Did The Increased Use Of Relief Pitching Cause A Decline In Clutch Hitting?

This is mainly an elaboration on last week's post called Clutch Hitting Over Time (1952-2008). What I found was a correlation between the fall in percentage of games completed and clutch hitting (as measured by the difference between non-close and late (NCL) situations and close and late (CL) situations). Here, I just turn things around and make the measure of clutch CL - NCL (the two stats I used were AVG and isolated power or ISO).

The table below shows the AL AVG in both CL and NCL for the given periods. I broke things down by 3 year periods because there was alot of volatility from year to year (the Retrosheet data on this in the AL starts in 1953 and 1952 for the NL). The period averages are simple averages. The DIFF column is just the first minus the second and the last column is the percentage of games not completed.



You can see that the difference has generally gotten more negative over time as the percentage of games not completed has increased (a proxy for the use of relief pitching). I was surprised to find that there were years when the AVG in CL situations was higher than in NCL situations. The next graph shows relationship between the last two columns from the table above.



The r-squared in the graph refers to the percentage of variation in clutch hitting (CL - NCL) explained by the percentage of games not completed (%NCG). It was 71.94%. Now the same two tables for the NL.





Interesting that the r-squared is so much lower in the NL. No reason comes to mind.

The next set of graphs does the same thing for ISO in the AL.





The .8652 seems very high. 86.52% of the variation in clutch is explained by the change in games not completed. Now for the NL.

Pujols wins triple crown

Okay, he has won the triple crown covering the years 2001-2008 in the NL. Here are the top 10 in AVG, HRs, RBIs with a 2000 PA minimum. Once we extend it to 2009, he will still lead in all 3. Maybe some other hitters have done this over a 9 year stretch or longer. Hornsby did for his entire NL career! Ted Williams did it for his entire career! So did Stan Musial! Anybody know who else had a long span triple crown? I will look at obvious choices when I get a chance. Data from the Lee Sinins Complete Baseball Encyclopedia.

AVERAGE
1 Albert Pujols .334
2 Todd Helton .326
3 Barry Bonds .325
4 Matt Holliday .319
5 Chipper Jones .317
6 Larry Walker .316
7 Miguel Cabrera .313
8 David Wright .309
9 Hanley Ramirez .308
10 Moises Alou .304

HOMERUNS
1 Albert Pujols 319
2 Adam Dunn 278
3 Barry Bonds 268
4 Lance Berkman 263
5 Andruw Jones 255
6 Aramis Ramirez 237
7 Pat Burrell 233
T8 Chipper Jones 219
T8 Jim Edmonds 219
10 Derrek Lee 207

RBI
1 Albert Pujols 977
2 Lance Berkman 879
3 Aramis Ramirez 815
4 Andruw Jones 770
T5 Pat Burrell 748
T5 Todd Helton 748
7 Chipper Jones 739
8 Jeff Kent 725
9 Adam Dunn 672
10 Luis Gonzalez 664

Hornsby had a career triple crown while in the NL. He lead the NL in all 3 stats (even with just a 1000 PA minimum) for his entire NL career, from 1915-33. So from 1915-1933, Hornsby lead in AVG, HRs, and RBIs. Maybe someone has said this before but I have not seen it. Here are the top 10

AVG
1 Rogers Hornsby .359 (.35936)
2 Chuck Klein .359 (.35907)
3 Lefty O'Doul .355
4 Paul Waner .346
5 Bill Terry .341
6 Riggs Stephenson .339
7 Babe Herman .332
8 Lloyd Waner .332
9 Kiki Cuyler .330
10 Spud Davis .330

HR
1 Rogers Hornsby 298
2 Cy Williams 247
3 Hack Wilson 238
4 Jim Bottomley 194
5 Chuck Klein 191
6 Mel Ott 176
7 Gabby Hartnett 154
8 George Kelly 148
9 Babe Herman 143
10 Bill Terry 138

RBI
1 Rogers Hornsby 1555
2 Jim Bottomley 1188
3 Pie Traynor 1176
4 Frankie Frisch 1084
5 Hack Wilson 1033
6 George Kelly 1020
7 Charlie Grimm 1015
8 Cy Williams 967
9 Bill Terry 892
10 Edd Roush 891

Now for Ted Williams

AVG
1 Ted Williams .344
2 Joe DiMaggio .322
3 Jimmie Foxx .315
4 Harvey Kuenn .313
5 Dale Mitchell .312
6 Barney McCosky .312
7 Luke Appling .310
8 Hank Greenberg .309
9 Bob Dillinger .308
10 Taffy Wright .308

HR
1 Ted Williams 521
2 Mickey Mantle 320
3 Yogi Berra 318
4 Joe DiMaggio 254
5 Larry Doby 253
T6 Vic Wertz 247
T6 Vern Stephens 247
8 Roy Sievers 243
9 Gus Zernial 237
10 Joe Gordon 228

RBI
1 Ted Williams 1839
2 Yogi Berra 1306
3 Mickey Vernon 1296
4 Vern Stephens 1174
5 Bobby Doerr 1153
6 Joe DiMaggio 1105
7 Vic Wertz 1092
8 Larry Doby 970
9 Mickey Mantle 935
10 Rudy York 922

Now Musial

AVG
1 Stan Musial .331
2 Hank Aaron .320
3 Willie Mays .315
4 Tommy Davis .313
5 Dixie Walker .312
6 Jackie Robinson .311
7 Orlando Cepeda .310
8 Vada Pinson .309
9 Richie Ashburn .308
10 Joe Medwick .305

HR
1 Stan Musial 475
2 Eddie Mathews 422
3 Willie Mays 406
4 Duke Snider 403
5 Gil Hodges 370
6 Ernie Banks 353
7 Ralph Kiner 351
8 Hank Aaron 342
9 Hank Sauer 288
10 Del Ennis 286

RBI
1 Stan Musial 1951
2 Duke Snider 1316
3 Del Ennis 1277
4 Gil Hodges 1274
5 Willie Mays 1179
6 Eddie Mathews 1166
7 Hank Aaron 1121
8 Carl Furillo 1058
9 Bob Elliott 1051
10 Ernie Banks 1026

The following sites also discussed these issues

http://www.philly.com/philly/sports/phillies/20090923_High___Inside__NL_Notes.html

http://www.baseballthinkfactory.org/files/newsstand/discussion/goold_albert_pujols_claim_to_a_triple_crown_or_two/

http://www.stltoday.com/blogzone/bird-land/bird-land/2009/01/albert-pujols-could-be-close-to-claiming-a-triple-crown-or-two/

Yes, We Should Have Kept An Eye On The Rockies

On June 8th, I asked Should We Keep An Eye On The Rockies? It was right after they swept the Cardinals in St. Louis, scoring alot of runs in a combination of blowouts and un-close games. Given that the Cards were (and still are) a very good team, I thought the sweep was an indicator of how good the Rockies might be.

But I sure got some other precictions wrong. Like Albert Pujols Has A Good Chance To Win The Triple Crown. He lead the league in HRs and RBI's on July 4th while only trailing Hanley Ramirez in average by .008 in average. I thought is better track record (including 2nd half hitting) gave him a good shot to lead in AVG over Ramirez and the other top hitters. But he may not even lead in RBIs.

And then there was Is Ryan Howard The New Mickey Vernon? (Or Is His Career Really In Decline?). His offensive winning percentage(OWP) had declined the last 2 years.

.777 (26)
.675 (27)
.582 (28)

So those declines were .102 & .093. If I had limited the study to declines of .093 or more, there were only 8 guys. The only one whose decline started before age 30 was Vernon. Here is what happened to Vernon:

.759 (28)
.465 (29)
.284 (30)

But he bounced back at age 31 with .579. And Howard, too, has bounced back. I don't have his OWP for this year, but his adjusted OPS the last 4 years, including this year have been (from Baseball Reference)

167
144
124
136

Vernon's 4 years were

160
99
73
113

So I guess I was right: Howard is the new Vernon. Any player under 30 with a .093 or more decline in OWP 2 straight years will bounce back the next year with a better season.:)

Clutch Hitting Over Time (1952-2008)

The Retrosheet site can tell you the league averages for various situations going back to 1952 for the NL and 1953 for the AL (plus some earlier years). Here I show the league differences in close and late (CL) situations for both batting average and isolated power. Each league is done separately and each data point is a 3 year average (just a simple average, with the exception being that the first AL data point is just 1953-4).

The first graph shows the NL AVG difference. That was calculated by the non-CL situations minus the CL situations. For example, in the NL in 1952, the non-CL AVG was .251 while the CL AVG was .248, for a difference of about .003. The next two years had differences of about 0.007 and 0.008, respectively. So the first NL data point was (.003 + .007 +.008)/3 or .006.



Notice how the difference is growing over time. This might be due to the greater specialization of relief pitching. But that is just a guess (although the correlation between the 3 year AVG difference and the % of games not completed is .74 in the NL-it was also about .74 for ISO-in the AL those correlations were .85 & .93, respectively-the last graph shows NONCL ISO MINUS CL ISO AS A FUNCTION OF GAMES NOT COMPLETED IN THE AL). Now the same graph but for isolated power (or SLG - AVG).



I wish I could explain what was going on in the late 1960s and early 1970s. It does not seem like it was that much harder to hit in CL situations than non-CL situations in the NL. Now the AL graphs.





The graph below shows how closely related the difference between clutch and non-clutch hitting is to the % of games not completed. The fewer games completed (the more relievers are used), the harder it is for hitters to keep up their normal power hitting in close and late games.

Some Interesting Stats On Gene Tenace

This is another one of those "Tenace was better than is commonly thought" articles. Here is the link to his Retrosheet page Gene Tenace. I was looking at the best hitting seasons by catchers to see where Joe Mauer this year ranks and I noticed that Tenace did fairly well. Using the Lee Sinins Complete Baseball Encyclopedia, here are the top 10 seasons since 1900 by catchers who qualified for the batting title in offensive winning percentage(OWP):

1 Mike Piazza 1997 .814
2 Mike Piazza 1995 .783
3 Mickey Cochrane 1933 .769
4 Mike Piazza 1996 .757
5 Roger Bresnahan 1906 .745
6 Darren Daulton 1992 .745
7 Chris Hoiles 1993 .741
8 Roy Campanella 1951 .739
9 Mickey Cochrane 1931 .732
10 Gene Tenace 1975 .732

(this means seasons where their most common position was that of catcher). Tenace had only 3 seasons as a catcher when he also qualified for the batting title. All 3 are in the top 60 seasons since 1900 for catchers. One is 44th (.680). The other is 56th (.668). The only 8 years he had 400+ PAs were from 1973-80. Here are the top 25 in OWP during that time period with 2500+ PAs:

1 Joe Morgan .737
2 Rod Carew .727
3 Reggie Jackson .724
4 Willie Stargell .717
5 Ken Singleton .703
6 Reggie Smith .696
7 Oscar Gamble .688
8 George Brett .684
9 Fred Lynn .684
10 Mike Schmidt .682
11 Gene Tenace .677
12 George Foster .676
13 Dave Parker .667
14 Bob Watson .658
15 Mike Hargrove .647
16 Cesar Cedeno .647
17 Ken Griffey Sr. .646
18 Jim Rice .645
19 Eddie Murray .645
20 Dave Winfield .643
21 Keith Hernandez .643
22 Greg Luzinski .642
23 Andre Thornton .635
24 Pete Rose .635
25 Jose Cruz .633

He does well (actually 16 of these guys had at least 4000 PAs and no one was under 2700). Of course, it his Tenace's prime years, so it is slightly biased in his favor. But he still looks good. Of the 10 guys ahead of him, 6 are in the Hall of Fame. And I see that he is ahead of 3 Hall of Famers shown here (and a few more ranked lower), who were generally going through the quality part of their careers. If not, they were certainly not in any kind of decline phase.

In those years, here is where he ranked in OWP:

1973 4th (.714)
1974 22nd (.597)
1975 6th (.732)
1976 4th (.693)
1977 9th (.680)
1978 11th (.665)
1979 10th (.668)

He did not qualify for the league lead in 1980 with just 416 PAs. But his OWP was .650 that year. 1974 was the only year in this period he was under .650.

I also came up with a point system several years ago (probably 5-7 years ago). I only counted seasons when a player had a .600+ OWP. I then multiplied his PAs that season times (OWP - .600). The idea was to calculate how much high quality hitting players did. Tenace ranked 131st among all hitters. He ranked 8th among catchers. But through 2008, he ranked only 161st in games at catcher with 914 (for some reason his own personal file at Sinins shows only 892 games at catcher). So his high rank is remarkable given he did not play that much at the position. He got exactly 1 vote for the Hall of Fame in 1989 and was, of course, dropped from futher balloting getting less than 5% (0.2%, actually). Anyway, here is the link: Ranking Hitters by Their Performance Above a .600 Offensive Winning Percentage. Here is the top 10 in that:

Mike Piazza 601.21
Bill Dickey 412.07
Mick. Cochrane 326.31
Yogi Berra 321.63
Carlton Fisk 301.47
Buck Ewing 268.16
Fred Carroll 266.11
Gene Tenace 265.57
Johnny Bench 259.78
Rog. Bresnahan 255.04

Bill James says that 20 Win Shares in a season constitutes an all-star type year. Tenace had at least 22 every year from 1973-79. In 1975, he had 32, tied for 4th place, 1 behind MVP Fred Lynn and John Mayberry and Ken Singleton. He averaged 24.7 WS per season over those 7 years. His 198 WS for the 1970s was the 31st highest total, including pitchers. He ranks 177th in Wins Above Replacement level at Sean Smith's Top 500 site.

If Tenace is counted as a 1B man, he would rank 19th all-time in OWP for a minimum of 5000 PAs. Here are the leaders

1 Lou Gehrig .797
2 Dan Brouthers .771
3 Jimmie Foxx .746
4 Johnny Mize .743
5 Mark McGwire .739
6 Jason Giambi .727
7 Jim Thome .724
8 Roger Connor .717
9 Hank Greenberg .715
10 Jeff Bagwell .704
11 Willie McCovey .702
12 Carlos Delgado .684
13 Todd Helton .680
14 Jack Fournier .679
15 Cap Anson .677
16 Will Clark .674
17 Bill Terry .674
18 Norm Cash .672
19 Rod Carew .667

If he is counted as a catcher, he would be 2nd

1 Mike Piazza .687
2 Bill Dickey .644
3 Mickey Cochrane .643
4 Yogi Berra .629
5 Gabby Hartnett .610
6 Johnny Bench .607
7 Jorge Posada .606
8 Wally Schang .592
9 Ted Simmons .591
10 Carlton Fisk .575

Of course, he had under 500 PAs after the age of 33, so his percentages don't suffer much from a decline phase. But still, he did rank pretty high on my OWP above .600 list, where no one is hurt by a decline phase.

The Mariners Have Been Very Average This Year

This issue got brought up at Seattle Sports Insider with M's Tilt at Pythag Windmill. They seem to be winning more games than their runs scored and runs allowed would normally indicate. Here are the comments I made about the issue:

"Maybe the Mariners are not so bad. Here is what their hitters have done:

OBP .313
SLG .400
OPS .713

Here is what their pitchers have allowed

OBP .312
SLG .395
OPS .712"

And

"The Mariners have allowed 63 unearned runs this year. The league average is 50.

A quick regression shows that runs per game in the AL this year is

R/G = -5.69 + 21.03*OBP + 8.04*SLG

That predicts them to score about 4.11 per game while it is actually 3.95. Over 148 games, it amounts to 24 runs below expectations. So now we have already accounted for 37 of their -53 run differential (584-637).

Now for runs allowed the equation is

R/G =-4.14 + 22.78*OBP + 3*SLG

(maybe fielding and relief pitching are the reasons this is different than the hitting formula-I really don't know-could be small sample or one year of data)

That says they should allow 4.266 per game and it is really 4.304. Over the 148 games it adds up to 5.59 runs. So now we are up to explaining over 42 of the -53 runs in their differential (which should be very close to zero given their stats I mentioned above).

So I think they have been a little unlucky hitting wise, scoring 24 fewer runs than expected. Then they are allowing too many unearned runs. Also, if I applied the hitting formual to their pitching stats, it would mean they have given up 26 more runs than expected. That could be bad luck or a weak bullpen. My guess is that they know about all this."

Yogi Berra As A Clutch Hitter & Clutch Hitting Over Time

Below are two messages I just posted to SABR-L. It looks like Berra's extra good performance in close and late situations is significant. I also found that there is a bigger gap now in how players hit in close and late situations versus overall than there was in Berra's time.

Post 1

On Septemeber 16, 1955, the Yankees beat the Red Sox 3-2. They came from behind with 2 solo HRs in the bottom of the 9th (by Bauer and Berra). That info is, of course, from Retrosheet. The win put the Yankees in a tie for first place with the Indians. It was the first day in Sept that the Yankees had at least a share of the lead. The Yankees had 9 more games and never fell out first place after this. Click here to Retrosheet's box score and play by play. And click here to see Baseball Reference's report on the game.

Berra's HR was an inside the park job. According to Baseball Reference, it was his only inside the park HR in his career (BR lists just 1). There must have been something going on that day, since Robin Yount was born. Click here to see Baseball Reference's breakdown of his HRs.

Anyway, it looks like a clutch hit for Berra. I came across this game looking for something else. So I checked Retrosheet's splits since Berra has a reputation for having been a clutch hitter. It turns out that for the years that Retrosheet has the data posted, 1953-1965, Berra batted .304 in close and late (CL) situations.

Using the none on/runners on data that Retrosheet lists, I found that Berra had an overall average .277 from 1953-62 (Retrosheet says "some situational stats are missing due to lack of play-by-play data for some games"). The combined AB total from the none on/runners data is a little less than his actual total, but it is close). I then found that Berra batted .272 in non-CL situations. So he batted 32 points higher in the "clutch" than he normally did.

That seems like a lot and I wondered if it was significant. To see, I used the formula for a Z-score that Pete Palmer used in his “Clutch Hitting One More Time,” article (from By the Numbers, March 1990). It not only takes into account how much different a player hits in a give situation, but factors in the normal or league average difference. I actually did not calculate the non-CL average in the AL from 1953-62 (it could be done with Retrosheet data). But here are the overall AL batting averages from 1953-62 followed by the CL average (note, this means right here I have not separated out CL and nonCL).

.262/.266
.257/.250
.258/.257
.260/.251
.255/.256
.254/.248
.253/.255
.255/.259
.256/.261
.255/.251

It looks like the overall AVG is just a little more than 1 point better. So the difference between the AL CL AVG and the AL nonCL AVG for these years is probably about 2 points or .002. I then came up with a Z-score for Berra of about 1.90. I think that is borderline significant (there must be people on the list who know more about the binomial distribution who could say if that is right).

But Berra had a .551 SLG in CL situations while it was about .475 overall. So that is about a .075 differential. Over this time, in the AL, the overall SLG was about .005 better than the CL SLG. So that gives Berra a swing of 80 points. My guess is that is significant, although SLG cannot be fit into the binomial model since it is not an either or event. But Berra's CL HR% was 6.6% while it was 4.8% overall. It looks like his NONCL HR% was about 4.5%. So Berra raised his HR frequency by nearly 50% in CL situations.

Now given that SLG was almost the same in overall situations and CL situations during these years in the AL, I assumed no league differential in doing the Z-score for HR%. I got a Z-score of 2.25. That is statistically significant.

Of course, a small % of players will, by random chance, have a Z-score this high. If you have more than 5% of the players be plus or minus 1.96 in Z, we don't know for sure which ones got there by luck and which ones were the true clutch hitters (as Willie Runquist would say). But we can see that there was at least some reason why people saw him as clutch.


Post 2

This is related to my post on Berra. I showed that from 1953-62, the AL batted about .001 less in CL situations than it did overall. Here are the overall/CL AVGs in the AL from 2004-08:

.270/.252
.268/.254
.275/.263
.271/.255
.268/.252

About a 15 point difference per year. Now the NL for the same years:

.263/.255
.262/.253
.265/.254
.266/.253
.260/.253

Almost a 10 point difference per year. From 1991-2000, using both leagues, it was .266/.256. My guess is that over time relief pitching cause the differential to rise.

I also mentioned that from 1953-62, the overall AL SLG was about 5 points higher than the CL SLG. From 1991-2000, using both leagues, it was .416/.388, a difference of 28 points. So, just like AVG, the differential has risen.

When I get some time, I will chart out all the years that Retrosheet has to see how these differences have grown or changed. But I won't mind if someone else does it.

A Yogi Berra interview. He was asked:

"You were known as a great clutch hitter. Somebody once said that the toughest hitter in baseball in the last three innings was Yogi Berra. What made you so good under pressure?

Yogi Berra: I don't know. Maybe I was just lucky. I loved to hit with men on base. Dickey used to holler at me, "You're wasting time at bat with nobody on base," he says."

The Retrosheet page of Yogi Berra's splits shows that he also hit very well with runners on base and with runners in scoring position. His average was about 30 points better than at other times.

Why Did Al Simmons Decline So Much In The Second Half Of His Career?

This is a follow up to my last post on "how good was he?" which you can read below. He batted .363 from ages 22-29 but only .309 from ages 30-37. That is a very big dropoff. I wonder if he had nagging injuries that eventually caught up to him (he missed about 140 games from ages 25-29). There are a couple of reasons mentioned below from his SABR bio.

To see how his declined compared to others, I found every player who had 4000+ PAs from ages 22-29 (390 players) using the Lee Sinins Complete Baseball Encyclopedia and every who had at least 3230 PAs from ages 30-37 (also 390 players). Then I found all the players who were in both groups (143) and the difference between their "young" average and their "old" average and ranked them from highest to lowest. The top and bottom tens are in the table below.



As you can see, Simmons is very near the bottom. He did not have the biggest decline, but it was still very big. As for Keeler and Davis, those two guys both had their careers cover both a high average period and a low average period. When Davis was 22-29, the NL league average was .287 (Keeler's ages 22-29 are almost identical to these years). Then from 1902-08, when Davis was 31-37, the AL league average was .250 (Davis switched leagues). Keeler spent most of his 30s in the AL, too. From 1903-09, when he was 31-37, the AL league average was .246. But the AL league average when Simmons was "young" was .293 and when he was "old" it only fell to .288. So his dropoff is more surprising. I also calculated the average park factor for the parks he played in when he was "old" and it came out to about 101, meaning average. There might be some lefty/righty issues in those parks, though, that I don't know about it. But it looks like his decline cannot be explained by a changing league average or tougher parks. The A's park had a park factor just under 104 when Simmons was aged 22-29.

The simple average of the change in average from "young" to "old" was about .007 (if I only used "old" guys who had 4000+ PAs,it was .006). So if Simmons could have had an average drop off, it would mean that he would have hit aroung .356 in the second half of his career. That would put his career average close to .360, still second to Cobb (I have not quite split his career evenly in two, so I could have overestimated this by a couple of points). Anyway, Rogers Hornsby is 2nd with a .358 average. Simmons is currently 21st all-time in average for players with 5000+ PAs.

I wonder if he had made it to 3000 hits if he would be better known today (he would have made it with the higher projected average). Also, he batted .390 or better twice. He was at .401 on July 17, 1927. He played only 5 more games in July and then did not play again until Sept. 6. Maybe some injury cost him .400. In 1931, when he batted .390, he only played 27 games in the final two months of the season. Yet he batted .465 & .426, in those two months, respectively. His combined average then was .442. Another 100 or so ABs at that rate would have gotten him to .400. A .400 season might have made him more famous. Maybe playing for the Yankees would have helped (although being a right handed batter would have hurt). Data in this paragraph came from Retrosheet.

His bio by Fred Stein at the SABR Baseball Biography Project suggests a couple of reasons for his decline, but I don't think it explains enough.

The bios says "In 1926 Simmons “slipped” to .341 and 199 hits. He was hampered by injuries the following two seasons although he hit for high averages." In 1930, at one point, he had a "swelling knee." He missed 16 games that year. Also, "Years later, Simmons admitted to a writer that he had accepted the White Sox’ second-division attitude and had slacked off in his customary strenuous practice habits." (but he actually did better in OWP, discussed below, in his first two years with the White Sox, .617 & .675, than in his last year with the A's, .590).

One curious thing it reports is that Simmons said "I’ve studied movies of myself batting." I wonder how many players did that back then?

The table below shows how Simmons declined in offensive winning percentage (OWP) when he got "old." OWP is the Bill James stat that says what your team's winning percentage would be with 9 identical hitters when you give up an average number of runs. In this case, it is park adjusted. As you can see, his dropoff was great. I think Sisler had some kind of eye problem and even missed a whole season. I don't know anything about Bottomley. Maybe sports medicine was not very good back then.



Simmons finished with a career OWP of .644, which is now ranked 117th among players with 5000+ PAs. The typical dropoff when getting "old" was about .024. If Simmons had a .698 OWP when he was "old" that would give him a career OWP of about .710. That would currently rank 35th, a big improvement over 117.

How Good Was Al Simmons?

You may have heard that Ichiro Suzuki recently became the second fastest player to reach 2000 hits. He did it in 1402 games. Al Simmons was fastest, doing it in 1390 games. The answer to the question is that he was very, very good. Great would work, too.

Simmons got his 2000th hit early in the 1934 season. His first season was 1924. Through the 1931 season, he was second only to Ty Cobb in lifetime batting average at .363 for players with 4500+ PAs (I used the Lee Sinins Complete Baseball Encyclopedia). He finished his career at .334. That may be a normal dropoff. I really don't know, but it seems like alot. Maybe injuries caught up with him. He missed 143 games between 1927 and 1931 (might actually be a little less than that since the A's did not play 154 games in all of those years).

Through 1931, he was also third in isolated power (SLG - AVG) with .233, trailing only Ruth (.360) and Gehrig (.300). He is one of 22 players to have at least 3 seasons with an .800+ offensive winning percentage (the Bill James stat that says what your team's winning percentage would be with 9 identical hitters when you give up an average number of runs). Ruth had 12, Cobb 11, and Ted Williams 10. So he is not close to them, but to be in that small a group is pretty good.

He had 375 Win Shares (the Bill James stat that includes all phases of the game). That was tied for 63rd through 2001 (including pitchers). Bill James ranked him as the 7th best leftfielder of all time and gave him an A as a fielder. He played 1377 games in LF and 771 in CF. He was 88th among position players in WS per PA. He led the league in Win Shares once (thanks to Ruth's 1925 stomach ache). But he had 5 other seasons in the top 10, including 3 in the top 3 (once losing to Foxx by a hair in 1929). He led all AL outfielders in fielding Win Shares twice and was third 3 times.

He is ranked 85th among position players in Wins Among Replacement at Sean Smith's site. Looks like he was one of the greatest players ever. Good to see him get mentioned in the news. I just searched his name through google news. Got 653 hits. But if I put in "-ichiro" it drops to 40 and some of those deal with other guys with the same name. But I did also learn that he "holds the major-league record with 11 straight 100-RBI seasons at the start of a career" from a Jeremy Sandler article. Geez, I almost forgot. He was of Polish ancestry: his birth name was Alois Szymanski.

Click here to go to his Baseball Reference page.

Click here to read his bio by Fred Stein at the SABR Baseball Biography Project

Click here to see Simmons on the cover of Sports Illustrated in 1996

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Is Chase Utley Undervalued By The Media?

"Maybe the media simply has no idea how valuable Chase Utley is,..." said Tom Tango at his The Book blog. If so, I think it might have something to do with Utley being a 2B man.

I looked at how many MVP awards have gone to each position as well as MVP award shares. 2B men have done very poorly. Go to MVP Awards And Award Shares By Position to see that study. And it is the media (the writers) who vote. 1B men have won 26 awards while 2B men have won only 10. The discrepancy is even bigger for award shares. Are 2B men really not that good? I don't think so. I counted about 11 2Bmen in the top 100 at Sean Smith's list of the best all-time players by wins above replacement value. And there were some other guys who played part of their careers at 2B.

I also did a study called Have Second Basemen Been Underpaid? It certainly is not conclusive, but it is suggestive. Maybe there is just a general mis-perception of how valuable 2B men are and that it is not limited to the media.

Is Prince Fielder Good At Hitting "Late" HRs?

David Pinto of "Baseball Musings" said "Fielder doesn’t hit many homers late, as only 10 of his 37 have come from the 7th inning on." See Special Day for Fielder. Here are the comments I left on that.

I am not sure I agree with what you are saying about Fielder not hitting many late HRs. 10 is 27% of 37 and the last 3 innings are 33% of the game. So it seems like he is pretty close to the proportion we might expect.

I went over to Baseball Reference and found that this year his HR%'s by the groups of 3 innings were

7.1 (1-3)
8.3 (4-6)
6.3 (7-9)

So it looks like he does not do so well from innings 7-9. But he also has 1 extra inning HR. So his % from the 7th inning on is 6.8%, very close to what he does early.

In his career, here are the %'s for the groups of 3 innings

5.94
6.9
6.86

So he looks like he does okay late. One year may not be enough data for a conclusion. If we do 7th inning on, including extra innings, then his career % is 6.96.

Also recall that he may be facing good closers in these situations. It might be normal for all players to see their HR% fall when it is late. In fact, this year, the overall MLB HR% is 3.09. From the 7th inning on it is 2.78% (data from ESPN). So give Mr. Fielder a break. Just because he's a vegetarian...

I should have said above that 9 of his 36 non-extra inning HRs were hit in innings 7-9, for 25%. But in his career, he has 148 non-extra inning HRs and 48 of those were hit in innings 7-9. That is 32.4%. Pretty darn close to the expected rate of 33% (and of course he may not always bat in the last of the 9th at home)

Have The Phillies Been A Little Lucky This Year? Should They Be Worried?

I got thinking about this thanks to a thread at Baseball Think Factory. It had to do with productive outs and how the Phillies were hitting with runners in scoring position (RISP). But the Phillies actually do better with RISP (and in close and late situations) than they normally do. It looks like a lot better. That is usually a case of luck and won't continue into the future. Here is what I posted over at BTF.

The Phillies have the highest OPS with RISP in the NL. They are tied for the 2nd hightest SLG and they are 6th in OBP (at .356 while the league average is .351). So they get on base at an above average rate with RISP and have great power with RISP. Sounds good. Here is the link to the NL RISP hitting data

http://espn.go.com/mlb/stats/team/_/stat/batting/split/39/league/nl/sort/OPS/order/true

The Phillies have an overall team hitting OPS of .780 while their pitchers have allowed an OPS overall of .757. So their differential overall is .023. Using my regression formula of

pct = 1.21*OPSDIFF + .5

I estimate they should have about a .528 pct. Yet it is actually .579. Their hitters have an OPS of .800 with RISP while their pitchers have allowed just .726. So their overall RISP performance is really good. I wonder what kind of training or drills they do to pull that off? Or maybe they know how to scout clutch players.:)

The Phillies hitters have a team OPS of .792 in close and late situations while their pitchers allow an OPS of .684. So that is a differential of .108. And as mentioned above their RISP differential is .074 while their overall differential is just .023. So they really know how to ramp up their performance when it counts.

Was The Left Hand Of God Responsible For The Red Sox Miracle In 1967? (Or, Yaz Hit Especially Great Against Lefites That Year)

I just submitted this as an email to the SABR list:

Yaz had a career average against righties of .299 while it was .244 against lefties (that and all data mentioned here are from Retrosheet). So what do you think was his only season when he had a higher AVG against lefites? Yes, 1967, when he batted .338 against lefties and .323 vs. righties. In 1974 he batted .001 better against righties but every other year in his career saw him hit at least .030 better against righties. In both 1965 and 1966, he batted at least 100 points higher against righties. In 1968 and 1969, it was 46 and 79 points, respectively.

So it looks like among all the great things he did that year, he also ramped up his hitting against lefties, even relative to what he did against righties. His OBP was .027 better against righties, but that was the second smallest differential of his career (it .077 for his whole career). The differential was over 100 points in 1965, 1966, 1968, and 1969.

Now his SLG was "only" .500 against lefties that year while it was .657 vs. righties. That differential of .157 was even bigger than his career differential of .121

His OPS differential was .184, slightly lower than his career differential of .198. But, the differential was .391 & .27 the two years before 1967 and .205 & .332 in the following two years. So at that point in his career, his relative performance vs. lefties may have been unusually good.

I tried to calculate how many batting runs he had per plate appearance vs. lefties and vs. righties for each year in his career and then calculated the difference. I used the following run values

1B .47
2B .78
3B 1.09
HR 1.4
BB & HBP .33
Out -.25

Plate appearances were just AB + BB + HBP. His differential in 1967 was .0518, his smallest in 5 years and it would not be less than that for another 5 years. It was the 7th lowest of his career. The average differential of the two previous years and the following two years was .0997. Now that is .0479 higher than in 1967. Suppose he did .0997 worse against lefties in 1967 rather than .0518. Over 144 PAs, it would mean 6.9 runs. With 10 runs usually being equal to one win, it is possible that his much better than usual relative performance against lefties tipped the pennant to the Red Sox.

Added Point: He also hit especially great after August 31. He batted .417 (40 for 96). He ended August with a .3085 AVG. If he had hit that the rest of the way, he would have had 29.61 hits. So this created an additional 10 hits. He slugged .760 after Aug. 31. Through that date it was .594. This created an additional 16 bases. His OBP after Aug. 31 was .504 while it was .401. A rough estimate is that he had 2 more HRs, 1 more 2B, 3 more singles and about 11.6 fewer outs than expected based on what he had done prior to Sept. 1. Using the linear weights values, that generated about 7.89 more runs than expected. That is almost enough runs to generate 1 more win. But Sept. had the lowest league totals for AVG, OBP and SLG that year (probably not that unusual). YAZ greatly increased his performance while everyone else was going down. So it brings it even closer to one win and that is how many games they won the pennant by.

Who Deserved The 1997 NL MVP Award?

This issue came up over at The Daily Something's Happy Birthday To Mike Piazza. Bill apparently thinks that Piazza got robbed when the award went to Larry Walker of the Rockies. The only thing I can cay about that is "Don't Mess With Texas!":)

The reason is that Craig Biggio of the Houston Astros was also deserving of the award (although both he and Piazza were clearly better choices than Walker). The Rockies actually finished 3rd, 7 games behind the West champs (the Giants). The Dodgers were 2nd, 2 GB. The Astros won the Central, but only went 84-78. The Dodgers went 88-74. Sabermetrically, it looks like Biggio and Piazza both had clearly better years than Walker.

Here are the leaders in Win Shares that year

Gwynn 39
Piazza 39
Biggio 38
Bonds 36
Bagwell 32
Walker 32
Rolen 29

Now the leaders in batting plus fielding wins (it is the Pete Palmer linear weights measure, from Retrosheet)

8.2 Biggio HOU
7.7 Piazza LA
6.2 Bonds SF
6.1 Bagwell HOU

Walker had 4.4

I do recall that maybe some writers saw that his home/road splits that year were balanced, so they did not discount for Coors. His AVG-OBP-SLG at home were .384-.460-.709 and on the road they were .346-.443-.733.

Biggio's AVG-OBP-SLG that year (home and road) were .309-.415-.501 playing in the Astrodome. He stole 47 bases with 10 CS and played 2B. Walker was a RFer.

Sean Smith has them with the following wins above replacement (WAR)

Biggio 9.6
Piazza 9.3
Walker 9.0

Biggio still comes out on top, but not by as much. Others already mentioned had

Gwynn 5.2
Bonds 8.8
Bagwell 8.1
Rolen 4.5

I don't see where Sean lists the year by year leaders, so I will have to leave it at that. Another thing to look at would be the NL leaders in various stats that year at Baseball Reference. Walker does well in adjusted batting runs and adjusted batting wins. I think these are park adjusted. So fielding wins would have to vault either Biggio or Piazza past Walker.

1. Walker (COL) 6.6
2. Piazza (LAD) 6.5
3. Bagwell (HOU) 6.0
Bonds (SFG) 6.0
5. Gwynn (SDP) 4.8
6. Biggio (HOU) 4.3
7. Lankford (STL) 4.1
8. Mondesi (LAD) 3.2
9. Caminiti (SDP) 3.1
10. Snow (SFG) 3.0
Olerud (NYM) 3.0
Hundley (NYM) 3.0

Earth In The Balance (Or The (Im)balance Of Power)

Okay, this post isn't really about those things-I just wanted to get Al Gore and Star Trek (TOS) fans to read it. Well, the imbalance of power is not that far off. Anyway, various researchers have wondered about whether or not teams score more runs if they have a balanced lineup compared to an imbalanced one, holding overall talent/performance constant. The latest attempt was

Does a balanced offense score more than an imbalanced one? by mgl at The Book blog.

There have been earlier attempts to analyze this question. I think the evidence is mixed but various methods have been used. So if simulations, Markov chains, regressions, correlations, standard deviations and covariance are your thing, you'll enjoy all of these posts.

Aim For The Head: Are Balanced Lineups Better? by Keith Woolner

Lineup Balance by David Gassko

The Impact of Lineup Balance on Scoring, 1920-89 (by me)

Are "Balanced" Teams More Successful? (by me)

Explaining The 1959 White Sox (Part 2)

Part 1 was Explaining The 1959 White Sox. There I showed that the White Sox had a much higher winning percentage than their underlying stats would indicate. Here I try to quantify how much their clutch performance helped. They had an actual winning percentage of .610.

But according to my study from a few years ago called Does Team Clutch Matter in Baseball?, they should have only had a pct of .522 based on their OPS differential. The Sox only had an OPS differential of about .020 since their hitters had an OPS of .691 while their pitchers allowed an OPS of .671. My team clutch study had a regression equation for winning pct of

(1) PCT = 0.49 + 1.27*OPS - 1.26*OPPOPS

That projects a team with an OPS differential of .020 to have a pct of .522. So how did the Sox end up with a .610 pct? Another regression equation was

(2) PCT = 0.501 + 0.918*NONCLOPS + 0.345*CLOPS - 0.845*OPPNONCLOPS - 0.421*OPPCLOPS

That is, OPS by the hitters and OPS allowed by the pitchers was broken down into "close and late" (CL) situations and non-CL situations. Here is where we can start to see how the Sox had such a good winning pct. Their hitters had a CL OPS of .742 and a non-CL OPS of .680. For the pitchers, those numbers were .623 and .682, respectively. Plugging those numbers into equation (2), the Sox would have a pct of .543. So it is possible that their superior CL performance (especially compared to nonCL), added about .021 to their pct. That is about 3.2 wins over 154 games.

The Sox also did extremely well with runners in scoring postion (RISP). Their hitters had a RISP OPS of .742 and a non-RISP OPS of .673. For the pitchers, those numbers were .639 & .680. How did this RISP performance affect their winning pct? Equation (3) estimates that. It is like equation (2), but broken down by RISP and non-RISP performance.

(3) PCT = 0.501 + 0.848*NONRISPOPS + 0.432*RISPOPS - 0.799*OPPNONRISPOPS - 0.462*OPPRISPOPS

It projects the Sox to have a pct of .553, .031 better than the .522 estimated by equation (1). Over 154 games, that is about 4.77 wins.

It is not clear how to combine the information generated by equations (1) and (2). I am not sure if we can simply add the .021 to the .031 to get .051 and then say that their clutch play add that much to their pct. There is going to be an overlap between the CL and RISP situations. Usually about 25% of plate appearances (PAs) are with RISP and about 15% are CL. Just multiplying the .25*.15 would get about .0375, meaning that 3.75% of PAs are both CL and RISP. So maybe their would not be much overlap and summing the .021 & .031 is okay. Maybe not. I'm just not sure.

But it could be that the combined RISP-CL situations are extremely important and maybe the Sox did well in those cases, further adding to their pct. Anyway, if we could add the two gains together, it would explain more than half the unexplained gap from equation (1), which was .088 (.610 - .522). Whatever the case, the Sox had no advantage over their opponents in non-CL and RISP situations but totally dominated when it was CL or RISP. This is probably the reason for their success. Since no one seems to know how to consistently perform well in the clutch, we have to view the 1959 White Sox has having been very lucky.

I also found that their starting pitchers allowed an OBP of .306, not counting sacrifice hits and IBBs. The starters had allowed 2.26% of hitters to hit HRs. These numbers for the relievers were .306 and 2%. So it could be argued that the White Sox had a great bullpen and that explains the great pitching performance in CL situations. But that is not the case.

Konerko Breaks The Tie With Mark McGwire, Catches Mike Lowell!

At the start of the season, the three of them were all tied for the record with the fewest triples with 5000+ ABs with 6. Lowell got one on April 27th. But tonight Paul Konerko hit one in the first inning in Boston (Lowell was not in the starting lineup). Cecil Fielder had 7. One more and Konerko can tie Mike Piazza. Only 302 more to go to catch Sam Crawford.

Did Drug Use Keep Dave Parker Out Of The Hall Of Fame?

This issue came up in ALAN ROBINSON's article titled Parker wonders if drug use keeps him out of HOF. It compares Parker to recent inductee Jim Rice and others in an attempt to show that Parker might be Hall-worthy. It generally relies on conventionals stats. I don't think Parker has an especially strong case. But it is not bad, either.

Here are some things I came up with. Parker ranks 308th in wins above replacement level among position players with 37.9 at Sean Smith's site Top 500 Position Players. Rice is ranked 257th with 41.5. Neither of those ranks seems impressive.

I have a site which ranks players by the Win Shares Per Plate Appearance. Through 2001, Rice ranked 197th with 20.17 WS per 648 PA among outfielders. Parker was Parker 176th 20.81. Again, not really impressive for either guy.

Baseball Reference has a couple of measures of who is Hall-worthy. One is the "Hall of Fame Monitor." BR says "This is another [Bill] Jamesian creation. It attempts to assess how likely (not how deserving) an active player is to make the Hall of Fame. It's rough scale is 100 means a good possibility and 130 is a virtual cinch. It isn't hard and fast, but it does a pretty good job." Click here to see the rankings. Parker has a score of 124 which ranks 110th while a "Likely HOFer ≈ 100." Rice ranks 90 with a score of 144. So this suggests that both had careers that normally get you in, based on the voters' preferences. Maybe the drug use could be affecting Parker here.

One issue here is that over the years players have gotten in by the BBWAA or the Veteran's committee. The latter has changed its procedures in recent years, so it may not be clear if these patterns will hold in the future and they probably did not always agree with the BBWAA. It would be interesting to have separate formulas for each. Anwyay, if you don't get in by the BBWAA, it usually takes awhile before you are eligible for the Veteran's Committee.

BR also has its Hall of Fame Standards Batting and says "It is used to measure the overall quality of a player's career as opposed to singular brilliance (peak value)." Parker has a score of 41 which ranks him 135th while the "Average HOFer ≈ 50." Rice is ranked 116th with a score of 43. So both guys fall a little short here. But they are close. Parker could argue he is close enough to Rice to get in.

A few months ago, I came up with my own regression based formulas for estimating the % of the votes a player might get in his first year of eligibility or his probability of getting in at all based on voting patterns and the apparent preferences of the voters.

One model was an OLS regression. You can read about that at What Determines Vote Percentage In The First Year Of Hall Of Fame Eligibility? (Part 2). This one estimated first year eligibility vote %. It took into the following variables MVP awards, getting 3000 hits, getting 500 HRs, all-star games, Gold Gloves, getting 500 stolen bases, world series performance and career plate appearances. The model estimated that Parker would get 28.8% while he actually got 17.5%. So a little less than expected but if you read the link you will see that others did even worse.

The other study I did was Predicting Who Makes The Hall Of Fame Using A Logit Model. That model took into account career AVG, number of seasons with 100 RBIs, all-star games, career plate appearances, MVP awards, world series performance, getting 3000 hits and being a catcher. Parker did not do well here but I concluded "If Dave Parker had 8 all-star game instead of 6, he goes from a P of 8% to over 60%." Maybe without the drug use he would have made more all-star games and it would put him in the Hall of Fame. Rice's probability of making the Hall was about 59.5%.

So I don't really see any strong case for Parker. But some evidence supports him.

Why Are The Angels Winning More Games Than Their Pythagorean Projection?

This is being discussed at Beyond The Boxscore and Baseball Think Factory. With 685 runs scored and 605 allowed, their Pyth pct is .561, good for 91 wins in a season. But they actually have a .613 pct, good for about 99 wins. So the gap is projected to be about 8 (this comes from the Bill James idea that a team's winning percentage is going to be close to their runs scored squared/(runs scored squared + runs scored allowed)).

The big reason why the Angels are doing better than expected is how well they are doing in close and late situations (situations "in the 7th or later with the batting team tied, ahead by one, or the tying run at least on deck" according to Baseball Reference, where I got my data from along with ESPN). Then they also hit extremely well with runners on base (as I explain in Part 2). Part 3 discusses how well the Angels are doing by a sophisticated measure called Win Probability Added.

Part 1
I did a study a few years ago called Does Team Clutch Matter in Baseball?. The equation for pct was

(1) PCT = 0.49 + 1.27*OPS - 1.26*OPPOPS

Where OPPOPS is the OPS allowed by a team's pithcers. Using only walks and hits in OBP, the Angels have an .804 OPS and a .787 OPS allowed. It predicts they will have a pct of a .519.

But if you break it down by close and late situations and non close and late situations it was

(2) PCT = 0.501 + 0.918*NONCLOPS + 0.345*CLOPS - 0.845*OPPNONCLOPS - 0.421*OPPCLOPS

For hitting, their OPS was .796 in NONCL and .852 in CL. For pitching, it was .799 & .707. So they get predicted to have a .553 pct. That .033 gain over 162 games is 5.41 wins.

With 685 runs scored and 605 allowed, their Pyth pct is .561, good for 91 wins in a seasons. But they actually have a .613 pct, good for about 99 wins. So the gap is projected to be about 8. But 5.4 (or about 2/3) of that is due to their close and late performance. And as the next part shows, some of the rest of the gap will be explained by how well they hit with runners on base (they don't pitch any better with runners on base than they do normally).

But the more important comparison is between the .519 predicted by equation (1) and and the Angel's actual pct of .613. The .519 says they should win about 84 games. So the gap is 15. The 5.4 explains about 1/3 of it. Still, a big chunk.

One more thing, and to make another long story short, and using formulas from my team clutch study linked above, taking RISP performance into account would add another .023 to the Angel's pct (similar to what I did with equation to and close and late situations). That amounts to 3.7 additional wins. It is probably not that much, since some RISP situations happen when it is close and late and I already did a calculation that took that into account. But if about 25% of PAs are with RISP and about 15% when it is C&L, then maybe 3.75% are both. Not sure if is that simple, but I think most of that 3.7 could be added to the 5.4 I got before and get us close to 9 wins, 60% of the differential of 15.

Part 2
The Angels have scored about about .25 more runs per game than expected. From a regression, the runs per game in the AL this year can be estimated by

R/G = 5.96*SLG + 24.96*OBP - 6.01

It says the Angels should score 5.50 R/G but they actaully have 5.76. So over the whole season, that is about an extra 40 runs. But they are doing it because of their phenomenal hitting with runners on base (ROB). The overall AVG-OBP-SLG this year are 0.290-0.354-0.451. But with ROB, they are 0.307-0.378-0.464. So their differential in all three with ROB are .019-.024-.013.

The AL league averages for AVG-OBP-SLG are .266-.335-.430. With ROB, they are .270-.345-.430. The differences are .004-.010-.000. So the Angels ramp it up alot more with ROB than most other teams.

Then I ran a regression with SLG and OBP broken down by ROB & NONROB. The equation was

R/G = 6.94*NONROBOBP + 4.46*NONROBSLG +17.18*ROBOBP + 1.72*ROBSLG -5.98

This predicts that the Angels would score about 5.63 runs per game. Over a whole season, it means they are scoring about 21 more runs than expected. So taking their ROB hitting into account, we reduce their differential by about half. When I did OBP for both regressions, I only included walks and hits. So the OBPs used are slightly different than what I report (from ESPN). Now their will be some overlap between close and late situations and ROB situations, but my guess is that the gap between actual and predicted wins from Part 1 will be even smaller once ROB is taken into account.

Part 3
The Fangraphs website has a more sophisticated measure. It uses WPA, or Win Probability Added. It is a stat which credits a player with how much he changes his team's probability of winning after a given plate appearance. A guy might get a hit, raising the prob. by .1 or make an out lowering it by .1. These probabilities are based on historical data of how often teams win games in certain situations (leading by 2 in the seventh inning, trailing by 4 in the 8th, etc.) It also takes into account how the base-out situation changes, which affects runs and the chances of winning.

So far this year, Fangraphs has the Angels as +4.79 clutch for their hitters and +4.18 for their pitchers. So that is 8.92 extra wins due to clutch performance (that is, doing better in clutch situations than they normally do). What it means is that the Angels, whether the batters or pitchers, are really coming through the closer and later the game and the more runners on base there are. You can see this data at

Fangraphs Win Probabilities for batters

Fangraphs Win Probabilities for pitchers

Have The Texas (Power) Rangers Discovered OBP?

I have written a few posts this season on how the Rangers are a great power hitting team but they are just about average in scoring because their team OBP is well below average. Click here to read about that.

But so far in August, the Rangers have a team OBP of .351 (overall this year it is .321). The league average for the year is .336. So the Rangers are well behind that. But the league average for August is .344, so now the Rangers are getting on base more than most teams.

Unfortuantely, they are only averaging 4.53 runs per game in August while the league average is 5.19. They also have an above average SLG this month (.459 vs. .451). So it is still probably a good sign that their OBP is coming up. Their below average runs per game this month is probably just bad luck. If they keep that OBP high, combined with their power, they should stay in the wild card chase.

Are The White Sox Really A Bunch Of Underachievers?

By now you proably heard that Sox GM Kenny Williams called them that. Is it true? To see if it is, I compared how individual players and pitchers are doing to what they were projected to do by Bill James in this year's Handbook.

The fist table has OPS and predictd OPS for all the guys who have 200+ ABs on the Sox this year. Some guys did not get projections. Overall it does not look too bad. Some guys are doing better than expected, some worse. I calculated the average differential at a negative -.010. Not huge.



Now pitchers and their ERAs. Things don't look too bad here, either. There is one more table after this that uses WHIP per 9 IP.



Now the WHIP table. Does not look so terrible.

When Buying a Bullpen, It’s Better to Go Cheap

That was the title of an article in the Wall Street Journal on Aug. 4. I have not seen much comment on it in the blogosphere, so I thought I would pass it along. Here is the link: When Buying a Bullpen, It’s Better to Go Cheap. Here is the key passage;

"Among the 54 pitchers who have been used regularly in high-leverage situations this season, the correlation between salary and performance is just .07 (where zero is no relation and one is a perfect bond)."

It would be intersting to see how this shakes out over time. One year can have alot of fluctuations. Maybe using a 3-4 year period would get a higher correlation. Also, I wonder how this compares to correlations between performance and salary at other positions. Then there is the issue of whether or not a given pitcher was eligible for free agency or arbitration. But still, it does seem like a pretty low correlation.

Maybe Stat Zombies Ignored The Marlins But Others Did, Too

The Marlins are actually doing just a bit better than this stat zombie would expect.

From a regression I ran a few years ago,

Winning Pct = .5 + 1.21*OPSDifferential

The Marlins have a .008 differential this year for a .50968 predicted winning percentage. For a 115 games, that would be 58.61 wins. They have 61. So 2.39 more wins than expected. Does not seem like a big deal. The standard error of the regression was 1.54 wins per season. So the Marlins are one win beyond that. We should also wait until the season is over to judge them. My regression equation is from the article An OPS Question.

But this issue comes from a Paul Lebowitz article called Why the stat zombies ignore the Marlins. (Hat Tip: Baseball Think Factory) Here is the relevant paragraph:

"Most of the stat zombies predicted them as winning between 66 and 74 games. But it doesn't matter whether they win 80, 85 or 90 games; whether or not they make the playoffs or fade out at the end. They've built an organization that should be admired in the way that Michael Lewis's creative non-fiction Moneyball canonized the Billy Beane A's. Lewis tried to create an "age of enlightenment" in baseball inserting the Ivy League-educated genius into the game at the expense of those who can look at an athlete and find his talent regardless of what his stat sheet says. The numbers don't fit into that kind of analysis, so it's best to ignore it and hope it goes away; but it's not going away."

So some stat zombies did not correctly predict the Marlin win total. Mr. Lebowitz does not say who they were or provide any links. Also, I don't know if any stat zombies claim they have perfect foresight. There will always be things no one can predict. For example, who could have predicted that both Jim Rice and Fred Lynn would have had such outstanding rookie seasons in 1975 to propel the Red Sox to the AL East title?

But as I hinted at earlier, stat zombies were not the only ones to "ignore" the Marlins. The following links show they were not highly regarded before the season by non-zombies:

Chris Bahr's Predictions at the Sporting News (he predicted they would be 4th)

Sporting News Power Poll (the Marlins at #20 in mlb, 11th in the NL)

CNN/SI MLB 2009 Preseason Predictions

The last one had 13 experts and none predicted the Marlins to win the division or be the wild card.

One last thing. One stat zombie, Tom Tippett, of Diamond Mind baseball fame, has done a pretty good job of making predictions. You can read about that at 2005 Predictions -- Keeping Score. Look for the 7 year composite rankings towards the end. He did a very good job of predicting standings using his simulations from the Diamond Mind game. He was a also such a good stat zombie that he got hired by the Red Sox.

Has The Dodgers Defense Been Good This Year And How Much Difference Has It Made?

The Dodgers lead the NL in both fielding percentage and defensive efficiency rating (DER). DER simply says what % of balls in play are turned into outs. The following link at mlb.com has the fielding stats Sortable Team Stats.

It seems unusual that at team would lead its league in both categories. A team (or player) can have a high fiedling percentage but not get to that many balls. But that is not the case with the Dodgers. They must be getting to alot of balls and are fielding them cleanly. So how many runs are they saving with all these balls they catch that other teams don't and all these errors they don't make?

Using data from ESPN, the Dodgers have allowed 452 runs this year with 422 of them being earned. So they have 30 unearned runs. The league average is 40. So that makes the Dodgers 10 runs better than average.

Then ESPN shows that the Dodgers DIPS% is 107, meaning that their pitchers would have an ERA that is 7% higher than it actually is if they allowed a league average of hits on balls in play (they are , of course, better than average). With their actual ERA being 3.61, then their DIPS ERA is 3.86. So here their fielders save .25 runs per game (that is, if the pitchers have nothing to do with batting average on balls in play). The Dodgers have played 115 games, so this is an additional 28.75 runs scored. Adding the 10 in from fewer unearned runs gives us 38.75 runs. Since it usually takes about 10 runs to win one game, a rough estimate is that the Dodgers have won close to 4 games this year with their fielding.

On the other hand, Fan Graphs Team Fielding shows them to have just about average defense using more advanced metrics.

Pedro Martinez Has A 2.16 DIPS ERA In His First Start

That is according to the the ESPN rankings. They say they use the DIPS 2.0 formula. Although he allowed 3 earned runs in 5 IP (for a 5.40 ERA), he struck out 5 while only walking 1 and allowed no HRs.

Explaining The 1959 White Sox

They won the pennant but their underlying stats were not that good. The reason they came in first is that they performed remarkably well in "clutch" situations.

First, let's look at their underlying stats. Yesterday I presented a formula that estimates a team's winning percentage. I found each team's HRs, BBs, and non-HR hits per game for both their pitchers and hitters and then converted this to a differential. Then I came up with the following formula for winning percentage using regression analysis:

Pct = .5 + .071*NONHR + .047*BB + .157*HR

(some technical notes on this at the end). This formula predicted that the 1959 White Sox would have a winning percentage of .512, while it was actually .610. So they exceeded their predicted pct. by .099 (not .098 due to rounding). This was the third highest positive differential since 1920. The highest belonged to the 1931 Cardinals. But Retrosheet does not have situational splits for them. Next is the 2007 Diamondbacks. But they did not make it to the World Series.

The table below shows the standings for the 1959 AL using the predicted pct. The White Sox are 4th.



The White Sox were actually out homered by their opponents (with the biggest negative differential). But they were third in both walk differential and nonHR differential. Now let's look at how they did in "clutch" situations vs. other situations. The table below shows how the Sox hitters did in various situations.



Now what the Sox pitchers did.



Now the differentials followed by some discussion.



The total line, of course, refers to all plate appearances. The Sox had modest differentials here. They batted .250 while the Sox pitchers held their opponents to a .242 AVG. With no runners on base, the differentials are even lower. But now look at their differentials with men on. For AVG, it is .013, much higher than the .005 with none on. For OBP, the differential jumps from .009 to .023. SLG goes from .001 to .010.

With runners in scoring position (RISP), they had a .040 differential in AVG!. It was actually negative in nonRISP situations. Sox pitchers held opposing batters to a .221 AVG with RISP. Their OBP differential jumped from .007 to .040 while SLG jumped from -.014 to .063. Incredible. Their hitters' SLG went up .032 with RISP while the pitchers lowered it by .044.

Moving to close and late situations, the Sox outhit their opponents by .024 while it it was only .005 in nonCL situations. The OBP differential rose from .010 to .043 while for SLG it went from -.012 to .076. Another stunning swing. The Sox hitters actually had an SLG of .400 in close and late situations, by far their highest for any case.

So it is pretty easy to see what happened that year. I have not looked at other teams, but the case of the 1959 White Sox must be very unusual.

Technical notes: The regression was linear. The r-squared was .806 and the standard error was .035, which amounts to 5.67 wins per 162 games. I also put each team's data in groups of 5 years and then did the same regression. The r-squared was .917 and the standard error in terms of wins fell quite a bit (I think it was about 2.2 wins but I left that data at the office). So some of the randomness is mitigated by aggregating over 5 years. The coefficient values were about the same in each regression.

Were The 1922 St. Louis Browns The 14th Best Team Since 1920?

Yesterday I mentioned that they have done very well in computer simulation seasons. I think that might be due to how well they performed statistically in real life. If the computer simulations don't include any situational adjustments (like performance with runners on base or in close and late situations), then a team's raw stats will dictate how well they do. Some teams have bad luck. They don't win as many games as their runs scored and runs allowed might suggest. And they might score fewer runs and give up more than their stats might predict. That might have happened to the Browns.

Ideally, we would rate all teams on their OBP & SLG, both by their hitters and what is allowed by their pitchers. But these data are not easily obtained going back so far in history. So I found each team's HRs, BBs, and non-HR hits per game for both their pitchers and hitters and then converted this to a differential. Then I came up with the following formula for winning percentage using regression analysis:

Pct = .5 + .071*NONHR + .047*BB + .157*HR

Then I calculated a predicted winning percentage for every team since 1920. The table below shows the top 25 in terms of predicted winning percentage. You can click on the table to see a bigger version. Not a surprise that the 1927 Yankees come out on top. The had an actual pct. of .714 but the predicted pct. is .744, so they fell .03 short of what the model says.




The 1931 Yankees did very well, coming in third. Yet they came in 2nd place, to the A's that year, about 13 games out! The 1931 A's are 31st according to the model. As you can also see, the 1922 Browns are 14th. The 1969-71 Orioles had 3 teams in the top 33. Now the teams with worst predicted percentages.



In case you are wondering where the 1962 Mets are, they get mentioned below. Now the teams that exceeded their predicted pct. the most. I suspect that these teams did especially well in clutch situations. I will have to look at their splits in Retrosheet (where available) to see if this is true.



Now the teams that underperformed the most. The 1962 Mets were the 2nd worst.



I will try to add some more discussion later when I get some time. Roger Godin told me that there was a book in written in 1950 by Tom Meany called Baseball's Greatest Teams and that the 1922 Browns were one of the ten teams discussed.

Are The 1922 St. Louis Browns An Unacknowledged Great Team?

There is a book about them. It is 1922 St. Louis Browns: Best of the American League's Worst by by Roger A. Godin. But I have not read it and I don't know if it attempted to rank them among all teams in baseball history (I will post something on this tomorrow and it will show that they rank very high).

I started thinking about this when I read about a simulation called the Seamheads Near Miss League at wezen-ball. The Browns did extremely well. Actually, they were an outstanding team statistically.

The Brown's team strikeout-to-walk ratio was about 34% above the league average. That is the 110th best ever in AL & NL history. With well over 2000 team-seasons, that puts them in the top 5%. Their team ERA was about 19% below the league average, good for 195th, still in the top 10%. But their park factor was 108, meaning that they pitched in a somewhat high run environment. They did give up a few more HRs than the average AL team in 1922. They gave up 71 HRs while the average for the other 7 teams was about 65. But their park gave up 91% more HRs than average.

On the hitting side, their team offensive winning percentage of .577, which ranks 191st. Again, that is in the top 10%. OWP is the Bill James stat that tells us if a team had 9 identical hitters and gave up an average number of runs, what would their winning percentage be? Since I used the Lee Sinins Complete Baseball Encyclopedia, it is park adjusted. So pretty impressive, being in the top 10% in both hitting and pitching.

What happened to them? Why didn't they win the pennant? Using Retrosheet, here are some interesting facts about the 1922 season:

The Browns finished 1 game out, behind the Yankees. But the Browns had a run differential of 224 to the Yankees 140 (of course Ruth missed 44 games, most probably due to his suspension. He also got off to a slow start in May, batting just .190 with 2 HRs in 42 ABs).

The Browns had 256 more hits than their opponents that year, 50 more walks and 27 more HRs. For the Yankees, it was 101-69-25. So the Browns stats look much better.

The Yankees beat the Browns 14 times out of 22 even though the Browns outscored the Yankees 105-100 in those games. It looks like the Yankees won all 8 of the 1-run games between the two teams that year, with 4 in extra innings.

In mid-Sept, the Yankees came to Stl. for the last series between the two teams that year. The Yanks were a half game ahead when the series started and won 2 out of 3. In the last game, the Browns led 2-0 after 7, but NY won it 3-2 with 2 in the 8th and 1 in the 9th. 2 of the 3 runs were unearned as the Browns made 3 errors.

Later, with 2 games for each team left, the Browns were 2 back. But both of them won game 153 and so it was over. But if the Browns had just one more win against NY, they would have been tied with 2 games left.

Udate at 7:43 am central time, 8-11-2009: Chirs Jaffe had a good discusssion of this team last year at October country’s refugees (part 2 of 2)

Rangers Power Update

(or maybe we should call them the "Power Rangers") With 2/3 of the season having been played, the Rangers have 168 HRs. If we simply increse that by 50%, they would finish with 252, 4th best all time. I first wrote about this issue last May with Texas Rangers On A Pace To Set Power Hitting Records. Obviously the Rangers have tailed off in their power hitting since then. But their isolated power is still .201, which would be tied for 3rd all-time. Their HR% is 4.56. If they finish with that, they would be 2nd.

But they are only scoring an average number of runs per game (4.84, the league average is 4.83). The reason they are only averge in runs per game even though they are doing all this power hitting is that their OBP is only .317 while the league average is .335.

Jim Rice and the Hall of Fame (Revisited)

Bob Ryan of boston.com recently wrote an article called The big picture is that Rice earned his plaque. He takes a swipe at "SABR people." But we're not monolithic and I don't know if all or even most SABR members agree with my views on Rice. But anyway, here is part of what Ryan said and I follow by summarizing some of my past posts on Rice that counter what Ryan says.

"The SABR people are resolutely anti-Rice. They’ve got numbers parsed by the truckload to downplay his impact, and to this I say, “Phooey,’’ or maybe even something stronger. For SABR people refuse to acknowledge the concept of anecdotal evidence when evaluating a ballplayer (no, not you, Bill James). So when I speak of the time Milwaukee manager Alex Grammas confirmed for me that, yes, indeed, he had ordered a sizzling Jim Rice pitched around (like, four straight unhittable balls out of the strike zone) in a sixth-inning, bases-loaded situation, or when fellow inductee Rickey Henderson says, as he did yesterday, that when the A’s had pitchers meetings prior to Red Sox series in the Rice era guys “trembled,’’ they say that’s nice, but irrelevant.

Sorry, it matters.

There was a three-year period from 1977-79 when Rice was The Man in the American League, averaging 41 homers, 127 RBIs, and 206 hits a year. And did you know he had back-to-back seasons (’77-78) of 15 triples? He was a feared - yeah, SABR people, feared - hitter, because he was very content to get a base hit in a key situation. He was, after all, just trying to win the game."

One of my posts was Was Jim Rice A Feared Hitter?. I showed that he did not draw very many intentional walks compared to other top hitters and that players who batted in front of him were not especially helped.

With Jim Rice and the Hall of Fame I showed that his clutch hitting stats, although better than his overall stats, were very close. I wrote "According to retrosheet, with ROB [runners on base], his AVG-SLG were .305 & .509. With RISP [runners in scoring position] he had .308 & .501. These are very close to his overall stats of .298 & .502." In fact, he was more likely to come up with runners on base in Fenway, a good hitters park. So naturally he would hit better in those situations. He most likely had a disproportionate number of ABs with RISP & ROB in Fenway. His close and late AVG-SLG were .274-.453. So that does not look very clutch.

He was helped by Fenway. His AVG-SLG in home games was .320 & .546 while on the road they were only .277 & .459. I also showed that his RBI-to-GDP ratio was very poor, even below average.

One commentor at Ryan's article mentioned that Rice had alot of clutch hits in Septmber 1986 when the Red Sox were in a tight divisional race. But his AVG was .310 in Sept while it was .324 for the whole season. He also grouned into 6 double plays that month. He had 19 for the whole season, so he had close to 1/3 in Sept. His SLG was .560 in Sept. while it was .490 for the whole season. So he did slug better even if he got fewer hits.

I have also found that Jose Cruz of the Astros may be just as Hall worthy as Rice. Go to Jim Rice vs. Jose Cruz.