The mystery of Troy Percival

While doing a wee bit of research for another piece that I was writing, I came across a small mystery in Troy Percival’s stat lines.  Open that up for a moment and take a look at his BABIP column over all the years that he’s been pitching.  (Prefer a graph?)  The numbers struck me as a little strange.  They’re almost all below .300.  (The one and only exception is 1997, when he had a BABIP of .313.)
BABIP is generally considered to be a matter of random fluctuation around a league mean, and the league mean is usually .300.  There’s a small mountain of evidence that says that there is very little repeatable skill in a pitcher’s performance on this particular stat from year to year, no matter how you slice it.  It looks like there’s a tiny bit, but randomness is far and away the bigger piece of what’s going on.
Then there’s Troy Percival.  From 1995 to 2004, he generally threw a little more than 50 innings a year and faced between 200-250 batters.  Again, only once was his BABIP above .300.  In 2005, he started giving up homeruns like crazy for the Tigers and soon was out of baseball… for a year.  His BABIP in 2005, however, was .192.  Then, last year, he caught on with the Cardinals and posted a .215 BABIP in 40 IP.  This year, he signed a contract with the Rays and has put up a mere .152  Assuming that BABIP fluctuates completely randomly and that I’m as likely to be above the mean as I am below the mean (a 50/50 shot), the chances of Percival being below the mean in 12 out of 13 seasons is about 0.17%, round it to 1 in 600.  Not astronomical odds, but certainly not odds I’d want to stake my life to.
Now, given that the BABIP reliability numbers that we have aren’t zero (just very very low), perhaps there can be a case made that Percival has a particular skill that we haven’t yet appreciated and that he’s not that lucky.  Still, there’s something a little mysterious about his statlines.

Party Poopers: Those Who Fail When All Others Succeed

This past Monday, the Phillies demolished the Rockies by a score of 20-5.  The Rockies had no answers as the Phightins were phiring on all cylinders.  Well, mostly all cylinders.  Pat Burrell, one of their top offensive threats, went 0-4.  It seemed so odd that someone could have such an unproductive game when the rest of his team goes nuts padding their stats, so I decided to see how often this happens.
Using the handy-dandy Baseball Reference Play Index, I looked at the box scores of every game (1956-present day) in which a team scored 20 or more runs.  My goal was to isolate the players who went at least 0-4.  While there were many who went 0-2 or even 0-3, I yearned for even worse relative games.  There have been 91 games since 1956 in which teams scored at least 20 runs, with last year’s Wes Littleton* topping out at 30.
*I named this game in honor of Rangers reliever Wes Littleton who managed to record a save despite his team winning 30-3.  Go figure.
Of the 91 games, I found a total of 20 players who met this criteria.  There were no instances of more than one player posting an “o-fer” in the same game and half of the players that met said criteria were on teams with exactly 20 runs scored.  Nobody went at least 0-4 when their team scored 25, 26, or 30 runs. 
When looking at the players, be sure to note that the slash numbers next to their game stats are the BA/OPS after the “o-fer.”  This way we can look at the performance levels of these players at the time of their party pooping.  Here are the qualifying players whose teams scored 20 runs:

  • 8/13/59, Sammy Taylor, 0-4, .257/.721
  • 8/1/70, Gene Alley, 0-5, .245/.662
  • 4/27/80, Rick Sofield, 0-4, .143/.408
  • 7/8/90, Rob Deer, 0-5, .198/.768
  • 5/4/91, Sandy Alomar, 0-6, .214/.514
  • 4/17/93, Alan Trammell, 0-4, .000/.333
  • 5/7/99, Richie Sexson, 0-4, .217/.699
  • 9/23/03, Chris Stynes, 0-6, .253/.742
  • 7/6/07, Michael Cuddyer, 0-6, .274/.789

There were three players–Alomar, Stynes, and Cuddyer–that went 0-6 while their team scored 20 runs.  Here are the players who accomplished similar “feats” when their team scores 21 runs:

  • 4/28/96, Dave Silvestri, 0-4, .189/.518
  • 8/23/99, Chuck Knoblauch, 0-5, .288/.828

Knoblauch’s numbers were very solid following his 0-5 and the game took place at a point in the season when those numbers were immune to small sample size syndrome; as of right now his game looks to be the worst.  How about 22 runs:

  • 5/31/70, Buddy Bradford, 0-4, .186/.559
  • 4/12/94, Tim Naehring, 0-4, .333/.993
  • 6/19/00, Chuck Knoblauch, 0-4, .277/.730
  • 7/23/02, Shea Hillenbrand, 0-5, .303/.814

Knoblauch again!?  In under a year Knoblauch was a part of two 20+ runs scored games and contributed zilch.  When scoring 23 runs:

  • 4/6/74, Fernando Gonzalez, 0-4, .167/.334
  • 9/30/00, Mark Bellhorn, 0-4, .154/.421

Eeek, Bellhorn stunk that year.  Interesting with regards to his 0-4 is that he actually pinch-hit in this 23-run game and managed to bat four times.  Lastly, since nobody performed this poorly in games of 25+ runs, here are those in games of 24 runs:

  • 4/21/86, Wade Boggs, 0-5, .347/.924
  • 4/24/96, Rich Becker, 0-5, .073/.272

Though early in the season, Becker was slumping at the time of his egregious performance.  It also seems he was celebrating the ten year anniversary of Boggs’ outing by going 0-5. 
As more runs are scored the number of players not taking part in the hits party significantly decreases.  These last two games appear to be the least productive–in terms of exceeding the minimum number of at-bats and the team exceeding the minimum runs scored–as both Becker and Boggs went 0-5 while the rest of their teams scored 24 runs.  Alomar, Stynes, and Cuddyer all went 0-6 in games of 20 runs, which is very poor when we consider that they had an extra non-walk plate appearance in which to contribute. 
Overall, though, what I’ve been saying about Chuck Knoblauch for years is finally evident in this research: He’s pretty good but stinks when his team scores 20+ runs.

Fantasy vs. reality: Know the difference

I have a very ambivalent relationship in my head with fantasy baseball.  On the one hand, reading about it when I was 10 probably had a direct influence on the fact that now I spend a lot of time playing with numbers in baseball.  I’ve played fantasy baseball (although not this year, oddly enough) and I’ve even written about it here and there (and there and there).  Plus, it’s a fun way to keep in touch with some friends and to have an excuse to talk about baseball all summer.  It’s just that fantasy baseball drives me nuts as a Sabermetrician because it seems to me that, when watching actual baseball, we’ve gotten to a point where players are evaluated (at least by broadcasters and the general public) more by how good they are as fantasy players, rather than, well, real life players.  After all, I bet most of the knowledge that people have about players on teams in other cities comes from fantasy-related publications, and if he’s good in fantasy, that must translate over into real life, right?
Maybe this effect is even carrying over into real life.  Over the past off-season, J.C. Bradbury, who wrote The Baseball Economist, and operates the must-read Sabernomics blog, pointed out that over the past off-season, Brewers’ free agents Scott Linebrink and Francisco Cordero both signed four year contracts at roughly about the same time.  If you cover up their “saves” columns for their careers, they both had roughly the same track record coming into the 2008 season.  Why did Cordero ($46 million) get twenty-seven million dollars more than Linebrink ($19 million) over those four years?  Saves, after all, are a fantasy category.
The problem, of course, is that fantasy baseball usually relies on a bunch of stats that make Sabermetricians cringe.  The usual AVG, HR, RBI, SB, and R for batters and W, SV, ERA, WHIP, and K for pitchers in a 5×5 league are a mixed bag of stats when you evaluate them.  So, I present to you 8 players who are much better in your fantasies than they are in real life and 8 players who you will overlook if you just use a fantasy lens.  A lot of the explanations mirror one another, but they’re ways that the fantasy stats can fool you into believing that a player is better or worse than he really is.
(All stats were current as of Saturday afternoon when I wrote this.  It doesn’t matter if the stats change a little bit.  These are archetypes that will appear again and again in baseball so long as fantasy players are out there.)
Players who are much better in your fantasies:
1) Gavin Floyd (owned in 70% of ESPN fantasy leagues).  Floyd has all the markers of a massive disappointment heading into the rest of the season.  A lot of owners noticed him when he almost threw that no-hitter, and I have to say, the numbers so far are tempting.  He’s 4-3, but with a 2.93 ERA and a 1.08 WHIP.  A fantasy owner’s… um, fantasy, right?  Maybe not.  The number one rule of looking at WHIPs is to know whether it’s the W or the H that’s driving that WHIP.  If a pitcher is giving up a lot of walks, that’s probably not going to change.  If he’s giving up a lot of hits, that very well could change, especially if he’s gotten lucky in the BABIP department.  Floyd is walking 4+ batters per nine innings (more than he’s striking out).  His BABIP is .177, well under the usual .300.  Prognosis: that WHIP will not last, because Floyd will probably give up more hits.  Sell high now while he still looks shiny.  See also: Ryan Dempster (100% ownership), Tim Redding (24%).
2) Andy Sonnanstine (9%), is getting some nibbles because he’s picked up six wins (against only two losses) and three of those wins have been gems (a shut out, and two performances of 8-innings, 1-run).  The 5.09 ERA and 1.32 WHIP are nothing great, but maybe out of a fifth starter spot, it’s nice to pick up a few wins.  Plus, he leads the Devil Rays in wins, and they’re in a surprising second place, and “wins are what it’s all about!”  Right?  Not exactly.  My colleague, Eric Seidman, has written about how a starting pitcher can get a little lucky with his win-loss record, not because he’s a good pitcher, but because his offense picks him up.  Check out Sonnanstine’s game log for this year.  He’s getting an average of 5.79 runs of run support, so that when he goes out and throws 6 innings of 3 or 4 run ball (a perfectly respectable… average… statline for a starter), which he often does, he’s still picking up some wins.  With that said, it’s not like his peripherals are awful (or terrific), but right now, he’s one of the leaders in the category of “wins” in baseball, which means that there will be some over-valuing of him by fantasy owners.  See also: Braden Looper (9%)
3) George Sherrill (100%).  17 saves so far.  He might just make the All-Star team.  And your fantasy bullpen loves it when he comes out to save the game.  There’s a little tiny problem.  Sherrill is like Floyd in that he’s living off of a .200 BABIP, and he walks more than 4 per nine innings.  Uh oh.  What’s more telling is that 62.7% of his balls in play are going for fly balls, although he’s only been burned by a home run twice.  Sherrill will keep collecting saves, because Baltimore will keep throwing him out there, but that WHIP will go up, and if your league counts blown saves, do you really want a guy who plays in Camden Yards and gives up a lot of flyballs (and has dodged fate so far) as your stopper?  See also: K-Rod (100%) at least this year .
4) Fausto Carmona (100%).  The 4-2 record with the 3.10 ERA was deceptive.  The 1.59 WHIP is not.  But, is Carmona really a 3.10 ERA guy?  Carmona’s FIP (which is an ERA projection that looks at the fielding independent stats of K’s, BB’s, and HR’s) is 4.56, and FIP has been shown to be a better predictor of future performance than actual ERA.  Carmona has one of the largest spreads in that direction in baseball.  So, Carmona, who is walking more batters than he strikes out and has managed to avoid the home run (principally because he’s one of the most extreme ground ball pitchers in baseball) is getting a little lucky.  That ERA looks pretty, but it’s not real.  See also: Gavin Floyd (again), Scott Olsen (77%)
5) Just about anyone who steals a bunch of bases and hits for a .310 OBP.  That’s great that you’re so fast, but please, in order to use it, you need to get on base.  And then there’s fantasy ball, where these guys find a home, because… well, at least they steal bases.  See: Willy Taveras (95.2%), Carlos Gomez (100%), Joey Gathright (9%).
6) Carlos Lee (100%).  And now a small rant against RBI’s.  Just about every game that Carlos Lee has started, he’s hit behind Lance Berkman and Miguel Tejada, who are having amazing OBP seasons.  Carlos Lee is among the league leaders in RBI.  Is this because he is amazing?  No.  In fact, he’s got an OBP of .311.   See also: Adrian Gonzalez (100%).
7) Cristian Guzman (100%).  I bet you love that .300 AVG.  But dig that .322 OBP.  See also: Bengie Molina (98.7%), Jose Lopez (91%).
8) Ryan Theriot (100%).  Theriot is, I suppose a nice little backup plan at SS for those who didn’t get Hanley Ramirez, Jose Reyes, or Jimmy Rollins.  After all, he’s put up a nice .320 average and has even stolen 9 bases.  There’s one problem with that.  Theriot is 9 for 16 in SB’s.  Fantasy baseball usually counts raw SB totals (they should count net), so you don’t see the ugly side.  Theriot also has the problem in real life that he’s not really a shortstop.  But, why worry about defense in fantasy baseball.  See also: Corey Patterson (14.4%).
Players who are much better in reality:
1) Brian Wilson (100%) gets his own category and a cheap reference to Barenaked Ladies.  He’s put up 14 saves (on a bad team!) so he’s getting some ownership love, but he’s killing your ERA (5.49) and WHIP (1.63), right?  Ah… not so.  Wilson’s WHIP has a lot to do with his .374 BABIP (although his walk rate is above 4 per 9 IP).  Still, his FIP is also 3.79, because he strikes guys out like crazy.  Here’s a little tip.  The guy who owns Brian Wilson in your league likes the saves, but is a little freaked out by the peripherals.  First off, closers pitch about 60-70 innings per year, while starters go 180-210, so you shouldn’t be as worried about a closer with a high WHIP and ERA… but he’s a moron and he’s worried.  If you have a closer who pitches for a losing team (remember that saves are team dependent) who has a better WHIP and ERA right now, maybe you’d flip him to that guy for Wilson and some other piece that you want from him.  After all, you’re trading down.
2) Ted Lilly (99.8%).  He strikes guys out (9 K’s per 9 IP), but is 5-4, with a 5.14 ERA, and you fantasy owners (and the North Side of Chicago with you) get ulcers on a regular basis over him.  His FIP is 3.75.  Does that make your tummy feel better?  See also: C.C. Sabathia (ERA 5.14, FIP 3.80), although no one’s going to give up on him.
3) Darren Oliver (0%).  A good relievers who get no saves.  Ugh.  See also: Damaso Marte (0%), Heath Bell (21.8%), and a bunch of others.
4) Joe Blanton (42.2%).  2-6 with a 3.87 ERA won’t get anyone in fantasyland excited.  Blanton has a skill set that while it doesn’t translate into fantasy points, translates into good pitching performance on the field.  He’s not a strikeout machine.  But, he rarely walks anyone, keeps the ball on the ground, and has shown some pretty reliable numbers in keeping fly balls from going over the wall.  It’s all very boring I suppose, but it does get the job done.  See also: Paul Byrd (1%).
5) Garrett Atkins (100%).  Atkins hits line drives.  In real baseball, line drives are very likely to go for hits, sometimes of the extra-base variety.  The problem for fantasy owners is that they don’t as often go for home runs.  Who needs a corner infielder who doesn’t hit home runs?  The nice part about real baseball is that the point of the game isn’t to hit the ball over the fence (although that’s nice.)  It’s to hit it where they ain’t.  (Yeah, I know, there’s no one over the fence.)  But for this particular “problem”, Atkins doesn’t really get any love, being relegated to being drafted behind A-Rod, David Wright, Miggie Cabrera, Ryan Braun, Aramis Ramirez(!), and Chone Figgins.  See also: Sean Casey (1%), and um, Chone Figgins (100%).
6) Brian McCann (100%).  It’s hard to make the case that a guy like McCann doesn’t get some love.  He was the third quickest drafted catcher on ESPN, but he has one of those fantasy profiles that’s just… a little… off for fantasy ball.  But, in real life, he’s rather amazing.  McCann’s greatest sin is that he prefers to hit doubles to home runs.  And he walks more than he strikes out.  Doubles help the batting average and perhaps the RBI, but walks do nothing.  McCann also gets credit for having the time to run for President in the off-season.  See also: Kevin Youklis (100%)
7) Adam Dunn (100%).  I got into an argument with my brother about Adam Dunn last weekend.  I told him I’d love to have him on my team, strikeouts and all.  Dunn is the prototypical guy that fantasy players and casual real life fans hate: the three true outcomes guy.  When Dunn comes to the plate, it usually ends in a walk, strikeout, or a home run.  The walks are boring, the strikeouts are hard to bear in the moment, but the homeruns give just enough of that slot machine jackpot feeling to keep the guy around.  In fantasy ball, the home runs are always welcomed, but the low batting average that comes from striking out a lot and having most of your other times on base be from walks make for a guy who always has a little waning label on him.  You want to own him, but never as a first choice.  Still, a guy like Adam Dunn, in real life, has a runs created per 27 outs (think: what would a lineup of 9 Adam Dunn’s do over a full game) of 8.06 runs, which puts him #10 among MLB outfielders.  See also: Pat Burrell (100%), Dan Uggla (100%), Geovany Soto (100%).
8) Adam Everett (0%… yes, I know he’s injured).  Defensive wizard.  But he hits .230, and no one plays defense in fantasy ball.  Still, in 2006, when Everett was healthy, he put up a number of 21 fielding runs above the average shortstop.  What he lacks at the plate, he makes up for in the field on a real team.  But if all you’re looking at are his offensive fantasy stats (you can read that with either emphasis), you won’t see that.  See also: Omar Vizquel (2%).

StatSpeak World Famous Roundtable: May 26

Welcome to the Memorial Day edition of the roundtable.  This week, we welcome back an old friend to StatSpeak, Mr. Mike Fast.  Mike’s been hanging out lately at The Hardball Times and at his own personal blog Fast Balls.  Today, we discuss the Marlins, re-aligning baseball, and what’s been going on in Sabermetrics for the last twenty years.
Question #1: In his 1987 Baseball Abstract, Bill James rates baseball statistics in terms of how meaningful they are on the basis of four criteria: (1) importance–does it correlate with winning?, (2) reliability–the extent to which the statistic truly reflects ability, (3) intelligibility–can the average baseball fan make sense of this information, and (4) construction–where James applied minor penalties for poor construction, such as Runs Produced giving only half credit to home runs.  James rated ERA, OBP, and SLG as the most meaningful traditional single-season statistics.  Among the more recent statistics, James did not evaluate his own creations, but rated linear weights the highest.  Which of the new statistics since 1987 are the most meaningful, and which are the most meaningful overall, including the traditional ones?
Mike Fast: What are the best or the most commonly used of the new stats over the last twenty years. I’m not sure whether BABIP originated with Voros McCracken’s DIPS work or not, but it’s been one of the most influential stats over the last decade. It tells us a lot about whether pitchers and hitters are getting lucky or not. Or at least we think it does, on the whole. We’re still learning. Along the same line, we have Fielding Independent Pitching (FIP), which is a good ERA predictor based on similar ideas to DIPS. Bill James introduced Win Shares, and it’s had some refinements, like Win Shares Above Bench, but I’m not sure it ever really caught on to the point that people understand it, and it has its flaws. There’s Value Over Replacement Player (VORP), by Keith Woolner, denominated in runs. Hmm…there’s lots of versions of the same kind of things, and I’m not sure how many of them are improvements. I do like the batting runs linear weights system from David Smyth, Base Runs. On the fielding side, we’ve improved a lot with the play-by-play data. Without it, the best of the bunch is probably Dan Fox’s Simple Fielding Runs (SFR). With the play-by-play data, which is privately held, we get systems like Ultimate Zone Rating (UZR), Probabilistic Model of Range (PMR), and BIS’s Plus/Minus.
It’s hard for me to evaluate the fielding metrics since I’m not privy to the data. On the pitching side, I’ll credit FIP as the best new stat, and on the hitting side, Base Runs. FIP is on the same scale as ERA, which we understand well as fans, and it includes only three main rates: HR/IP, BB/IP, and K/IP, which are also well understood by fans. It also correlates well with winning and true talent of the pitcher. Base Runs is an improvement on Pete Palmer’s linear weights. It has all the same advantages in terms of reporting runs relative to league average, which we fans understand fairly well, but it does a little better job of correlating with actual run scoring. In the end, though, I have to stick with the same choices Bill James made: ERA, OBP, and SLG. For all their flaws and supposed flaws, they do a pretty good job, and we fans have years and years of context stuck in our brains with which to interpret them.
Eric Seidman: FIP is my favorite statistic primarily due to most of my research dealing with pitchers and my fascination with ingratiating non-statheads into the saber-community.  Many are intimidated by statistical analysis and so statistics like FIP, EqA, and wOBA work so well, in my eyes, because they explain more than the barometers they represent while staying on a familiar scale.  A casual fan knows that a 3.00 ERA and .288 BA is solid; when they see these numbers represented in a statistic that accounts for more variables they are more likely to understand and appreciate them.
The best part about using FIP in comparison with ERA is we can see a measure of actual results vs. skill based results to see which pitchers are outperforming their skills or underperforming their skills.  Unearned runs are a result of errors, which come from those official scorers making decisions, so they are a little fishy in and of themselves.  Walks, strikeouts, and home runs, the key ingredients of FIP are not arbitrary.
As far as the traditional statistics, I like to use ERA but only in the comparison mentioned above; I also like looking at the slash line BA/OBP/SLG.  I don’t like any of the three on their own, with the newer statistics available but looking at all three side by side explains more than any on their own.  Jason Giambi has a .236 BA this year… he also has a .384 OBP and a .516 SLG.  Looking at those three on their own I would determine, in order: Giambi is a terrible hitter this year; oh, ok, Giambi is either walking a lot or hitting well and walking some of the time; and Giambi is a power hitter.  Put together and we can see that his OBP and SLG are leagues higher than his BA, meaning he walks a ton and, while he doesn’t get hits quite often, they are usually extra-base hits.
Pizza Cutter: The research methods professor in me chuckles at Bill’s choice of words (The first criteria is actually “predictive validity” and the second is “construct validity”).  Sabermetricians have focused on those first two, often at the expense of #3.  I’m not so sure that’s a bad thing, although it does mean that we can’t really whine that the general public hasn’t quite gotten the hang of this yet.  Of all the ones that are out there, the win probability added suite makes the most sense to me as something that’s intuitive to the general public, can be easily presented, and clearly tracks winning.  I’d agree that the linear weights approach is the most informative and probably best meets criteria #1 and #2 for evaluating individual players, although it’s not really intuitive to explain to someone.  Of the traditional stats, it seems like all that’s really needed is for people to understand rate stats per PA.  So, perhaps K/PA or BB/PA or HR/PA could become fairly standard.  That would be ideal.  (If it could, then linear weights could probably gain a mainstream foothold.)
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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:

TYPE

WS ACQ.

WS LOST

NET

WINS/YR

Trades

272

378

-106

-5.89

Free Agents

327

319

+8

+0.44

Waivers

42

47

-5

-0.28

Rule V

14

2

+12

+0.67

Overall

655

746

-91

-5.06

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.
Trades
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:

TYPE

WS ACQ.

WS LOST

NET

WINS/YR

SP

125

267

-142

-8

RP

172

122

+50

+3

C

76

13

+63

+4

1B

41

32

+9

+0.5

2B

22

89

-67

-4

SS

33

94

-61

-4

3B

166

17

+149

+9

OF

20

112

-92

-5

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. 
Conclusion
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 Most Important Pitch: A Look at Greg Maddux and 1-1 Counts

There are twelve possible ball-strike counts in a given plate appearance.  Ranging from the initial 0-0 to the dramatic 3-2, these counts shift in favor of either the batter or the pitcher.  A 2-0 count favors the hitter; if the pitcher misses the count will run to 3-0.  Along similar lines, an 0-2 count favors the pitcher because the batter will theoretically be more likely to swing at junk in an effort to protect himself.
Of all twelve, Greg Maddux considers the 1-1 count to be of utmost importance.  Though some may spot the identical numbers and deem the count neutral, the linear weights run expectancy shows it favors the pitcher.  Missing on a 1-1 count shifts the momentum back towards the hitter whereas a successful 1-1 pitch can move the count’s favor further in the direction of the pitcher.  The 1-1 count brings with it a run expectancy of -0.012 from the batter’s perspective; a ball shifts it to +0.037 whereas a strike causes a jump to -0.079.  Maddux is right.
This is the third and final (for now) look at Greg Maddux’s theory and selection in certain situations using Pitch F/X data.  Previously, we have looked at Maddux’s “playbook” vs. Bengie Molina as well as his selection, location, and results in 0-2 counts, in which he does not like to throw waste pitches.  Here we are going to conduct a similar analysis to the 0-2 article but with regards to his 1-1 counts.  Be sure to note that not all of his starts were recorded by the Pitch F/X system last year.
Results
Maddux primarily throws his two-seam fastball, a changeup, and a cutslide. Though “slutter” sounds funnier for the combo cutter/slider, this blog has a PG rating… though nowadays even PG allows naughty words and innuendos.. anyway, back to baseball. Here is a breakdown of Maddux’s pitches and results to lefties and righties:
maddux11countresults.JPG
Since he has thrown more pitches to righties, seeing the percentages of pitches thrown to each batting handedness can help show discrepancies in either approach or selection. To righties, Maddux has thrown 58.9% fastballs, 25.2% changeups, and 15.9% cutslides; to lefties, 55.3% fastballs, 32.5% changeups, and 12.3% cutslides. Clearly, he uses the cutslide sparingly. Maddux has thrown three percent less fastballs to lefties, as well as three percent less cutslides; the difference has been made up with over six percent more changeups.
Location
Here is a location chart of his fastballs thrown to both lefties and righties, with lefties always on the left:
madduxfa11count.JPG
The biggest difference between results here is the amount of called strikes. When facing righties, Maddux has gotten many called strikes on 1-1 counts whereas he has just four when pitching to lefties. Though he clearly favors the outside corner to both types of batters, lefties have made contact with the corner pitches while righties seem to be more inclined to take the pitch. Due to his fastball having the tailing movement, righties tend to think pitches like this are outside; when it tails back to the outside corner for a strike it catalyzes many glances back at the umpire.
Here is a location chart of the changeups thrown:
madduxch11count.JPG
The results of his changeups thrown to each batting handedness do not differ too much; even if they did it is too small of a sample to garner anything worthwhile from. Despite this, the visualization helps us see that he has thrown a higher percentage of changeups in the strike zone and down the middle to righties; to lefties he continues to hit the outside corner. Regardless, the pitches that have worked the best for him in these situations have been changeups to lefties and all offspeed pitches to righties. Essentially, throwing it in the general vicinity of down the middle has not yet hurt him in 1-1 counts in the Pitch F/X era.
Location Results
No, I didn’t just combine the headings of the previous two sections no matter how much it may seem like that. Maddux’s fastball has not been particularly effective to lefties or righties in these counts. Therefore, I want to look at the nine zone sections–up and away, down and in, etc–and see what types of results his fastballs have produced. Unfortunately, small sample size syndrome has forced me to combine the nine sections into three: away, middle, in. Here are the results:
lhhrhh-locationresults.JPG
These are not large samples either but we can still discern some potential strategies to watch for over the remainder of the season. He has had his most success with the fastball away, to both types of hitters, even though righties have still done well with the balls in play. I hate even attempting to draw conclusions from these small samples, but based on the non-BIP results and the BIP results, it seems Maddux’s best chance at getting the 1-2 as opposed to the 2-1 would be to stick to his offspeed stuff (cutslide or changeup) but if he had to throw the fastball, make sure it is away to lefties and, more specifically, down and away to righties.

StatSpeak World Famous Roundtable: May 20

The roundtable this week is a little late due to some techincal issues.  This week, we had hoped to welcome David Chase of Brock for Broglio for a discussion of some “most likely to…”, the current second place team in the AL East, and the role of advanced stats in the front office, but David had to pull out at the last minute.  So, this week, the roundtable is just Eric and I.
Question #1From the players, to the managers/coaches, to front office executive’s.. How much has progressive statistical analysis penetrated major league baseball?
Eric Seidman: Statistical analysis has made great strides towards infiltrating major league baseball.  At the website friarforecast.com, there is a list of all the statistical analysts working for teams and plenty of teams are listed; those not listed still likely have at least a consultant on their payroll.  Even before Moneyball teams utilized stats; perhaps not as much as the A’s but teams did not ignore statistics and go solely on scouting.  To do so would be irresponsible.  The same can be said for going solely on stats.  Many major league teams have found the proper balance between scouting and statistics.  When it comes to developing players, a factor not often discussed, many teams are struggling, but this has more to do with personnel developing the players than the players themselves.  The Pirates front office representatives acknowledged this in a discussion at Penn State not too long ago.  They weren’t sure if it was who they were drafting or who was developing those drafted, an issue most tend to ignore.
Pizza Cutter: The question for me isn’t how many analysts are working in the offices or how much data is being collected behind the scenes or whether there’s good research going on, it’s how much of a voice this gets at the decision-making table.  Too often, I see decisions that are being made both in terms of signing and game strategy that could be avoided by simply reading Baseball Between the Numbers.  I’m guessing that at this point, just about everyone has a quant working for them, some of them a whole team of quants.  The problem seems to be that at times, I wonder if the marketing department is the one making all the decisions or if the GM hears out the person who runs the numbers and uses it only as a way to justify what he was already thinking while dismissing it if it doesn’t.
Read more of this post

Power scores (or at least my attempt)

A while ago, I took on the task of building a better speed score.  Bill James had come up with his formula some time ago, and for what it’s worth, I found that his formula (warning: PDF) was pretty good.  My formula was much more stable over time… but it was also a lot harder to calculate.  So, while we can get a single number to describe speed, something that has a bearing on several different events in a game (stolen bases, triples, staying out of a double play), I’ve never seen an attempt made at a “power score.”  I figured I might as well give it a shot.
First, we identify events in a game that might involve “power.”  For example, power would clearly be involved in home run hitting, but what else might be involved?  I made a list of stats and rates that might just be involved.

  • Home runs per fly ball.  It’s hard to hit a home run on a ground ball.  But, a player with power would likely have the power to put a fly ball over the fence.
  • Fly balls fielded by outfielders rather than infielders (whether caught on the fly or not).  It seems sensible that even if a fly ball doesn’t leave the park, it is still the mark of a more powerful hitter that he would put more balls further away (the outfield) from him than close to him (the infield).  (Formula: OF fly balls / total fly balls and popups)
  • Doubles and triples per ball in play.  A grounder or line drive could end up going for a double or triple, but certainly, they are more likely on a fly ball that hits the wall.  In either case, the ball was probably hit pretty hard.
  • Line drive rate.  Part of hitting for power is making good solid contact, and line drives are the sign of good solid contact.
  • Ground balls fielded by outfielders rather than infielders.  Again, a ground ball that goes through the infield is more likely to have been hit harder (or perhaps just placed better?) than if it was fielded by an infielder.  Again, it doesn’t matter if it went for a hit (the shortstop got to it, but couldn’t make the throw), just where it landed.
  • ISO.  This is supposed to be a measure of “isolated power.”  The formula is SLG-AVG.  Let’s see what happens.
  • BABIP.  Not often that we get to talk about BABIP from the batter’s perspective.  But again, balls in play that go for hits mean that fielders had a hard time getting to them.  What’s one way to give a fielder a hard time getting to the ball?  Hit it really fast or really hard.

Like my methodology for calculating speed scores, I was dealing with a lot of probability numbers (with the exception of ISO).  Probability distributions are notoriously not normal, so I applied a normality transformation by taking the natural log of the odds ratio.  I restricted myself to players who had at least 100 PA in the season in question, and I had a database stretching from 2000-2007.  I converted all natural logs of the odds ratio to Z-scores based on the distribution present in the year in question (to get everything into the same basic range of scale).  I then subjected these Z-scores to an exploratory factor analysis, with a Varimax rotation, to see which of these variables hung together.  I saved factors with an Eigenvalue over 1.00.  If you have no idea what I just said, just trust me on this one. 
The results were a little bit surprising.  I got two factors (gory detail: picked up 59.7% of the variance present.)  So far, so good.  HR/FB, XBH, and ISO hung together, as might be expected.  Outfield flies also was part of this factor, although it loaded negatively.  So, we would expect someone who hits a lot of homeruns and doubles to hit fewer fly balls to the outfield (or more to the point, more infield flies.)  The other factor that emerged was a combination of BABIP, ground balls that go through to the OF, and line drive rate.  (gory detail: There was very little in the way of cross-loading factors.)
So, home runs generally are accompanied by other extra base hits, and that generally pushes the ISO up (not a huge surprise that those would all hang together).  But, something that speaks of a power hitter is actually (comparatively) a lot of infield pop ups.  We already know that power hitters are given to striking out and that they hit a lot of foul balls, but it also looks like they have a propensity to hit infield pop ups.  Seems that trying to hit big fly balls has plenty of risks.  Swinging really hard is bought at a price of lowered plate coverage, but also it looks like the ability to control the bat angle goes.  Get the horizontal angle wrong, hit it foul.  Get the vertical (bat impact angle) wrong, hit a harmless popup.  Get it all right, fireworks.
To check to see whether my two new scales were consistent over time, I looked at their intraclass correlation over four years (2004-2007).  The first factor (call it “big fly power”) had an ICC of .740 indicating excellent consistency over time.  The second factor (call it “solid contact power”) had an ICC of only .380, which really isn’t all that good (not horrible, but not great).  Since two of the components are getting the ball through the infield on a ground ball and getting a hit when the ball’s in play (we might, thus, call this one “hitting for average”), there’s something to be said for the fact that this skill is only moderately consistent from year to year.  What might stand in the way of those two skills?  The defense.  Trying live off of hanging back and making solid contact has its own risks.  If you hit it where they ain’t, bully for you.  If the defense can cover a lot of ground, you’re going to have some issues.  There’s no defending a fly ball that either hits the wall or goes off it.
Now, why go to all this trouble (and what the heck is exploratory factor analysis?) to figure out power numbers.  Can’t one use ISO or HR/FB or something like that on their own?  Sure, ISO and HR/FB correlate well with the “big fly” factor (.931 and .872, respectively, meaning that they parallel each other very closely)  But they are not as consistent over the years for batters (ICC’s of .648 and .675, still quite good, but not as good as the total factor).  Here’s the beauty of exploratory factor analysis and scale construction.  Put a few things together that are correlated anyway and the possible random variations to the extreme in one can be balanced out the others and make for a more stable whole.  If you want a good number that will stay more consistent over the years, use my power number.  If you want a quick and dirty number that’s really easy to get, go with ISO.
While I was in the neighborhood though, I ran a correlation matrix and found that ISO and BABIP are actually un-correlated with one another.  Looks like the old scouting adage about “hit for power” and “hit for average” being two separate tools is accurate.  One can be both or neither (well, if you’re in MLB, you’re at least one), but one doesn’t tell you much about the other.
For those interested, I’ve posted the 2007 list here, sorted as always by Retrosheet ID.

Interviewing David Pinto

I was planning on posting the third and final part of my Greg Maddux analysis today; however, I am making updates to my Pitch F/X database and will have to delay the final part, a look at his selection and results in 1-1 counts, until next Thursday.
There are several great baseball websites with statistical analysis as its calling card but few are more popular and well-known than Baseball Musings.  Founded and maintained by David Pinto, the site offers a wide array of features ranging from news updates across the blogosphere to a nifty little tool that allows readers to find their team’s optimum run-producing lineup.
I recently sat down with (e-mailed) David and spoke (typed) to him about his career as a baseball writer and analyst as well as a few other topics.
ES: Let’s start with an introduction for anyone reading who does not happen to know who you are.  If you could sum up your work and career into three sentences, what would you say?
DP: I started as a biochemistry major, was working in immunology, got interested in computers, and found my way to STATS, Inc.  They supplied me to ESPN as a consultant for ten years, where I was essentially the lead researcher for Baseball Tonight.  I’ve also done research for Fox Sports and Baseball Info Solutions, as well as hosting Baseball Tonight Online.
ES: When working for STATS, Inc and ESPN, what was your normal day like, if you had a normal day, in terms of your responsibilities and hours?
DP: The two jobs overlapped.  I worked from my home for STATS, and would spend the morning working on any programming issues that came up.  In the afternoon I would travel to Bristol (ESPN HQ) and spend my first hour there putting together reports and coming up with graphic ideas for the show meeting.  Other people would bring up graphics ideas and I would spend a couple of more hours working on those.  I’d then spend some time working on game notes for the ESPN telecasts.  While games were going on, new graphic ideas would come up and I’d work on those as well.  During the show I was just off camera offering any support needed.  During the offseason, though, I would spend all day programming for STATS.
ES: Looking back on your time at both of these jobs, including hosting BBTN Online, how would you rate your overall experience?  Fun?  Or more along the lines of an everyday job?
DP: The experience was very good.  It was grinding at times, but always fun.  It definitely wasn’t just another job.
ES: Now, in 2002, you started blogging full time with Baseball Musings.  So… Baseball Musings… great website?  Or the greatest website?
DP: People seem to like it, so I’m happy.
ES: How does one get to blog full time?  Is it based solely on ad-based revenue or did it need some start up from investors?
DP: I had been blogging for three years when I lost my job.  I decided blogging was what I really wanted to do.  My business plan was Blog -> ? -> Profit.  Fortunately, the ?, filled itself in as advertisers found me.
ES: Musings is slowly becoming what MLB.com used to be for me in terms of showing interesting stories spanning the entire league, but there is so much more your site offers that many still fail to take advantage of.  Could you explain how features like the Day-to-Day Database, Lineup Analysis, and Defensive Charts could enhance someone’s work?
DP: The Day to Day database has batting and pitching lines for each player back to 1957.  Because it’s date based, you can look at a player or group of players over any time period.  There is also batting event data going back to 2000.  Again, since this is date based, you can find out for example what someone is hitting with RISP over the last two weeks or last two years.  More splits out there are season based.  The Lineup Analysis is a fun tool that, given nine on-base percentages and slugging percentages, calculates the optimum lineup.  The defensive charts are from the Probabilistic Model of Range, a new way of measuring range based on the probability of turning a batted ball into an out based on a number of factors.
ES: In terms of blogging full time, something I’m sure many, many people would love to do, what goes into running and maintaining an extremely popular blog?
DP: I get up early, update the Day to Day Database, look at late box scores and headlines, and when I see something interesting I write about it.  About 10 AM I go off to my part time job.  I get back after 2, watch games, write articles or prepare for my radio show, and blog about anything interesting that happens.  I stay up late if there’s anything interesting happening in the late games.
ES: I hate beating a dead horse, and this is not directed towards Bissinger’s comments, but what do you think of the disgust and ignorance directed towards blogs by the mainstream media to the point that some have no idea how to differentiate between a post and a comment?
DP: Doesn’t matter to me.  There are many more journalists who read blogs every day.  I’m linked by many more beat writers who blog than there are columnists who don’t care for the medium.  Bissinger proved himself to be a dinosaur.  I like new things.  I got into blogging early, which is a reason the site is popular today.  New, in general, tends to be better.  I’ve learned more from blogs about all aspects of the news than I ever learned from a newspaper.
ES: When did you realize statistical analysis would be in your future?
DP: I’ve pretty much done it in all of my jobs.  That’s what science is all about.
ES: What area of science first interested you in this aspect?
DP: We did a lot of it in genetics class, and I really loved that.
ES: Working with science got you in the door, so to speak, but how did you get attracted to the statistical analysis of baseball?
DP: Probably when I read my first Bill James Abstract.  That pretty much clarified things for me and I knew this type of research would be cool.
ES: Give me one position player and one pitcher you would like to build a team around.
DP: Hanley Ramirez and Cole Hamels.  When you’re building, you want young, great players.
ES: What about if you had to win right now?
DP: Alex Rodriguez and Johan Santana.
ES: I remember reading you were very interested in the analyst position with the Mets a few years back.  Have you ever worked for a team in such a capacity and is it something you would like to do in the future?
DP: I have done some consulting for a team but, at this point, I just want to write the blog.
ES: What do you see as the next big area of exploration for sabermetrics?
DP: The whole PITCHFX has opened up a new world of exploration, although I do hope people work on macro issues as well.  I’m much more interested in those at the moment.  I really believe that baseball should radically redesign divisions and scrap the draft and reserve clause and everyone becomes a free agent at the end of their contract.
ES: Moving away from baseball for a minute… favorite movie?
DP: It depends on the day.  Comedy – Annie Hall.  Musical – My Fair Lady. SciFi – Blade Runner.  Drama – Casablanca.  Rockumentary – Hard Day’s Night.  I also love Hitchcock’s 39 Steps and Lady Vanishes as well as anything with Kate Winslet.
ES: TV show?
DP: Now, probably House.  Of all time…. Cheers.
ES: Okay, back to baseball.  Who was your favorite player growing up?
DP: Thurman Munson.  I liked the way he blocked the plate.
ES: Has blogging full time effected your allegiances to the team you grew up rooting for?
DP: My childhood team was the Yankees.  When I started working for STATS in 1990, I was already moving away from rooting for a single team toward rooting for good organizations.  When I joined ESPN I decided I would be a baseball fan, and not a fan of a particular team.
ES: Okay, before I let you go, of the following, who is a Hall of Famer, and why?  Andy Pettitte, Mike Mussina, Fred McGriff, Tim Raines, Roberto Alomar, Barry Larkin.
DP: Mussina – dominant pitcher for a long time.  He has a large repertoire of pitches that he threw equally well.  And Tim Raines – the second best leadoff hitter of all time.
Well, thanks a ton David, I really appreciate you taking the time to talk.  If you haven’t been there yet, definitely check out Baseball Musings as it is not just a one stop shop for interesting stories but also has some very underrated statistical tools.
Next week I will finish off my analysis of Greg Maddux on Thursday and begin the first in a monthly series of General Manager Evaluations, looking at JP Ricciardi.

Waste This: An Analysis of Greg Maddux's 0-2 Pitch Selection

Last week we took a look at a bunch of plate appearances between Greg Maddux and Bengie Molina, in an attempt to see if there were any discernable patterns or tendencies on the part of either participant.

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