World Famous StatSpeak Roundtable: July 14

At the All-Star break, the roundtable is pleased to welcome a special guest to our StatSpeak weekly discussion of all things baseball.  This week, we welcome Bill James, author of multiple books concerning Sabermetrics and founder of Bill James online, which showcases his work.  Bill has also spent some time working as a consultant to the Boston Red Sox.  Bill joins Eric and Pizza in a discussion of instant replay, team chemistry and of course, the All-Star game.
Question #1: Is it possible to argue that a player’s actions in the clubhouse have no impact on the performance of his teammates, or is it axiomatically true in all situations in which people interact that they make one another more or less efficient?
Bill James: I pose the question because I think it is axiomatically true that co-workers make their co-workers more effective or less effective, and therefore I think it is silly to argue that what a player does “off the field” doesn’t have an impact on the performance of the team. To believe that is to believe that sports are different from every other human activiity. There are no doubt jobs in which one’s co-workers are unable to make one more or less productive. . .being a forest ranger, for example, or. Well, actually, I can’t come up with a second example, even as a joke. But if you have an opposite opinion, I’d like to hear why.
Eric Seidman: This is really interesting because it delves into the psychology of players, something Pizza Cutter is admittedly much more in tune with than I, but what I will say is that baseball players are generally very confident, and very confident people are less susceptible to “outside interference” than others; whereas someone less sure of himself will take more to heart, a confident person is more likely to brush something similar off.  However, put a group of these very confident people together in a room and suddenly we may see some egos start to crack.  For instance, if someone random in the stands offers up hitting advice to (first player to come to mind) Nick Swisher, he will avoid responding and pretend he didn’t hear it, whereas if Jermaine Dye, his teammate, offers the same exact advice, he will not only listen, but perhaps even take advice to heart and alter his hitting approach or whatever that advice pertained to.
With this “idea” in mind–that a peer can have an effect on another player–I think we have to believe that on some level players are not immune to problems in the clubhouse; however, one of the things that separates major leaguers from minor leaguers would be the ability to deal with adversity and do their job in the process.  I don’t think players can completely block out these distractions but I also don’t think the distractions have a huge, huge impact on the overall performance simply due to the selection bias of baseball players and the confidence that accompanies them.  Overall, it will have an affect on players but I don’t truly believe it is the difference between someone posting an .880 OPS or .940 OPS based on how he gets along with teammates.  I recall a Derek Zumsteg article at USS Mariner early this season discussing how, regardless of how great the team chemistry was, or how little clubhouse problems existed a few years ago, the Mariners for lack of a better word stunk.  Athletes are known to push each other, as well, whether it be on the field or in the gym, and considering these people are mostly human I don’t see how they could, with complete 100% accuracy, turn a switch off to the point that off-the-field antics have no effect on them.
Pizza Cutter: The professional literature on the subject shows that workplace “climate” affects employee productivity.  No doubt on this one.  That’s why companies spend so much time and money on human resources departments and conflict mediation and employee reward programs and why they allow for company money to be used for the office Christmas party.  Employees that work well together and like each other are more productive.  Now, that effect isn’t as strong for jobs in which there’s limited interaction with one’s co-workers, and baseball can be a solitary game, especially in the batter’s box.  I don’t know how big the effect is in baseball (and really, that’s what we’re asking here), but that’s an empirical question.   My guess is that it wouldn’t be a big effect, but I doubt that it’s zero.  It’s also the type of question that if MLB would grant me the access (and someone would grant me the funding) I could do that study.  The measures of workplace connectivity are out there.  Baseball performance is easy to measure.  The problem is that to get enough data, I’d need to measure multiple clubhouses at multiple times over a few years.  (I could use hierarchical linear modeling!)  But, it’s just an engineering problem.
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When They Were Young

Generally speaking, aging curves of players have shown that up until 28 years old someone can be expected to improve.  Once past that benchmark a skillset will begin to decline.  For some it will be more gradual than others but this is what those in the analytical community have come to expect.  On Thursday we looked at the only ten pitchers from 1956 until now that have been primarily starters from the age of 38-44, finding that Nolan Ryan has the highest average game score while Roger Clemens’ 134 ERA+ slightly outdoes the 131 of Randy Johnson for the lead.  Also of note is how all ten pitchers posted an above average ERA+, with David Wells and Tommy John’s 101 as the minimum.
I mentioned how there would be an inherent bias in that only the pitchers effective enough on the mound would be able to hang around as a starter this late into their years so it should almost be expected to find quality numbers.  We then looked at all pitchers age 45+ and saw the group whittle down to five, with Jamie Moyer emerging as the best of the bunch.  This catalyzed a new train of thought: how do the age 38+ seasons for these ten pitchers compare with their first seven or so seasons?  Were they better via these metrics as old-timers?
The goal involved comparing the first seven years to the age 38-44 years, however some players started their careers as relievers so, as long as they became a starter within a reasonable amount of time (2-3 years) I went ahead and recorded their statistics during their first seven years as a starter.  The only pitcher this could not be done for was Charlie Hough, who was a reliever for a significant portion of his career’s beginning before becoming a starter a bit later on.  I also could not use the Average Game Score as a measure because Warren Spahn’s career began prior to 1956; before that point the Retrosheet game logs are fewer and further between in terms of existence or proofing (some may exist but are not 100% sure to be correct).
Here are the comparisons, with A representing their early years and B representing elderly prformance, all in terms of their average yearly performance:

  • Nolan Ryan A: 207 IP, 1.82 K/BB, 113 ERA+
  • Nolan Ryan B: 208 IP, 2.75 K/BB, 114 ERA+
  • Randy Johnson A: 205 IP, 2.14 K/BB, 121 ERA+
  • Randy Johnson B: 158 IP, 4.59 K/BB, 131 ERA+
  • Roger Clemens A: 216 IP, 3.10 K/BB, 125 ERA+
  • Roger Clemens B: 179 IP, 2.96 K/BB, 134 ERA+
  • Warren Spahn A: 267 IP, 1.46 K/BB, 107 ERA+
  • Warren Spahn B: 246 IP, 1.90 K/BB, 105 ERA+
  • Phil Niekro A: 267 IP, 2.48 K/BB, 125 ERA+
  • Phil Niekro B: 265 IP, 1.76 K/BB, 114 ERA+
  • Charlie Hough A: 106 IP, 1.30 K/BB, 106 ERA+
  • Charlie Hough B: 221 IP, 1.31 K/BB, 107 ERA+
  • Gaylord Perry A: 281 IP, 2.79 K/BB, 122 ERA+
  • Gaylord Perry B: 213 IP, 2.27 K/BB, 106 ERA+
  • Jamie Moyer A: 122 IP, 1.64 K/BB, 93 ERA+
  • Jamie Moyer B: 210 IP, 2.20 K/BB, 107 ERA+
  • David Wells A: 176 IP, 2.52 K/BB, 109 ERA+
  • David Wells B: 162 IP, 3.45 K/BB, 101 ERA+
  • Tommy John A: 194 IP, 1.88 K/BB, 118 ERA+
  • Tommy John B: 160 IP, 1.27 K/BB, 101 ERA+

Looking at the results for each of these ten pitchers we see that nine of them tended to post better numbers in their younger years.  Sure, there are instances of these nine posting better K/BB ratios as elder statesmen, but their durability and ERA+ would decrease.  Nolan Ryan’s ERA+ and IP stayed the same but his K/BB shot up when older.  Randy Johnson actually had a better K/BB and ERA+ while older despite many more IP as a youngster.  Roger Clemens had better IP when young, a better ERA+ when old, and the same K/BB essentially.
Warren Spahn had better IP when young, a better K/BB when old, and essentially the same ERA+.  Phil Niekro was better at everything when young.  Charlie Hough is tough because his early years were as a reliever, but, still, he posted virtually idential K/BB and ERA+ numbers.  Gaylord Perry joined Niekro as posting better counts in all three categories when young.  David Wells had better IP and ERA+ numbers when young but a much better K/BB later on.  And Tommy John posted better numbers of these three metrics when young.
That leaves just Jamie Moyer, who was the only one here to post better numbers in all three categories when older.  He was below average as a youngster and increased that to 7% above average as an old-timer.  Not only is Jamie Moyer potentially the best 45+ pitcher in the Retrosheet era (1956+) but he is the only one of these ten to legitimately improve in all three of these areas (durability, strikeouts to walks, ERA+) from his first seven seasons to his age 38-44 seasons.

The Middle 80%

In a recent interview with Kevin Orris at Major League Report, veteran lefty Jamie Moyer said something particularly interesting with regards to how he likes to evaluate himself and other pitchers.  According to Moyer, “I have a couple of outings a year where I am just not good.  But, if you look at any starting pitcher during the course of the season when you’re getting 30 or more starts, we’re all in the same boat.  It’s just how bad are you?  The way I look at it is if you take those 3-5 bad starts away and remove the 3-5 good starts, the bulk of your season is in the remainder of 25 or so starts.  If you pitch well in those games you have a chance to make big contributions to your ball club.”
While it’s probably not the ideal way to evaluate pitchers it piqued my interest nonetheless.  Since the number of starts a pitcher makes in a season can constitute a small sample size–even in the early 1900s–the average Game Score of a pitcher may be inflated or deflated due to 3-4 tremendous or terrible outings.  Now, this isn’t to say that these starts should be removed when evaluating said pitchers, but what happens if they are removed?  Would there be significant differences in the Game Score averages?  If Moyer is right–many could argue for and against his idea–that every pitcher making that many starts will have a couple clunkers and a couple standout performances, then looking at the remaining bulk would offer up more of a general range of consistency.
With that in mind I probed the Baseball Reference Play Index for the highest average Game Scores in a single season from 2000-2007, of those making 30+ starts.  I then removed the top and bottom three Game Scores from each pitcher and re-calculated their averages.  Essentially, since the pitchers all fell between 30-35 starts, the six removed starts accounted for 20% of their total outings, leaving the middle 80% to look at… hey, that sounds like a catchy title.
The Play Index query brought back a plethora of names but the top twenty-five all happened to have averages of 60 or higher, so it seemed like a logical cutoff point.  Now, this analysis isn’t done to necessarily suggest we evaluate pitchers this way, by any means, but sometimes it’s just fun to look at interesting ideas and toy around with the numbers.  Perhaps we’ll find that the great or terrible starts really did have a true effect on the season even with their relatively small percentage of the whole.  For starters, here are the twenty-five pitchers, their seasons, and their overall average Game Scores:

  1. Randy Johnson, 2002: 67
  2. Randy Johnson, 2001: 67
  3. Johan Santana, 2004: 65
  4. Randy Johnson, 2004: 65
  5. Pedro Martinez, 2002: 65
  6. Curt Schilling, 2002: 64
  7. Randy Johnson, 2000: 64
  8. Roger Clemens, 2005: 63
  9. Johan Santana, 2005: 63
  10. Pedro Martinez, 2005: 63
  11. Ben Sheets, 2004: 63
  12. Mark Prior, 2003: 63
  13. Curt Schilling, 2001: 63
  14. Kevin Brown, 2000: 63
  15. Jake Peavy, 2007: 62
  16. Johan Santana, 2006: 62
  17. Jason Schmidt, 2004: 62
  18. Chris Carpenter, 2005: 61
  19. Jake Peavy, 2005: 61
  20. Oliver Perez, 2004: 61
  21. Kerry Wood, 2003: 61
  22. Andy Pettitte, 2005: 60
  23. Kevin Brown, 2003: 60
  24. Derek Lowe, 2002: 60
  25. Odalis Perez, 2002: 60

After removing the top and bottom three starts from each, here is a table showing the before and after photos, so to speak, ranked by differential.  So, someone with a 60 who shot up to a 62.5 after those starts were removed would have a 2.5; the opposite would result in a -2.5 since the pitcher’s average lessened after removing these starts. In theory, those who benefited the most from three tremendous starts will see their averages decrease while those who suffered from three bad starts will see their averages increase.





Johnson, 2000




Clemens, 2005




Carpenter, 2005




Pettitte, 2005




Wood, 2003




Peavy, 2007




Santana, 2004




Peavy, 2005




Schmidt, 2004




Schilling 2002




Martinez, 2002




Johnson, 2001




Johnson, 2002




Perez, 2004




Santana, 2005




Brown, 2003




Prior, 2003




Santana, 2006




Schilling, 2001




Brown, 2000




Lowe, 2002




Martinez, 2005




Sheets, 2004




Johnson, 2004




Perez, 2002




What initially stands out is that so few of these twenty-five players actually saw their average game score decrease. A closer look shows that not many increased either. I mean, in a relatively speaking type of sense, all but the final three “increased” but said increase was so minimal that I would say Johnson’s 2000 and Clemens’ 2005 season were the only two to experience somewhat significant increases while Odalis Perez’s 2002 season took a big hit. Everyone else may have increased or decreased a bit, but for an initial look at something like this it does not seem that removing these starts really has that big of an effect on the overall averages. So, Jamie, for now, it seems that it isn’t really hurting you to look at pitchers this way but there really isn’t any need to get rid of the starts.

GM Report Card – JP Ricciardi

In December 2002 I can vividly remember calling friends and family, excited that the Phillies had just acquired Kevin Millwood.  With the imminent return of Mike Lieberthal, Johnny Estrada had become an expendable commodity and Millwood had been a key cog in the Braves rotation.  Two years later, my evaluation of the trade had changed.  Millwood had not been the answer to the Phillies pitching woes and Estrada turned out to be the lone Braves representative on the all-star team.
Of course hindsight is always 20/20 but general managers are, more often than not, evaluated by the production levels of the players they acquire and send away as well as how these production levels translate to wins.  Millwood did not meet expectations while Estrada exceeded them; therefore, it was Ed Wade’s fault for making a bad move.
With this in mind I decided to begin a bi-weekly or monthly feature evaluating general managers.  The method is somewhat of a combination of Geoff Young’s trade-tracking chapter in the 2008 Ducksnorts Baseball Annual, and Dan Levitt’s analysis of Terry Ryan at Baseball Analysts.  Win Shares is the statistic used to evaluate moves and they are assigned to all players acquired and lost during a GMs tenure.  The major difference between what I will do here and what was done in Levitt’s wonderful analysis is that he assigned Win Shares to lost players for every subsequent year; I am only assigning them for the years on the first new team they join.
For instance, in the Millwood/Estrada deal, Ed Wade would be debited for Estrada’s tenure with just the Braves.  When the Braves sent Estrada to the Diamondbacks, he then became a player lost by John Scheurholz.  Otherwise, the evaluations are pretty straight-forward.  For those unfamiliar with Win Shares, it is a statistic created by Bill James and explained in the self-titled book by James and Jim Hentzler and it measures the contribution of a player to his team’s total wins.  3 Win Shares = 1 Win; 20+ is an all-star season and 30+ is an MVP season.
To kick off this series of evaluations I chose to look at J.P. Ricciardi, GM of the Toronto Blue Jays.
Meet J.P.
A disciple of Billy Beane, Ricciardi took over the Toronto reigns in November 2001.  He replaced Gord Ash, who had more recently found himself embroiled in the Shouldergate controversy; he also hired a manager that feigned fighting in Vietnam.  The team had struggled to finish higher than third place and hoped that Ricciardi’s knack for quantifying players would pay off major dividends.
Now in the midst of his eighth season at the helm, the team is still yet to experience the success envisioned at the time of his hiring.  Sure, they finished in second place in 2006, but it did not result in a playoff berth.  In fact, they have not been in the playoffs since 1993, when some guy I have erased from memory hit a world series winning walkoff home run.
Overall Results
Before looking at each area of moves on their own, here are the overall results of his moves:











Free Agents










Rule V










As mentioned above, one win equals three WS.  For example, based on the free agents Ricciardi has signed, as opposed to those released or lost, the net of +8 WS equates to about three added wins.  Over the course of his six years he has added about a half-win per season in free agent moves.
Elsewhere, he has not made many Rule V moves or waiver claims, resulting in very little net Win Shares.  In trades, though, Ricciardi has bombed.  His trades have cost the Blue Jays approximately 8 wins per year.  Now this is contingent upon the traded away players performing the same way in Toronto as they did in their new destination; however, as mentioned at the start of the evaluation, whether fair or not, this is how GMs are evaluated.
Free Agent Signings
Ricciardi has received 42 WS, or 14 wins, from the free agents he has signed, starting in November 2001.  During his tenure these signings have added just about 0.5 wins per season. 
Click here to view the results for all of his free agent signings.
Of the forty signings, fourteen resulted in ten or more WS; only one, Victor Zambrano, produced negatively.  Frank Catalanotto is far and away the best signing he made, providing the team with around 17 total wins, or 4/yr.  The next highest is Gregg Zaun, previously a backup catcher who recently found himself the primary backstop for the Jays.
The highest single-season signing is a tie between BJ Ryan in 2006 and Frank Thomas in 2007.  Each had seventeen shares and contributed as much as six whole wins in the respective seasons.
Free Agents Lost
This category not only refers to players lost to free agency but also those who were released.  While Ricciardi’s 40 signings produced an aggregate 42 WS, the 29 players let go produced 47 for their new team.  Now, as I mentioned earlier, I only look at the very next team for a lost player.  Doug Davis was released and signed with the Brewers; I debit Ricciardi all of Davis’s WS while on the Brewers.  Once he joined the DBacks, he becomes Brewers GM Doug Melvin’s “problem.”
Of the 29 lost or released, five produced WS totals of 30 or more: Esteban Loaiza (30), Kelvim Escobar (51), Carlos Delgado (31), Chris Carpenter (48), and Doug Davis (36).  Looking at the yearly averages: Loaiza (15/yr), Escobar (13/yr), Delgado (31/yr), Carpenter (12/yr), Davis (9/yr).
Click here to view the results for all free agents lost/players released.
Ricciardi has made 29 significant trades from 2002-2007; trades that resulted in at least one win share on either his, or the other, side.  A trade was considered insignificant if nobody made the major leagues or both parties summed to 0 WS.  Overall, his trades have been the worst facet of his moves.  The players acquired produced an aggregate 272 WS–91 wins–which comes to +15 wins/yr.
The players lost, however, produced 378 WS for their new clubs.  378 WS = 126 wins = -21 wins/yr.  Though rounded a bit, he brought in 15 wins/yr with these trades but lost 21 wins/yr.  The net of -5.89, or -6 really leaves a significant stain on his Toronto resume.
The best trade pulled off involved getting Eric Hinske and Justin Miller in exchange for Billy Koch on 12/7/2001.  Koch played just one year with Oakland, bringing in 19 WS; Hinske and Miller combined for 65 WS.
He also made two really bad trades that, on their own, account for much of the net loss.  Both trades involved ridding the Jays of major league commodities for prospects that never cut the mustard.  The first, completed just six days after the Hinske deal on 12/13/2001, saw Luke Prokopec head to Toronto in exchange for Cesar Izturis and Paul Quantrill.  Prokopec contributed 0 WS in a brief 2001 stint while Quantrill and Izturis combined for 66 WS from 2001-2006.  The other one, completed almost a year later on 12/15/2002, saw Jason Arnold join the Jays while Felipe Lopez headed to Cincinnati.  Essentially the same story, Arnold contributed nothing while Lopez produced 43 WS from 2003 to 2006.
In terms of trades, commenter Darren pointed out that certain players were being double-counted; he was correct and these are now fixed.  What he meant can be explained in the Bobby Kielty deals; the Jays traded Shannon Stewart for Kielty mid-2003 and I counted Kielty’s one half-year with the Jays and Stewart’s 3+ years with the Twins.  In the end this gave Kielty 4 WS for JP and Stewart -39 WS against JP.  This was not correctly done on my part because Kielty was traded the next year for Ted Lilly.  At that point, Stewart’s WS with the Twins should have stopped and it would then be Kielty vs. Ted Lilly.  So, the Stewart-Kielty would be +4 vs -9 and then the Kielty-Lilly would remain the same.  Otherwise, it would be Stewart counting against Kielty even though the K-Man was not there anymore.  This did not happen too often in the trade log but I did make the corrections reflected in the results.
Click here to see the results for all players acquired and lost through trades.
Waiver Wire
Another way to acquire free talent or get rid of the undesirables is the waiver wire.  Ricciardi was essentially even in this acquisition aspect, bringing in 42 WS and giving away 47.  His most productive waiver claims were Pete Walker (12) and Frank Menechino (11).  Of players he lost to waivers, Scott Eyre produced 19 WS for the Giants and Bruce Chen chimed in with 16 for the Orioles.
Click here to see the results for his waiver moves.
Rule V
Ricciardi’s Rule V selections and losses were often than not returned; in other cases, they simply never amounted to anything.  The only three Rule V picks that were significant resulted in 14 WS gained and 2 WS lost.  Though a small sample this happened to be his best area.  Corey Thurman gave him 4 WS in 2001 and Aquilino Lopez gave him 10 in 2002; Matt Ford contributed 2 WS to Milwaukee in 2002.
Position Evaluation
Another interesting way to analyze his moves is to look at how he fared by position.  Perhaps he had a knack for finding relievers but struggled to sign quality shortstops.  Here are the results:














































These numbers are much more rounded than the overall results but you can see Ricciardi has fared best with third baseman and worst with starting pitchers and outfielders.  In fact, 14 of those 20 WS for outfielders belong to Matt Stairs; most of the other OFs he acquired did nothing. 
I hope this shed some light on what Ricciardi has done and how it effected his team’s success.  There is still room to improve the system and one such facet I am considering would be to compare the lost players to their replacements; for instance, Orlando Hudson was traded away but how did he compare to the incumbent second baseman?  Perhaps he would not count as much against Ricciardi when we see Aaron Hill’s numbers. 
Until we have a bunch of these analyses conducted we cannot rank the GMs but, based on Win Shares, Ricciardi certainly will not be amongst the leaders as he has cost his team about five wins per season with his transactions.
I am still deciding who the next GM for this should be, so if anyone has thoughts, leave them in the comments section.  I’d prefer it be a somewhat current time frame and, whoever you pick, also specify the team; don’t just say Pat Gillick, say Gillick with the Mariners or Gillick with the Blue Jays, etc.

The Batting Hall of Current

A topic that never ceases to cause debate in the baseball writing community is who does or does not belong in the hall of fame.  Most of the debate revolves around whether or not a player has “the numbers.”  The majority of those chiming in intuitively know what makes up a worthy player but, because no common denominator exists, we rever to statistical milestones in order to base judgments.  While this is not wrong, by any means, there are also those who possess the mindset that a healthy combination of solid stats and contributions to the game is a better way to gauge induction-worthiness.
I personally feel the hall of fame should work more along the lines of an historical document that will serve to inform future generations which players from the past are really worth knowing about.  The fact of the matter is that there are many different ideas and definitions about what the Cooperstown hall is or should be; this plethora of ideas is one of the key reasons we so fervently debate.
In my favorite baseball book (as of now) Whatever Happened to the Hall of Fame? Bill James attempts to uncover what makes a hall of fame player as well as why Player A got in and Player B did not.  While he did not necessarily find a common denominator he did notice that a large percentage of those enshrined reached certain statistical milestones.  With that in mind he created a few tests to determine the likelihood of a player getting inducted.
The test I like to examine the most is the Hall of Fame Monitor.  For a full explanation click the link of the title, but it essentially weights different milestones and awards points as players positively distance themselves from said achievements.  Anyone with a score of 100+ is considered to have a shot; anyone with 130+ is considered a virtual shoe-in.  For instance, Ken Griffey Jr. currently has a 225 and Alex Rodriguez has a 316; based on what others currently inducted have done, these two players would be no-doubters if they retired today or tomorrow.
There are currently 35 batters with 100+ not yet eligible for induction.  I thought it might be fun to show them and get your thoughts on whether or not they are worthy, as well as why or why not.  If we can get enough of a response we’ll have an official fan ballot.  In just taking a cursory scan of these 35 I have a strong sense we will find some players with 130+ that are not necessarily worthy of induction based on the standards of some.  Here are the seven above 200:

  • Barry Bonds, 350
  • Alex Rodriguez, 316
  • Ivan Rodriguez, 228
  • Ken Griffey Jr, 225
  • Derek Jeter, 221
  • Mike Piazza, 205
  • Sammy Sosa, 201

The bookends of that list bring up the topic of steroids and magical performance elixirs (what I imagine Sesame Street would call PED’s) but I am only mentioning them due to a quota of PED mentions in articles in need of being reached.  Here are the players above 150:

  • Frank Thomas, 194
  • Roberto Alomar, 193
  • Manny Ramirez, 187
  • Rafael Palmeiro, 178
  • Vlad Guerrero, 174
  • Craig Biggio, 172
  • Ichiro Suzuki, 170
  • Albert Pujols, 166
  • Todd Helton, 162

It’s very interesting to see Albert Pujols and Vlad in there so highly due to them still having a nice portion of their careers left.  Here are the players above 130:

  • Jeff Bagwell, 149
  • Larry Walker, 147
  • Gary Sheffield, 146
  • Chipper Jones, 141
  • Jim Thome, 139
  • Bernie Williams, 133
  • Edgar Martinez, 131

And here are the players between 100 and 130:

  • Jeff Kent, 121
  • Nomar Garciaparra, 120
  • Juan Gonzalez, 120
  • Barry Larkin, 118
  • Miguel Tejada, 114
  • Andres Galarraga, 114
  • Omar Vizquel, 104
  • Andruw Jones, 101
  • Luis Gonzalez, 101
  • Carlos Delgado, 100
  • Magglio Ordonez, 100
  • Fred McGriff, 100

These are the 35 batters currently with Hall Monitor scores of 100+.  The players below the 130 mark are definitely more easily debatable so let’s focus the discussion on the players with scores higher than 130, the players James considers to be virtually assured at getting in.  I’d rather hear your thoughts and discuss this in the comments thread than sit here and ramble about my own personal beliefs but I do think that, steroids aside, the first seven players mentioned–those above 200–are all deserving, and very few, if any, of those below 130 are deserving. 

StatSpeak World Famous Roundtable: April 14

Another Monday means another Roundtable.  This week’s roundtable finds us chatting with Lisa Gray from The Astros Dugout and The Hardball TimesRead on as Lisa, Eric, and Pizza talk about Bill James’ recent senitiments on Sabermetrics, pitchers hitting 8th, and the trouble with the Tigers.
Question 1: Both Tony LaRussa and Ned Yost are batting the pitcher in the 8th spot this year. Do you think this is good strategy? Do you think it would be good strategy for any NL team?
Lisa Gray:  Yes, I think it is a great idea and I don’t know why it hasn’t been done regularly. I’ve seen managers put a pitcher who is a very strong hitter in the 8-hole in front of a very weak hitter, but I think that a weak hitting pitcher, who is usually going to bunt anyway, would be better off using up the second out and leaving the PA to a better hitter.
Eric Seidman: All of the analyses I have read have shown that, at its maximum, a pitcher batting eighth will produce slightly positive results.  Since positive results are better than, say, any other type of results, it seems like it would be a sound strategy, but it is not anything so revolutionary that managers are idiots for not following suit.  It also depends on the pitcher.  The strategy applied by LaRussa, as he says himself, is done to allow him an extra leadoff batter.  The problem here is that teams do not bat-around or bring all nine to the plate in every single inning.  For the innings where teams can IBB the 8th batter to get to the pitcher this strategy may or may not pay off in preventing the IBB.  Generally speaking, the 8th batter is the worst non-pitcher hitter, so flipping him to ninth in the order in favor of the pitcher batting eighth does not even sound as if it would produce incredible results unless the team found themselves in the midst of a rally that a pitcher coming to the plate could kill.  If the pitcher was a great hitter I would definitely move him up in the batting order over, say, Adam Kennedy, but I would go in not necessarily expecting an explosion of runs due to the move. 
Pizza Cutter: Let’s see, the Brewers are generally batting Jason Kendall ninth and the Cardinals have put Aaron Miles and Cesar Izturis there.  So, it’s not like we’re dealing with Albert Pujols or Ryan Braun here.  In general, the lineup optimization research says that it doesn’t really matter all that much how you place the specific batters in the lineup and that at most, it’s worth a few runs over a season, but I suppose every little bit helps.  Mark Pankin actually took a look specifically at Tony LaRussa’s penchant for batting the pitcher in the 8-hole.  Then, in his presentation at SABR 37 last year, Pankin put together a more complete model and suggested that all things considered, there wasn’t much of a benefit either way.  Others like StatSpeak friend Tango Tiger have found a small benefit to the pitcher hitting 8th.  Seems like the pitcher hitting 8th isn’t the key to oodles of runs, but it does look like it’s a bit better than the pitcher hitting ninth.
Or we could just give in and tell the NL about the DH.
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This Week in News and Sabermetrics: 4/6-4/12

Welcome to the first edition of TWINS – This Week in News and Sabermetrics.  This will be a weekly article recapping the goings on in the baseball world, ranging anywhere from top games of the week or oddest stats to frontrunners for awards based on my formulas and links to great articles.  Expect one of these bad boys every Saturday.  If anybody has suggestions for additions they would like to see feel free to post them in the comments.  Without further delay:
Interesting Bits of Tid
Well, the Tigers finally won a game after starting the season 0-7 and worrying the moustache off of Jim Leyland (not in a literal sense).  Unfortunately, any hope of a winning streak was put to rest when Tim Wakefield took the mound the next night.  Two weeks into the season the team expected to score 1,000 runs in 162 games (6.17/gm for anyone wondering) has scored 28 runs in 9 games (not 6.17/gm for anyone wondering).  To show how bad things have been Placido Polanco even made errors in consecutive nights.
Staying in the AL, Travis Buck of the Athletics started the season by going 0-21, with 9 strikeouts, and a .043 OPS… out of the leadoff spot.  He was about as effective as Travis Buckley–the other guy that appears when you type “Travis Buck” into Baseball Reference–but then remembered how to hit.  In his next three games Buck went 7-16, with 6 doubles, 4 RBIs, and a 1.284 OPS.
MVP Predictor
I came up with a pretty simple formula to see who would win the MVP should the season end at any given point.  The formula is: (OPS+ / 2) + VORP + VB.
OPS+ compares production to the rest of the league; VORP offers how important a player proved to be in accounting for runs than a replacement level player; VB is a Victory Bonus, just like in the James Cy Young Predictor, that awards points to a division leader.  In this case, +10 for first place and +6 for second place.  It’s simple but effective in determining how important a player statistically performed.  It does not take into account the more human factors of the game but the MVP is usually awarded to the best hitter on the best team; this formula measures that. 
I will be revising this throughout the season I am sure but for now it will work fine.  Here are the top five in the NL:

  1. Kendall, MIL, 135.7
  2. Ramirez, FLA, 130.9
  3. Burrell, Phi, 123.5
  4. Pujols, StL, 120.9
  5. Upton, Ari, 107.2

And the AL:

  1. Pierzynski, CHW, 131.4
  2. Scott, BAL, 126.7
  3. Crede, CHW, 123.2
  4. Dye, CHW, 120.0
  5. Drew, BOS, 114.8

Cy Young Predictor
In The Neyer/James Guide to Pitchers Bill James presented a formula that could, with pretty good accuracy, predict the eventual Cy Young Award.  For a description of the formula, click here.  Though I altered his formula in previous articles to account for old-time players, his works great here.  Here are the top five in the NL:

  1. Jake Peavy, SD, 23.3
  2. Brandon Webb, ARI, 19.6
  3. Micah Owings, ARI, 18.8
  4. Ben Sheets, MIL, 18.6
  5. Jason Isringhausen, StL, 18.4

And the AL:

  1. Daisuke Matsuzaka, BOS, 23.2
  2. Zach Greinke, KC, 22.2
  3. Edwin Jackson, TB, 22.0
  4. Chien-Ming Wang, NYY, 20.3
  5. Brian Bannister, KC, 19.9

Beane Count
Over at Rob Neyer created a really cool stat I had never heard of until earlier this month, titled Beane Count.  The stat measures all of the contributions Athletics GM Billy Beane looks for in players and evaluates the teams that best fit his desires.  The total is found by adding the team rank in home runs hit, walks, home runs allowed, and walks allowed.  Interestingly enough, as of right now, both the Chicago White Sox and Chicago Cubs lead their respective leagues–and by significant margins.
Cain Watch
Many readers here should know that I have some crazy manlove for Matt Cain, despite having no allegiances to the Giants, and really cannot stand how unlucky he gets on the mound.  In 2007 he went 7-16, though my Adjusted W-L system had him pegged at 16-7; my SP Effectiveness System even scored him a +50, just meeting the cutoff for a #1 pitcher.  Each week I will look at his starts and see if the unlucky trend continues.

  • #1, 4/1/08, 5.2 IP, 3 H, 0 R, 0 ER, 4 BB, 5 K, ND.  Records an AQND because it was an Adjusted Quality Start.  Game Score of 64.  From what I saw and heard he was squeezed and really should have only walked two batters.
  • #2, 4/7/08, 4.1 IP, 7 H, 5 R, 4 ER, 5 BB, 5 K. Loss.  Does not record an AQS and legitimately deserved to lose.  Unlike his first start he was not terribly squeezed and this was not a good start by any means.

Game Scores of the Week
Bill James created the Game Score statistic to measure the exact quality of a pitched game.  Info on the easy to calculate figure can be found here.  For the record, a GSC of 50 or higher is good.  Below are the top three game scores of the week of 4/6-4/12.

  • Ben Sheets, April 6th: 9 IP, 5 H, 0 R, 0 ER, 0 BB, 8 K – 85 GSC
  • Edwin Jackson, April 10th: 8 IP, 2 H, 0 R, 0 ER, 4 BB, 6 K – 80 GSC
  • Wandy Rodriguez, April 7th: 7.1 IP, 3 H, 0 R, 0 ER, 0 BB, 6 K – 78 GSC

Weekly Oddibe Award
The Oddibe Awards are given to the hitter with the slash stats (BA/OBP/SLG) closest to the league average and are named after Oddibe McDowell, whom RJ Anderson of Beyond the Box Score determined to have the career slash line closest to the league average from 1960-2006.  As of this week the league average slash line is .257/.327/.403.  Should the season for some odd reason end today, the 2008 Oddibe Award recipient would be – Orlando Hudson, Ari: .270/.325/.405.
If the Season Ended Today
Speaking of whether or not the season ended today I think it will be interesting to look at the playoff matchups each week if it did end.  This way we can see which teams were in it all year as opposed to burning out or surging in. Note – this was done at 11:16 PM EST, so the As had played while the Angels were still playing.

  • Baltimore Orioles (AL East) vs. Chicago White Sox (Wild Card)
  • Kansas City Royals (AL Central) vs. Oakland Athletics (AL West)
  • Arizona DBacks (NL West) vs. Winner of Tiebreaking Game between CHC/MIL
  • Florida Marlins (NL East) vs. St. Louis Cardinals (NL Central)

In Case You Missed It
Here are some great sabermetrics articles from this past week: