellar years Edmonds and Castilla project to 19 and 18 homers. Now it may well be that 1995 is a true reflection of their talents, that they made impressive strides and from here on out will continue to hit 30+ homers, in which case these baselines are wrong. But what the baseline is reflecting is that prior to 95 these guys weren’t top rung power hitters. In 1994 Edmonds hit one homer every 58 at bats. Castilla, who didn’t play much in 94, had a rate of 1 every 39 AB in 93 and 94 combined. Their 1995 rates (1 every 17 and 16, respectively) may turn out to be accurate, but the baseline says be careful, chances are their talents, and future production, lie somewhere in between.

Yet Cordova, who has played only one big league season and hit 26 (prorated) home runs, projects out to hit 22, more than either Edmonds or Castilla. You might ask, how can that be? And I’m glad you did, because I need to explain how regression works (and how we developed our regressions). What follows is going to have some technical stuff in it, so be forewarned, but it isn’t a technical section. If you want you can skip it, but if you’re interested in the assumptions we made to get us to the predictions we’ve got, here’s the dope.

The Regression Digression

Until last year I used a player’s career average (in all seasons with more than 250 AB) for my baseline projections. I’d come to this by way of Bill James, who in one of his books some years ago (before the STATS books) had said that the best way of guessing what a player would do in the upcoming year was to look at his career averages. Up to that point I had tried a variety of averages, three year, two year, variously weighted, to make my projections, but when I tested the different methods I found that James was pretty much right: The way to get the most accurrate predictions for the most players was to use career average. (Career average had a much wider error at the extremes, that is, the predictions that were wrong were more wrong, but it seemed to me that what was most important was depth of accurracy, rather than breadth.) One year I spent an unholy amount of time deriving different factors for a wide range of variables, like age, and whether a player’s last season had been better or worse than his career average (and by how much). I was amazed by all the numbers I generated, and how difficult they were to incorporate into the spreadsheet on my old XT class PC without running out of memory. So, I stopped using the factors and, for a few years, used career averages as the basis for my tweaks. They were nearly as accurate and whole lot easier to prepare.

Then I met Les. As you’ve probably noticed, Les is a big fan of regression. It has been the primary tool he’s used developing his pricing systems. When he and I started talking, last year, about my doing predictions for this book he suggested I use regression to help hone my formulas. I resisted, of course, I didn’t really want to get involved with a whole new system, but ultimately he convinced me that it was worth a try. Although at first I didn’t understand it all, I started putting together different tests. Eventually, even though I still didn’t really understand what was happening, I came up with a formula that seemed to work pretty well. And most importantly, it worked better than career averages and was easier to calculate. It was only then that I explored what the regression equation meant.

In a nutshell, it goes like this: Any group of numbers has a relationship to any other group of numbers. If the two groups of numbers are totally unrelated, the number of Mercedes imported to the US in any given year and the per capita sauerkraut consumption, for instance, when compared the two will increase and decrease in completely different ways. But if there is an underlying relationship, let’s imagine a fad for German culture that sweeps the nation, the two numbers might rise at the same time. Regression would sort out the random part of the relationship–the part that is unrelated to the fad–and give us a formula that reflects how the two phenomena are linked. If there were enough of a relationship, it might even give us a way to predicting next year’s sauerkraut comsumption based on this year’s sales of Mercedes.

Now, we know there is a linkage between what a baseball player has done before and what he will do in the future. So, if we run regressions on a player’s past stats, we should be able to get a measure of what he’ll do in the future. And this is what I did. I set up a spreadsheet that listed player stats in each of many categories for the previous years (I tested up to five years before the “predicted” season) and then ran multiple regressions. Quattro Pro has a multiple regression function, so all I had to do was point to the cells that showed the history, designate the column that had the results (the predicted values), click my mouse and viola! A result.

Not a good result, at first. What I discovered is that there a great many conditions to every situation in a player’s career. There are many players with one year of experience, others with two years, others with three or more. In any of those years they may have had wildly fluctuating, or increasing or decreasing, AB totals. The stars, of course, barring injury, are pretty consistent, but for almost everyone else, there are periods of platooning and benching, or even a year spent in Japan. And once you figure in the fabulous assortment of injuries, from the freak to the chronic, there is a remarkable diversity to the shapes of players’s careers. Trying to fit everyone into one formula proved fruitless, yet the prospect of tailoring specific formulas to specific situations didn’t do much good either. After all, the idea was to create a simple method for sucking all we can know about a player’s future level of performance from his past performance. What we ended up with is a rather straightforward formula derived not from the experiences of all baseball players, but one that instead charts the year to year fluctuations of the most consistent players–those with more than 200 AB for each of at least four consecutive seasons.

The other great illusion was that there would be hidden factors that would reveal remarkably valuable insights into the game. To this end I ran countless regressions that led nowhere, simply because the randomness of the data overwhelmed its predictive capacity. Strikeout to walk ratio was the one I thought most promising, but it didn’t work out. There is an examination of why later on. Suffice it to say that only the most direct comparisons seemed to show a meaningful relationship. For a player with more than 200 AB each of the last three years, the formulas use data in each category for each of the last three years. For example, for a player with three or more years of 200 AB, the regression predicts his HR for next year by taking the following steps:

1) Multiply his 3 year ago HR by .08
2) Multiply his 2 year ago HR by .32
3) Multiply his last years HR by .39
4) Multiply his Age minus 28 by .36
5) Add 2.07 (which is a constant that every player starts with).

When we do this for Vinny Castilla we get:

3 Years Ago,9,*,0.08,=,0.7,,,,,
2 Years Ago,3,*,0.32,=,0.9,,,,,
Last Year,32,*,0.39,=,12.4,,,,,
Age – 28,0,*,0.36,=,0,,,,,
Projection,,,,,16 homers in 96,,,,,

Whoops. That isn’t the projection I talked about earlier. But after a brief moment’s panic I realized what the difference was. And like many things irritating, it is strike related. If we prorate Vinny’s homers for the shortened seasons (to 35 and 4), we end up with a projection of 18.

Now, Marty Cordova has played only one year in the big leagues, so we don’t have three years of numbers to mull over. But it turns out that players in their first year (regressed from the historical stats of those who became consistent getters of AB) do produce a formula that is valid, although not as accurate as the three or two year formulas. We also discovered that it is pointless to go back further than three years. What a player did four years ago has no statistical relevance to what we can expect from him today. As you can see in the Vinny Castilla example, the three year ago factor in home runs represents less than 3% of the prediction. It isn’t very important. The four year ago factor is even less so.

Cordova’s projection goes:
Step,HR (prorated),*,Factor,=,Value,,,,,
Last Year,26,*,0.71,=,18.46,,,,,
28 – Age,1,*,0.34,=,0.34,,,,,

What’s interesting about these numbers are the various weights the past years are given. And how significant the decisions the predictor makes are. For instance, Castilla in 1994, had 130 AB, which prorates (to account for the shortened season) up to 184 AB. Since that is below the 200 AB threshold a case could be made for using the same formula for Castilla that I’m using for Cordova. The one year formula. If I did, Castilla would project out to 29 homers, instead of 18, which would change significantly his baseline projected price for 1996. But I won’t do that because when it comes to the baselines, I don’t make judgement calls. Or, should I say, I’ve made my judgement calls already. There is a formula in my spreadsheet to determine which formula applies to each player. In Castilla’s case, his 337 AB in 1993 put him over the threshold and he is evaluated by the three year formula. The spreadsheet doesn’t know why he had so few AB in 1994, was he benched or was he hurt? The spreadsheet doesn’t care. It assumes he’s showing a level of competancy (or incompetancy) and makes its calculations based on what he’s put on the board. If I disagree with what it kicked out, I always have the opportunity later to tweak.

One serious, and for a time baffling problem about the baselines, had to do with an apparent lack of AB. The following is a blast from the unpublished past, last year’s prediction chapter. I quote it here not only because it explains the problem pretty clearly, but because it sheds light on how regressions work. It also contains contains one of my best predictions from last year: Ken Griffey’s injury. Please keep in mind, this was written in November 1994.

“A quick look at our projections seems to reveal a serious lack of ABs. How can Marquis Grissom, who in the previous years had more than 600 ABs, be projected to 580? Similar anomolies exist for all the top AB getters in both leagues. What the hell is happening here?

“Believe me, Les and I said the same thing. How could we publish projections in which the number of ABs was clearly too low? That question haunted us. It’s one thing to look stupid because you turn out to be wrong, it’s another thing to look at a list in which you know 10 to 15 players are going to have 600 ABs and realize that you’ve predicted none to do so. That appears not only to be stupid, but also dumb. But after way too many hours of debate, discussion, attempts to raise the ABs by adding a constant to all the projections, and coffee, we stumbled upon a single simple fact: Our projections for the 168 players we predict to be drafted in a regular rotisserie league, are almost identical to the actual stats the top 168 players actually accumulated in each year (For 1995 we project 129,197 AB for the top 168 players in each league combined. In 1993, the only year that wasn’t strike shortened and in which both leagues had 14 teams, the same number of top players actually had 131,368 AB, a difference of 6.4 AB per player. The other stats, which fluctuate more from year to year, were also within the normal range of variation.). The next question was: If we’re so right, how can we so clearly be wrong? We decided that rather than adjust the numbers we’d make an explanation, and we’ll leave it to the tweakers to rectify what turns out not to be a problem but rather a different way of looking at what’s happening.

“Our regressions scanned the entire list of players and found single numbers to explain the relationship between their previous performance and their next year’s actual stats. To do so the regression draws a line through all the data points, trying to find as many fits as it can. But what happens is that at the high end the line goes below the more extreme data points (the top guys in each category), and at the low end the line goes above the part time players. As a matter of course the system compresses the range of projections. Ken Griffey hit 45 homers two years ago, and had 40 (57 adjusted for the strike) (in 1994) in two-thirds a season. And he’s just 25. It’s hard not to say that he’s going to hit more than 45 homers in 1995, but the regression gives him just 42. Is that right?

“We think so, at least for our purposes. What the regression is saying is that the guys at the extremes, usually, come back to the middle. How much depends on a wide variety of factors that the regression, which is after all simply a tool, cannot compensate for. I’m certainly not going to bet against Griffey hitting 48 homers (in 1995). I think he’s going to hit 50, easy. But what happens to that projection if on opening day Griffey is plunked on the hand by a fastball? Or he pulls a hamstring? Or some unutterable tragedy strikes that prevents him from playing or causes him to lose focus and concentration? The fact is that guys at the top of the spectrum have a lot farther to fall than they have to gain, and the odds are that a number will. The regression takes this into account. And those probabilities are reflected in our projections.

“The regression similarly boosts guys at the lower end. If we published the bottom fifty projected guys in each league you’d see two score of cup-a-coffee-types projected at around 100 ABs. That’s because the constant for ABs is near 100. Some of the guys in that list are tomorrow’s stars and will play full time. They bring up the average for the others at the bottom of the list, even though many of those guys will never play in the bigs again. Similarly, the SB constant of 1.15 assures that we’re going to project all but the oldest players with one steal. And while we don’t think Cecil is going to steal a base in 1995, the probability is that enough guys you wouldn’t expect to steal will steal that that makes sense. Barely, come to think of it.”

Well, okay, I didn’t exactly predict Griffey would run into the wall and break his wrist, but the day the Kid did just that Les (who paid $51(!) for Griffey in our draft) called me up and accused me of putting a hex on his most valuable player. Hey, I said, I just make predictions, like Jeane Dixon. And I grinned when I said it, even though it isn’t a very good joke, because I knew one formidable team had just bitten the dust.

Cosmic Slop: A Mock Draft

Just how good is our predictive system? How good is anyone’s set of predictions for that matter? Not good enough to overcome the basic fact that more often than not ball players are inconsistant. One year Craig Biggio steals thirty eight bases and hits six home runs. The next he hits 21 home runs and steals but 15 bases, and then goes on the next year (prorated 1994) to hit six home runs and steal 54 bases. Oh, and then in 1995 he puts it all together: twenty two homers and thirty three steals in a shortened season (25 and 36 prorated). No forecaster, no mathematical equation, nothing could account for those swings.

But that doesn’t keep us from making a 1996 prediction for Biggio: 20 homers and 32 steals. And if he ends up signing with Colorado? According to the Stats ballpark effects charts, the Astrodome has supressed homers by about 20% (Biggio has hit one-third his homers at home) and 89% more homers were hit at Coors in its first year than in the other NL parks. The straight math says Biggio in a Rockies uniform for a full season will hit 32, but you have to think that playing in a hitter’s park affects the way a hitter approaches each at bat. I wouldn’t go crazy in the bidding, but it’s hard not to think that forty taters would be a possibility. [This section was written when it looked like Biggio would become a Rocky. As we know now, by the good grace of Jeff Bagwell, Biggio re-upped with Houston. The ideas here remain valid, however, even if they’ve lost the frission of possibility. So this section remains.] If Biggio becomes a Rocky, where we have to assume his job would be to lead off and set the table for the big boppers, I’ll predict he doesn’t go too crazy: 29 homers and 35 steals. [As it turns out, staying in Houston, I stick with his baseline projection: 20 homers and 32 steals. A safe bet.] [Note: He ended up with 15 homers and 33 steals.] Still, if you look at his career breakdowns, you see that whether he was leading off or batting second, Biggio has been a remarkably similar and consistent hitter. So maybe that won’t make a difference. But it could, and if someone gets hurt and he ends up batting third? Well, anything could happen.

And that’s the point. Anything does happen, and nearly as often as not those of us in the prediction game (and all of us who play roto are in the prediction game, whether we call it that or not) are wrong. Biggio has re-signed with Houston, so we won’t get to test the Coors park effects. But the question lingers: When it comes to predictions, what is wrong (or right, if you prefer)? We know that the stats of the most stable players fluctuate by about 25%, up and down(!), each year, for no discernable reason. Which means if you establish an expected range for a player it might sweep from 15 to 25 homers, or 9 to 15 wins. (A few years ago, in an attempt to solve the prediction quandry, I set up a Neural Net, which is a computer program that analyzes data by emulating the way the human brain discerns patterns. Since it susses out its own weights for the data, it sees patterns our predjudiced human minds might miss. Neural Nets are used by stock analysts, football touts and in optical character recognition, among other industries, but no matter how I set up the data, the Net kept returning its predictions as broad ranges, that is that X — whose lifetime average is .275 — will hit between .253 and .299 this year. Which isn’t much help at all.) The differences between the high and low numbers add up to real dollars you can’t afford to squander. So what is a good prediction?

The simple answer is: The one that gets you the players in the draft that help you win your league. You don’t have to be perfect (you won’t be perfect), you just have to be better. Let’s look at a few lists:

Top 20 1995 AL Pitchers w/ predictions

“MESA, JOSE”,$1 ,$8 ,$5 ,$5 ,$11 ,$44 ,PMK,35,,
“JOHNSON, RANDY”,$20 ,$26 ,$28 ,$31 ,$23 ,$31 ,ADL,2,,
“AGUILERA, RICK”,$26 ,$24 ,$25 ,$23 ,$25 ,$29 ,Labr,3,,
“SMITH, LEE”,$27 ,$20 ,$21 ,$22 ,$22 ,$29 ,Labr,6,,
“MUSSINA, MIKE”,$28 ,$27 ,$27 ,$33 ,$27 ,$28 ,ADL,E,,
“WETTELAND, JOHN”,$37 ,$28 ,$41 ,$38 ,$38 ,$27 ,Patbid,-12,,
“HERNANDEZ, ROBERTO”,$34 ,$32 ,$32 ,$34 ,$27 ,$27 ,ADL/Labr,-6,,
“MONTGOMERY, JEFF”,$29 ,$26 ,$35 ,$33 ,$34 ,$27 ,Patbid,-8,,
“CONE, DAVID”,$25 ,$33 ,$26 ,$28 ,$24 ,$24 ,Hunt,-5,,
“ECKERSLEY, DENNIS”,$25 ,$22 ,$21 ,$24 ,$23 ,$24 ,Labr,-1,,
“WAKEFIELD, TIM”,,,,,,$24 ,No Bid,,,
“ROGERS, KEN”,$12 ,$16 ,$9 ,$10 ,$14 ,$23 ,Hunt,8,,
“MARTINEZ, DENNIS”,$16 ,$19 ,$18 ,$23 ,$15 ,$19 ,ADL,-1,,
“APPIER, KEVIN”,$20 ,$28 ,$25 ,$26 ,$24 ,$19 ,Hunt,-8,,
“AYALA, BOBBY”,$28 ,$25 ,$33 ,$36 ,$27 ,$19 ,ADL,-15,,
“MCDOWELL, JACK”,$22 ,$23 ,$22 ,$27 ,$23 ,$18 ,ADL,-6,,
“HERSHISER, OREL”,$10 ,$12 ,$7 ,$9 ,$7 ,$18 ,Hunt,7,,
“GUBICZA, MARK”,$1 ,$4 ,$1 ,$0 ,$2 ,$16 ,Hunt,13,,
“FERNANDEZ, ALEX”,$25 ,$27 ,$18 ,$21 ,$21 ,$16 ,Hunt,-10,,
“BELINDA, STAN”,$1 ,$6 ,$1 ,$0 ,,$16 ,Hunt,14,,
Total,$387 ,$406 ,$395 ,$423 ,$387 ,$478 ,,-30,,

First off, the prognosticators are: LABR=Baseball Weekly’s League of Alternate Baseball Reality actual prices, HUNT=John Hunt, Baseball Weekly’s Fantasy columnist, PATBID=Alex Patton’s Bid dollars from his Spring Training update, rather than actual projections, ADL=American Dreams League actual prices, PMK=My tweaked projections, published on ESPNet Sportzone on the World Wide Web last April, ACT=Les’s prices for each pitcher. A $0 bid in LABR and ADL means the player was taken in the reserve round.

This isn’t meant to be a comprehensive list. If I had Benson and Shandler’s prices in my spreadsheet I’d include them as well, and the results might well be different. It is a look at the dynamics that come into play at the draft, and how the predictions we make in the preseason effect them.

The winning bid went to the guy with the highest bid, for $1 more than the next highest bid. In the case of a tie I gave the player to both “teams” for the bid price, because it seemed in this small sample unrepresentative not to.

The results are: LABR got 4 pitchers for a net of +$2; Hunt got 7 pitchers for a net of +$19; Patton got 2 pitchers for a net of -$20; ADL got 6 pitchers for a net of -$26; and PMK got 1 pitcher for a net of +$35.

Hmmmm, I seem to be the winner here, but then anyone who had Mesa last year had a good chance of being a winner. So consider me lucky and give the prize, so far, to John Hunt. He certainly did better than ADL, which spent more than he did and showed a net loss. ADL is the league Peter G and Les and I (and Alex Patton, too) are in, so I know a little bit about why the ADL prices are so high. Two ADL teams last year, the Burn Bags and the Nova, went for the Sweeney Plan (thus named for Hugh Sweeney, who devised it, but hereafter called the Nova Strategy, in honor of its first successful implementation), which means dumping homers and ribbies and concentrating on average, steals and, most importantly, pitching. Both teams spent more than $130 — each — on pitching. and between them bought four of the pitchers (Johnson, Hernandez, Martinez and McDowell) the ADL bought here.

If the ADL prices weren’t included here Hunt would also have gotten Martinez and McDowell, for a net of -$5. Nine of 19 pitchers, when it would be expected he’d get 5. Is Hunt spending too much? Or am I (I wouldn’t get any of the ADL pitchers) spending too little?

Top 20 11 1995 Bargain AL Pitchers w/ predictions

“MESA, JOSE”,$1 ,$8 ,$5 ,$5 ,$11 ,44,*PMK,35,,
“BELINDA, STAN”,$1 ,$6 ,$1 ,$0 ,,16,Hunt,14,,
“OGEA, CHAD”,,,$1 ,$0 ,$0 ,10,Patton,9,,
“GUBICZA, MARK”,$1 ,$4 ,$1 ,$0 ,$2 ,16,Hunt,13,,
“LEITER, AL”,$1 ,$3 ,$1 ,$0 ,$5 ,14,PMK,10,,
“LIRA, FELIPE”,,,$1 ,,,9,Patton,8,,
“ROGERS, KEN”,$12 ,$16 ,$9 ,$10 ,$14 ,23,Hunt,8,,
“RADKE, BRAD”,,,$1 ,$0 ,,7,Patton,6,,
“HERSHISER, OREL”,$10 ,$12 ,$7 ,$9 ,$7 ,18,Hunt,7,,
“TIMLIN, MIKE”,$0 ,,$2 ,$0 ,,7,Patton,5,,
“NELSON, JEFF”,$3 ,$6 ,$3 ,$0 ,$0 ,11,Hunt,7,,
,$29 ,$55 ,$32 ,$24 ,$40 ,$175 ,,122,,
* denotes previously purchased

Hunt is still on a rampage, getting nearly half of the bought bargains (I started out with 20, but 9 wouldn’t have been bid on, so I took them out of the chart.) for a net of $42. PATTON makes up for the two bums he got last round, with four bargains for a net of $28. PMK gets Mesa again (which I left in just cause it feels good) and Leiter, for a net of $10. The real draft leagues are shut out of the bargains, which makes sense: these are the guys that no one was very high on. Patton’s four “buys” are of guys who, in the ADL, were chosen in the reserve round. The $10 listed as the ADL price for Kenny Rogers is what I paid for him in the draft, not the $14 I projected. And notice that in spite of my $11 tout of Mesa, he went in my league for just $5 (and in LABR, whose draft was a week earlier, for $1). And I didn’t get him. I guess it must’ve been the situation.

Two more charts:

20 18 AL Pitchers Around $10 w/ predictions

“STOTTLEMYRE, TODD”,$5 ,$7 ,$4 ,$1 ,$7 ,$13 ,PMK/Hunt,$6 ,,
“CASTILLO, TONY”,$0 ,$7 ,$2 ,$1 ,$10 ,$12 ,PMK,$4 ,,
“TAVAREZ, JULIAN”,,,$2 ,$2 ,$7 ,$12 ,PMK,$9 ,,
“NELSON, JEFF”,$3 ,$6 ,$3 ,$0 , ,$11 ,Hunt,$7 ,,
“ERICKSON, SCOTT”,$2 ,$7 ,$4 ,,$7 ,$11 ,PMK/Hunt,$6 ,,
“GORDON, TOM”,$8 ,$15 ,$8 ,$9 ,$12 ,$11 ,Hunt,($2),,
“PAVLIK, ROGER”,$1 ,$10 ,$2 ,$5 ,$2 ,$10 ,Hunt,$4 ,,
“OGEA, CHAD”,,,$1 ,$0 ,$0 ,$10 ,*Patton,$9 ,,
“HITCHCOCK, STERLIN”,$1 ,$7 ,$3 ,$4 ,$6 ,$10 ,Hunt,$3 ,,
“CLEMENS, ROGER”,$22 ,$20 ,$20 ,$19 ,$14 ,$9 ,LABR,($12),,
“PERCIVAL, TROY”,$4 ,,$5 ,$3 ,,$9 ,Patton,$4 ,,
“LIRA, FELIPE”,,,$1 ,,,$9 ,*Patton,$8 ,,
“STEVENS, DAVE”,$1 ,,$5 ,$7 ,,$9 ,ADL,$3 ,,
“MCDOWELL, ROGER”,,$5 ,$2 ,$0 ,,$8 ,Hunt,$5 ,,
“BONES, RICKY”,$7 ,$15 ,$8 ,$10 ,$14 ,$8 ,Hunt,($7),,
“PLUNK, ERIC”,$10 ,$19 ,$15 ,$12 ,$12 ,$8 ,Hunt,($8),,
“ONTIVEROS, STEVE”,$10 ,$18 ,$13 ,$17 ,$14 ,$8 ,Hunt,($10),,
“ALVAREZ, WILSON”,$17 ,$19 ,$21 ,$23 ,$20 ,$7 ,ADL,($15),,
,$44.00 ,$76.00 ,$119.00 ,$69.00 ,$60.00 ,$175.00 ,,$14.00 ,,

20 14 Bottom AL Pitchers w/ predictions

“SMITH, ZANE”,$8 ,,$8 ,$10 ,$7 ,$2 ,ADL,($7),,
“VAN POPPEL, TODD”,$1 ,,,$0 ,,$2 ,LABR,$1 ,,
“BOSKIE, SHAWN”,,,$2 ,$0 ,$3 ,$2 ,PMK,($1),,
“WEGMAN, BILL”,$3 ,$8 ,$3 ,$4 ,$5 ,$2 ,Hunt,($4),,
“BUTCHER, MIKE”,$1 ,,$1 ,,,$2 ,LABR/Patton,$1 ,,
“BERGMAN, SEAN”,,,$2 ,$1 ,$5 ,$2 ,PMK,($1),,
“RYAN, KEN”,$22 ,$23 ,$29 ,$27 ,$23 ,$2 ,Patton,($26),,
“MEACHAM, RUSTY”,$2 ,$9 ,$4 ,$0 ,$7 ,$2 ,Hunt,($6),,
“MCDONALD, BEN”,$19 ,$21 ,$19 ,$21 ,$19 ,$2 ,Hunt/ADL,($18),,
“WICKMAN, BOB”,$5 ,$13 ,$7 ,$3 ,$8 ,$2 ,Hunt,($7),,
“GUTHRIE, MARK”,$1 ,,,$0 ,,$1 ,LABR,E,,
“POOLE, JIM”,,,$1 ,$0 ,$0 ,$1 ,Patton,E,,
“DELEON, JOSE”,,,$2 ,$1 ,$4 ,$1 ,PMK,($2),,
“OLIVER, DARREN”,$10 ,$8 ,$6 ,$9 ,$6 ,$1 ,LABR,($9),,
,$72 ,$83 ,$84 ,$77 ,$87 ,$24 ,,($11.00),,

By grabbing another 13 pitchers for a net of -$31, it becomes clear that Hunt has way too much money. If he’s going to take all those high priced pitchers, and those bargains, he can’t also be buying midpriced guys and cheapies. But in this context he most certainly is. PMK takes 7 for a net of $21. Patton gets 4 for -$21. The leagues reappear, the ADL getting 4 for -$37 and LABR takes 5 for -$19. And I note, I did very well here, indeed.

The overall results:

Tout,# of Pitchers,Net,,,,,,,,
Hunt,29,$30 ,,,,,,,,
PMK,9,$66 ,,,,,,,,

I didn’t stack the deck, really. The selections of pitchers were random, not manipulated. I just pointed the cursor at a price and took the 20 guys below it. You don’t have to believe that, but please note that I’m not claiming victory. I don’t believe this exercise proves anything, at least as far as prognostication goes. What’s clear is that Hunt has too much money allocated to pitchers. Every pitcher he liked he got. To his credit they didn’t do too badly, not badly at all, but there is something dangerously askew here. To use Hunt’s prices with a $200 pitching budget might work, but it would still be wrong. The rest of us split the remainder pretty evenly, with the ADL suffering because of the two teams implementing the Nova Strategy. (I should point out, parenthetically, that even spending all that money on pitchers, Mark Starr and Ron Given’s Nova won the ADL title this year, and the Bags finished a strong sixth. Which goes to show, I guess, that an awful lot in this game is dependent on the value we give each of the categories.)

But back to predictions. What is of interest here is that the four prognosticators who allocated a proper amount of money (around $1050) each ended up with virtually the same number of pitchers. That doesn’t seem remarkable, at first. We all end up with the same number of pitchers after the draft, too, But at the draft we have to end up with 9 pitchers, the rules won’t let us buy more or fewer. In this cockeyed little exercise, as John Hunt demonstrates, the sky’s the limit. So why do the leagues (LABR and ADL, which are contrained by their rules to spend $3120 for all their players) and Alex and I, who also allocate that budget to our projections, hoping we get every one right, end up so close?

The answer, I think, is that the differences between projections, when spread out over the whole spectrum of 108 pitchers, becomes very small. For all of us the cheapest pitcher is $1 (except Hunt), and the most expensive is somewhere around $40 (even Hunt), with the average pitcher about $9 (Hunt’s is higher). Our job, as soothsayers, prognosticators, touts, whatever, is to say which pitcher goes where on that scale. And since we know a thing or two about baseball, and have all the information history can provide at our fingertips, I’d venture that collectively we do about as well as can be done. So if LABR (which after all, represents the collective wisdom of 12 very serious roto players, and hence, prognosticators) grabs 4 pitchers out of the first group, and I get only 1, it means that they believe you should pay for the good pitchers and so they distribute their money towards the top, while I believe you should find value in the middle (where I copped 4 pitchers to their 2).

One of the first ways I tried testing overall projections, some years ago, was to compare the actual player value to the predictor’s. I thought if I then added up the net values for each predictor I’d get a measure of their system. But of course, that was very, very wrong. The only differences that appeared were those of the initial budgets. That is, there was a $52 difference between a $1044 budget and a $1096. Because if you spend more at the top you’re going to spend less at the bottom, and one way or another, over the whole spread, the differences will even out. Except for the differences of the initial budget and the pitchers selected by one tout not selected by another.

So for us to be successful, to help you draft a winning team, we have to be more right about the right players, the ones who do better than expected and those who do worse, no matter where on the scale they fall, than the other guy. This exercise didn’t tell us that my predictions are better than the others, but it does go to show that small differences can yield big results.

Random Notes

Walter Shapiro suggests that we might be better off without predictions.

Walter, an ADL owner and political columnist for Esquire and USA Today, is an information freak. When I need to know where an ageing career minor leaguer who has just made an improbable jump to the bigs from the Pioneer League (read Jeff Grotewald) qualifies, Walter knows. He’s already looked it up. Walter also warns me that Grotewald’s a replacement player, so I make no claim on Grotewald and he goes somewhere else. Walter is always looking for an angle to help his Nattering Nabobs, so during a recent interview for one of his columns he asked an economist about predictions. The economist raised the spectre of “price fallacy,” the idea that by predicting a price we guarantee we’ll overpay for otherwise undervalued goods. In roto, for example, once we make predictions for all the players and set our prices for them, the ones we’re bound to get are the ones we value more highly that our opponents value less highly. Which is exactly what happens.

Each year, after the draft, I enter all the prices paid for players into my spreadsheet. Although I try to resist, I then sort by team and total up the categories. Most years my team is the best of the bunch, and it always has the best “value,” that is the difference between price paid and value prospectively obtained. But when the season is over, it is often clear that that wasn’t at all true. Other owners, who use Alex Patton’s draft software during the draft, can spit out “final” standings as soon as our auction is finished. Depending on whether they use Alex’s predictions, or have incorporated their own, the standings vary widely. Which demonstrates that predictions are in no way objective reality (even the baselines), but are rather an expression of preferences. The guys we obtain, the players who make up our teams, are the guys we value more than the other owners in the league. It is a dynamic of the auction that I daresay is inevitable.

Walter suggested there was futility in doing predictions, and for those of us trying to measure our success, there is a discouragement that comes from knowing that in the final analysis so many of our predictions are wrong. But the fact is that predictions are the first important step we have to take if we’re to play roto competitively (in order, Predict, Value, Draft, Juggle). I think our predictions, whether we quantify them or not, most effect how well our team does, unless we completely disregard the established principals of valuation. Which means, rather than do away with predictions, we need to make our predictions better.

Once Les’s Roto Optimizer works in real time, we’ll have a much better grasp of what players are worth in the contest of our league and draft, but even this state-of-the-art valuing system will be reliant on the predictions entered into it before the draft. Valuing systems matter, but it is better predictions that force the other guys to be more fallacious, and make our own price fallacy less so.

What about leading indicators? Are there any that can help us in our tweaking?

Sure, in our tweaking, but not in our mechanical baselines, so far as I can see.

One of the factors I didn’t get a chance to test last year, when making the predictions, was the walk to strikeout ratio as a harbinger of a batter’s improvement or decline. For a few years now, Ron Shandler has had a category in his stats called “Eye,” which tells us how the hitter is dealing with the strike zone. One of the things we hear about players, from announcers and scouts and other people around the game, is that they improve their hitting as their command of the strike zone improves. It seemed to me if I could figure out a way to incorporate an “Eye” trend in the regressions we use to make our predictions, the baselines would become more accurrate.

After setting up the problem in many different ways, I’ve given up. While there is definitely a correlation between guys having their best years when their strikeout to walk ratio is best, there doesn’t seem to be any consistent progression to the process. It’s like saying a guy has his best year the year he hits the most homers and has the highest average. It doesn’t follow that the previous year he had his second best year for homers and second highest average, and the year before that the third best. These numbers jump around too much (maybe in part because of inconsistent umpiring?) and don’t necessarily correspond to improvements in hitting..

A quick look at Chuck Knoblauch and Albert Belle shows us the troubling inconsistency. (Eye is calculated by dividing strikeouts into base-on-balls.)

Belle’s big leap came between 1993 and 1994, yet his Eye those years stayed practically the same. And the vast improvement in his ratio between 1994 and 1995, seems not to be reflected in his slugging percentage, which stayed virtually the same (a small argument could be made that 1995’s SLG is actually better than 1994’s, since it lead the league, but for our purposes it suffices to say that it didn’t change that much). Last year Belle walked less and struck out less than the year before. His AVG dropped from .357 to .317. His OBA dropped too, though that can be attributed to the change in his AVG. Perhaps most significantly in 1995, Belle was walked intentionally much less frequently: From 13 times in ‘93, 13 (prorated) again in ‘94, to 6 (prorated) in ‘95. Can we attribute the changes to improved “protection” in the lineup? There were cetainly better hitters behind him each successive year. But why would that hurt his AVG, and improve his Eye?

Until 1994 Knoblauch had not had an Eye below 1.0 (below 1 means more strikeouts than walks). The turnaround in 94 would seem to indicate a change in approach, as he got more aggressive he swung less discriminately and struck out more. But the hard numbers show that the change comes not from a decrease in walks but rather an increase in strikeouts. If ever there was a leading indicator for a hitter in decline, one would think, that would be it. But not only did Knoblauch’s SLG improve that year, but so did his AVG and OBA. The ratio improvement in 95 was accompanied by even more improvement in his SLG, AVG and OBA, and was caused by a steadying of the K rate and an increase in BB. Which says to me that Knoblauch is getting better because he’s swinging the bat harder, not because his command of the strike zone (what one would hope Eye measures) is changing.

Whatever the reasons for these swings, regression tells us that there is just too much noise (data inconsistent with one result) in the strikeout to walk ratio to make it a reliable leading indicator.

But then what about tweaking?

Well, you’ve got to use the info you’ve got, and the generally true idea that a player’s best years are those he has the best ratio, tells us that Eye means something. At least some of the time.

I don’t tweak guys like Belle and Knoblauch, who have played full time for more than three years, unless they’ve changed leagues, suffered a major injury or are slated for a change in playing time. In any given year their mechanical projections may be more or less right or wrong, but there isn’t anything I can add to them other than my own prejudices. I mean, watching Belle during the World Series I had the feeling I was hearing him think. Each at bat he seemed to edge closer to the plate, trying to find a way to extend his arms out to those strikes that are called just off the outside corner of the plate. It was as if he had to make the move slowly, to sneak up to the plate so that maybe the pitcher wouldn’t notice. The homer off Smoltz was the payoff, a beautiful, opposite field shot that told me that Albert was a great hitter. That he did the exact same thing the next game against Maddux tells us, I think, all we need to know about pitchers and hitters making adjustments. For one moment, at least, Maddux was literally and figuratively, behind the curve. Wow. My point being, if I let my heart speak I’d be projecting Albert for 56 homers, which he certainly could do. The thing is that it is imprudent to do so, and so I let the baselines reign my heart in.

Okay, back to the point. The guys we tweak are those who don’t have an established level of performance. Garret Anderson, for example. Or Vinny Castilla. Maybe we look at their box of stats and decide that the mechanical projection is right. After all, I would pay for Vinny Castilla’s projected 18 homers, if I could. Coincidentally, Anderson’s baseline homer projection is also 18. That’s a tougher call. Last year the surprising rookie hit 18 (prorated) homers in 410 (prorated) ABs, six more than he’d hit in the power friendly PCL the previous year. In 100 more Abs. That’s the surprising part. His K to BB ratio, throughout the minors and in his rookie year, has been remarkably consistent, about 3 to 1. His AVG, OBA and SLG are also remarkably consistent, especially considering the PCL’s ability to pump up hitting stats. The only difference lies in his doubles, where he dropped off significantly in the majors.

Because Anderson seemed to improve as a hitter in the majors, without improving his ratio, I’m going to bet conservative and dock his power numbers. It’s not that I don’t believe he can hit, I’ve seen him swing, and it’s not that he shouldn’t be able to hit for power, he’s a big guy (6’3” and 190), it’s just that big league pitchers are better than PCL pitchers, and AL parks are bigger than PCL parks. My guess is Anderson returns to being the kind of hitter he was in the PCL, gets knocked around a little, and then as he gets older makes some adjustments and adds more power. Next year? .305 with 12 homers (and 37 doubles).

Should the totals of one’s final projections add up to the expected totals of the big leagues?

I wouldn’t worry about it. At least not in the details.

Your projections, my projections, whoever’s projections, are intended to help us draft a good roto team. They reflect our best thinking about what the players in a league are going to do. But as I’ve said probably a few too many times already, there is much we don’t know, and can’t possibly know. If we set a fixed number of projected stats for the big leagues, then apportion those stats among the possible players, we’re not only opening ourselves to making a mistake in the initial prediction (how much stats there will be in 1996, let’s say), but compounding the mistake with every subsequent mistake we make up and down the line.

In the Mets outfield, you and I know Lance Johnson is going to play full time. But how do you allocate playing time between Everett, Thompson, Buford, Jones and Ochoa. Any one of them could spend significant time in the minors. Thompson or Buford could be traded, or Buford could be a defensive specialist and Jones solely a pinch hitter. Thompson may wash out, Ochoa might emerge. Or in each case, not. Maybe during spring training the situation will become clearer. Until we know something, however, I suggest you tweak the numbers to reflect the probability of the maximum time that each is going to play.

I think Johnson will play full time, so I give him 615 AB. I guess that Everett will play most of the time, sitting against some lefties, so I pump him up to 545. Either Thompson or Ochoa could play full time, or part time, so I give them both half time totals (347 and 325 respectively). Thompson’s is his baseline, Ochoa is a guesstimate. Buford (200) and Jones (235) also get their baselines, because I just don’t know. The net result, in the current projections, is that the Met outfielders have better than 2200 AB. Too many, for sure, but a lot more helpful than trying to allocate exactly 1800. Here’s why:

As Spring Training proceeds decisions are going to be made. At that point I’m going to know much more about the Mets’s outfield situation than I could possibly know now. At that point I’ll have an idea who the starters are, and who will be platooned and how. As I make my final tweaks I’ll sort the spreadsheet so that each team is listed as a whole. I’ll put a column indicating the expected role of each player, and then I’ll evaluate playing time. If Ochoa is cut I’ll cut his projection to zero. If Thompson is traded I change his team, resort, and deal with the questions that raises. I keep everyone with a chance to play active now (keeping the baseline’s proportions of homers, ribbies and stolen bases), because it’s easier to locate overages than reinsert guys you’ve erased because you thought they didn’t have a chance. Slowly and surely, bit by bit, I’ll sculpt a profile of each team and each league that will resemble what I think is going to happen for the year. Still, the totals won’t be perfect.

And I won’t be too concerned. Because a lot happens after opening day. Spring Training talk is rife with dissembling, misdirection and best-intentions. With my predictions I’m trying to give the best evaluation of what each player is likely to do this year. If I start adding a hundred AB here and cutting a hundred there, ignoring what the player’s history tells me, I’m liable to make two mistakes rather than just one. For instance, if I start pumping up Mike Benjamin and John Patterson’s AB because Robby Thompson misses a lot of time and rookie SS Rich Aurilia is untested and unproven, I’m ignoring their modest histories. And if I predict that Aurilia and Thompson are going to play full time, I’m ignoring the probablility they won’t. So I end up predicting 1000 AB for the group rather than 1200, knowing that I could be underestimating any of them, but happy not that by doing so I won’t be overpaying.

Having said that, I do add up each of the categories to make sure that the relationship between avg, home runs, rbi and stolen bases stays pretty constant. If it gets out of whack (unless you make the adjustment on purpose) you’ll be inadvertantly skewing the values of the players toward one category or another.

For 1996, I’d suggest shooting for these numbers, and if you go high or low, just make sure that the ratios between them stay about the same:


How did your projections really do last year?

The best way we’ve found to compare sets of numbers is to use a statistical tool called correlation. Correlation looks at a pair of lists and tells you how similar they are. A score of 1 means they’re exactly in align (think of them of lines on a graph: if they run parallel they correlate exactly), a score of 0 means there is no relationship between the two (no point on the one line bears any relationship to any point on the other line, in fact the number are so scattered there aren’t any lines at all), and a score of -1 means that the two lists are opposites, that is that the high point on one light relates directly to the low point on the other line, and vice versa.

The correlations for the predicted prices of the three touts (Hunt, Patton and me) and LABR, which has no freezes), to their actual prices, turn out to be: (best correlation in bold)

Situation, Hunt, Patton, PMK, LABR,
NL Top Hitters, .519, .478, .521, .487,
NL All Hitters, .680, .686, .726, .694,
NL Top Pitchers, .601, .654, .658, .589,
NL All Pitchers, .438, .478, .584, .487,
AL Top Hitters, .465, .539, .495, .466,
AL All Hitters, .632, .714, .717, .637,
AL Top Pitchers, .465, .571, .637, .500,
AL All Pitchers, .454, .535, .571, .495,

Needless to say, when I finished compiling these numbers I was pleased. I had promised myself I would run them no matter what the results, so my first reaction was relief, then excitement. And then I started looking for problems.

The biggest question is: What are we comparing? These are comparisons of each of the projected groups of players (from Hunt, Patton, LABR and me) to the players who actually earned the best, priced using Les’s pricing system. The Top designation means the 50 biggest earners in each group, the All designation means the 168 players and 108 pitchers in each league. When I relayed the results of these correlations to Alex Patton, he hit me with the big question: What are we comparing? I was crushed. I had to agree with him. It seems inevitable that, since I used Les’s price formula to value the players, some measure of bias creeps into the results. How much bias? I don’t know. Back in November Alex sent me his 1995 prices, so I could run the same correlations using them as the Actual Prices. I know he figured he would score better than he does here, and he may well have.

But I put off running those tests until just now, when I sit with my back against a deadline and Peter G ringing the phone off the hook. So please understand my dismay (and, I’m sure, Alex’s) when I opened the files and they were corrupt. My first thought was to can this section, my doubts about the bias getting in the way of my common sense and salesmanship. In lieu of running corroborating correlations using someone else’s price formula, which may also have crowned me the winner, as it were, or may have not, let me make my case:

I like Les’s prices, and since you’re reading this book, you probably do, too. Unless you’re a first-time reader, if in your past encounters with Les’s prices you found them to be bunk, I’d guess you wouldn’t be poring over this book now. That being the case, there isn’t anything wrong with comparing our projected prices to Les’s. And if Les’s prices are the best prices, then these correlations and their happy results (for me) are completely valid.

But I still have doubts. First off, I completely admire Patton’s work. I have learned much from Les and Craig Wright and John Thorne and Pete Palmer and John Benson. I very much enjoyed the Elias books and the irrepressible Hirdts, and Ron Shandler often has excellent insights pleasingly presented, but the only baseball/roto analyst who casts a shadow on Alex as a writer is Bill James. And as a writer myself I admire that no end. With good writing comes sensual pleasure, the chance to lose oneself in a consuming universe of ideas and impressions. Alex has often brought me to that place, where I’m conducting three arguments at once, all inside my head. And I’m grateful for that. So I don’t want to make too much of these correlations. The fact is no single pricing system is bullet-proof.

Les’s prices are based on the expected behavior of first place teams. When teams win using a different strategy, as Nova did this year in the ADL, the range of prices in any given league is going to change. On top of that, Les himself explores the limitations of a single pricing theory elsewhere in this year’s book, and his alternative Two Price system gives us a whole new dimension to explore. Paramount is the concept that, when the season’s over, it’s not the player’s Draft Price, but rather, his Point Price that best describes his year. Yet Point Price, while of preseason analytical interest, doesn’t directly help us at the draft. And what all the touts publish, and what we can compare our prices against, is the expected Draft Price of players.

So I can’t suggest you disregard the possible problems. I think Les’s prices are the best, and frankly, I’d be surprised if any other turn out to be better. But I’ll leave it to you to decide whether the possibility of bias makes a difference or not. And I promise that next year (if I’m invited back) we’ll run comparisons of a broad range of touts against a range of pricing systems, no matter what the results. But even then, the point won’t be to crown a winner (although that will inevitably happen), but to see what we can learn about predicting.

First off, notice that the correlations for the Top hitters in each league are consistently worse than they are for All the hitters. You would think that the best hitters would also be the most consistent hitters, and so would yield the best scores. But what actually happens is that the Top hitters do have variations from year to year and, as we noted before, since they rise the highest they have the farthest to fall. Think of Griffey and Williams, Sheffield and Palmer. In general, the mediocre players have a much smaller range of possibility, and so it’s easier to predict their value. It seems the more bottom end of the spectrum hitters we include in the correlations, the better our score gets.

Pitchers are different because they’re more unpredictable. Whether it’s because of a mathematical quirk, inescapable injuries, the game’s natural ebbs and flows or some sort of cosmic joke, pitchers cause us more problems. Yet we see that our correlations of the top 50 pitchers shows a greater accuracy than for All pitchers, in both leagues. How are pitchers different than hitters?

Oh, let me count the ways. But most importantly for our purposes, not only is it hard to predict how well pitchers will do each year, it’s exceedingly hard to know which ones to pick. The All hitters correlation goes up because, while we may get lots of the predictions wrong, we’re at least predicting the right players. But with the pitchers, once you get past the first tier, a lot of the guys we pay for turn out not to be the guys we wanted. By the end of the season many of the top 108 pitchers are those that everyone ignored, like let’s say, Tim Wakefield. Or Steve Sparks. And sometimes we don’t want the first tier guys, either. Did I hear anyone mention Jason Bere?

The other important thing about the correlations is how well they confirm that we don’t know much. The top score, .76 out of a perfect 1, isn’t that bad, but the average is more like .55. Comparisons of the individual categories that go into Price, like HR and Saves and Ratio, tend to be somewhat better than these scores, but the point is well made here that we’re all shooting, thus far, at a pretty small target. In general, the touts do lots better than sticking pins, considerably better than using last year’s stats and somewhat better than using a 2-1-1 weighted average or career average. It’s hard to say just how much of the difference is structural, that is because we use a better method (including tweaking) of predicting than the other guys, and how much is just lucky. My prediction is we’ll know more next year.

Why aren’t Pitchers as predictable as Hitters?

Undoubtedly it’s a combination of things, including the statistical “two category” effect Les talks about, but my bet is that injuries are the main factor. Pitchers have to work awfully hard and the overhand throwing motion, orthopedic surgeons will tell you, is far from the most natural. I don’t have much space here but since this is one of my pet peeves I’m going to push it a little. We’ll get to the predictions in a moment.

One irritating recent baseball trend has been for some traditionalists and most blowhards to complain about the lack of pitching. They point out that modern pitchers don’t throw as many innings and don’t complete as many games as old time pitchers (from the sixties on back) did. They throw their hands up, indignant, and demand to know why pitchers today aren’t as good as pitchers then? They moan: How can baseball expand when there aren’t enough good pitchers? I get the distinct impression that what they’re really saying is that pitchers today are spoiled, they don’t have the grit to tough it out the way the pitchers of their youth did. I say it’s because pitchers today work too hard.

Craig Wright and Tom House do a fine job of examining the issue of pitching stress and injuries in The Diamond Appraised, which is well worth checking out. A lot of what I know and believe about pitching comes from them. But I also think that one simple observation makes a thoroughly convincing case for the modern hurler as well: For more than 100 years, since pitchers stopped having to throw the ball where the batter wanted it, either high or low, every change in the rules that effects the pitcher/hitter match-up has favored the hitter, except one. The exception was the restoration in 1963 of the pre-1950 strike zone, a change that ushered in a period of the least productive hitting since the teens. The result? Within a few years the strike zone was again tightened, the mound was lowered and the designated hitter stepped into the on deck circle. And in spite of the recent hitting onslaught there are those today who think the split-finger fastball has given the pitcher too much an edge. I’ve heard people advocate reducing the number of balls in a walk to three, heaven forbid. I think we can say with certainty that it’s harder to be a pitcher today than at any other time.

So pitchers work harder, in Wright and House’s parlance they throw more stressful pitches, and–consequently–they get hurt. Sometimes this means they miss the whole year, or a big part of it. Of more importance to us roto players, sometimes they pitch hurt; usually not nearly as well as they do when they’re healthy. Steve Ontiveros, afterall, was the AL ERA Champ in 1994. It was only surprising because it had been such a long time since he’d been healthy. When we look at the best pitchers what we see is consistency–Rocket was as good as gold for a while–and we pay for it, but when they get hurt–suddenly Roger was human–the bottom falls out. And we really pay for it. Because of his odd motion experts have been predicting an injury of some type since the start of Kevin Appier’s career. After a tremendous start last year it happened? Until he got hurt Appier was the best pitcher in the American League. And after? With condolences to Peter G’s Veecks, who crashed along with Appier, let me simply and gently say he was no longer one of the best.

The injury factor makes paying for good pitchers like playing musical chairs. It’s a perverse kind of fun until you end up flat on your ass. And, of course, the less talented pitchers are all over the place anyway, musical chairs unto themselves, depending on their skills and health and smarts and how on any particular day they come together. Or not. That’s why winning teams pay less for pitchers than they do for comparably talented hitters. And as Les’s Two Price theory shows, even though they have the potential to earn more. When it comes to pitching, winning teams can never be sure that even their best predictions are going to get a chance.

The Predictions–Tweaked, Sort Of

What follows are predictions for a whole bunch of hitters and pitchers. Many of these are rock solid and will be the predictions I go into my drafts with next March (the season starts March 29th!). But many of these predictions are guesstimates. We don’t know if Ron Gant is going to be a Red, or something else. We don’t know where Kevin Mitchell will play. We don’t know if Rich Aurilia is the Giant’s shortstop. Perhaps it will be Shawon Dunston. Why do I have a nagging feeling Dunston will end up with the Yankees? No, forget about it. That would be too stupid.

But would it be too stupid for Benito Santiago to sign with San Diego? Well, it would be if I had a keeper on Brad Ausmus. But that’s another story. This story is that it’s the middle of December, the snow is blowing across the Long Island Sound, and I don’t know where David Cone is pitching next year. But at least I know David Cone will be pitching, and I know how talented he is, and so I feel fairly secure telling you he’s going to pitch well. But you already know that. The predictions that follow are an extension of that principal: they’re an assessment of talent tempered by what I know right now about 1996’s baseball situations.

The majority of player predictions are their baseline projection. I didn’t think, at the time I was putting together the list, there was any reason to adjust them, though there may well be a reason come Spring Training. The players with an * next to their name, however, have been tweaked. For a variety of reasons I felt their situation warranted special attention. For some–Griffey and Williams–it means they’ve recovered from freakish injuries, for others–Franco and Sandberg–it means they’re returning from Japan or retirement, for yet others–Aurilia and Jeter–it means they will probably be given a shot to play in the bigs, while for others–Whitaker and Lyons–it probably means the end.

The hitters are laid out by position, sorted most valuable to least. Any player who appeared in more than 10 games at a position in the big leagues is listed at that position. Players who played in fewer than 40 games are listed at the position at which they made the most big league appearances. At the bottom of each of the positions there are players who are simply listed. While many of these hitters really are worth less than zero, some are simply bubblin’ under, biding their time for an opportunity to play in the bigs. They have no value in my predictions because the baselines didn’t give them any, I don’t have enough info right now to give them that chance by tweaking, But for more than a handful they will move up this year into the list of the valued. The extra names are listed here to help you consider each and every one of the possibilities.

Only the top pitchers are listed because there are simply too many others to list. These 225 or so pitchers are my choices for guys to pick this spring. They have talent, or enough moxie to fake it. But to dig deeper is an exercise in randomness. On most teams, who knows who the fifth, or even fourth starter, is going to be? And until we know which pitchers are in the rotation, which pitchers are healthy and then which pitchers actually make their teams (remember, many of those fifth starters are sent to the minors out of camp to keep working until the schedule gets more full) that we’re stuck giving each pitcher 75 mediocre innings with 3 wins and a save, which doesn’t do much to tell us who to select.

So these lists scope out the terrain, they don’t make the tough decisions that will have to be made come March. But that’s why we’re here, to provoke, prod and pretend (whether we know if David Hulse is going to play for the Brewers next year. Or not).

Finally, while many players have team names listed, quite a few don’t. As I write this there are many, many free agent players still unsigned, and more are about to become free agents through the clubs’s recent fad of non-tendering. The number of players who are now or could shortly become free agents makes it impossible to offer lists broken out by league. These lists could still conceivably be used on draft day: Simply cross out all the players on each list who are in the other league, then conduct your draft crossing out the remaining players as they’re selected. You’ll want to adjust the prices, of course, but this does give these lists some additional utility.

Good luck.

The Catchers,,,,,,,,,,
,”PIAZZA, MIKE”,LA,484,0.324,28,98,2,$18 ,$28 ,
*,”LOPEZ, JAVIER”,ATL,463,0.3,19,70,2,$11 ,$18 ,
,”STANLEY, MIKE”,BOS,439,0.283,18,85,2,$11 ,$17 ,
,”HOILES, CHRIS”,BAL,411,0.268,22,65,2,$10 ,$15 ,
,”RODRIGUEZ, IVAN”,TEX,520,0.297,13,71,3,$10 ,$15 ,
,”SURHOFF, BJ”,,425,0.285,13,72,7,$10 ,$14 ,
*,”JOHNSON, CHARLES”,FLO,439,0.269,18,66,2,$9 ,$13 ,
,”DAULTON, DARREN”,PHI,391,0.262,17,64,4,$8 ,$12 ,
,”STEINBACH, TERRY”,OAK,458,0.275,13,69,3,$8 ,$12 ,
,”EUSEBIO, TONY”,HOU,427,0.289,10,65,2,$8 ,$11 ,
,”ALOMAR, SANDY”,CLE,292,0.285,14,43,5,$7 ,$11 ,
,”HUNDLEY, TODD”,NYM,345,0.26,16,57,2,$7 ,$10 ,
,”AUSMUS, BRAD”,SD,391,0.279,8,41,15,$8 ,$10 ,
,”MACFARLANE, MIKE”,KC,417,0.25,16,58,3,$7 ,$10 ,
,”LEYRITZ, JIM”,,331,0.275,14,48,2,$6 ,$9 ,
*,”OLIVER, JOE”,,435,0.262,11,66,3,$7 ,$9 ,
,”SANTIAGO, BENITO”,,347,0.265,13,51,3,$6 ,$9 ,
,”FLETCHER, DARRIN”,,401,0.27,12,54,1,$6 ,$9 ,
,”PARENT, MARK”,DET,342,0.247,15,47,2,$5 ,$8 ,
,”TAUBENSEE, EDDIE”,CIN,286,0.277,9,48,3,$5 ,$7 ,
,”KARKOVICE, RON”,CWS,369,0.224,15,55,3,$5 ,$7 ,
*,”WILLIAMS, GEORGE”,OAK,285,0.288,9,46,0,$4 ,$7 ,
*,”GIRARDI, JOE”,NYY,493,0.272,5,58,3,$6 ,$7 ,
,”SERVAIS, SCOTT”,CHC,331,0.246,11,52,3,$5 ,$7 ,
*,”OWENS, JAYHAWK”,COL,350,0.28,11,33,0,$4 ,$6 ,
,”WILSON, DAN”,SEA,437,0.261,7,54,3,$5 ,$6 ,
,”MYERS, GREG”,MIN,317,0.258,8,41,2,$3 ,$4 ,
,”WILKINS, RICK”,,295,0.248,10,32,2,$3 ,$4 ,
,”JOHNSON, BRIAN”,SD,292,0.265,6,39,2,$3 ,$3 ,
,”MANWARING, KIRT”,,428,0.26,4,43,2,$3 ,$3 ,
,”FABREGAS, JORGE”,CAL,308,0.265,5,33,2,$2 ,$3 ,
,”FLAHERTY, JOHN”,DET,305,0.249,7,37,1,$2 ,$2 ,
*,”ZAUN, GREGG”,BAL,218,0.26,6,29,1,$1 ,$2 ,
,”O’BRIEN, CHARLIE”,TOR,238,0.227,11,28,0,$1 ,$2 ,
,”MAYNE, BRENT”,NYM,345,0.26,4,34,2,$2 ,$2 ,
,”PAGNOZZI, TOM”,,297,0.244,7,28,1,$1 ,$1 ,
*,”WALBECK, MATT”,MIN,296,0.249,4,34,2,$1 ,$1 ,
,”SLAUGHT, DON”,,217,0.283,3,25,1,$1 ,$1 ,
,”MERULLO, MATT”,,234,0.282,1,32,0,$1 ,$1 ,
,”HASELMAN, BILL”,,182,0.243,6,28,0,$0 ,$0 ,
,”BERRYHILL, DAMON”,,196,0.229,7,24,1,$0 ,$0 ,
,”SHEAFFER, DANNY”,,267,0.24,4,36,1,$1 ,$0 ,
,”BORDERS, PAT”,,248,0.244,5,25,2,$0 ,$0 ,
,”KREUTER, CHAD”,CWS,177,0.247,5,20,1,$0 ,$0 ,
,”WEBSTER, LENNY”,,180,0.267,5,17,0,$0 ,$0 ,
,”HEMOND, SCOTT”,,213,0.212,7,20,3,$0 ,($1),
*,”SWEENEY, MIKE”,KC,150,0.24,6,12,1,($1),($1),
,”TINGLEY, RON”,,149,0.226,5,22,0,($1),($1),
,”MARTINEZ, ANGEL”,TOR,229,0.241,2,30,0,($1),($1),
,”STINNETT, KELLY”,,235,0.219,5,22,2,$0 ,($1),
,”LAKER, TIM”,,169,0.234,4,24,0,($1),($1),
,”VALLE, DAVID”,,168,0.241,3,17,2,($1),($2),
*,”PENA, TONY”,CLE,207,0.238,3,22,1,($1),($2),
*,”ENCARNACION, ANGEL”,PIT,240,0.25,2,17,1,($1),($2),
,”MATHENY, MIKE”,,199,0.247,0,25,2,($1),($2),
*,”WIDGER, CHRIS”,SEA,225,0.24,3,18,1,($1),($2),
,”LAMPKIN, TOM”,,91,0.276,1,11,2,($1),($2),
,”DECKER, STEVE”,,160,0.226,4,16,1,($1),($2),
*,”PEREZ, ED”,ATL,100,0.23,4,12,0,($2),($2),
,”LAVALLIERE, MIKE”,,118,0.245,1,23,0,($2),($2),
,”PARRISH, LANCE”,,214,0.202,5,26,0,($1),($2),
,”KNORR, RANDY”,,158,0.212,4,19,0,($2),($3),
,”NATAL, BOB”,,52,0.233,2,7,0,($3),($3),
*,”HERNANDEZ, CARLOS”,LA,188,0.207,4,16,0,($2),($3),
,”REED, JEFF”,COL,136,0.265,0,11,0,($2),($3),
,”KMAK, JOE”,,,,,,,,,
,”ALLANSON, ANDY”,,,,,,,,,
,”SPEHR, TIM”,,,,,,,,,
,”TUCKER, SCOOTER”,,,,,,,,,
,”LEVIS, JESSE”,,,,,,,,,
,”BRITO, JORGE”,,,,,,,,,
,”LIEBERTHAL, MIKE”,,,,,,,,,
,”PRINCE, TOM”,,,,,,,,,
,”GOFF, JERRY”,,,,,,,,,
,”SIDDALL, JOE”,,,,,,,,,
,”NOKES, MATT”,,,,,,,,,
,”POSADA, JORGE”,,,,,,,,,
,”LYONS, BARRY”,,,,,,,,,
,”MUNOZ, NOE”,,,,,,,,,
,”DEVAREZ, CESAR”,,,,,,,,,
,”TURNER, CHRIS”,,,,,,,,,
,”HUBBARD, MIKE”,,,,,,,,,
,”DALESANDRO, MARK”,,,,,,,,,
,”ROWLAND, RICHARD”,,,,,,,,,
,”TREMIE, CHRIS”,,,,,,,,,
,”SASSER, MACKEY”,,,,,,,,,
,”HELFAND, ERIC”,,,,,,,,,
,”PRATT, TODD”,,,,,,,,,
,”THOMAS, FRANK”,CWS,527,0.319,38,114,4,$22 ,$35 ,
*,”BAGWELL, JEFF”,HOU,540,0.32,31,104,14,$23 ,$34 ,
,”GALARRAGA, ANDRES”,COL,577,0.305,32,106,12,$22 ,$33 ,
,”VAUGHN, MO”,BOS,569,0.299,32,122,11,$22 ,$33 ,
,”PALMEIRO, RAFAEL”,BAL,579,0.3,35,104,6,$20 ,$31 ,
*,”WILLIAMS, MATT”,SF,472,0.297,40,96,3,$19 ,$31 ,
,”MCGRIFF, FRED”,ATL,558,0.287,35,98,5,$18 ,$28 ,
*,”BONILLA, BOBBY”,BAL,560,0.298,29,92,2,$16 ,$25 ,
,”MCGWIRE, MARK”,OAK,385,0.271,33,94,3,$15 ,$24 ,
,”COLBRUNN, GREG”,FLO,562,0.28,22,93,12,$16 ,$23 ,
,”CAMINITI, KEN”,SD,553,0.278,22,95,11,$16 ,$23 ,
,”CONINE, JEFF”,FLO,528,0.3,20,105,3,$15 ,$23 ,
,”KARROS, ERIC”,LA,571,0.275,25,100,5,$15 ,$22 ,
,”JONES, CHIPPER”,ATL,558,0.277,24,90,9,$16 ,$22 ,
,”MARTINEZ, TINO”,NYY,535,0.277,25,106,2,$15 ,$22 ,
,”VENTURA, ROBIN”,CWS,526,0.281,22,95,5,$14 ,$21 ,
,”GRACE, MARK”,CHC,572,0.308,14,90,6,$15 ,$20 ,
,”THOME, JIM”,CLE,483,0.294,22,76,5,$14 ,$20 ,
,”BROGNA, RICO”,NYM,534,0.287,24,81,2,$13 ,$20 ,
,”MURRAY, EDDIE”,CLE,492,0.274,23,87,6,$14 ,$20 ,
*,”FIELDER, CECIL”,DET,533,0.254,31,89,0,$13 ,$20 ,
,”PHILLIPS, TONY”,,559,0.272,21,67,14,$14 ,$19 ,
,”CASTILLA, VINNY”,COL,508,0.296,18,82,3,$13 ,$19 ,
,”GAETTI, GARY”,STL,532,0.254,25,93,4,$13 ,$18 ,
,”JEFFERIES, GREGG”,PHI,517,0.317,11,64,12,$14 ,$18 ,
,”CLARK, WILL”,TEX,497,0.297,16,94,2,$12 ,$18 ,
,”MERCED, ORLANDO”,PIT,520,0.293,15,83,7,$13 ,$18 ,
,”FRYMAN, TRAVIS”,DET,593,0.283,15,87,5,$13 ,$17 ,
,”JOYNER, WALLY”,KC,501,0.294,14,84,4,$12 ,$17 ,
,”SNOW, J.T.”,CAL,536,0.26,20,94,3,$12 ,$17 ,
,”HAYES, CHARLIE”,,558,0.285,13,86,6,$12 ,$16 ,
*,”BERRY, SEAN”,HOU,456,0.287,16,71,7,$11 ,$16 ,
,”OLERUD, JOHN”,TOR,523,0.312,12,64,2,$11 ,$16 ,
,”KING, JEFF”,PIT,482,0.271,14,86,7,$11 ,$15 ,
*,”HERNANDEZ, JOSE”,CHC,421,0.262,21,63,4,$10 ,$14 ,
,”SORRENTO, PAUL”,,387,0.257,20,81,2,$10 ,$14 ,
,”SURHOFF, BJ”,,425,0.285,13,72,7,$10 ,$14 ,
*,”BLOWERS, MIKE”,LA,465,0.272,16,78,3,$10 ,$14 ,
,”JAHA, JOHN”,MLW,377,0.277,17,68,4,$9 ,$14 ,
,”BOGGS, WADE”,NYY,498,0.303,10,68,2,$10 ,$14 ,
*,”CARREON, MARK”,SF,384,0.286,17,61,2,$8 ,$13 ,
,”PENDLETON, TERRY”,FLO,528,0.265,16,77,3,$9 ,$13 ,
*,”DAVIS, RUSS”,SEA,470,0.276,16,62,1,$8 ,$12 ,
,”MORRIS, HAL”,CIN,433,0.301,10,61,3,$9 ,$12 ,
,”ZEILE, TODD”,CHC,480,0.263,16,64,3,$8 ,$12 ,
,”SEGUI, DAVID”,MON,489,0.278,11,70,3,$9 ,$12 ,
,”MANTO, JEFF”,,332,0.263,20,47,2,$7 ,$12 ,
,”SEITZER, KEVIN”,MLW,512,0.289,6,72,4,$8 ,$11 ,
*,”GIAMBI, JASON”,OAK,400,0.275,14,55,3,$7 ,$11 ,
,”SPRAGUE, ED”,TOR,549,0.254,15,75,2,$8 ,$11 ,
,”NAEHRING, TIM”,BOS,465,0.29,9,62,2,$8 ,$11 ,
,”BROSIUS, SCOTT”,OAK,436,0.255,16,52,5,$7 ,$10 ,
,”PALMER, DEAN”,TEX,237,0.283,17,40,3,$6 ,$10 ,
,”BATES, JASON”,COL,388,0.277,12,54,5,$7 ,$10 ,
,”MABRY, JOHN”,STL,444,0.297,10,50,2,$7 ,$10 ,
*,”JOHNSON, MARK”,PIT,303,0.244,18,44,7,$7 ,$10 ,
*,”YOUNG, KEVIN”,PIT,500,0.252,14,63,4,$7 ,$10 ,
,”CIRILLO, JEFF”,MLW,393,0.281,9,48,8,$7 ,$10 ,
,”LEYRITZ, JIM”,,331,0.275,14,48,2,$6 ,$9 ,
*,”ANDREWS, SHANE”,MON,453,0.238,15,61,4,$7 ,$9 ,
,”WILLIAMS, EDDIE”,SD,367,0.265,13,55,2,$6 ,$9 ,
,”VELARDE, RANDY”,CAL,414,0.278,8,50,6,$7 ,$9 ,
,”SCARSONE, STEVE”,,314,0.27,12,39,5,$6 ,$8 ,
,”DUNCAN, MARIANO”,NYY,348,0.275,10,47,4,$6 ,$8 ,
*,”VITIELLO, JOE”,KC,312,0.254,15,50,0,$5 ,$8 ,
,”MARTINEZ, DAVE”,CWS,358,0.267,9,42,8,$6 ,$8 ,
,”WALLACH, TIM”,CAL,407,0.246,15,52,1,$5 ,$8 ,
,”BRANSON, JEFF”,CIN,358,0.265,11,47,3,$5 ,$7 ,
,”COOPER, SCOTT”,,433,0.265,11,50,2,$5 ,$7 ,
,”ALFONZO, EDGAR”,NYM,400,0.278,8,50,3,$5 ,$7 ,
*,”GRUDZIELANEK, MARK”,MON,457,0.264,6,41,12,$7 ,$7 ,
,”LEIUS, SCOTT”,MIN,429,0.254,9,56,3,$5 ,$7 ,
,”SHIPLEY, CRAIG”,,306,0.27,8,33,7,$5 ,$6 ,
*,”LEWIS, MARK”,DET,308,0.286,6,52,1,$4 ,$6 ,
*,”PHILLIPS, J.R.”,SF,390,0.237,13,48,4,$5 ,$6 ,
,”LIVINGSTONE, SCOTT”,,269,0.298,5,37,3,$4 ,$5 ,
,”MAGADAN, DAVE”,,388,0.285,3,48,3,$4 ,$5 ,
,”ANTHONY, ERIC”,,235,0.259,10,34,4,$4 ,$5 ,
,”STAHOVIAK, SCOTT”,MIN,339,0.275,5,34,7,$4 ,$5 ,
*,”PETAGINE, ROBERTO”,SD,446,0.234,11,61,0,$4 ,$5 ,
,”HOLLINS, DAVID”,,272,0.247,11,36,2,$3 ,$5 ,
*,”PERRY, HERBERT”,CLE,194,0.315,7,28,1,$3 ,$5 ,
,”RODRIGUEZ, HENRY”,MON,246,0.26,10,34,1,$3 ,$4 ,
,”PAQUETTE, CRAIG”,OAK,311,0.217,10,49,6,$4 ,$4 ,
*,”SWEENEY, MARK”,STL,270,0.3,5,26,1,$2 ,$4 ,
,”GOMEZ, LEO”,,234,0.241,11,27,1,$2 ,$3 ,
*,”COOMER, RON”,MIN,250,0.248,9,39,0,$2 ,$3 ,
*,”SAMUEL, JUAN”,,269,0.259,6,31,5,$3 ,$3 ,
,”HUNTER, BRIAN”,,193,0.241,9,32,3,$2 ,$3 ,
,”VANDERWAL, JOHN”,,186,0.281,6,29,3,$2 ,$3 ,
*,”AUDE, RICH”,PIT,262,0.244,6,46,2,$2 ,$3 ,
,”ARIAS, ALEX”,FLO,272,0.264,6,32,2,$2 ,$3 ,
*,”LOCKHART, KEITH”,,175,0.297,4,22,5,$2 ,$3 ,
,”CIANFROCCO, ARCI”,,204,0.251,7,37,2,$2 ,$3 ,
,”OWEN, SPIKE”,,300,0.25,5,37,4,$2 ,$2 ,
,”JOHNSON, HOWARD”,,257,0.215,9,33,4,$2 ,$2 ,
,”REBOULET, JEFF”,,285,0.27,4,31,2,$2 ,$2 ,
,”ALDRETE, MIKE”,CAL,234,0.255,6,32,2,$1 ,$2 ,
*,”CLARK, TONY”,DET,240,0.233,10,25,0,$1 ,$2 ,
,”STRANGE, DOUG”,SEA,246,0.251,4,32,2,$1 ,$1 ,
*,”OLIVA, JOSE”,STL,220,0.219,10,24,0,$0 ,$1 ,
,”BENJAMIN, MIKE”,,223,0.22,4,14,13,$2 ,$1 ,
,”GALLEGO, MIKE”,,233,0.246,6,23,1,$1 ,$1 ,
*,”SNOPEK, CHRIS”,CWS,175,0.291,3,17,2,$0 ,$1 ,
*,”HUSKEY, BUTCH”,NYM,250,0.212,10,23,1,$0 ,$0 ,
,”FLOYD, CLIFF”,MON,198,0.226,4,27,6,$1 ,$0 ,
,”MCCARTY, DAVE”,,154,0.253,6,15,2,$0 ,$0 ,
,”BOGAR, TIMOTHY”,NYM,209,0.244,3,27,2,$0 ,$0 ,
*,”PEREZ, EDUARDO”,,240,0.254,5,19,0,$0 ,$0 ,
,”HUSON, JEFF”,,193,0.248,1,23,6,$1 ,$0 ,
,”ESPINOZA, ALVARO”,CLE,238,0.254,2,26,2,$0 ,$0 ,
,”HARRIS, LENNY”,,236,0.208,2,19,12,$1 ,($1),
,”HANSEN, DAVE”,LA,217,0.287,1,17,0,($1),($1),
*,”ORTIZ, LUIS”,TEX,260,0.254,3,24,0,$0 ,($1),
*,”MARTINEZ, CARLOS”,,138,0.239,4,18,1,($1),($1),
,”MASTELLER, DAN”,,238,0.237,4,25,1,$0 ,($1),
,”DONNELS, CHRIS”,,109,0.3,2,13,0,($1),($1),
,”MORDECAI, MIKE”,,90,0.28,4,13,0,($1),($1),
,”STUBBS, FRANKLIN”,,139,0.25,2,23,0,($1),($2),
,”GONZALES, RENE”,,110,0.297,1,14,2,($1),($2),
,”WEHNER, JOHN”,,128,0.308,0,6,4,($1),($2),
*,”CLARK, JERALD”,,174,0.228,2,18,4,($1),($2),
,”SILVESTRI, DAVE”,,86,0.264,2,8,2,($1),($2),
,”SPIERS, BILL”,,181,0.238,1,22,3,($1),($2),
,”COLES, DARNELL”,,166,0.225,4,19,0,($1),($2),
,”GREBECK, CRAIG”,,185,0.26,1,17,0,($1),($2),
,”BUSCH, MIKE”,,,,,,,,,
,”PAGLIARULO, MIKE”,,,,,,,,,
,”NEVIN, PHIL”,DET,,,,,,,,
,”INGRAM, GAREY”,,,,,,,,,
,”OWENS, ERIC”,,,,,,,,,
,”FOLEY, TOM”,,,,,,,,,
,”LEDESMA, AARON”,,,,,,,,,
,”MAAS, KEVIN”,,,,,,,,,
,”SCHALL, GENE”,,,,,,,,,
,”SHARPERSON, MIKE”,,,,,,,,,
,”BUECHELE, STEVE”,,,,,,,,,
,”SIMMS, MIKE”,,,,,,,,,
,”MATTINGLY, DON”,,,,,,,,,
,”GROTEWOLD, JEFF”,,,,,,,,,
,”SABO, CHRIS”,CIN,,,,,,,,
,”CASTRO, JUAN”,,,,,,,,,
,”UNROE, TIM”,,,,,,,,,
,”PIRKL, GREG”,,,,,,,,,
,”SEFCIK, KEVIN”,,,,,,,,,
,”HYERS, TIM”,,,,,,,,,
,”MCGINNIS, RUSS”,,,,,,,,,
,”RANDA, JOE”,,,,,,,,,
,”DUNN, STEVE”,,,,,,,,,
,”PYE, EDDIE”,,,,,,,,,
,”GREENE, WILLIE”,,,,,,,,,
,”READY, RANDY”,,,,,,,,,
,”PERRY, GERALD”,,,,,,,,,
Middle Infielders,,,,,,,,,,
,”BIGGIO, CRAIG”,HOU,578,0.296,20,79,32,$22 ,$29 ,
,”KNOBLAUCH, CHUCK”,MIN,568,0.307,12,66,43,$23 ,$28 ,
,”LARKIN, BARRY”,CIN,534,0.298,12,69,45,$23 ,$27 ,
,”BAERGA, CARLOS”,CLE,582,0.313,20,94,12,$19 ,$26 ,
,”VALENTIN, JOHN”,BOS,531,0.295,18,97,17,$18 ,$24 ,
,”ALOMAR, ROBERTO”,,544,0.307,12,69,29,$19 ,$24 ,
,”VERAS, QUILVIO”,FLO,488,0.273,7,42,52,$20 ,$21 ,
,”CORDERO, WILL”,MON,544,0.285,15,58,11,$12 ,$16 ,
,”YOUNG, ERIC”,COL,404,0.288,7,43,33,$14 ,$16 ,
,”KENT, JEFF”,,514,0.282,18,72,4,$11 ,$16 ,
,”RIPKEN, CAL”,BAL,578,0.267,18,91,2,$11 ,$16 ,
,”DESHIELDS, DELINO”,LA,463,0.273,6,44,37,$15 ,$16 ,
,”DUNSTON, SHAWON”,,504,0.281,15,69,10,$12 ,$16 ,
,”LANSING, MIKE”,,506,0.273,8,64,25,$13 ,$15 ,
*,”SANDBERG, RYNE”,CHC,500,0.282,14,72,6,$11 ,$15 ,
*,”HERNANDEZ, JOSE”,CHC,421,0.262,21,63,4,$10 ,$14 ,
,”BOONE, BRET”,CIN,539,0.282,13,72,6,$11 ,$14 ,
,”CLAYTON, ROYCE”,STL,536,0.263,7,61,23,$12 ,$13 ,
,”DURHAM, RAY”,CWS,514,0.272,8,59,18,$11 ,$13 ,
,”VIZQUEL, OMAR”,CLE,545,0.268,4,58,26,$12 ,$13 ,
*,”RODRIGUEZ, ALEX”,SEA,488,0.256,14,58,14,$10 ,$13 ,
*,”GATES, BRENT”,OAK,522,0.308,7,59,4,$9 ,$12 ,
,”ABBOTT, KURT”,FLO,463,0.262,16,62,5,$8 ,$12 ,
,”BELL, JAY”,PIT,559,0.279,11,60,4,$9 ,$11 ,
*,”MILLER, ORLANDO”,HOU,468,0.272,13,54,6,$8 ,$11 ,
,”MCLEMORE, MARK”,TEX,499,0.266,6,48,21,$10 ,$11 ,
,”VALENTIN, JOSE”,MLW,392,0.245,11,58,16,$9 ,$11 ,
,”GARCIA, CARLOS”,,435,0.282,8,54,11,$9 ,$10 ,
,”BATES, JASON”,COL,388,0.277,12,54,5,$7 ,$10 ,
,”CORA, JOEY”,SEA,463,0.279,5,46,17,$9 ,$10 ,
,”GONZALEZ, ALEX”,TOR,426,0.266,13,51,6,$7 ,$10 ,
,”ALICEA, LUIS”,BOS,440,0.274,8,49,12,$8 ,$10 ,
,”CIRILLO, JEFF”,MLW,393,0.281,9,48,8,$7 ,$10 ,
,”GOMEZ, CHRIS”,DET,463,0.255,12,60,5,$7 ,$9 ,
,”MORANDINI, MICKEY”,PHI,507,0.275,6,52,10,$8 ,$9 ,
,”VIZCAINO, JOSE”,NYM,541,0.281,4,59,8,$8 ,$9 ,
,”MEARES, PAT”,MIN,422,0.268,8,52,10,$7 ,$9 ,
,”FERNANDEZ, TONY”,,441,0.269,8,53,8,$7 ,$9 ,
,”SOJO, LUIS”,SEA,381,0.282,10,44,5,$6 ,$9 ,
,”ROBERTS, BIP”,,381,0.282,3,34,21,$8 ,$9 ,
,”VELARDE, RANDY”,CAL,414,0.278,8,50,6,$7 ,$9 ,
,”BORDICK, MIKE”,,477,0.258,8,50,11,$7 ,$8 ,
,”SCARSONE, STEVE”,,314,0.27,12,39,5,$6 ,$8 ,
,”DUNCAN, MARIANO”,NYY,348,0.275,10,47,4,$6 ,$8 ,
,”BLAUSER, JEFF”,ATL,478,0.256,11,42,8,$7 ,$8 ,
,”LEE, MANNY”,,148,0.501,4,15,2,$4 ,$8 ,
,”BRANSON, JEFF”,CIN,358,0.265,11,47,3,$5 ,$7 ,
,”ALFONZO, EDGAR”,NYM,400,0.278,8,50,3,$5 ,$7 ,
*,”GRUDZIELANEK, MARK”,MON,457,0.264,6,41,12,$7 ,$7 ,
,”DISARCINA, GARY”,CAL,427,0.274,6,47,7,$6 ,$7 ,
,”FONVILLE, CHAD”,LA,387,0.283,0,28,20,$7 ,$7 ,
,”ALEXANDER, MANNY”,BAL,321,0.26,8,34,12,$6 ,$7 ,
*,”GAGNE, GREG”,LA,477,0.259,7,56,5,$6 ,$7 ,
,”CEDENO, ANDUJAR”,SD,440,0.257,10,42,6,$5 ,$7 ,
,”LIRIANO, NELSON”,,328,0.271,9,46,3,$5 ,$7 ,
*,”GIL, BENJI”,TEX,501,0.235,13,54,4,$5 ,$6 ,
,”SHIPLEY, CRAIG”,,306,0.27,8,33,7,$5 ,$6 ,
,”KELLY, PAT”,,341,0.265,5,39,9,$5 ,$5 ,
,”BARBERIE, BRET”,,331,0.272,8,34,4,$4 ,$5 ,
,”GUILLEN, OZZIE”,CWS,463,0.268,3,48,7,$5 ,$5 ,
,”VINA, FERNANDO”,,360,0.269,5,39,7,$5 ,$5 ,
,”SANCHEZ, REY”,CHC,460,0.282,2,35,6,$5 ,$5 ,
,”OFFERMAN, JOSE”,KC,453,0.265,6,41,4,$4 ,$5 ,
*,”FRYE, JEFF”,TEX,360,0.293,2,36,5,$4 ,$5 ,
,”WEISS, WALT”,COL,482,0.258,2,35,15,$6 ,$4 ,
,”STOCKER, KEVIN”,,445,0.275,2,39,6,$4 ,$4 ,
,”LEMKE, MARK”,ATL,449,0.265,5,45,3,$4 ,$4 ,
*,”BELL, DAVID”,STL,340,0.259,8,37,3,$3 ,$4 ,
,”EASLEY, DAMION”,,411,0.256,6,41,6,$4 ,$4 ,
,”LISTACH, PAT”,,351,0.255,2,30,12,$4 ,$3 ,
*,”JETER, DEREK”,NYY,360,0.294,1,22,5,$3 ,$3 ,
,”HOWARD, DAVID”,,332,0.259,4,30,7,$3 ,$3 ,
,”ARIAS, ALEX”,FLO,272,0.264,6,32,2,$2 ,$3 ,
*,”LOCKHART, KEITH”,,175,0.297,4,22,5,$2 ,$3 ,
,”CIANFROCCO, ARCI”,,204,0.251,7,37,2,$2 ,$3 ,
,”GUTIERREZ, RICKY”,HOU,254,0.267,5,23,6,$2 ,$2 ,
,”PENA, GERONIMO”,STL,203,0.264,6,21,5,$2 ,$2 ,
,”OWEN, SPIKE”,,300,0.25,5,37,4,$2 ,$2 ,
*,”AURILIA, RICH”,SF,270,0.263,5,27,4,$2 ,$2 ,
,”THOMPSON, ROBBY”,SF,366,0.246,7,31,3,$2 ,$2 ,
*,”HUDLER, REX”,,150,0.267,5,18,7,$2 ,$2 ,
,”REBOULET, JEFF”,,285,0.27,4,31,2,$2 ,$2 ,
,”CROMER, TRIPP”,MIN,408,0.25,7,29,2,$2 ,$2 ,
,”BROWNE, JERRY”,,284,0.267,4,27,3,$2 ,$2 ,
*,”REED, JODY”,,330,0.262,2,31,5,$2 ,$1 ,
,”MARTIN, NORBERTO”,,192,0.269,2,20,6,$1 ,$1 ,
*,”STYNES, CHRIS”,KC,250,0.268,2,19,6,$1 ,$1 ,
,”BENJAMIN, MIKE”,,223,0.22,4,14,13,$2 ,$1 ,
,”GALLEGO, MIKE”,,233,0.246,6,23,1,$1 ,$1 ,
,”PATTERSON, JOHN”,,285,0.236,3,29,7,$2 ,$0 ,
*,”TRAMMELL, ALAN”,DET,154,0.274,4,17,2,$0 ,$0 ,
,”OQUENDO, JOSE”,STL,303,0.236,5,28,3,$1 ,$0 ,
*,”JORDAN, KEVIN”,PHI,240,0.263,4,21,0,$0 ,$0 ,
*,”LORETTA, MARK”,MLW,180,0.267,2,15,5,$0 ,$0 ,
,”BOGAR, TIMOTHY”,NYM,209,0.244,3,27,2,$0 ,$0 ,
,”HUSON, JEFF”,,193,0.248,1,23,6,$1 ,$0 ,
,”ESPINOZA, ALVARO”,CLE,238,0.254,2,26,2,$0 ,$0 ,
*,”CRUZ, FAUSTO”,OAK,280,0.254,3,22,2,$0 ,($1),
,”SCHOFIELD, DICK”,CAL,161,0.254,3,21,3,$0 ,($1),
,”SMITH, OZZIE”,STL,274,0.233,2,24,6,$1 ,($1),
*,”HOCKING, DENNY”,MIN,200,0.26,2,16,3,$0 ,($1),
,”FLETCHER, SCOTT”,,260,0.24,2,26,4,$0 ,($1),
,”MORDECAI, MIKE”,,90,0.28,4,13,0,($1),($1),
,”TREADWAY, JEFF”,,154,0.262,1,21,2,($1),($1),
,”CEDENO, DOMINGO”,,193,0.236,5,17,0,($1),($2),
,”DIAZ, MARIO”,,171,0.266,1,17,1,($1),($2),
,”LIND, JOSE”,,245,0.251,1,21,3,$0 ,($2),
,”MEJIA, ROBERTO”,COL,149,0.224,4,15,2,($1),($2),
,”SILVESTRI, DAVE”,,86,0.264,2,8,2,($1),($2),
*,”FERMIN, FELIX”,,200,0.245,1,19,3,($1),($2),
,”CACERES, EDGAR”,,140,0.239,1,20,2,($1),($2),
,”HULETT, TIM”,,116,0.231,3,12,1,($1),($2),
,”GREBECK, CRAIG”,,185,0.26,1,17,0,($1),($2),
,”BELL, JUAN”,,127,0.228,2,14,2,($1),($2),
,”RIPKEN, BILLY”,,,,,,,,,
,”HANEY, TODD”,,,,,,,,,
,”HOLBERT, RAY”,HOU,,,,,,,,
,”PEREZ, TOMAS”,,,,,,,,,
,”CARABALLO, RAMON”,,,,,,,,,
,”SHUMPERT, TERRY”,,,,,,,,,
,”ELSTER, KEVIN”,,,,,,,,,
,”PENN, SHANNON”,DET,,,,,,,,
,”FRANCO, MATT”,,,,,,,,,
,”CORREIA, ROD”,,,,,,,,,
,”BELLIARD, RAFAEL”,,,,,,,,,
,”BRADY, DOUG”,,,,,,,,,
,”WHITAKER, LOU”,,,,,,,,,
,”RAABE, BRIAN”,,,,,,,,,
,”POZO, ARQUIMEDEZ”,,,,,,,,,
,”COUNSELL, CRAIG”,,,,,,,,,
,”RODRIGUEZ, STEVE”,,,,,,,,,
,”ZOSKY, EDDIE”,,,,,,,,,
,”BELTRE, ESTEBAN”,,,,,,,,,
,”EENHOORN, ROBERT”,,,,,,,,,
,”GIOVANOLA, ED”,,,,,,,,,
,”BONDS, BARRY”,SF,536,0.304,38,105,30,$29 ,$40 ,
,”BELLE, ALBERT”,CLE,569,0.313,42,125,7,$25 ,$39 ,
,”BICHETTE, DANTE”,COL,606,0.309,35,124,15,$26 ,$38 ,
,”SOSA, SAMMY”,CHC,584,0.278,34,114,32,$27 ,$37 ,
*,”GRIFFEY, KEN JR”,SEA,536,0.296,49,87,10,$23 ,$37 ,
,”LOFTON, KENNY”,CLE,528,0.319,11,59,54,$26 ,$31 ,
,”SANDERS, REGGIE”,CIN,520,0.284,22,97,33,$23 ,$30 ,
,”WALKER, LARRY”,COL,527,0.294,27,102,17,$21 ,$29 ,
*,”GANT, RON”,,499,0.277,27,103,25,$22 ,$29 ,
,”MONDESI, RAUL”,LA,564,0.296,23,88,24,$21 ,$29 ,
,”SALMON, TIM”,CAL,555,0.302,29,104,6,$19 ,$28 ,
,”GWYNN, TONY”,SD,563,0.348,10,90,16,$19 ,$27 ,
,”RAMIREZ, MANNY”,CLE,501,0.294,27,103,7,$17 ,$26 ,
*,”BONILLA, BOBBY”,BAL,560,0.298,29,92,2,$16 ,$25 ,
,”BUHNER, JAY”,SEA,503,0.268,32,116,1,$16 ,$25 ,
,”BELL, DEREK”,HOU,502,0.303,14,86,26,$19 ,$25 ,
,”CORDOVA, MARTY”,MIN,548,0.281,22,88,20,$18 ,$25 ,
*,”KLESKO, RYAN”,ATL,454,0.296,27,87,6,$16 ,$24 ,
*,”GRISSOM, MARQUIS”,ATL,583,0.281,11,52,46,$21 ,$24 ,
,”PUCKETT, KIRBY”,MIN,568,0.296,21,104,5,$16 ,$24 ,
,”O’NEILL, PAUL”,NYY,498,0.309,22,97,3,$16 ,$24 ,
,”CONINE, JEFF”,FLO,528,0.3,20,105,3,$15 ,$23 ,
,”HILL, GLENALLEN”,SF,509,0.277,17,82,24,$18 ,$23 ,
,”JONES, CHIPPER”,ATL,558,0.277,24,90,9,$16 ,$22 ,
,”LANKFORD, RAY”,STL,522,0.266,21,82,22,$17 ,$22 ,
,”JOHNSON, LANCE”,NYM,615,0.291,7,62,37,$19 ,$22 ,
,”CURTIS, CHAD”,DET,605,0.274,16,70,28,$18 ,$22 ,
,”BERROA, GERONIMO”,OAK,557,0.285,20,90,8,$15 ,$21 ,
,”WHITE, RONDELL”,MON,516,0.293,14,64,25,$16 ,$21 ,
,”CARTER, JOE”,TOR,583,0.253,25,87,12,$15 ,$20 ,
,”SHEFFIELD, GARY”,FLO,304,0.3,21,58,18,$14 ,$20 ,
,”JORDAN, BRIAN”,STL,488,0.289,14,76,21,$16 ,$20 ,
*,”EVERETT, CARL”,NYM,541,0.272,23,92,6,$14 ,$20 ,
,”EDMONDS, JIM”,CAL,557,0.286,19,97,3,$14 ,$20 ,
,”ANDERSON, BRADY”,BAL,582,0.262,16,68,26,$16 ,$19 ,
,”JUSTICE, DAVE”,ATL,458,0.276,24,82,5,$13 ,$19 ,
,”JAVIER, STAN”,SF,492,0.278,10,60,33,$17 ,$19 ,
,”FINLEY, STEVE”,SD,575,0.279,12,50,32,$17 ,$19 ,
,”PHILLIPS, TONY”,,559,0.272,21,67,14,$14 ,$19 ,
,”WILLIAMS, BERNIE”,NYY,581,0.29,15,83,10,$14 ,$19 ,
*,”NILSSON, DAVE”,MLW,531,0.276,21,91,5,$13 ,$19 ,
*,”GOODWIN, TOM”,KC,522,0.278,5,38,47,$18 ,$19 ,
,”SIERRA, RUBEN”,NYY,521,0.257,23,92,7,$13 ,$19 ,
,”JEFFERIES, GREGG”,PHI,517,0.317,11,64,12,$14 ,$18 ,
*,”GREEN, SHAWN”,TOR,545,0.289,19,77,4,$12 ,$18 ,
,”MERCED, ORLANDO”,PIT,520,0.293,15,83,7,$13 ,$18 ,
,”MCRAE, BRIAN”,CHC,598,0.282,9,55,27,$15 ,$18 ,
*,”ANDERSON, GARRET”,CAL,490,0.308,12,75,7,$12 ,$17 ,
,”HENDERSON, RICKEY”,,447,0.272,9,56,31,$15 ,$17 ,
*,”HUNTER, BRIAN L”,HOU,504,0.295,5,50,31,$15 ,$17 ,
,”GILKEY, BERNARD”,STL,514,0.285,13,72,13,$13 ,$17 ,
*,”HOSEY, DWAYNE”,BOS,326,0.3,14,39,22,$13 ,$16 ,
,”GREENWELL, MIKE”,BOS,507,0.287,13,77,9,$12 ,$16 ,
,”MARTIN, AL”,PIT,466,0.283,14,48,19,$13 ,$16 ,
,”CORDERO, WILL”,MON,544,0.285,15,58,11,$12 ,$16 ,
,”TARASCO, TONY”,MON,486,0.266,14,49,24,$13 ,$16 ,
,”YOUNG, ERIC”,COL,404,0.288,7,43,33,$14 ,$16 ,
,”KELLY, ROBERTO”,,541,0.289,8,61,19,$13 ,$16 ,
,”COLEMAN, VINCE”,,506,0.265,6,37,42,$16 ,$16 ,
,”BUTLER, BRETT”,LA,547,0.286,4,45,31,$14 ,$15 ,
,”TETTLETON, MICKEY”,TEX,470,0.24,25,81,2,$10 ,$15 ,
*,”BECKER, RICH”,MIN,447,0.286,10,69,14,$12 ,$15 ,
*,”NIXON, OTIS”,TOR,600,0.27,4,48,34,$15 ,$15 ,
*,”SANDBERG, RYNE”,CHC,500,0.282,14,72,6,$11 ,$15 ,
,”GONZALEZ, LUIS”,CHC,509,0.283,11,75,9,$11 ,$15 ,
,”RAINES, TIM”,CWS,533,0.274,11,70,14,$12 ,$14 ,
,”SURHOFF, BJ”,,425,0.285,13,72,7,$10 ,$14 ,
*,”ALOU, MOISES”,,419,0.293,16,58,4,$10 ,$14 ,
,”O’LEARY, TROY”,,453,0.296,12,57,6,$10 ,$14 ,
*,”CARREON, MARK”,SF,384,0.286,17,61,2,$8 ,$13 ,
,”EISENREICH, JIM”,PHI,423,0.296,9,60,10,$10 ,$13 ,
,”WHITE, DEVON”,FLO,479,0.27,12,59,13,$10 ,$13 ,
,”TINSLEY, LEE”,,404,0.283,8,50,18,$11 ,$13 ,
,”BURKS, ELLIS”,,325,0.281,15,54,7,$9 ,$13 ,
*,”DAMON, JOHNNY”,KC,451,0.282,7,55,17,$11 ,$12 ,
,”MUNOZ, PEDRO”,MIN,414,0.281,16,60,1,$8 ,$12 ,
,”NUNNALLY, JON”,KC,372,0.265,16,51,7,$8 ,$12 ,
,”SANDERS, DEION”,,411,0.276,6,36,26,$11 ,$12 ,
*,”HAMILTON, DARRYL”,TEX,414,0.277,6,47,21,$10 ,$12 ,
,”SEGUI, DAVID”,MON,489,0.278,11,70,3,$9 ,$12 ,
,”LEWIS, DARREN”,CWS,520,0.258,5,34,32,$12 ,$11 ,
*,”NEWFIELD, MARC”,SD,400,0.295,13,58,0,$7 ,$11 ,
*,”PLANTIER, PHIL”,DET,433,0.25,17,64,4,$8 ,$11 ,
,”WHITEN, MARK”,PHI,306,0.272,14,49,9,$8 ,$11 ,
,”MCLEMORE, MARK”,TEX,499,0.266,6,48,21,$10 ,$11 ,
,”BRUMFIELD, JACOB”,PIT,413,0.28,7,32,20,$9 ,$11 ,
*,”HOLLANDSWORTH, TOD”,LA,375,0.264,15,48,7,$8 ,$11 ,
*,”BAUTISTA, DANNY”,DET,483,0.257,15,52,8,$8 ,$11 ,
*,”GREER, RUSTY”,,449,0.278,11,66,2,$8 ,$11 ,
,”HIGGINSON, BOB”,DET,462,0.254,16,52,7,$8 ,$11 ,
,”GOODWIN, CURTIS”,BAL,360,0.277,7,34,22,$9 ,$11 ,
,”MAY, DERRICK”,HOU,303,0.294,11,51,6,$7 ,$10 ,
,”BROSIUS, SCOTT”,OAK,436,0.255,16,52,5,$7 ,$10 ,
,”MABRY, JOHN”,STL,444,0.297,10,50,2,$7 ,$10 ,
,”DEVEREAUX, MIKE”,,392,0.257,13,59,6,$8 ,$10 ,
,”CARR, CHUCK”,MLW,394,0.256,6,30,27,$10 ,$10 ,
,”MOUTON, JAMES”,HOU,366,0.264,4,34,25,$9 ,$9 ,
,”HOWARD, THOMAS”,,355,0.287,4,36,17,$8 ,$9 ,
,”TARTABULL, DANNY”,OAK,368,0.245,17,49,1,$6 ,$9 ,
,”ROBERTS, BIP”,,381,0.282,3,34,21,$8 ,$9 ,
,”VELARDE, RANDY”,CAL,414,0.278,8,50,6,$7 ,$9 ,
,”NIEVES, MELVIN”,SD,314,0.243,16,47,4,$6 ,$8 ,
,”DIAZ, ALEX”,,345,0.263,6,37,18,$8 ,$8 ,
*,”DELGADO, CARLOS”,TOR,400,0.258,16,41,1,$5 ,$8 ,
,”DAWSON, ANDRE”,,312,0.243,17,47,2,$5 ,$8 ,
,”HULSE, DAVID”,,397,0.269,3,49,17,$8 ,$8 ,
,”MARTINEZ, DAVE”,CWS,358,0.267,9,42,8,$6 ,$8 ,
,”MIESKE, MATT”,MLW,334,0.263,11,55,4,$6 ,$8 ,
,”AMARAL, RICH”,SEA,309,0.273,5,28,19,$7 ,$8 ,
,”DYKSTRA, LENNY”,PHI,335,0.275,8,29,13,$6 ,$7 ,
,”FONVILLE, CHAD”,LA,387,0.283,0,28,20,$7 ,$7 ,
*,”MOUTON, LYLE”,CWS,350,0.271,11,41,1,$4 ,$7 ,
,”THOMPSON, RYAN”,NYM,347,0.25,12,42,4,$5 ,$6 ,
*,”STRAWBERRY, DARRYL”,,313,0.276,11,34,2,$4 ,$6 ,
*,”TUCKER, MICHAEL”,KC,424,0.259,10,40,4,$5 ,$6 ,
,”ASHLEY, BILLY”,LA,298,0.259,12,37,2,$4 ,$6 ,
,”HAMMONDS, JEFFREY”,BAL,266,0.275,7,35,5,$4 ,$5 ,
*,”OCHOA, ALEX”,NYM,325,0.289,5,28,7,$4 ,$5 ,
,”BASS, KEVIN”,,348,0.266,5,40,8,$5 ,$5 ,
,”ORSULAK, JOE”,FLO,358,0.271,6,45,3,$4 ,$5 ,
,”ANTHONY, ERIC”,,235,0.259,10,34,4,$4 ,$5 ,
,”MALDONADO, CANDY”,,228,0.263,11,36,1,$3 ,$5 ,
,”POLONIA, LUIS”,,328,0.276,2,27,14,$5 ,$5 ,
*,”BENITEZ, YAMIL”,MON,187,0.289,7,29,6,$4 ,$5 ,
,”TIMMONS, OZZIE”,CHC,205,0.263,10,34,4,$3 ,$5 ,
*,”KINGERY, MIKE”,PIT,405,0.257,8,35,6,$4 ,$5 ,
,”KIRBY, WAYNE”,,265,0.252,7,26,11,$4 ,$5 ,
,”RODRIGUEZ, HENRY”,MON,246,0.26,10,34,1,$3 ,$4 ,
,”CANGELOSI, JOHN”,HOU,187,0.299,3,21,12,$4 ,$4 ,
,”JONES, CHRIS”,NYM,235,0.281,6,35,3,$3 ,$4 ,
,”WARD, TURNER”,,249,0.256,7,34,7,$4 ,$4 ,
,”PAQUETTE, CRAIG”,OAK,311,0.217,10,49,6,$4 ,$4 ,
*,”PEMBERTON, RUDY”,DET,225,0.271,8,22,5,$3 ,$4 ,
*,”WALTON, JEROME”,,194,0.29,5,26,6,$3 ,$4 ,
,”BULLETT, SCOTT”,,180,0.273,4,26,10,$3 ,$3 ,
,”WILLIAMS, GERALD”,,218,0.247,7,34,5,$3 ,$3 ,
*,”SAMUEL, JUAN”,,269,0.259,6,31,5,$3 ,$3 ,
,”LISTACH, PAT”,,351,0.255,2,30,12,$4 ,$3 ,
,”CLARK, DAVE”,,235,0.281,5,29,4,$2 ,$3 ,
,”COLE, ALEX”,,208,0.296,2,24,7,$3 ,$3 ,
,”SMITH, DWIGHT”,,223,0.271,6,31,2,$2 ,$3 ,
,”VANDERWAL, JOHN”,,186,0.281,6,29,3,$2 ,$3 ,
*,”KELLY, MIKE”,ATL,200,0.26,5,23,8,$3 ,$3 ,
,”MCGEE, WILLIE”,STL,269,0.275,4,26,6,$3 ,$3 ,
,”HOWARD, DAVID”,,332,0.259,4,30,7,$3 ,$3 ,
,”LONGMIRE, TONY”,,125,0.356,4,23,1,$1 ,$3 ,
*,”BENARD, MARVIN”,SF,215,0.288,3,19,7,$2 ,$2 ,
,”TAVAREZ, JESUS”,FLO,228,0.289,2,16,8,$2 ,$2 ,
,”THOMPSON, ROBBY”,SF,366,0.246,7,31,3,$2 ,$2 ,
*,”FLORA, KEVIN”,PHI,270,0.256,6,23,5,$2 ,$2 ,
,”JOHNSON, HOWARD”,,257,0.215,9,33,4,$2 ,$2 ,
*,”HUDLER, REX”,,150,0.267,5,18,7,$2 ,$2 ,
,”GALLAGHER, DAVE”,,236,0.268,7,22,1,$1 ,$2 ,
,”BROWNE, JERRY”,,284,0.267,4,27,3,$2 ,$2 ,
,”ALDRETE, MIKE”,CAL,234,0.255,6,32,2,$1 ,$2 ,
,”GREGG, TOMMY”,,187,0.237,7,24,4,$1 ,$2 ,
,”THOMPSON, MILT”,,232,0.243,4,30,6,$2 ,$1 ,
,”BUFORD, DAMON”,NYM,200,0.235,5,19,8,$2 ,$1 ,
,”BURNITZ, JEROMY”,,120,0.361,2,13,2,$0 ,$1 ,
,”NEWSON, WARREN”,,188,0.261,6,18,2,$1 ,$1 ,
*,”LAWTON, MATT”,MIN,150,0.28,1,19,8,$1 ,$1 ,
,”MARTIN, NORBERTO”,,192,0.269,2,20,6,$1 ,$1 ,
,”BRAGG, DARREN”,,174,0.234,4,14,11,$2 ,$1 ,
*,”YOUNG, ERNIE”,OAK,180,0.261,7,18,0,$0 ,$1 ,
,”PEGUES, STEVE”,,205,0.246,7,19,1,$0 ,$1 ,
,”JAMES, DION”,,249,0.222,5,31,4,$1 ,$0 ,
,”HUFF, MIKE”,,230,0.264,3,24,3,$1 ,$0 ,
*,”STEVERSON, TODD”,DET,250,0.244,4,23,4,$1 ,$0 ,
,”MARSH, TOM”,,131,0.294,4,18,0,$0 ,$0 ,
*,”CEDENO, ROGER”,LA,200,0.27,1,14,7,$1 ,$0 ,
*,”STEWART, SHANNON”,TOR,185,0.276,0,13,8,$1 ,$0 ,
,”HUBBARD, TRENT”,COL,70,0.31,4,11,2,($1),$0 ,
,”TOMBERLIN, ANDY”,,102,0.212,5,12,5,$0 ,($1),
*,”PEREZ, ROBERT”,TOR,185,0.276,2,15,2,$0 ,($1),
*,”CUMMINGS, MIDRE”,,360,0.233,4,31,2,$0 ,($1),
,”SMITH, MARK”,BAL,125,0.231,4,18,4,$0 ,($1),
,”CHAMBERLAIN, WES”,,151,0.225,5,15,2,($1),($1),
,”RHODES, KARL”,,155,0.204,6,17,3,($1),($1),
,”MASTELLER, DAN”,,238,0.237,4,25,1,$0 ,($1),
,”GWYNN, CHRIS”,,167,0.257,2,20,1,($1),($1),
*,”HERRERA, JOSE”,OAK,220,0.245,2,14,5,$0 ,($1),
,”STUBBS, FRANKLIN”,,139,0.25,2,23,0,($1),($2),
,”WEHNER, JOHN”,,128,0.308,0,6,4,($1),($2),
,”SANTANGELO, FP”,,118,0.296,1,11,1,($1),($2),
,”MORMAN, RUSS”,,86,0.278,4,8,0,($2),($2),
,”CUYLER, MILT”,,177,0.23,2,16,4,($1),($2),
,”FRAZIER, LOU”,,119,0.212,0,10,11,$0 ,($2),
,”HATCHER, BILLY”,,129,0.199,5,14,2,($1),($2),
*,”CLARK, JERALD”,,174,0.228,2,18,4,($1),($2),
,”JAMES, CHRIS”,,98,0.268,2,10,1,($2),($2),
,”STAIRS, MATT”,,106,0.261,1,20,0,($2),($2),
,”THURMAN, GARY”,,,,,,,,,
,”ROBERSON, KEVIN”,,,,,,,,,
,”NEVIN, PHIL”,DET,,,,,,,,
,”VARSHO, GARY”,,,,,,,,,
,”BATTLE, ALLEN”,STL,,,,,,,,
,”GILES, BRIAN”,,,,,,,,,
,”HIATT, PHIL”,,,,,,,,,
,”MCDAVID, RAY”,SD,,,,,,,,
,”PULLIAM, HARVEY”,,,,,,,,,
,”PARKER, RICK”,,,,,,,,,
,”AMARO, RUBEN”,,,,,,,,,
,”VOIGT, JACK”,,,,,,,,,
,”LEONARD, MARK”,,,,,,,,,
,”MAHAY, RON”,,,,,,,,,
,”TATUM, JIM”,,,,,,,,,
,”BRADSHAW, TERRY”,,,,,,,,,
,”BRUMLEY, MIKE”,,,,,,,,,
,”HARE, SHAWN”,,,,,,,,,
,”NORMAN, LES”,,,,,,,,,
,”CAMERON, MIKE”,,,,,,,,,
,”GIBRALTER, STEVE”,,,,,,,,,
,”JOSE, FELIX”,,,,,,,,,
,”MILLER, KEITH”,,,,,,,,,
,”SIMMS, MIKE”,,,,,,,,,
,”CLARK, PHIL”,,,,,,,,,
,”WILSON, CRAIG”,,,,,,,,,
,”WALKER, CHICO”,,,,,,,,,
,”VAN SLYKE, ANDY”,,,,,,,,,
,”PRIDE, CURTIS”,,,,,,,,,
,”RIVERA, RUBEN”,,,,,,,,,
,”MCCRACKEN, Q”,,,,,,,,,
,”COOKSON, BRENT”,,,,,,,,,
,”WEBSTER, MITCH”,,,,,,,,,
,”KOWITZ, BRIAN”,,,,,,,,,
,”BROWN, JARVIS”,,,,,,,,,
,”GARCIA, KARIM”,LA,,,,,,,,
,”WILLIAMS, REGGIE”,,,,,,,,,
,”FANEYTE, RIKKERT”,,,,,,,,,
,”BEAN, BILLY”,,,,,,,,,
,”WILSON, NIGEL”,,,,,,,,,
,”GIANNELLI, RAY”,,,,,,,,,
,”BARRY, JEFF”,,,,,,,,,
,”HALL, JOE”,,,,,,,,,
,”OTERO, RICKY”,,,,,,,,,
,”FOX, ERIC”,,,,,,,,,
,”SINGLETON, DUANE”,,,,,,,,,
DH/No Position,,,,,,,,,,
,”MARTINEZ, EDGAR”,SEA,529,0.308,21,105,6,$17 ,$25 ,
,”GONZALEZ, JUAN”,TEX,425,0.295,27,89,2,$14 ,$22 ,
,”CANSECO, JOSE”,BOS,460,0.279,25,86,7,$14 ,$21 ,
,”DAVIS, CHILI”,CAL,475,0.281,24,91,4,$14 ,$21 ,
*,”FRANCO, JULIO”,CLE,500,0.302,13,83,2,$11 ,$16 ,
*,”MOLITOR, PAUL”,MIN,510,0.288,14,64,11,$12 ,$16 ,
,”VAUGHN, GREG”,,447,0.25,19,66,11,$11 ,$14 ,
*,”BAINES, HAROLD”,CWS,292,0.288,14,46,1,$6 ,$10 ,
,”HAMELIN, BOB”,KC,299,0.236,15,46,2,$5 ,$7 ,
,”JEFFERSON, REGGIE”,,209,0.288,7,35,1,$3 ,$4 ,
*,”BATTLE, HOWARD”,PHI,245,0.253,7,21,4,$2 ,$2 ,
,”BENZINGER, TODD”,,154,0.238,4,20,2,$0 ,($1),
,”HALE, CHIP”,,124,0.262,2,22,0,($1),($1),
,”HAJEK, DAVE”,,,,,,,,,
,”FORDYCE, BROOK”,,,,,,,,,
,”HATTEBERG, SCOTT”,,,,,,,,,
,”MARZANO, JOHN”,,,,,,,,,
,”KRUK, JOHN”,,,,,,,,,
,”GIBSON, KIRK”,,,,,,,,,
,”BRITO, BERNARDO”,,,,,,,,,
,”WINFIELD, DAVE”,,,,,,,,,
,”HARPER, BRIAN”,,,,,,,,,
,”BENNETT, GARY”,,,,,,,,,
,”MOTA, JOSE”,,,,,,,,,
,”INGRAM, RICCARDO”,,,,,,,,,
,”HORN, SAM”,,,,,,,,,
,”MESA, JOSE”,CLE,77,2.45,5,8.95,39,$47 ,$39 ,
,”MADDUX, GREG”,ATL,203,1.88,19,8.25,0,$55 ,$35 ,
,”HENKE, TOM”,,83,2.42,5,9.81,33,$41 ,$33 ,
,”FRANCO, JOHN”,NYM,71,3.21,5,12.48,34,$36 ,$32 ,
,”HOFFMAN, TREVOR”,SD,63,3.61,6,10.67,33,$36 ,$31 ,
,”WETTELAND, JOHN”,,71,2.44,4,8.99,31,$39 ,$30 ,
,”BECK, ROD”,SF,61,3.33,4,11.38,34,$35 ,$30 ,
,”MONTGOMERY, JEFF”,KC,81,3.12,4,11.45,31,$35 ,$30 ,
,”MYERS, RANDY”,BAL,73,3.49,2,12.09,35,$34 ,$29 ,
,”BRANTLEY, JEFF”,CIN,88,3.11,5,10.41,29,$37 ,$29 ,
,”WORRELL, TODD”,LA,79,3.87,6,10.66,30,$35 ,$29 ,
*,”SMITH, LEE”,CAL,78,3.56,3,10.73,33,$35 ,$29 ,
,”ECKERSLEY, DENNIS”,OAK,88,3.88,7,11.17,28,$34 ,$28 ,
,”JOHNSON, RANDY”,SEA,207,2.89,19,10.06,0,$41 ,$28 ,
*,”WOHLERS, MARK”,ATL,66,3.5,5,11,28,$32 ,$26 ,
,”ROJAS, MEL”,MON,78,3.58,3,11.55,30,$31 ,$26 ,
,”HERNANDEZ, ROBERTO”,CWS,72,3.74,4,12.2,29,$30 ,$26 ,
,”CONE, DAVID”,,215,3.21,18,10.55,0,$38 ,$25 ,
,”HENNEMAN, MIKE”,,76,3.18,3,12.96,25,$26 ,$22 ,
,”MUSSINA, MIKE”,BAL,188,3.84,18,10.66,0,$31 ,$21 ,
,”GLAVINE, TOM”,ATL,187,3.54,17,12.55,0,$25 ,$20 ,
,”NOMO, HIDEO”,LA,165,2.55,13,9.64,0,$33 ,$20 ,
*,”CHARLTON, NORM”,SEA,78,3,4,7.72,19,$30 ,$20 ,
,”HILL, KEN”,,195,3.79,17,12.25,0,$25 ,$19 ,
,”AYALA, BOBBY”,SEA,70,4.61,6,12.78,21,$22 ,$19 ,
,”HERSHISER, OREL”,CLE,181,3.42,15,11.34,0,$27 ,$19 ,
*,”JONES, TODD”,HOU,86,3.12,5,11.23,17,$25 ,$19 ,
,”MCDOWELL, JACK”,CLE,203,3.8,16,11.85,0,$26 ,$19 ,
,”FETTERS, MIKE”,MLW,54,3.06,2,14.06,24,$21 ,$18 ,
,”WELLS, DAVID”,CIN,198,3.76,15,10.99,0,$28 ,$18 ,
,”ROGERS, KEN”,,194,4.02,17,11.95,0,$24 ,$18 ,
,”FINLEY, CHUCK”,,204,3.81,16,12.22,0,$24 ,$18 ,
*,”VALDES, ISMAEL”,LA,157,3.05,11,9.22,1,$30 ,$17 ,
,”APPIER, KEVIN”,KC,185,3.61,14,11.27,0,$26 ,$17 ,
,”FASSERO, JEFF”,MON,173,3.12,13,11.47,0,$26 ,$17 ,
,”PORTUGAL, MARK”,CIN,179,3.46,14,11.8,0,$24 ,$17 ,
,”NEN, ROBB”,FLO,61,4.65,1,12.67,24,$20 ,$17 ,
,”BURKETT, JOHN”,FLO,184,3.87,15,11.95,0,$23 ,$16 ,
,”RUSSELL, JEFF”,,58,3.46,2,12.07,20,$20 ,$16 ,
,”MARTINEZ, RAMON”,LA,184,3.94,15,12.1,0,$22 ,$16 ,
*,”MARTINEZ, DENNIS”,CLE,175,3.49,13,12.02,0,$22 ,$16 ,
,”CANDIOTTI, TOM”,LA,199,3.18,10,11.23,0,$26 ,$15 ,
,”DRABEK, DOUG”,HOU,189,3.68,12,11.08,0,$25 ,$15 ,
,”JONES, BOBBY”,NYM,182,3.52,12,11.91,0,$22 ,$15 ,
,”FERNANDEZ, ALEX”,CWS,185,3.9,13,11.69,0,$22 ,$15 ,
,”MARTINEZ, PEDRO”,MON,152,3.58,12,10.94,0,$22 ,$14 ,
*,”ISRINGHAUSEN, JASO”,NYM,160,3.43,11,11.19,0,$22 ,$14 ,
,”NAGY, CHARLES”,CLE,160,4.11,14,12.63,0,$18 ,$14 ,
*,”AGUILERA, RICK”,MIN,150,3.84,11,11.1,2,$21 ,$14 ,
*,”TEWKSBURY, BOB”,SD,167,4.13,13,11.51,0,$20 ,$13 ,
,”SMOLTZ, JOHN”,ATL,177,3.84,11,11.52,0,$21 ,$13 ,
*,”SLOCUMB, HEATH”,PHI,70,3.96,5,13.38,13,$16 ,$13 ,
,”HANSON, ERIK”,,173,4.03,12,11.91,0,$19 ,$13 ,
*,”MICELI, DANNY”,PIT,66,4.78,3,11.88,18,$17 ,$13 ,
*,”SCHOUREK, PETE”,CIN,168,4.19,13,11.22,0,$20 ,$13 ,
,”LANGSTON, MARK”,CAL,184,4.42,14,12.56,0,$17 ,$13 ,
,”CLEMENS, ROGER”,BOS,157,3.71,11,11.44,0,$20 ,$13 ,
,”TAPANI, KEVIN”,,207,4.73,15,12.76,0,$16 ,$13 ,
*,”ASTACIO, PEDRO”,LA,145,3.79,11,10.3,0,$21 ,$12 ,
,”BENES, ANDY”,,199,4.34,13,12.13,0,$18 ,$12 ,
,”ONTIVEROS, STEVE”,,139,3.27,10,10.71,0,$21 ,$12 ,
*,”ABBOTT, JIM”,CAL,198,4.02,11,11.79,0,$20 ,$12 ,
,”RUFFIN, BRUCE”,COL,63,2.93,2,12.79,14,$16 ,$12 ,
,”REYNOLDS, SHANE”,HOU,158,3.4,9,11.24,0,$20 ,$12 ,
,”HENTGEN, PAT”,TOR,182,4.38,13,13.03,0,$15 ,$11 ,
,”MCMICHAEL, GREG”,ATL,82,3,6,11.64,8,$17 ,$11 ,
,”BULLINGER, JIM”,CHC,137,3.87,11,12.07,1,$17 ,$11 ,
*,”PULSIPHER, BILL”,NYM,166,3.85,10,11.44,0,$19 ,$11 ,
,”BROWN, KEVIN”,,174,4.11,11,12.51,0,$16 ,$11 ,
,”GORDON, TOM”,,166,4.33,12,13.37,0,$13 ,$10 ,
*,”SABERHAGEN, BRET”,COL,153,3.99,10,11.45,0,$17 ,$10 ,
*,”STEVENS, DAVE”,MIN,74,4.5,3,11.8,13,$14 ,$10 ,
*,”LEITER, AL”,FLO,168,4.04,10,12.54,0,$15 ,$10 ,
,”ALVAREZ, WILSON”,CWS,159,3.93,10,13.1,0,$13 ,$10 ,
,”GUBICZA, MARK”,KC,182,4.24,11,12.92,0,$14 ,$10 ,
,”SMILEY, JOHN”,CIN,158,4.41,11,11.74,0,$15 ,$10 ,
,”STOTTLEMYRE, TODD”,OAK,184,4.65,12,13.33,0,$12 ,$10 ,
,”BELINDA, STAN”,BOS,72,4.21,6,11.7,8,$13 ,$9 ,
,”RAPP, PAT”,FLO,142,3.97,11,13.54,0,$12 ,$9 ,
,”HOLMES, DARREN”,COL,72,4.83,4,15.45,13,$9 ,$9 ,
*,”RISLEY, BILL”,MON,69,3.37,5,9.97,7,$15 ,$9 ,
*,”HITCHCOCK, STERLIN”,SEA,135,4.17,9,11.62,1,$15 ,$9 ,
,”HAMILTON, JOEY”,SD,158,3.31,6,11.16,0,$18 ,$9 ,
,”PEREZ, CARLOS”,MON,124,3.76,9,11.09,0,$15 ,$9 ,
,”SWIFT, BILL”,COL,121,3.98,10,12.64,0,$12 ,$9 ,
,”SWINDELL, GREG”,HOU,154,4.44,11,12.61,0,$12 ,$9 ,
,”PETTITTE, ANDY”,NYY,144,4.25,11,12.98,0,$12 ,$9 ,
,”AVERY, STEVE”,ATL,158,4.26,9,11.39,0,$16 ,$9 ,
,”FOSTER, KEVIN”,CHC,138,3.86,9,11.88,0,$14 ,$9 ,
,”JACKSON, MIKE”,,64,2.23,6,8.96,3,$15 ,$8 ,
*,”WAKEFIELD, TIM”,BOS,150,4.03,9,12.03,0,$14 ,$8 ,
*,”JONES, DOUG”,,83,4.57,3,13.26,11,$11 ,$8 ,
*,”BERGMAN, SEAN”,DET,145,4.11,9,11.53,0,$15 ,$8 ,
,”HAMMOND, CHRIS”,FLO,138,3.48,8,12.13,0,$14 ,$8 ,
*,”PLUNK, ERIC”,CLE,74,2.8,7,11.92,3,$13 ,$8 ,
*,”VERES, DAVE”,MON,87,2.62,5,11.6,4,$14 ,$8 ,
,”LEITER, MARK”,SF,165,4.42,9,12.21,1,$13 ,$8 ,
*,”GARDNER, MARK”,FLO,125,4.52,9,11.21,1,$14 ,$8 ,
*,”NAVARRO, JAIME”,CHC,173,4.71,11,12.58,0,$12 ,$8 ,
,”FERNANDEZ, SID”,PHI,128,4.15,9,11.23,0,$14 ,$8 ,
,”WITT, BOBBY”,,189,4.53,11,13.92,0,$9 ,$8 ,
*,”MERCKER, KENT”,BAL,145,4.47,10,12.35,0,$12 ,$8 ,
,”BOSIO, CHRIS”,SEA,162,4.21,9,13.13,0,$11 ,$8 ,
*,”HAYNES, JIMMY”,BAL,145,3.97,8,11.73,0,$14 ,$8 ,
,”CLONTZ, BRAD”,ATL,74,3.72,7,12.41,4,$11 ,$8 ,
,”BONES, RICKY”,MLW,177,4.63,10,12.8,0,$11 ,$8 ,
,”PETKOVSEK, MARK”,STL,116,3.74,8,11.29,0,$14 ,$8 ,
,”BOEVER, JOE”,DET,112,4.47,8,13.78,4,$9 ,$8 ,
*,”BOTTALICO, RICKY”,PHI,75,2.53,3,11.06,6,$14 ,$8 ,
,”LESKANIC, CURT”,COL,89,4.61,4,13.03,9,$10 ,$7 ,
,”SMITH, ZANE”,,128,4.27,9,12.3,0,$12 ,$7 ,
*,”BURBA, DAVE”,CIN,125,3.96,8,11.52,0,$13 ,$7 ,
,”PENA, ALEJANDRO”,FLO,87,4.58,6,10.71,5,$12 ,$7 ,
,”MCDOWELL, ROGER”,BAL,94,3.64,6,14.13,4,$9 ,$7 ,
,”CASTILLO, FRANK”,CHC,141,3.94,8,11.77,0,$13 ,$7 ,
*,”VAN POPPEL, TODD”,OAK,145,4.5,9,11.79,0,$12 ,$7 ,
,”BELCHER, TIM”,,183,4.87,11,13.8,0,$8 ,$7 ,
*,”BERE, JASON”,CHW,143,4.56,10,12.75,0,$10 ,$7 ,
,”REED, STEVE”,COL,84,3.66,6,12.17,4,$11 ,$7 ,
*,”GROSS, KEVIN”,TEX,164,4.81,12,14.48,0,$6 ,$7 ,
,”KAMIENIECKI, SCOTT”,NYY,110,3.97,9,13.37,0,$9 ,$7 ,
,”MCDONALD, BEN”,,111,4.07,8,11.93,0,$11 ,$7 ,
*,”BENES, ALAN”,STL,110,3.68,8,13.01,0,$10 ,$7 ,
,”ASHBY, ANDY”,SD,165,4.6,9,12.36,0,$11 ,$7 ,
,”NEAGLE, DENNY”,PIT,163,4.93,10,12.49,0,$10 ,$7 ,
,”JACKSON, DANNY”,STL,137,4.21,9,13.01,0,$10 ,$6 ,
,”PERCIVAL, TROY”,CAL,80,2,2,7.86,3,$17 ,$6 ,
,”DELEON, JOSE”,MON,108,4.19,7,11.09,1,$12 ,$6 ,
*,”MIMBS, MIKE”,PHI,121,4.21,8,13.59,1,$8 ,$6 ,
,”SPARKS, STEVE”,MLW,177,4.6,9,13.28,0,$9 ,$6 ,
,”RITZ, KEVIN”,COL,148,4.92,10,13.76,1,$7 ,$6 ,
*,”RADKE, BRAD”,MIN,155,4.99,9,11.66,0,$11 ,$6 ,
,”RYAN, KEN”,BOS,42,3.88,2,14.01,10,$8 ,$6 ,
,”CARRASCO, HECTOR”,CIN,82,3.38,3,12.97,6,$9 ,$6 ,
,”CLARK, MARK”,CLE,116,4.73,9,12.96,0,$8 ,$6 ,
*,”HERNANDEZ, XAVIER”,CIN,91,4.53,7,12.22,3,$9 ,$6 ,
,”VOSBERG, ED”,TEX,64,3.24,5,12.73,4,$9 ,$5 ,
,”QUANTRILL, PAUL”,TOR,128,4.96,10,13.47,0,$7 ,$5 ,
,”OROSCO, JESSE”,BAL,79,3.47,5,11.02,3,$11 ,$5 ,
,”MCCASKILL, KIRK”,CWS,98,3.97,6,13.32,3,$8 ,$5 ,
*,”CASTILLO, TONY”,TOR,81,2.95,3,11.75,3,$10 ,$5 ,
,”HENRY, BUTCH”,,113,4.03,7,11.78,0,$10 ,$5 ,
*,”ANDERSON, BRIAN”,CAL,117,4.57,8,12.38,0,$8 ,$5 ,
,”HEREDIA, GIL”,SF,105,3.97,6,12.18,1,$9 ,$5 ,
*,”PAVLIK, ROGER”,TEX,171,4.89,9,13.21,0,$7 ,$5 ,
,”NELSON, JEFF”,NYY,75,3.2,5,11.66,1,$9 ,$5 ,
,”LIRA, FELIPE”,DET,125,4.39,8,13.06,0,$7 ,$5 ,
*,”RUETER, KIRK”,MON,95,3.6,6,11.46,0,$10 ,$5 ,
,”ASSENMACHER, PAUL”,CLE,64,3.01,6,10.69,0,$10 ,$5 ,
*,”MLICKI, DAVE”,NYM,145,4.28,6,11.23,0,$12 ,$4 ,
*,”RIJO, JOSE”,CIN,82,4.68,9,13.72,0,$6 ,$4 ,
*,”VALENZUELA, FERNAN”,SD,115,4.46,7,12.44,0,$8 ,$4 ,
,”VANLANDINGHAM, BIL”,SF,105,3.84,6,12.53,0,$8 ,$4 ,
,”DELUCIA, RICH”,,85,3.73,6,11.31,0,$9 ,$4 ,
,”SCOTT, TIM”,MON,69,3.33,4,11.83,3,$8 ,$4 ,
*,”JUDEN, JEFF”,PHI,115,3.99,6,12.44,0,$8 ,$4 ,
,”SCHILLING, CURT”,PHI,111,4.06,6,11.15,0,$10 ,$4 ,
,”TRACHSEL, STEVE”,CHC,138,4.46,7,13.26,0,$7 ,$4 ,
,”GUZMAN, JUAN”,TOR,160,5.02,9,13.34,0,$6 ,$4 ,
*,”GUTHRIE, MARK”,LA,92,4.64,7,12.55,1,$7 ,$4 ,
,”ERICKSON, SCOTT”,BAL,175,5.47,11,14.2,0,$3 ,$4 ,
*,”PARRIS, STEVE”,PIT,125,4.25,6,11.38,0,$10 ,$4 ,
*,”SANDERSON, SCOTT”,CAL,90,4.51,7,12.61,0,$7 ,$4 ,
,”ELDRED, CAL”,MLW,86,4.25,7,12.57,0,$7 ,$4 ,
,”WICKMAN, BOB”,NYY,83,4.23,5,12.51,3,$7 ,$4 ,
,”WEGMAN, BILL”,,98,4.72,7,13.24,1,$6 ,$4 ,
*,”MATHEWS, TERRY”,FLO,80,3.69,4,12.26,3,$8 ,$4 ,
*,”RODRIGUEZ, FRANK”,MIN,125,4.71,7,12.02,0,$8 ,$4 ,
,”KILE, DARRYL”,HOU,128,4.63,8,13.75,0,$5 ,$4 ,
,”AROCHA, RENE”,STL,72,4,4,12.97,4,$7 ,$4 ,
*,”DIBBLE, ROBBIE”,CHC,46,3.67,1,10.83,7,$8 ,$3 ,
,”HAMPTON, MIKE”,HOU,112,3.86,6,13.02,0,$7 ,$3 ,
,”DOUGHERTY, JIM”,HOU,85,4.89,8,13.52,0,$5 ,$3 ,
*,”EMBREE, ALAN”,CLE,77,4.09,6,10.99,0,$8 ,$3 ,
*,”WATSON, ALLEN”,SF,120,4.55,6,11.64,0,$9 ,$3 ,
,”FREEMAN, MARVIN”,COL,109,4.29,7,13.19,0,$6 ,$3 ,
*,”PARRETT, JEFF”,STL,88,3.39,4,10.18,0,$10 ,$3 ,
*,”TAVAREZ, JULIAN”,CLE,85,4.02,5,12.81,1,$6 ,$3 ,
*,”MATHEWS, TJ”,STL,100,3.15,4,12.69,0,$8 ,$3 ,
,”TIMLIN, MIKE”,TOR,52,4.17,3,13.5,5,$5 ,$3 ,
,”BARTON, SHAWN”,SF,77,4.18,5,11.33,1,$7 ,$3 ,
,”WAGNER, PAUL”,PIT,145,4.78,6,13.64,1,$5 ,$3 ,
*,”RIVERA, MARIANO”,NYY,95,4.4,6,12.03,0,$7 ,$3 ,
,”PLESAC, DAN”,PIT,76,4.24,4,12.4,3,$6 ,$3 ,
,”MORGAN, MIKE”,STL,138,4.82,7,13.55,0,$4 ,$3 ,
,”PICHARDO, HIPOLITO”,KC,69,4.85,7,14.22,1,$4 ,$3 ,
,”VERES, RANDY”,FLO,75,3.83,4,12.61,1,$6 ,$3 ,
,”HABYAN, JOHN”,,83,3.61,4,12.69,1,$6 ,$3 ,
*,”HANEY, CHRIS”,KC,99,4.35,5,10.8,0,$8 ,$3 ,
,”FOSSAS, TONY”,,68,3.44,5,11.13,0,$7 ,$2 ,
,”BORLAND, TOBY”,PHI,73,3.21,1,13.44,5,$6 ,$2 ,
,”MULHOLLAND, TERRY”,PHI,152,5.23,8,13.11,0,$4 ,$2 ,
*,”BREWINGTON, JAMIE”,SF,110,4.25,5,11.54,0,$8 ,$2 ,
,”WEST, DAVE”,PHI,67,3.42,5,12.34,0,$6 ,$2 ,
*,”LOAIZA, ESTEBAN”,,155,4.82,7,13.47,0,$4 ,$2 ,
,”CLARK, TERRY”,,80,3.46,3,13.17,1,$5 ,$2 ,
*,”SHAW, JEFF”,CHW,79,5,4,13.21,4,$4 ,$2 ,
*,”WALKER, MIKE”,CHC,70,3.19,3,13.99,2,$4 ,$2 ,
*,”HUDSON, JOE”,BOS,66,3.71,4,12.82,1,$5 ,$2 ,
*,”HONEYCUTT, RICK”,NYY,79,3.83,3,12.18,2,$6 ,$2 ,
*,”BALDWIN, JAMES”,CHW,90,4.6,6,13.1,0,$4 ,$2 ,
,”PEREZ, MELIDO”,NYY,99,5.14,7,12.68,0,$4 ,$2 ,
,”MOYER, JAMIE”,,124,4.76,6,12.99,0,$5 ,$2 ,
,”HARRIS, GREG”,,92,4.76,5,13.07,1,$4 ,$2 ,
,”HENRY, DOUG”,NYM,73,4.37,4,13.55,3,$4 ,$2 ,
*,”SCHMIDT, JASON”,ATL,100,4.32,5,12.69,0,$5 ,$2 ,
,”CORMIER, RHEAL”,,102,4.86,6,11.92,0,$6 ,$2 ,
,”OSBORNE, DONOVAN”,STL,90,3.89,4,12.35,0,$6 ,$1 ,
*,”BYRD, PAUL”,NYM,58,2.95,3,9.47,0,$7 ,$1 ,
*,”CORSI, JIM”,OAK,68,3.6,3,10.79,1,$7 ,$1 ,
*,”ANDUJAR, LUIS”,CWS,86,3.77,4,12.77,0,$5 ,$1 ,
*,”REKAR, BRYAN”,COL,101,4.72,5,11.5,0,$6 ,$1 ,
,”LLOYD, GRAEME”,MLW,48,4.33,1,11.41,5,$5 ,$1 ,
*,”ACEVEDO, JUAN”,COL,110,5.65,8,13.83,0,$2 ,$1 ,
*,”DOHERTY, JOHN”,DET,118,5.63,7,14.01,1,$1 ,$1 ,
*,”WOLCOTT, BOB”,SEA,105,4.8,5,11.49,0,$6 ,$1 ,
,”MEACHAM, RUSTY”,KC,66,4.52,3,12.79,3,$4 ,$1 ,
,”KRUEGER, BILL”,,95,5.07,7,15.2,0,$0 ,$1 ,
,”WHITESIDE, MATT”,TEX,61,4.71,4,13.12,3,$3 ,$1 ,
*,”BOROWSKI, JOE”,BAL,59,3.36,3,9,0,$7 ,$1 ,
,”SELE, AARON”,BOS,61,3.5,4,12.99,0,$4 ,$1 ,
,”KARL, SCOTT”,MLW,109,4.22,5,14.2,0,$2 ,$1 ,
,”BOCHTLER, DOUG”,SD,58,3.64,3,11.6,1,$5 ,$1 ,
*,”DIPOTO, JERRY”,NYM,88,4.6,4,12.68,1,$4 ,$1 ,
*,”LOPEZ, ALBIE”,CLE,71,3.55,3,10.65,0,$6 ,$1 ,
,”MONTELEONE, RICH”,,45,3.31,4,11.44,0,$4 ,$1 ,
*,”VILLONE, RON”,SD,60,4.62,3,13.11,3,$3 ,$1 ,
,”YOUNG, ANTHONY”,CHC,68,3.94,3,12.84,1,$4 ,$1 ,
,”HARNISCH, PETE”,,109,4.58,4,12.27,0,$5 ,$1 ,
*,”CORNELIUS, REID”,NYM,100,4.86,5,12.06,0,$5 ,$1 ,
,”HARVEY, BRYAN”,,36,2.42,1,6.76,3,$7 ,$1 ,
*,”GREENE, TOMMY”,PHI,100,4.59,4,11.16,0,$6 ,$1 ,
*,”HURTADO, EDWIN”,SEA,100,4.54,4,11.8,0,$5 ,$1 ,
,”JOHNS, DOUG”,,72,4.61,5,11.67,0,$4 ,$1 ,
,”BLAIR, WILLIE”,,115,5.11,6,14.42,1,$1 ,$1 ,
*,”MARTINEZ, PEDRO A”,NYM,57,3.79,3,12.16,1,$4 ,$1 ,
,”GRAHE, JOE”,COL,59,5.14,4,16.14,4,$0 ,$1 ,
,”SANDERS, SCOTT”,SD,94,4.7,5,11.92,0,$5 ,$1 ,
,”JAMES, MIKE”,,73,3.89,3,12.28,1,$4 ,$1 ,
*,”NITKOWSKI, CJ”,DET,124,4.94,5,12.41,0,$4 ,$1 ,
*,”WAGNER, BILLY”,HOU,55,3.76,4,12.44,0,$4 ,$1 ,
*,”BREWER, BILLY”,LA,54,3.66,3,11.38,1,$5 ,$1 ,
*,”GREEN, TYLER”,PHI,110,4.9,6,14.71,0,$0 ,$1 ,
*,”MIRANDA, ANGEL”,MLW,99,4.45,4,12.27,0,$4 ,$1 ,

That’s it. Tons of players, tons of fun. But surely there’re a lot missing. Well, rookies anyway.

Scouting rookies is a job, one for which I regret I don’t have the time. I’ve gone through all the stats and read Baseball America and Baseball Weekly. So I think I know a thing or two about rookies, but Peter G tells me that John Benson is doing a rookie roundup elsewhere in the book. If you want to know about rookies, study his stuff. He has a lot of knowledge and information. And perhaps most importantly, he talks to some of the decisionmakers in baseball, the front office guys and managers, and the brains in baseball, the scouts.

Rookies are an unholy proposition for predictors. Not only is our information about their skills and readiness to play in the bigs incomplete, there are a range of other issues–citizenship and political, among them–that effect their opportunity. All last year I was baffled by the Royals refusal to give Dwayne Hosey a try. He had been American Association MVP the year before and was having another fine season in Omaha. It wasn’t like the KC squad was hard to crack. As they called up a seemingly endless string of replacement players, like Lockhart, Carceras and Grotewald, and had the light hitting David Howard playing the outfield, I couldn’t fathom why they wouldn’t give this man a break. Actually, the fact that the Royals weren’t doing what it seemed so logical for them to do made me suspect that there were hidden factors. Somebody up there (the front office, or his manager in Omaha, or the scouts), for some reason, valid or not, didn’t like Dwayne.

But I didn’t know anything about him, really, and as much as I bugged my friend who’s a Royals fan (and had Hosey on his reserve list) and others who know more about the minors than I do, I learned nothing. Eventually there was a story about Hosey in Baseball Weekly. Lisa Winston described his background, which included a bad attitude, and his present, which featured a turnaround. According to Winston, the reason he’d suddenly gone from being an underachiever to an achiever, at the advanced age of 27, is because he’d matured. He’d put the distractions of his youth behind him, discovered a way to apply himself, and improved his skills. Or at least his ability to express them.

Since the Royals seemed to have been part of that turnaround–they got him just before his breakthrough 1994 season–it’s still hard to understand why they didn’t give him a shot last year. They claimed they had younger prospects, Tucker and Damon and Goodwin, they wanted to give a chance, and that may be true. Hosey’s a small guy, 5’10” and 175 lbs, so it’s understandable that they wouldn’t expect him to be a big league power hitter. But this is a team that was running strikebreakers onto the roster all year long. And while the scabs didn’t really do badly, there is no rational way the Royals can claim Jeff Grotewald had more talent or ability than Hosey. And he doesn’t play the field. So something else was at work here, my point being that those of us outside the corridors of power just don’t, and maybe can’t, know.

What we do know is that once Hosey landed in Boston he played very well. It’s way too soon to tell whether he’ll actually make it in the bigs. The Royals could still be proved right. His size and age are working against him. But the Royals could also be proved wrong. For some reason I’ve taken a shine to Dwayne, I liked his patience and humility in the Winston article and the way he played in Boston in September, so I hope they are.

The End and a Plug

I’m going to be spending March in Florida. I’ve got family there now and it’s the best time to visit. I was down last year, from mid February to mid March, and while I had a nice time, I couldn’t get over the fact that the Spring Training camps were filled with replacement players. The papers howled about all the people who were showing up at the camps. I just couldn’t bring myself to go and be added to the owner’s headcount, which was then used against the players. So I boycotted.

But this year I’m going down again and, barring the unthinkable, I won’t be boycotting. I’ll be playing some golf, eating some grouper and watching some baseball. As I look out the window now it is a clear day here, the sky is blue with just a few wisps of clouds, but the ground is covered with a foot or so of snow. In a few hours, if I’m going to mail this off to Peter G today, I’m going to have to dig my car out of a sizable drift. So when it comes to Florida, I can’t wait.

Les suggested that perhaps I’d like to offer updated, tweaked predictions to anyone who was interested. For a small fee. Which I would like to do. The idea isn’t so much to make money, just yet anyway, as it is to get you the best, most timely info we have come the last week of Spring Training. I expect I’ll have some observations from the camps that are valuable. I’ll certainly have some opinions. All these will find their way into the projections. So the charge won’t be a lot, just enough to cover our costs and, if enough people order, to make it a little bit worth our while. If your interested please drop me a line at:

[I updated the email address to write to and deleted an old snail mail address that no longer works.]

E-mail is preferred, because it will reach me no matter where I am. But I’ll eventually get snail mail from my post as well. I’ll send out details on what we’re offering this year, and what the cost will be. Like I said, it won’t cost much and will be in the vein of what you’ve read above. If there’s anything special you’re interested in, please let me know, and we’ll see if we can do anything. We’re always looking for good ideas.

And, if you have any questions about anything at all I’ll try to answer all email promptly. I can’t make the same guarantee for the US mail.

Well, time to go. My thanks to Peter Golenbock and Les Leopold, for letting me have all this fun. And best of luck for all of you this coming season.

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