Evaluating Pitcher Winning %, Pt. 1

How best to evaluate a pitcher’s W-L record? While it has plenty of contextual biases, one that it does not have is park/era, since W% always is .500 for the league as a whole. This makes pitcher win-loss record a fairly interesting thing to look at, at least on the career level.

But of course the biggest pollution is the quality of the team around him. So it only seems natural that for many years, would-be sabermetricians have compared a pitcher’s W% to that of his team. Usually, this comparison is done only after the pitcher in question’s decisions have been removed. The reasoning for this is that we do not want to compare the pitcher to a standard that he himself has contributed to. Anyway, Ted Oliver’s Weighted Rating System was the first such approach, and the one most commonly used:
Rating = (W% - Mate)*(W + L)

Where Mate, to borrow a designation from Rob Wood, is the W% of his teammates (TmW - W)/(TmW + TmL - W - L). Oliver’s rating gives a number of wins above what an average teammate would have achieved in the same number of decisions. We could also call this Wins Above Team as Total Baseball does.

A related question is what is the projected W% of this pitcher on an otherwise .500 team? I’ll call this Neutral W%, to use the same abbreviation but a different name then Bill Deane does, so that my general term won’t get confused with his specific one. For the Oliver approach:
NW% = W% - Mate + .500

If this is not intuitively obvious, consider a 20-10 pitcher on a .540 Mate team. His WAT is (.667 - .500)*(20 + 10) = +3.8. If he is 3.8 wins above average in 30 decisions, this implies that he is 3.8 wins better then 15-15, or 18.8-11.2. This is an equivalent W% of 18.8/30 = .627, the same result as .667-.540+.500.

What begins to become clear as you look at how the method works is that it assumes that a .500 pitcher on this team would have a .540 record. This means that all of the team’s deviation from .500 is attributed to the offense or fielders. This assumption is clearly wrong, at least for a randomly selected team--given a random team, we should assume that they are equally skilled on offense and defense. Obviously, in some cases this assumption will be dreadfully wrong--but it will be correct more often then assuming that EVERY team deviates from .500 only because of offense and one particular pitcher whom we isolate to calculate his WAT/NW%.

We can find some historical examples where the assumption of the Oliver method really causes problems. The most notorious case is that of Red Ruffing, who so far as I know is the only Hall of Fame starter with a W% worse then that of his teammates. For his career, Ruffing was 273-225(.548), while the rest of his team was .554. This is a .494 NW% and -3 WAT. As a side note, WAT is also equal to (NW%-.500)*(W+L).

Ruffing did pitch for Yankee teams with great offenses, but he also had mound teammates like Lefty Gomez, Johnny Allen, and Spud Chandler (at various times). In 1936, for example, Ruffing was 20-12(.625), while the rest of the team was .678, for -1.7 WAT and a .447 NW%. His team did score a whopping 1065 runs, but they also led the league with 731 runs allowed. An average pitcher in the 1936 AL (who would have a 5.67 RA), would figure to have only a .594 record if supported by New York’s 6.87 runs/game.

We’ll check in on Ruffing more as we go. Bill Deane, formerly a Senior Research Associate at the Hall of Fame, developed his own method to divorce a pitcher’s W% from that of his team. Deane’s insight was that the further above .500 a team’s W% was, the less margin there was to improve upon it. A .500 team could be bettered by .500; a .625 team only by .375. A bad team could be improved by even more. So Deane rated equally pitches who improved their teams by equal percentages of the potential margin.

A .550 pitcher on a .500 team improved his team by .050 out of a possible .500 (10%); so did a .460 pitcher on a .400 team (.060/.600 = 10%). Thus, they are each credited with the same .550 NW% (Deane used the term Normalized W% for this). If it is not clear why the normalized percentage should be .550 for each pitcher, it is because a .500 team has a .500 margin for improvement, and 10% of .500 is .050. Following this logic, Deane would up with these formulas for NW%:
If W% >= Mate:
NW% = (W% - Mate)/(2*(1 - Mate)) + .500
If W%< Mate:
NW% = .500 - (Mate - W%)/(2*Mate)

The second formula comes from the fact that on a .600 team, there is a .600 margin for lowering the W%; a .550 pitcher did this by 8.33%, so .0833*.5 = .042, for a NW% of .458. Total Baseball (unlike Thorn & Palmer’s earlier Hidden Game which used Oliver’s formula) used Deane’s methodology to calculate WAT. A poster child for considering the margin for improvement is Steve Carlton in 1972, who was 27-10(.730) for a team that was otherwise 32-87(.269). Under the Oliver methodology, this is a nearly impossible .961 NW% and +17.1 WAT. Using Deane’s approach, it is an .815 NW% and +11.7 WAT (still the highest since Lefty Grove in 1931).

How does Ruffing fair under this approach? Career-wise, since his W% was so close to Mate to begin with, not much changes--he now sports a .495 NW%(v. .494) and -2.7 WAT(v. -3). In 1936 he moves from .447 to .461 and from -1.7 to -1.3 WAT.

Third and Third

As I write this, yesterday Oregon State, pretenders to the abbreviation of OSU, won the College World Series (yes, I really did write this in June). I figured it would be a good time to look back at the season of The OSU.

The Buckeyes finished third in the B10 regular season, crippled by a sweep in the heart of darkness. Northwestern shockingly was able to grab second after a horrific non-conference performance. Minnesota had a second consecutive year where they were not a major player in the race for the regular season title, but still qualified for the six-team tournament, which was filled out by Purdue and Illinois.

In the tournament, OSU beat Purdue and Northwestern but were tripped up in the winner’s bracket final by Minnesota and then lost the loser’s bracket final to those who shall not be named. They who shall not be named beat Minnesota two straight as the Gophers for the second straight year placed second in the tournament (to OSU in 2005). So those who shall not be named got the only B10 bid to the NCAA tournament.

While the Buckeyes fell short of a championship, they still had a solid season. Considering all games, OSU was second in W% at 37-21, .638 (those guys led at .672). But the Buckeyes paced the conference in EW%(.721; Minnesota was second at .627) and PW%(.728 with Minnesota second at .628). The Buckeyes also led in R/G(6.66; MSU second at 6.18) and RA/G(4.17; the bad guys second at 4.42). Northwestern’s W%, EW%, and PW% were .411(ninth of ten), .467(fifth), and .417(ninth), a simply bizarre combination for a second-place team. They were lucky that they did not have to face OSU, but even had they played the Bucks and been swept they would have finished in the first division.

With that, I will take a look at the individual performances of OSU players. Incidentally, all of the spreadsheets I used will be posted soon on my website if you are interested. Offensively, the Buckeyes were led by B10 MVP Ronnie Bourquin, the third baseman who was a second round pick to the Tigers. He narrowly missed the B10 triple crown, and hit 416/490/612 with 67 RC, a 12.2 RG(versus a conference average of 5.63, and +36 RAA. As you can see, his ISO was .196, but various scouting reports I saw before the draft said that he had power potential he had not shown in games. I have no trouble believing this, and can certainly understand why nobody on the collegiate level tried to mess with the form of a .400 hitter.

The Ohio offense was solid from top to bottom--sophomore centerfielder and leadoff man Matt Angle improved greatly, with a .449 OBA, 25-29 stealing, and +21. Sophomore catcher Eric Fryer was great again, with more power but less walks then Angle, resulting in nearly identical values(Angle created 54 runs and 9.2 per game; Fryer 54 and 9.2 per game). Senior captain and eighth round Oriole selection Jeddidiah Stephen finished his career at +16, 8.2, and his junior double play partner Jason Zoeller was second to him on the team in isolated power, +11 runs and 7.8 per game.

Junior Jacob Howell struggled through hamstring injuries, but hit a sizzling 402/448/500, 9.9, +15 RAA when able to play. The two weak spots in the lineup were Justin Miller, a freshman first baseman who started slowly but improved as the year went on, finishing at 4.3, -5. Junior rightfielder Wes Schirtzinger struggled greatly at the plate, with 257/321/296, 3.8, -11. The other hitters with over 100 PA were freshman OF/1B/P JB Shuck (6.1, +2) and DH Adam Schneider (4.7, -4).

The Buckeye pitching was solid again, tops in the B10 without a real standout. The ace was junior lefty Dan DeLucia with a 3.67 RA, +27 RAA, and 5.8 K per game, which may be why he went undrafted. Cory Luebke, a 22nd round pick of the Rangers as a draft eligible sophomore was 4.34 and +15. Freshman Jake Hale was the (relative) weak link at 4.92, +7. B10 Freshman of the Year JB Shuck probably looked better with traditional stats, as is 4.56 RA was a full two runs higher then his 2.51 ERA. Shuck, depending on your perspective, was victimized by his defense or had some mistakes obscured by the silly points of the earned run rule. His 4.52 eRA and .298 H/BIP lead me to the latter. But for a freshman, 79 innings and 12 runs above average is nothing to sneeze at.

There were really only four pitchers who got significant innings out of the pen. Rory Meister served as closer and had a 4.36 RA despite a 5.76 eRA. His control was very poor, walking 28 in 33 frames, but his H/BIP was a very high .382. Josh Barerra, a true freshman, had similar issues, walking 20 in 38 innings with a 5.68 RA and 7.16 eRA but a .429 hit rate. Both pitchers struck out a lot of batters and have shown evidence that they can be effective, but certainly need some polish. Trey Fausnaugh was pounded again with a 6.11 RA and 8.13 eRA. As were the other key relievers, he was victimized by a high hit per BIP rate at .409. Dan Barker was good again in 4 starts and 14 relief appearances, with a 4.15 RA and 3.57 eRA.

This was a fairly young team, but with only two (potentially three if Luebke was to sign with Texas) major losses, and a solid performance, it looks as if Ohio State will once again be a major player in the 2007 Big Ten race.

Bafflement

I have always considered myself to be much more a fan of baseball in general then of any specific team. This is not the case for me in other professional sports. That is not to say I am not interested in NFL games that do not involve the Browns, but when the Browns were moved, my interest level in the NFL plummeted. I do not at all believe that this would be the case if the Indians were to dissipate.

I have always rooted for all Ohio teams, although since I have lived in the Cleveland and Columbus areas, I am more partial to those teams then to those from Cincinnati (of course, Columbus has just one pro franchise, and it's the only team from Ohio in the NHL, so there's really no conflict there at all. But if it comes down to Indians/Reds or Browns/Bengals, I definitely side with Cleveland).

Anyway, all of this personal rambling is to get the point that my dual status of 1) being a bigger fan of the sport in general then of any specific team and 2) the Reds' status as my second favorite, rather then favorite team is quite a fortunate thing today. Otherwise, I would be infuriated that the Reds traded away two everyday players, 26 years of age, and both rated as above average hitters for their position a year ago (Lopez by quite a bit, Kearns by the skin of his teeth), in exchange for a couple of relief pitchers, Royce Clayton, and Brendan Harris. It is simply unbelievable to me.

And what really makes it galling is that Jim Bowden, whose tenure to this point in Washington has been embarassingly bad, has completely stuck it to his old employers.

All-Stat Stat Check

I thought it would be a useful exercise, for myself at least, to run a quick check of the all-star break statistics. There are great, updated sabermetric stat resources updated at The Hardball Times and Baseball Prospectus, but I always like to figure my own stuff myself rather then look at somebody else’s work. But for the rest of you, using those sites may well be more edifying then reading what follows.

First, teams. I have Expected W%, based on Runs and Runs Allowed, and PW%, based on RC and RC Allowed. I have presented these in the order W%, EW%, PW%, and interspersed flippant comments. At various points I will use words like “luck” to describe variation from EW% or PW%. This usage is not meant to indicate that the differences are conclusively due to chance and not systematic factors:
DET(670, 662, 619)
Not surprisingly lucky, but (depending on your perspective) surprisingly still first in baseball in PW%
CHA(648, 612, 579)
Exceeding expectations again
BOS(616, 581, 591)
NYN(596, 579, 568)
Lead NL in all three W% types
NYA(581, 582, 607)
TOR(557, 544, 580)
STL(552, 517, 505)
Perhaps even more vulnerable then they appear to be in the standings.
MIN(547, 532, 499)
SD(545, 531, 548)
LA(523, 562, 554)
OAK(511, 482, 449)
Disappointing despite being in first place. I’m sure Mr. Beane is aware of this, in some form.
TEX(511, 524, 540)
COL(506, 515, 508)
CIN(506, 484, 506)
SF(506, 515, 505)
MIL(489, 417, 477)
LAA(489, 489, 526)
ARI(489, 476, 483)
SEA(483, 506, 483)
HOU(483, 468, 467)
CLE(460, 548, 567)
The Anti-White Sox. Again. I’ve seen it suggested that Wedge should be fired, with the underperforming Pythagorean expectation in a big way two years in a row cited as a reason. I don’t buy this for a second. The answer this year almost certainly lies in the run distribution, and unless somebody can give me hard evidence to the contrary, it’s darn silly to believe the manager has control over this.
PHI(460, 461, 443)
BAL(456, 431, 423)
ATL(449, 490, 475)
FLA(442, 487, 476)
TB(438, 412, 430)
WAS(422, 428, 442)
CHN(386, 387, 424)
Worst EW% in the NL
KC(356, 357, 357)
Equally bad by any measure, and last in baseball in all of them
PIT(333, 429, 421)
Last in PW% in the NL, in addition to real W%

Now onto the pitchers. I will give the top five and bottom five in each league in a few “payoff” categories--i.e. run-based stuff, not components. For reference, the AL as a whole is hitting .273/.336/.437 with 5.05 runs per game; the NL is at .265/.330/.425 with 4.77. I used 75 innings as the qualification standard:
AL RA LEADERS:
Liriano, MIN (1.84)
Halladay, TOR (3.07)
Santana, MIN (3.16)
Verlander, DET (3.19)
Contreras, CHA (3.46)
AL eRA LEADERS:
Liriano, MIN (2.43)
Lackey, LAA (2.76)
Santana, MIN (3.12)
Halladay, TOR (3.24)
Bonderman, DET (3.29)
AL RAR LEADERS:
Halladay, TOR (+47)
Santana, MIN (+46)
Liriano, MIN (+44)
Zito, OAK (+39)
Verlander, DET (+38)
I guess Santana would get my mid-season Cy Young vote, but Liriano and Halladay are having superb seasons as well.
AL RA TRAILERS:
Silva, MIN (7.70)
McClung, TB (7.41)
Lopez, BAL (7.20)
Weaver, LAA (6.94)
Johnson, CLE/BOS (6.88)
AL eRA TRAILERS:
Silva, MIN (7.22)
Johnson, CLE/BOS (6.75)
McClung, TB (6.72)
Lopez, BAL (6.57)
Radke, MIN (6.56)
The greatness of having the Santana/Liriano combo is lessened by having two of the league’s worst performers.
AL RAR TRAILERS:
Silva, MIN (-14)
Lopez, BAL (-11)
McClung, TB (-10)
Weaver, LAA (-6)
Johnson, CLE/BOS (-5)
Now some lists that will be presented with leaders and trailers back-to-back, since they’re not really “good/bad” indicators:
HIGHEST RA/eRA ratio, AL:
Lackey, LAA (3.49/2.76)
Santana, LAA (4.52/3.62)
Johnson, NYA (5.68/4.61)
Vazquez, CHA (5.33/4.64)
Wakefield, BOS (4.61/4.11)
LOWEST RA/eRA ratio, AL:
Liriano, MIN (1.84/2.43)
Lilly, TOR (4.54/5.32)
Verlander, DET (3.19/3.73)
Kazmir, TB (3.67/4.30)
Radke, MIN (5.65/6.56)
HIGHEST $H, AL:
Johnson, CLE/BOS (.354)
Millwood, TEX (.345)
Radke, MIN (.344)
Silva, MIN (.342)
Weaver, LAA (.340)
LOWEST $H, AL:
Lackey, LAA (.237)
Beckett, BOS (.253)
Elarton, KC (.259)
Zito, OAK (.261)
Verlander, DET (.262)
NL RA LEADERS:
Webb, ARI (2.91)
Penny, LA (2.91)
Schmidt, SF (3.00)
Johnson, FLA (3.06)
Young, SD (3.30)
NL eRA LEADERS:
Schmidt, SF (3.17)
Martinez, NYN (3.24)
Webb, ARI (3.49)
Young, SD (3.61)
Johnson, FLA (3.62)
What a nice surprise Josh Johnson has been. I think Texas would like to have Chris Young back right about now too (although there are no park factors considered here, and Petco does cut into runs by about 5%).
NL RAR LEADERS:
Webb, ARI (+47)
Schmidt, SF (+42)
Arroyo, CIN (+37)
Penny, LA (+37)
Capuano, MIL (+36)
I guess that makes Webb or Schmidt the Cy Young choice.
NL RA TRAILERS:
Perez, PIT (7.58)
Moehler, FLA (7.34)
Madson, PHI (6.66)
Claussen, CIN (6.55)
Suppan, STL (6.52)
NL eRA TRAILERS:
Moehler, FLA (6.99)
Perez, PIT (6.87)
Madson, PHI (6.85)
Sosa, ATL (6.64)
Herandez, WAS (6.60)
What the heck happened to Oliver Perez? His strikeout rate has dropped now as well to 7.2
NL RAR TRAILERS:
Perez, PIT (-14)
Moehler, FLA (-12)
Hernandez, WAS (-7)
Madson, PHI (-7)
Suppan, STL (-6)
HIGHEST RA/eRA ratio, NL:
Bucholz, HOU (5.43/4.20)
Cain, SF (5.53/4.47)
Martinez, NYN (3.91/3.24)
Olsen, FLA (4.84/4.27)
O’Connor, WAS (4.80/4.24)
LOWEST RA/eRA ratio, NL:
Penny, LA (2.91/3.76)
Oswalt, HOU (3.38/4.19)
Glavine, NYN (3.86/4.70)
Maholm, PIT (5.29/6.41)
Webb, ARI (2.91/3.49)
HIGHEST $H, NL:
Maholm, PIT (.351)
Moehler, FLA (.348)
Pettitte, HOU (.346)
Madson, PHI (.341)
Snell, PIT (.336)

Let’s look at hitters now (200 PA needed to qualify):
AL BA LEADERS:
Mauer, MIN (.378)
Johnson, TOR (.365)
Jeter, NYA (.345)
Suzuki, SEA (.343)
DeRosa, TEX (.332)
AL OBA LEADERS:
Hafner, CLE (.457)
Mauer, MIN (.451)
Ramirez, BOS (.436)
Catalanatto, TOR (.431)
Johnson, TOR (.424)
AL SLG LEADERS:
Thome, CHA (.651)
Hafner, CLE (.650)
Dye, CHA (.646)
Thames, DET (.634)
Ramirez, BOS (.615)
AL RC LEADERS:
Hafner, CLE (79)
Ortiz, BOS (75)
Ramirez, BOS (74)
Thome, CHA (73)
Wells, TOR (69)
AL RG LEADERS:
Hafner, CLE (10.24)
Ramirez, BOS (9.15)
Thome, CHA (9.04)
Mauer, MIN (8.86)
Dye, CHA (8.67)
AL SEC LEADERS:
Giambi, NYA (.585)
Hafner, CLE (.577)
Thome, CHA (.547)
Ramirez, BOS (.540)
Ortiz, BOS (.508)
AL BA TRAILERS:
Anderson, CHA (.192)
Lee, TB (.197)
Reed, SEA (.217)
Sexson, SEA (.218)
Ellis, OAK (.219)
AL OBA TRAILERS:
Reed, SEA (.256)
Hall, TB (.258)
Uribe, CHA (.259)
Berroa, KC (.265)
Ellis, OAK (.271)
I really don’t understand the Hall and Hendrickson for Seo and Navarro trade at all. Sure, Hendrickson is an upgrade over Seo, at least for the present, but you give up a pretty good young catcher and get an older catcher whose never done much. But it makes sense to Brian Sabean and his acolytes, apparently
AL SLG TRAILERS:
Lee, TB (.291)
Ellis, OAK (.311)
Kendall, OAK (.314)
Anderson, CHA (.324)
Ford, MIN (.324)
A first baseman last in the league in slugging is never a good thing. At least Jason Kendall has a homer this year.
AL RG TRAILERS:
Lee, TB (2.60)
Ellis, OAK (2.79)
Anderson, CHA (2.91)
Berroa, KC (2.93)
Reed, SEA (3.01)
AL SEC TRAILERS:
Polanco, DET (.110)
Berroa, KC (.129)
Kendall, OAK (.138)
Loretta, BOS (.142)
Betancourt, SEA (.144)
Now the Neanderthal League:
NL BA LEADERS:
Garciaparra, LA (.358)
Sanchez, PIT (.358)
McCann, ATL (.343)
Holliday, COL (.337)
Lamb, HOU (.337)
NL OBA LEADERS:
Bonds, SF (.460)
Abreu, PHI (.451)
Pujols, STL (.432)
Cabrera, FLA (.428)
Helton, COL (.421)
Neither league’s OBA leader is in the All-Star game.
NL SLG LEADERS:
Pujols, STL (.703)
Berkman, HOU (.607)
Beltran, NYN (.606)
Holliday, COL (.587)
Howard, PHI (.582)
NL RC LEADERS:
Wright, NYN (72)
Pujols, STL (71)
Cabrera, FLA (70)
Berkman, HOU (69)
Bay, PIT (68)
NL RG LEADERS:
Pujols, STL (10.27)
Bonds, SF (8.55)
Garciaparra, LA (8.47)
Berkman, HOU (8.46)
Cabrera, FLA (8.30)
NL SEC LEADERS:
Bonds, SF (.640)
Pujols, STL (.590)
Dunn, CIN (.519)
Ensberg, HOU (.511)
Beltran, NYN (.509)
NL BA TRAILERS:
Lane, HOU (.205)
Barmes, COL (.208)
Guillen, WAS (.211)
Abercrombie, FLA (.221)
Molina, STL (.222)
NL OBA TRAILERS:
Barmes, COL (.234)
Guillen, WAS (.254)
Molina, STL (.256)
Castilla, SD (.259)
Burnitz, PIT (.269)
NL SLG TRAILERS:
Ausmus, HOU (.299)
Taveras, HOU (.308)
Schneider, WAS (.311)
Barmes, COL (.318)
Everett, HOU (.319)
Houston cannot expect to contend again with their sorry offense. Of course Berkman, Lamb, and Ensberg are having pretty good years, but when you have three black holes playing every day, you let that go to waste.
NL RG TRAILERS:
Barmes, COL (2.23)
Molina, STL (2.43)
Castilla, SD (2.57)
Everett, HOU (2.84)
Cedeno, CHN (2.90)
NL SEC TRAILERS:
Cedeno, CHN (.117)
Taveras, HOU (.118)
Eckstein, STL (.122)
Castilla, SD (.126)
Pierre, CHN (.135)
I didn’t include steals in Secondary Average here, so Pierre and Taveras might not be in the bottom five if you include them.

Now let’s look at the top and bottom three players, ranked by PRAR, at each position, for both leagues combined (although I will list the best and worst in each league if they are not among the extreme three).
CATCHERS:
Mauer, MIN (+39)
Martinez, CLE (+27)
Barrett, CHN (+23)
Hall, TB (0)
Ausmus, HOU (-1)
Molina, STL (-5)
Incredibly, A.J. Pierzynski, who is largely reviled, and is sixth among AL catchers in this category, is in the All-Star Game. Martinez’ throwing has been horrible this year, but he is still a great hitter for the position.
FIRST BASE/DH:
Hafner, CLE (+45)
Pujols, STL (+42)
Thome, CHA (+37)
Sexson, SEA (-4)
Everett, SEA (-6)
Lee, TB (-12)
Hafner leads the world in PRAR, and yet he doesn’t get that much respect on a national level. Pronk coming into this year had a career line of 293/388/556 while David Ortiz was 282/366/534. I realize Ortiz is the “clutch God”, is charismatic, and was a member of a world championship team, so I’m not surprised that he is more famous, but I think it is disproportionately so. And I’m not trying to put down Big Papi--he was next on this list at +33. I’d love to have either of them in my lineup. Adrian Gonzalez is last in the NL at +6.
SECOND BASE:
Utley, PHI (+33)
Uggla, FLA (+29)
Hall, MIL (+25)
Kennedy, LAA (+2)
Polanco, DET (+1)
Ellis, OAK (-4)
Perhaps the greatest advantage for the Neanderthals is at second base. You have to go all the way down to seventh on the list to find the AL leader, Brain Roberts, at +15. Aaron Miles at +3 is the worst in the NL.
THIRD BASE:
Cabrera, FLA (+40)
Wright, NYN (+38)
Rolen, STL (+31)
Bell, PHI (+5)
Beltre, SEA (+4)
Boone, CLE (-2)
ARod for all of the grief he gets, leads the AL at +28 (Chipper is ahead of him in addition to the three above). Aaron Boone continues to stink, and while Andy Marte has not had the best season at AAA, it’s time to let the guy play in the majors. It can’t get much worse then it already is.
SHORTSTOP:
Jeter, NYA (+35)
Reyes, NYN (+34)
Tejada, BAL (+31)
Everett, HOU (-1)
Berroa, KC (-2)
Barmes, COL (-7)
Remember the SI article in the 1996 baseball preview about how New York would have the best pair of shortstops in either league with Jeter and Rey Ordonez? They were ten years ahead of their time. Reyes, while his style is still not the kind of baseball I prefer, has been a favorite of mine this year as I used my second round fantasy pick on him, which prompted a comment of “What?” from another league member. I still wouldn’t pick him in the second round for a real team, but he still has time to change that, and he’s a pretty good player at this moment.
CORNER OUTFIELD:
Ramirez, BOS (+40)
Berkman, HOU (+37)
Dye, CHA (+34)
Abreu, PHI (+34)
Holliday, COL (+31)
Bay, PIT (+31)
Francouer, ATL (0)
Markakis, BAL (0)
Burnitz, PIT (-1)
Guillen, WAS (-4)
Ford, MIN (-5)
Abercrombie, FLA (-6)
I didn’t bother splitting them up into left/right, so I listed the top and bottom six. Jeff Francouer has eight walks in 369 at bats. JC Bradbury at Sabernomics has started a tracker to follow Francouer’s quest to make the most outs in a single season. He is simply not a good player, at least not at this point in his development.
CENTER FIELD:
Beltran, NYN (+37)
Wells, TOR (+35)
Matthews, TEX (+28)
Kotsay, OAK (-2)
Taveras, HOU (-4)
Anderson, CHA (-5)
Weren’t the New York fans booing Beltran at the beginning of the year too? Good grief, you’re the best in the league at your position and you get booed. If I had MVP votes, they would go to Hafner and Pujols, although Joe Mauer could make a good case depending on how his defense is (I’m not a big defensive stats maven and I’m not looking it up right now).

Useless Trivia of the Day

I don't have enough readers to turn this into a trivia game, although if you by chance happen to read this and want to take a stab at it, feel free to leave a comment.

Nolan Ryan pitched for four teams in his career: the Mets, the Angels, the Astros, and the Rangers. What do these four teams have in common?

Obviously I am thinking of something specific--there may be other common threads betweem the teams that you can think of. I will say that it has nothing to do with on-field performance, i.e. "None of them have ever won the World Series" or some such (yes, yes, I know the Mets and now the Angels have won the World Series).

Meandering Thoughts

The American League has beaten up the National League in interleague play this year, and of course has a string of recent success in the All-Star game, and has won seven of the last ten World Series, etc. But when you look at World Series history, it’s largely a story of the AL getting the better of the NL. AL teams lead the World Series 59-42. Some might object that the AL gets an unfair advantage from the Yankees and their 26 titles. That may be true to some extent, but if the strongest franchise in the history of the game is in your league, that’s a good feather in the cap to have. Besides, if you remove all series won by the Yankees, the AL only has 33, but to be fair you should remove all series won by the Cardinals, the most successful NL team in World Series play. If you do that, it’s even at 33. Furthermore, if you take out all series played by the Yankees, you take out 13 losses in addition to the 26 wins, so the AL record is 33-29.

What’s more remarkable to me is the way that the AL has put together streaks of consecutive WS wins while the NL has not. Only three times in the history of the game have NL teams won three straight world titles, and all three streaks lasted the minimum standard of three years: 1907-1909(Cubs, Cubs, Pirates); 1963-1965(Dodgers, Cardinals, Dodgers); 1980-1982(Phillies, Dodgers, Cardinals). The AL on the other hand has eight such streaks, three of them being three years: 1972-1974(A’s), 1991-1993(Twins, Blue Jays, Blue Jays), 1998-2000(Yankees). But the AL also has four four-year streaks: 1910-1913(A’s, A’s, Red Sox, A’s); 1915-1918(Red Sox, Red Sox, White Sox, Red Sox); 1927-1930(Yankees, Yankees, A’s, A’s). There is also a five-year streak, 1935-1939(Tigers, Yankees x 4), and a seven-year streak, 1947-1953(Yankees, Indians, Yankees x 5).

I guess I just never noticed this before. Given the penchant people have for silly facts, I’m surprised this isn’t more well known. Similarly, in horse racing, a historic streak ended this year when for the first time since 2000 no horse won two legs of the Triple Crown (Point Given in 2001, War Emblem in 2002, Funny Cide in 2003, Smarty Jones in 2004, and Afleet Alex in 2005 constituted the run). That was the longest such occurrence since 1939-1944 (Johnstown, Bimelech, Whirlaway, Shut Out, Count Fleet, Pensive). At least these useless facts are more relevant then the one I heard the other day about Ryan Howard. I can’t recall exactly what it was, but I think the gist of it was: “Ryan Howard set a record for the fewest games needed to reach 25 homers in a player’s second season in the majors”.

This is absurd on so many levels--first, 25 homers seems to be a cherry-picked milestone; the important homer milestones are usually considered to be 30, 40, 50, etc. Secondly, “a player’s second season in the majors” signifies much different things for different players. Howard is 27 this year, so his second season in the majors is not exactly on the same footing as that of Alex Rodriguez or Joe DiMaggio. This is one of the sillier “records” I can remember hearing about.

In a similar vein of poor record keeping, I have recently been re-reading one of my favorite baseball books from when I was a kid, Great Baseball Feats, Facts, and Firsts by David Nemec. Nemec is a great writer, a fellow Buckeye, and I consider his Great Encyclopedia of Nineteenth Century Major League Baseball to be one of the best baseball books ever published. So I’m not at all trying to put down on Nemec here. The aforementioned book lists various miscellaneous records and leaders in categories, and includes a list of firsts, like “so and so was the first to wear a batting helmet”, and other such things. It would probably contain something of interest to most baseball fans. However, as a kid, I loved that book, because I loved silly trivia facts and could recite the 500 home run club, and other such things (I no longer can do this, although I do know every World Series winner). Then I got into evaluating what the numbers mean instead of memorizing what the numbers are, and here I am.

That digression is to get to the point that in the book, Nemec breaks the game into five or six eras, and compiles records by each era, which is not at all a bad approach. But in each era section, he lists the World Series results for each team, and the list is ordered by the percentage of series won. I have seen this sorting method used in other books, and yet I am still befuddled by it. If one was to make a post-strike era list of WS winners by this criteria, the Marlins would top the list at 2-0, followed by the Diamondbacks, Angels, Red Sox, and White Sox at 1-0. Then, at sixth place, one would finally get to the Yankees and their lowly 4-2 mark, and then the 1-2 Braves.

Is that not the most insane way to make a list you’ve ever heard? Sure, if the list is “WS W%”, that’s what you have to do. But the list is not stated as being that; since no explanation is given, I am left to assume that it is a list of teams ordered by WS success. It seems obvious to me that the proper way to do this would be to rank teams first in order of WS titles, and then, to break ties, go back and put the team with MORE WS losses on top. The Braves going 1-2 in the World Series is clearly an indicator of a more accomplished team over that period then the 1-0 Diamondbacks.

Oh well, rant over. A final complaint is that in the Indians/Cardinals game last night, Mike Hegan referred to Albert Pujols numerous times as Luis Pujols. I can understand getting a solid player, say Ronnie Belliard, mixed up with a marginal player, like Rafael Belliard. And I can certainly understand saying Cal Ripken when you meant Billy Ripken. It’s natural to associate the name Ripken with Cal without too much thought. But if somebody calls Alex Rodriguez “Aurelio”, or calls Albert Pujols “Luis”, that’s befuddling.

A Review of "The Mind of Bill James"

The Mind of Bill James by Scott Gray, subtitled "How a Complete Outsider Changed Baseball", is a book that cannot decide exactly what it wants to be. Is it a biography of Bill James? Or a summary of his work? Or an attempt to describe what a day in the life of Bill James is like? Or a chronicle of how his work has affected the baseball world? It tries(or at least ends up as) to be all of those things, and therefore does not do a great job of any of them.

As a biography, it probably does about as good of a job as you would want, because while I don’t mean any offense to Mr. James, his life probably would not make for a stirring biography anymore of that of an anonymous scientist at some university--it is their work that is the most interesting thing about them, not biographical minutiae. Personally, I would say the same thing about Thomas Jefferson or other such figures, but somebody apparently likes them. Even so, I don’t think that Bill James would be on the top of the wish lists of biography fans.

As a summary of his work, it is woefully incomplete, as it would probably take a book of this size solely devoted to just that goal in order to be adequate. Gray does include an appendix which summarizes some of his work, but focuses on concepts(e.g. Defensive Spectrum, Law of Competitive Balance, Johsnon Effect) rather then specific applications/formulas(e.g. Runs Created, Win Shares, Component ERA). Of course, to someone who has read most of James’ work, like myself, this is not something that is necessary, and to me the appendix dragged on more then much of the book.

As far as describing how James’ insights have been adopted in baseball, the book discusses his role with the Red Sox, and refers to Moneyball a few times, but again, is not at all close to comprehensive on this front.

One aspect of the book you may or may not find interesting is that Gray reprints some emails and conversations between him and James about non-baseball topics, such as politics and criminality. On one hand, it is interesting to see how James approaches these other topics. On the other hand, I can think Bill James is the greatest baseball mind ever born but think his position on NAFTA is silly. Now the fact that I disagree with his position on NAFTA makes me think no less of his baseball work. But I’m sure there is somebody out there who will be affected in that way, which is why I don’t talk about politics and other such things here (in addition to the fact that I am mildly surprised that anybody cares what I have to say about baseball, and would be truly stunned if anybody cared what I thought about anything else).

I don’t mean to criticize the book too strongly, because I did find it an enjoyable read, with no glaring factual errors or misrepresentations. I have two standards of recommendation for a book--one is that it is great and you will want to read it again and again and you will refer back to it frequently. The second is that you should probably read it as you might find it enjoyable and somewhat informative, but it is not a part of an essential sabermetric library. Under the first standard, this book falls short, but under the second, The Mind of Bill James is a read I would recommend.

"Statistical Analysis" v. "Sabermetrics"

In the last week, there was a lively discussion on the mailing list of the SABR Statistical Analysis Committee on a proposal to change the name of the committee to the Sabermetric Committee or something similar. Some of the debate has been based on how the term “sabermetrics” is viewed by other members of the organization, and other internal organizational politics-type stuff. I know little about SABR politics and care little about SABR politics, so I will ignore that element of it.

As to which is the better name for the committee, though, I believe 100% that it is “sabermetrics”. One objection has been that there is no readily agreed upon definition of sabermetrics, and that the definitions often employed, especially by those who are not themselves sabermetricians, are essentially equivalent to “statistical analysis.” This is true, although I don’t believe that a misunderstanding of what sabermetrics is by the uninformed should change the way the term is used by those who understand it.

Bill James had two explicitly stated definitions for sabermetrics that he published in the Abstracts. The first was “the mathematical and statistical analysis of baseball records”. Oops. But Bill quickly realized that this was not adequate, so he used “the search for objective knowledge about baseball”.

That definition is too broad in my opinion, as there are many things that are objectively knowable that do not really fall under the purview of sabermetrics. For example, a list of owners of the Red Sox is objective knowledge about baseball, but under this broad definition practically all baseball research is sabermetrics.

Personally, I think that the best single definition of sabermetrics was that presented by Craig Wright in his forward to the 1985 Abstract, which was “the scientific research of the available evidence, to identify, study, and measure forces in professional baseball.” My only quibble here is that I believe the “professional” qualifier is unnecessary. I especially like the use of the word “measure”--James' word, if not his definition, already captured this by using the suffix “-metrics”. It measurement that sets a list of Red Sox owners, or Cal McLish’s full name, and other such things outside of the realm of sabermetrics and into the other categories of baseball research.

I also believe that the “objective knowledge” part of the James definition is best left by the wayside. Much of the statistical data that we work with is not purely objective. Take the distinction between a hit and an error, between a wild pitch and a passed ball, etc. These are subjectively determined, although there is an established framework by which one is supposed to make the distinction. However, scouting reports could also be incorporated in some way into evaluation, if the biases and error ranges are considered--just as we should for our statistical methods. That is not to say that all information is equally valid or equally useful, but we should not throw it all out the window right off the bat.

Wright also included some clarifications about the properties of sabermetrics which are very instructive in this particular debate: “A sabermetrician is not a statistician. Sabermetricians do not study baseball statistics. Sabermetricians are actually involved in research, scientific study, and the object is baseball.” (emphasis in original)

Statistical analysis and sabermetrics are not synonymous. Much of the statistical analysis that is done can certainly be put under the umbrella of sabermetrics--but it is a subset of sabermetrics, not the definition of it. I have always been uncomfortable with the term “statistical analysis” for other reasons--it implies a rigorousness and an approach that is not inherent to sabermetrics.

Statistics is a science unto itself, with its’ own principles and its’ own approach to questions. If a statistician is presented with the problem of estimating team runs from component statistics, his first move is not going to be to sit down and try to build a theoretical model of how runs are scored. He will likely look at correlations between the events, run regressions, etc. I am not here to say that those approaches are bad, just that sabermetrics encompasses more then that. Statistical tools are not going to allow you to create a method like Base Runs. If you created the model though, they will help you validate it.

At the risk of repeating myself, statistical analysis is certainly an important part of sabermetrics, but it is a poor choice of term to describe everything that sabermetricians do. And therefore, I don’t see why the committee should carry that name.