Wednesday, July 17, 2013

Ranking Pitchers By Fielding Independent ERA Adjusted For The League Average And Park Effects

I am not sure how much sense this makes and someone else may have done this. There may be problems with this approach that I have not thought of (and there are some that did occurr to me that maybe I could discuss in the future).

Here is how it works: I used Fielding Independent ERA from Fangraphs. But first I used RSAA or "Runs Saved Above Average" from the Lee Sinins Complete Baseball Encyclopedia. It adjusts for the league average and park effects.

Take Pedro Martinez, for example. He had 2,827.33 IP and an RSAA of 496. So he saved about 1.58 runs per 9 IP. Suppose we are in a league that has an average of 4 runs per game. It means he would allow about 2.42 runs per game.

But, according to Fangraphs, Martinez had an FIP ERA of 2.91, .02 lower than his actual ERA. So I subtracted .02 from 2.42 to get 2.40. Below is the top 25 pitchers with 2500+ IP from 1876-2011.

Pedro Martinez2.40
Roger Clemens2.63
Randy Johnson2.75
Lefty Grove2.77
Curt Schilling2.82
Roy Halladay2.86
Rube Waddell2.99
Mike Mussina3.07
Bret Saberhagen3.08
John Smoltz3.10
Greg Maddux3.11
Bob Gibson3.17
Dazzy Vance3.18
Cy Young3.20
Hal Newhouser3.20
Kevin Brown3.21
Walter Johnson3.21
Andy Pettitte3.22
Bert Blyleven3.26
Kevin Appier3.29
Kid Nichols3.31
Dizzy Trout3.31
Dennis Eckersley3.33
Christy Mathewson3.34
Rick Reuschel3.34
 
If I get time tomorrow, I will create a link with all 238 pitchers. Koufax is at about 3.08. But he only had  2325 IP. Catfish Hunter was 229th at 4.25.

I also used relative runs allowed. Martinez, for example allowed 1006 runs in his career. Since he saved 496, that means the average pitcher would have allowed 1502. Since 1006/1505 is about .67, I gave him about 2.68 runs allowed per game (since .67*4 = 2.68). But then I lowered it by .02. To get 2.66. Here are the top 25 using the relative method

Pedro Martinez2.66
Roger Clemens2.85
Curt Schilling2.94
Randy Johnson2.95
Rube Waddell2.99
Roy Halladay3.06
Lefty Grove3.12
Bret Saberhagen3.18
John Smoltz3.18
Walter Johnson3.18
Bob Gibson3.19
Mike Mussina3.20
Greg Maddux3.23
Ed Walsh3.25
Dazzy Vance3.27
Christy Mathewson3.30
Bert Blyleven3.30
Hal Newhouser3.31
Kevin Brown3.32
Andy Pettitte3.34
Eddie Plank3.37
Cy Young3.37
Rick Reuschel3.37
Dennis Eckersley3.38
Chief Bender3.38

Sunday, July 14, 2013

Trout's Relative Slugging Percentage For Ages 20-21 Near An All-Time High

Last year his SLG was .564 while the league had .409. This year he has .568 and the league has .411. So he is about 38% above the league average. That gives him a relative score of 138 (since each year his SLG divided by the league SLG is about 1.38 and multiplied by 100 you get 138).

The table below shows the top 20 in relative SLG for ages 20-21 with at least 800 plate appearances.

 
The 138 would put him in 5th place and in some very great company. He also has 523 total bases combined in 2012-13. That is already the 16th best total at ages 20-21. I have him on a pace to get 368 this year. Then his total for the two years would be 683, an all-time high. Here is the top 16:
 
Ted Williams 677
Alex Rodriguez 670
Al Kaline 648
Mel Ott 647
Frank Robinson 642
Vada Pinson 638
Orlando Cepeda 625
Eddie Mathews 599
Ken Griffey 576
Buddy Lewis 567
Ty Cobb 559
Cesar Cedeno 543
Jimmie Foxx 542
Tony Conigliaro 539
Hank Aaron 534
Mike Trout 523
 
It should not be too hard for him to reach Cepeda. He would just need to SLG about .362. Slugging about .444 would get him to Kaline. He needs to slug about .547 to tie Williams, assuming he plays every game and gets about the same ABs per game he has so far this year.


Thursday, July 11, 2013

Players Who Have Gone For 6 For 6 In A Major League Game

Click here to see the list that I maintain on this. I just updated it since Alex Rios went 6 for 6. A few years ago I downloaded the list the Sporting News had compiled and that is basically what this is. If there are any missing, let me know (that includes any other 6 hit games as well). Please let me know if you spot any mistakes.

Click here to see the list of all AL & NL 6-hit games at mlb.com

Monday, July 8, 2013

Update On Trout

Three weeks ago I had a post about Trout having another season of at least 160 OPS+. If he did, he would join Ty Cobb and Ted Williams to be the only players to have two such seasons through the age of 21. If I used 150, there would only be two more guys to join the list, Mel Ott and Rogers Hornsby. See Trout Could Join A Group That Includes Only Ted Williams And Ty Cobb.

Trout now has a 164 OPS+. If he could get around 134-136 the rest of he way, he would finish with at least 150. Then he would be on the list of Cobb, Hornsby, Ott and Williams. Four of the greatest hitters ever.

Sunday, July 7, 2013

How Well Did Carl Yastrzemski Hit In Clutch Situations During September And October Of 1967?

Very well, as you will see in the table below. This does not include World Series games. I used the Baseball Reference Play Index. My definition of Close and Late here is from the 7th inning on with the batting team ahead by 1, tied or behind by 1.

Clutch Stats For Sept/Oct 1967


Notice how well he hit in the clutch late in the season, even compared to his great full-season stats. He did not make a habit, though, of exceeding his normal performance by so much in the clutch during his entire career. The next table shows this. But we can see that he certainly did not let the pressure of a very tight pennant race get to him.

Clutch Stats For Entire Career



Monday, July 1, 2013

Chris Davis Has A .710 SLG In His Last 401 ABs

That includes this year and September and October of last year. He has .728 this year. The AL average is .409 this year. So Davis is 78% better than the league aveage. That would be the 17th best ever. See Chris Davis Has A .722 SLG Over His Last 302 ABs. He is actually slugging .747 in road games and .711 in home games this year. But according to the Bill James handbook, Camden Yards has a HR rating of 129 for lefties over 2010-2012, meaning their HR rate is 29% higher there than average. So he could be helped by his park.

He also has a 202 OPS+ this year as does Miguel Cabrerra. The last time there was a season with 2 guys getting at least 200 was 2001. Before that 1994 and before that is was 1961. Prior to this year, there have been 59 seasons of at least 200 in OPS+, going back to 1876.

Click here to see the all-time leaders at Baseball Reference. Here are all the years when at least two guys reached 200 in OPS+.

Friday, June 28, 2013

Why Did Lou Gehrig Hit Better On The Road Than At Home?

To see his career splits at Retrosheet, click here. For his career, his home AVG-OBP-SLG were .329, .435, .620. On the road they were .351, .457, .644. There is at least a 20 point difference for each stat. This seems strange since players usually hit better at home.

Fack Youk looked at this issue a bit in 2009. See Jeter, Gehrig, And Home/Road Splits. It does not seem like they got into too much detail, perhaps because of data limitations that may no longer be a problem.

The table below shows how Gehrig and the AL did from 1925-1938 (so not quite all of his career). H means home games and A means away games. Some data was from Retrosheet and some from Baseball Reference.

It is pretty clear that AL batters from 1925-38 hit better at home. Their AVG was 14 points higher at home while Gehrig's was 22 lower. So that is a swing of 36 points. The swing for SLG was 47 points.

This next table shows Gehrig compared to the top 10 Yankee left-handed batters over this period. I used BB% here instead of OBP (BB/(AB + BB)). Who those 10 guys were is listed at the end of this post

It is pretty clear that those other Yankee lefties did better at home (except in batting average on balls in play (BABIP)). But even there Gehrig had a bigger difference, .033 vs. .012. This just about explains his lower AVG, SLG, OBP at home. Notice his ISO (SLG - AVG) is just about the same, home and away. The other Yankee lefties did much better in ISO at home.

Maybe Gehrig was more of a line drive hitter, so he did not get as many HRs into the short rightfield porch as we might have expected.  That would also explain why he got hurt even more on BABIP. If the right fielder is playing in closer, he catches more of those line drives and your BABIP suffers.

Here are the top 10 Yankee lefties in PAs from 1925-1938 not counting Gehrig (I also removed Lefty Gomez, since he was a pitcher). These guys collectively had about 12,900 PAs at home and about 14,000 PAs in away games.

Earle Combs                6470  
Babe Ruth                  6013  
Bill Dickey                4939  
Red Rolfe                  3115  
George Selkirk             2025  
Joe Sewell                 1754    
Tommy Henrich               817  
Jack Saltzgaver             760  
Gene Robertson              623  
Cedric Durst                529  


Here is what they each did, home and away

Friday, June 21, 2013

Have The Cardinals Been Lucky So Far This Year?

Maybe a little. I started wondering about this when I saw that their AVG with runners in scoring position was .340 while it was .242 with none on (also, it is .321 with runners on). Averages are usually a bit better with RISP and runners on, but those are huge differentials. The table below shows how the Cards and the league have done in various situations. If you look at the pitching numbers carefully, you can see Cards pitchers are doing a bit better than the league average as well.




To estimate how many wins the "luck" has given the Cards, I used my equation for generating winning pct based on OPS differential.

Pct = 1.21*OPSDIFF + .5

The Cards batters have an OPS of .747 while their pitchers have allowed a .663 OPS. So their differential is .084. That should give them a pct of .602 while in reality it is .644. That gives them an extra 3.08 wins. But they would still have a very good record without the luck.

It is mostly on offense where they are getting their luck. Over the years 2010 - 2012 here is the regression generated team runs per game based on OBP and SLG

R/G = 14.71*OBP +  10.37*SLG - 4.57

The  Cards have a 0.337 OBP this year and a  0.410 SLG. The formula predicts they would score 4.64 runs per game while it is actually 5. So that is about .36 runs extra per game, probably due to their great hitting with RISP and runners on. The only team over the years 2010-12 to have a greater positive differential than the Cards this year were the 2010 Rays, who had .45. The next best team this year in all of MLB is the Mets, at .29.

The regression equation for runs allowed is

R/G = 16.95*OBP +  10.67*SLG - 5.41

The Card pitchers have allowed a .302 OBP this year and a .360 SLG. The formula says they should allow 3.55 runs per game while it is actually 3.479, for a difference of 0.071 runs per game.

So combining the .36 runs from batting with the .071 fewer runs allowed, we get .435 total extra runs per game (there is some rounding going on). Over 73 games, that is an extra 31.76 runs. But they are only the 2nd luckiest team so far this year. The table below shows how all the teams have done.


Monday, June 17, 2013

Trout Could Join A Group That Includes Only Ted Williams And Ty Cobb

Trout has a 161 OPS+ so far this year and last year he had 171. So he could have 2 seasons of 160 or higher through the age of 21 (he won't turn 21 until after June 30). Below are all the seasons of OPS+ of at least 160 through the age of 21, with at least 400 plate appearances. Both Cobb and Williams did it twice. Trout could join them this year. Even having one season like this puts you in very impressive company. They generally had long, productive, if not Hall of Fame, careers.

 
 
What if I lower the standard to a 150 OPS+? Here are all the players to have 2 seasons of at least a 150 OPS+ through the age of 21.
 
Mel Ott
Rogers Hornsby
Ted Williams
Ty Cobb
 
It seems like Trout has a great chance to join that group. Here are all the players who had 1 or more seasons of at least a 150 OPS+ through the age of 21
 
Al Kaline
Albert Pujols
Alex Rodriguez
Cesar Cedeno
Denny Lyons
Eddie Mathews
Fred Carroll
Hal Trosky
Jimmie Foxx
Ken Griffey
Mel Ott
Mickey Mantle
Mike Tiernan
Mike Trout
Rogers Hornsby
Sam Crawford
Stan Musial
Ted Williams
Tom McCreery
Tris Speaker
Ty Cobb
 
Again, a very outstanding group of players.
 

Wednesday, June 12, 2013

Josh Hamilton's Strange Season

He has 23 extra-base hits this year and only 20 RBIs. As I show later, he his hitting terribly with runners on base this year while in his career he has generally been pretty good. He is on  a pace to get 57 XBHs this year. Below is a list of all the players in the last 10 years to get 50+ XBHs yet have more XBHs than RBI. They tend to look like guys who bat 1-2 while Hamilton has been batting mostly 4-5 this year with 23 ABs batting 2.


I think Sizemore in 2006 was the only player ever to get 90+ XBHs in a season yet have more of those than RBI. He batted mostly leadoff. I think the record positive difference is Frank Baumholtz in 1953. He had 46 XBHs and 25 RBI. He was pretty much a 1-2 man.

Here are Hamilton's splits for this year and his career.

 

Click here to go to Hamilton's Yahoo page

Wednesday, June 5, 2013

Players Who Had 90+ Extra-Base Hits In A Season Before They Turned 25

Here is the list


I got this from the Lee Sinins Complete Baseball Encyclopedia. The age column shows their age as of June 30. So if a guy turned 25 on July 1, his age for that season is listed at 24. But all of these guys turned 25 after the season in question. They are all pretty much Hall of Famers or guys who put up Hall of Fame type numbers, except for Sizemore and Trosky.

But even Trosky was very good. Through age 27, he is 124th in career WAR among position players. If he had finished with that rank for his whole career, he would be a borderline case. He only played 312 games after the age of 27. His SABR bio says he suffered from very bad headaches starting around that time. He also finished in the top 7 in OPS+ 4 times.

Sizemore had 53 2Bs, 11 2Bs, 28 HRs.

Through age 25, he was 48th in career WAR among position players. He had 4 straight years in the top 10 including a number 1. Maybe he was headed for a Hall of Fame career before injuries.

And here all the guys that had from 85-89 under age 25 (I did not check to see if they turned 25 in the season in question so a few of them might have made the cutoff after their 25th birthday). It is also a pretty impressive list.


Monday, June 3, 2013

Chris Davis Has A .722 SLG Over His Last 302 ABs

That goes back to last year, including Sept and Oct. He has 30 HRs in that stretch. His SLG this year is .754. That is about 83% higher than the league average, which is .413. If he kept that up for the whole season, it will be one of the greatest relative SLGs ever, at 183. Here are the top 20.


 
 
 See also
 
Chris Davis' Absurd Season by Matt Hunter of "Beyond the Boxscore"

Friday, May 31, 2013

How On-Base Percentage and Slugging Percentage Affect Winning

This is something that I posted at Beyond the Boxscore in 2006.

It's probably obvious that if a team increases its on-base percentage (OBP) or slugging percentage (SLG), its winning percentage will go up. Get more runners on and hit for more power, you win more games. But how many more? If OBP goes up by as much as SLG, will they both lead to the same increase in wins? What does it mean for OBP to go up by as much as SLG? The same number of points? By the same percentage? Or should we look at something slightly more sophisticated, like a one standard deviation increase for each one? And what about reducing the OBP and SLG of your opponents? How many wins will that bring?

To try to get a handle on this, I used linear regression to find an equation for team winning percentage (I looked at all teams from 1989-2002). This is what I got

PCT = .493 + 2.01*OBP + .858*SLG - 2.06*OPPOBP - .806*OPPSLG

OPPOBP and OPPSLG are, respectively, the OBP and SLG teams allow their opponents. Given this relationship, how many more games will team win if they increase OBP and SLG (or reduce their opponents' OBP and SLG)? Table 1 shows the various increases in wins for a given change in performance


For example, if team OBP goes up by .010, wins over a 162 game season will increase by 3.26 (2.01*.01*162 = 3.26). For team SLG, it will go up 1.39 wins. The next column shows that the OBP increase is 2.35 times as important as the SLG increase. Lowering your opponents OBP and SLG have about the same effect and relationship.
 
The average team OBP was about .331. So a 10% increase would be about .033. The average team SLG was about .411, so a 10% increase would be about .041. The numbers were the same for OPPOBP and OPPSLG. A 10% increase in OBP adds 10.79 wins while a 10% increase in SLG adds 5.71. This makes OBP 1.89 times as important as SLG. The changes are about the same on the pitching side.
 
Standard deviation (SD) is a measure of spread or dispersion. The SD of OBP was .0149. That increase would add 4.85 wins. The SD for SLG was .0311. That increase would add 4.32 wins. In this case OBP is 1.12 times as important as SLG. On the pitching side, the SDs were about the same, so the results are similar.
 
So the relative win value of OBP and SLG can depend on how you frame the question or what kind of change you are looking at. In regressions with team runs per game as the dependent variable instead of winning percentage, the coefficient value on OBP is usually about 1.5 or 1.6 times that of SLG. It is more than double here for some reason. I am not sure why.
 
I also did the analysis with isolated power (ISO) instead of SLG. ISO is SLG minus AVG and is a better measure of power hitting than SLG, since a guy could get a single every time up and have an SLG of 1.000 with no extra base power. In this case, the regression equation was
 
PCT = .499 + 2.52*OBP + .962*ISO - 2.54*OPPOBP - .923*OPPISO
 
Table 2 shows the various win increases. I won't discuss those results since it would just repeat the previous discussion. The numbers mean the same things they meant in Table 1. The average ISO was .147 and the SD of ISO was .0227. Those were about the same on the pitching side.
 
 
Technical notes: The r-squared for the first regression .817, meaning that 81.7% of the variation in team winning percentage is explained by the equation. The standard error was .0297. That is about 4.8 wins a season. All of the independent variables were statistically significant, with all T-values above 8 or less than -8. The r-squared for the second regression .818, meaning that 81.8% of the variation in team winning percentage is explained by the equation. The standard error was .0297. That is about 4.8 wins a season. All of the independent variables were statistically significant, with all T-values above 8 or less than -8. There were 394 teams.
 
Now the comments
 

Correlation

How much do OBP and SLG correlate with each other? If there is a high correlation between the two, OBP might be sucking up some of the effect of SLG%. Isolated power might help reduce some of that but not all. If there something you could use that breaks OBP into its component parts, Walks and Hits?
 

Correlation

The correlation between OBP & SLG was .777. For OPPOBP & OPPSLG, it was .838. For ISO & OBP it was .616. For OPPOBP & OPPISO it was .703. Those seem high, so collinearity may be a problem. But I had low standard errors for the coefficient estimates, which is usually an indication that collinearity is not a problem.
 
Another way to check for multicollinearity is to run regressions in which one IV is a function of all of the other IVs. In the first model with OBP and SLG, the r-squared was about .5 when OBP was the dependent variable and the other variables (SLG, OPPOPB, OPPSLG) were the independent variables. There is a stat called the "variance inflation factor" or VIF. It is 1/(1 - r-squared). So if r-squared was .5, 1 - .5 = .5. Then 1/.5 = 2. A couple of sources I looked at suggested that if the VIF is under 10, multicollinearity is not a problem. So in this case, the VIF is only about 2. For the other 3 cases, VIF only got as high as 4. I did come across one source that said there is no rule about the value of VIF and multicollinearity.
 
But I did run the following regression based on your suggestions
 
PCT = .491 + 1.04*EXB +2.72*H + 2.53*W - 1*OPPEXB - 2.7*OPPH - 2.54*OPPW
 
EXB is extra bases/PA (PA = walks + ABs)
H is hits/PA
W = walks/PA
 
So it looks like a pretty big difference between getting hits and getting on base and hitting for power. Here are the win changes for a 1 SD improvement
 
EXB 3.38
W 4.64
H 4.43
OPPEXB 3.06
OPPH 5.19
OPPW 4.15
 

Sunday, May 26, 2013

Players Who Had A Line Drive Percentage Of At Least 30%

Sean Forman compiled this list for me. I noticed the other day that Miguel Cabrerra had 30% so far this year. I wondered what the record was and Sean was kind enough to come up with an answer. Interesting that about 90% of them are between 1996 and 2002 yet the stat goes back to 1988. Not sure why no one has done it the last 10 years. It is the % of all balls put in play that are line drives.


Wednesday, March 13, 2013

Rabbit Maranville, Mr. RBI

Why him? The following Bill James formula predicts his RBIs better than any other player:

RBI = (TB/4) + HRs

It predicts he would have had 883.75 RBIs while he actually had 884. For every 700 PAs, or about a full season, that is only off by +.016. That is the most accurate prediction for all players with 5,000+ PAs from 1876-2012 (I used Baseball Reference and RBIs might not be available for all pre-1900 years). Click here to see the rankings. The rankings are arranged by how much over or under a player was predicted.

Cap Anson was predicted to have 77.24 RBIs per 700 PAs while he actually had about 130. So he gets +52.76. Of course, this does not mean he was necessarily a great clutch hitter (although he could have been-he did lead the league 8 times in RBIs according to Baseball Reference and if you notice, he is 7 RBIs ahead of the next best guy, so he looks like a bit of an outlier). But his team led the league in OBP several times back then and in other years was often near the top.

So what might be going on with Anson? For one, he did not hit many HRs (just 97). But no one did back then so you had low HR guys batting in the middle of the order, where you would get more than the average number of RBI opportunities. Second, he might have played in some years when the league OBP was high. Third, more players reached on errors back then, creating even more opportunities.

Over the last 10 years, the formula has predicted about 20 more RBIs per team each year in the AL than they actually got. In the NL, it is about 25 more. So the prediction is coming in around 3% too high. Again, we are in a low error period, so not as many runners are reaching on errors as in other period's in baseball's history.

In, fact there is a high correlation between how often runners reach (by whatever means) and the size of the prediction error for a whole league in any given year. I added the OBP each year to the error rate (ERATE) each year (ERATE is 1 - fielding percentage). That sum was then correlated with how big the prediction error was per team (the more teams you have the bigger the error might be). For all of NL history, that correlation is .87 and for the AL it is .85. So years when an entire league had more RBIs than predicted it most likely had alot more baserunners than normal, by hits, walks, HBP and errors.

Now getting back to Maranville, he tended to bat leadoff, 2nd or 7th. Hardly great RBI slots. So you might expect him to get less than the number of RBIs expected. But he did play mainly in the 1920s and 30s, when OBPs were high and the ERATE was higher. He also hit well with runners on base. Retrosheet only has about 1300 of his 8800 career ABs broken down for this. But with none on he batted .277. With runners on, .317 and with runners in scoring position, .324. Click here to see his splits.

If you look at the rankings from the first link, you can see that many of the batters who had the biggest negative differentials (meaning they got fewer RBIs than expected) were leadoff men.

This formula may apply best to power hitters who bat in the middle of the order. So I also looked at how well the formula predicted for all players with 300+ career HRs. Click here to see that link. The guy that jumps out there is Al Simmons. He got about 25 more RBIs than expected per season and the next highest is Greenberg at about 18.

Now Simmons batted 4th most of his career (especially with the A's) and had Max Bishop leading off alot of that time. Bishop had a career OBP of .423. The 2-3 hitters probably averaged around .365. So he had alot of opportunities. But Retrosheet has an even smaller number of on-base splits for Simmons so it is hard to tell if he was a clutch hitter.

I was surprised to see Willie Mays so far down. He had 15 fewer RBIs than predicted per season yet he hit well with runners on.Click here to see his splits. Maybe he got intentionally walked alot with runners in scoring position. Mantle and Barry Bonds are also near the bottom and the same thing could have happened to them. Alfonso Soriano has batted leadoff nearly half of his PAs, so that may be why he is last.

Bill James discusses this formula in his book Solid Fool's Gold: Detours on the Way to Conventional Wisdom

I have published two articles about RBI prediction:

RBIs, Opportunities and Power Hitting

Do Hitter’s Get Their Expected RBIs?

Monday, March 11, 2013

Rich Gossage vs. Mariano Rivera

Below is something I posted in June 2011. Tango just had a post on this issue because Gossage is talking again about his work load being tougher. Tango posted something on this yesterday. See Mo v Goose. Now my post from 2011.

...“I wasn’t a closer, I was a relief pitcher,” Gossage said. He made a great point that he was not just the closer, but the seventh and eighth inning man. He pointed out that he came on with inherited runners in the seventh or eighth inning many times. Some of those situations required that he keep the ball out of play.

Gossage went on to say that “Mariano doesn’t come in with inherited runners. He gets to start out the ninth with nobody on… Easy? It is a piece of cake compared to what we use to do.”
From Baseball Think Factory, quoting an article by Mike Silva.

Yes, relievers were used differently in Gossage's time. From 1977-1985, one of the time periods I will look at for Gossage, most of the top 50 seasons in both saves and games finished were by pitchers who pitched over 100 innings (with only a couple of cases of even 1 game started). From 1997-2005, the period I will look at for Rivera, there were no 100+ IP seasons and even 90+ IP was rare (less than 5 for both stats).

So I want to compare both Gossage and Rivera to the average relievers of their times. I picked Gossage's 1977-1985 years since that seems to be his prime years and he was very good throughout the period. It does leave out his great 1975 season as a reliever (he was a starter in 1976). So for Rivera, I look at his first 9 years as a closer, 1997-2005 (which leaves out a very good 1996 seaon). The fact that Rivera has continued to pitch great since then is a plus in his favor. Gossage supporters might say that Rivera's relatively low IP totals have helped his longevity. Gossage was just average after 1985.

The average relief pitcher from 1977-1985 had an ERA of 3.68 while Gossage had 2.10. If we turn that into a winning pct. using the Pythagorean formula created by Bill James to estimate team winning pct. using runs and runs allowed, we get .754. From 1997-2005, Rivera's years, he had an ERA of 2.04 while the league average was 4.31. That gets us a pct of .817. So Rivera edges Gossage .817-.754. (I checked park factors for each pitcher and the simple average of their teams pitching park factors was the same, 97.56, meaning that they each got a little help from their parks, which were about 2.5% lower than average in scoring). All the data I use here is from Baseball Reference or The Lee Sinins Complete Baseball Encyclopedia.

I also found the top 10 pitchers in saves in each era and then calculated the combined ERA of the other 9 (taking out Gossage and Rivera). The best 9 in Gossage's years had 2.87. That gets a .651 pct. The best 9 in Rivera's years had 3.07, getting us a pct of .694. Again, edge to Rivera.

So far, when being compared to contemporaries with a similar role, Rivera is ahead. But ERA can be misleading, since the fielders play a role here (and ERA may not be the best way to judge relievers who are supposed to come in and put out fires).

To avoid this problem, I am going to look at how each guy comapared to his peers in the fielding independent stats (HRs, BBs, SOs). Then I will convert that into a run value using the values below

HR: 1.40
BB: .33
SO: -.22

Those are the values used in what is called "Fielding Independent ERA" formulas. The table below shows how each guy compared to the average reliever of his time in these stats per 9 IP. For example, Gossage allowed .508 HRs per 9 IP while the average reliever allowed .724. So he was .216 better. Multiplying that by 1.4 we get .3024 (it is negative in the table, meaning how much below average Gossage was). Then this is done for the other stats and for Rivera. The last line shows the combined run value each guy was below average using all three sats.



So Rivera is farther below average than Gossage. If I use the average reliever ERAs from each period, then Gossage gets 2.49 (3.68 - 1.19). Rivera gets 2.76 (4.31 - 1.55). The Pythagorean winning pct for Gossage is then .686 and for Rivera it is .709. The next table does the same thing but only for the other 9 pitchers in the top 10 in saves in each period.



Going right to the bottom line, we can see that they are almost even. Gossage would get a Pythagorean pct of .620 and Rivera would get .611. Very close. Now Gossage may have been better than Rivera, but I think the evidence shows that he should not belittle his greatness. Rivera seems to be at least close to Gossage as measured by how good they were relative to their peers.

One weakness of looking at the others in the top 9 is that park effects and fielders might play a big role since they don't represent the entire league. It is possible that the other 9 guys Rivera gets comapred to pitched in great hitters parks so they look weak in comparison to him. Or maybe Rivera had much better fielders behind him. I have not checked that. And when I did the top 10, it included both leagues whereas when I used the league average, it was just the league they pitched in (for Gossage it was the NL from 1977 and 1984-5 and the AL from 1978-83).

Sunday, February 17, 2013

Rick Reuschel Had Bad Defense Behind Him While Pitching In A HR Friendly Park

Adam Darowski recently had a good article on Reuschel and his Hall of Fame worthiness. See Beyond ERA+: Why Rick Reuschel Had Hall of Fame Value. Adam uses some of the more advanced fielding stats.

Here I will just look at DER (defensive efficiency rating) and fielding pct. But first, I came up with an approximate relative ERA for Reuschel and then converted that into Wins Above Replacement (WAR).

I found all the pitchers from 1920-2011 with 2000+ IPs from the Lee Sinins Complete Baseball Encyclopedia. I also found their ERA, walks, strikeouts and HRs all relative to the league average. I ran a regression with relative ERA as the dependent variable and the others as independent variables.

Here is the equation:

ERA = 34.42 + .264*SO + .24*HR + .179*BB

Here were Reuschel's numbers

ERA 108
SO 95
HR 127
BB 138

The 108 means his ERA was 8 lower than the league average (this is without park adjustments). The 95 menas he struck out 5% fewer batters than average. His predicted relative ERA was 114, good for 64th out of the 293 pitchers. Not a super high rank, but good. He is ahead of Hall of Famers Juan Marichal and Jim Bunning, who each had around the same number of  IP as Reuschel.

If I use the predicted relative ERA for each pitcher and base it on a league average of 4.00 and a replacement level of .400, Reuschel would finish 38th or in the top 13%. He is ahead of Marichal, Jim Palmer, Whitey Ford and Ted Lyons, just to name a few Hall of Famers.

Remember that I am not making any park adjustments. From 1972 –1984, the years Reuschel was on the Cubs, he was 23rd among all major league pitchers with 1000+ IP in HRs allowed relative to the league average. He allowed 25% HRs fewer than the average pitcher would have, pitching in Wrigley Field! Wrigley was a great HR park during this period, compared to other NL parks, allowing 42% more HRs than average.

Now this is all based on defense independent stats. Maybe one reason we don't see how great Reuschel is that the defense behind him was not very good. The table below shows how the Cubs ranked in DER (defensive efficiency rating) and fielding pct for the years when Reuschel pitched alot for them. DER is just what % of balls in play are turned into outs.


They were last 5 of these 9 years and Reuschel only pitched 129 innings in 1972 and well over 200 the rest of the years. They did not do too well in fielding pct, either. This next table shows the simple average for all the NL teams in these years. The Cubs were by far the worst in DER and were still below average in fielding pct.

Wednesday, January 23, 2013

Musial and great power/contact hitters

Dave Cameron addresses this over at Fangraphs. See Translating Stan Musial’s Numbers into 2012 Norms. He compares Musial's isolated power to his strikeout frequency and then converts them into numbers for the 2012 season. He looked specifically at Musial's 1943 season and also his entire career.

A couple of things to remember. That article talks about Musial in 1943. Recall that it was a war year and alot of good pitchers were gone, so that would help him strikeout less and hit more HRs, everything else being equal. And Sportsman's park was a good hitter's park. Musial had a home ISO of .246 and a road ISO of .211 for his career.  DiMaggio had .231 home and .277 away. And notice how close their SO rates were in the table below. DiMaggio had an overall career ISO of .254 while Musial had .228. And notice how close their SO rates were in the table below.

I did a study on this before and I divided a guy's relative HR rate by his relative K rate. Now that is not the same as ISO but my guess is that ISO and HR rate are highly correlated. The guy that really looks great in this analysis was DiMaggio

Musial was 70% better than the league average in ISO and DiMaggio 95% better. So, for Musial we have 1.7/.55 = 3.09. For DiMaggio we have 1.95/.59 = 3.30. DiMaggio had a better relative ISO rate divided by SO rate and he played in a bad park for righties while Musial played in a great park, especially for lefties.

Here is the link

Which Players Had The Best HR-To-Strikeout Ratios?

Musial does well but not as well as some other big names. Now here is all of that post

I looked at every player with 5000+ PAs since 1920. I found their relative HRs and their relative strikeouts. Then found the ratio of the two. Ken Williams, for example, hit 3.70 times as many HRs as the average player of his time and league while striking out only 75% as often as the average player. Since his ratio of ratios (3.7/.75 = 4.93) is the highest of anyone in the study, he is ranked first. The data comes from the Lee Sinins Complete Baseball Encyclopedia. The table below shows the top 25:



DiMaggio hit only 41% of his HRs at home in his career while Williams hit 72%. So it is likely the case that DiMaggio would rank first, and probably by a wide margin, if HRs were park adjusted. Ted Williams hit less than 50% of his HRs at home.

The next table shows which players had the lowest relative strikeout rates among guys who hit 40+ HRs. Again, no pikers here. In 2004, Bonds had only 41 strikeouts while the average player would have had 100. I am so proud to see the demonstration of Polish power with 3 for Ted Kluszewski and 1 for Carl Yastrzemski (whose 1970 season ranks 27th). Don't forget Stan Musial is 13th on the above list.



Update: I found all the guys from 1920-2011 with 5000+ PAs. I also found their ISO relative to the league average and their strikeouts relative to the league average. Then I took the ratios. Here are the top 12

Tris Speaker 4.105263158
Tommy Holmes 4.035714286
Joe Sewell 3.772727273
Frank McCormick 3.102564103
Joe DiMaggio 3.0625
Yogi Berra 3.02
Nellie Fox 2.894736842
Vic Power 2.875
Albert Pujols 2.857142857
Tony Gwynn 2.806451613
Don Mattingly 2.785714286
Stan Musial 2.741935484

The league average in the above case was based on outs or how many outs each guy made. The next one is based on PAs (these are the choices you have using the Lee Sinins data base). So the guy that really stands out is Speaker and DiMaggio still beats Musial

Tris Speaker 4.727272727
Tommy Holmes 4.185185185
Joe Sewell 3.952380952
Joe DiMaggio 3.322033898
Albert Pujols 3.214285714
Frank McCormick 3.102564103
Stan Musial 3.090909091
Yogi Berra 3.081632653
Tony Gwynn 3
Don Mattingly 2.925
Nellie Fox 2.894736842
Ted Williams 2.848101266

Friday, January 11, 2013

Bagwell Is A Clear Hall Of Famer Unless He Used PEDs

He is 36th in career WAR among position players. He twice led the league and 2 other times was 5th. By my count there are 147 position players in the Hall who played in the majors (I am not counting Negro league players only because we don't have a WAR ranking for them).

This puts Bagwell in the to 25% of career value among the top 147. Maybe he will get in eventually, but his gain this year was slight.

Bagwell had 387 career Win Shares. Through 2001, that would have been tied for 56th among all players, including pitchers. Not sure where he would rank now, definitely still in the top 100.

I count 63 pitchers, so there are 210 players + pitchers. Bagwell clearly is above the bar by two different measures, WAR and Win Shares. I doubt both of these measures are so seriously flawed that he should not be in.

Is there some reason to keep waiting to see if proof of using PEDs is found? I don't think so.

Thursday, January 10, 2013

Was Biggio Treated Differently Than Other Members Of The 3000 Hit Club?

It looks like 12 of the last 13 guys with 3000+ hits got elected in their first year of eligibility. The one who did not is Palmeiro. The streak might even be longer since I am starting with Brock in 85. Two of these guys had less career WAR than Biggio (62.1). Winfield (59.4) and Brock (42.8). Murray (63.4) and Gwynn (65.3) were just slightly higher.

Wednesday, January 9, 2013

How Does Piazza's Vote Percentage Compare To Other Great Catchers?

The table below shows the top 10 in career WAR for guys who played 50%+ games at catcher















Carter actually fell to about 33% in his 2nd year and slowly rose to make it. So Piazza did better than Carter but not as well as Berra. Carter jumped to about 49 in his 3rd year and to 64 in his 4th year. He had 72.7 in year 5. We could look at Piazza's vote as pretty normal for a catcher. We don't have to say that he was necessarily affected by the steroids era.

Ted Simmons caught more games than Johnny Bench, Ray Schalk, Bill Dickey and Yogi Berra. More innings than Bench.

The votes for Dickey, Hartnett and Cochrane are a little strange as the rules were in flux. Their vote histories from Baseball Reference are below. What I use for 1st year above is 6 years after their last year, which would be in line with today's rules, although Cochrane got no votes in that year (1943). As you can see below, he did get votes in 1942, so I used that above.

Hartnett jumped 20 points in 1953, from about 39 to 59 and got in the next year

Hall of Fame-Dickey
1945 BBWAA ( 6.9%)
1946 Final Ballot (12.2%)
1946 Nominating Vote (19.8%)
1948 BBWAA (32.2%)
1949 BBWAA (42.5%)
1949 Run Off (20.9%)
1950 BBWAA (46.4%)
1951 BBWAA (52.2%)
1952 BBWAA (59.4%)
1953 BBWAA (67.8%)
1954 BBWAA (80.2%)
Selected to HOF in 1954 by BBWAA

Hall of Fame-Hartnett
1936 BBWAA ()
1945 BBWAA ( 0.8%)
1946 Nominating Vote ( 1.0%)
1947 BBWAA ( 1.2%)
1948 BBWAA (27.3%)
1949 BBWAA (22.9%)
1949 Run Off ( 3.7%)
1950 BBWAA (32.1%)
1951 BBWAA (25.2%)
1952 BBWAA (32.9%)
1953 BBWAA (39.4%)
1954 BBWAA (59.9%)
1955 BBWAA (77.7%)
Selected to HOF in 1955 by BBWAA

Hall of Fame-Cochrane
1936 BBWAA (35.4%)
1939 BBWAA (10.2%)
1942 BBWAA (37.8%)
1945 BBWAA (50.6%)
1946 Final Ballot (24.7%)
1946 Nominating Vote (39.6%)
1947 BBWAA (79.5%)
Selected to HOF in 1947 by BBWAA

Monday, January 7, 2013

Did Good Hitters Behind Jim Rice Prevent Him From Being Intentionally Walked?

Probably not. This is a follow up on a post I did a few years ago. See Was Jim Rice A Feared Hitter?

Someone on Twitter suggested that he had good hitters behind him that kept him from being intentionally walked. But it seems like his IBBs were pretty low for a slugger like him.

One thing I should check is if Rice came up with a man on 1st more than average. That would tend to lower IBBs.

But here is a brief analysis. It looks like Rice's best years were 77-79 and 83. The numbers below show the AVG-OBP-SLG of how the slot behind him did on the Red Sox for those years. Then I show it for the league average in those years. I don't think the differences are that great.

Red Sox

1977(5th) .274-.345-.486
1978(4th) .271-.358-.417
1979(5th) .283-.346-.474
1983(4th) .224-.260-.440

Look at how weak the hitters did behind Rice in 1983 and yet he had only 10 IBBs (tied for 9th). Singleton led with 19.

League Average for those slots, same years

1977(5th) .272-.337-.450
1978(4th) .271-.342-.440
1979(5th) .280-.349-.454
1983(4th) .280-.347-.460

Again, it does not look like Rice was followed by unusually good hitters compared to the league average.

In 1978, Ben Ogilvie had 10 IBBs and Rice had 7. Remmber, this was Rice's best year, with 46 HRs. The guys batting 6th on Milwaukee that year had AVG-OBP-SLG of .301-.359 -.448, far better than the guys behind Rice. Ogilvie batted mostly 5th that year and had 18 HRs and a .303 AVG. Rice hit 46 HRs with a .315 AVG. Rice had more impressive stats and weaker hitters behind him yet he had fewer IBBs. Carew actually led the league with 19.

Here are the numbers on the percentage of time Rice came up with a man on 1st (but not 1st only), then followed by the league averages.

1977 31.1%
1978 30%
1979 36.3%
1983 40.8%

League average

1977 30.5%
1978 30.7%
1979 31%
1983 30.7%

So in two of these years, Rice was at the league average. That would not stop him from getting walked intentionally. The other years he did have a man blocking an IBB on 1st more than average. But again, in 1978 he had his best year, the guys behind him did not hit very well and he only had an average number of guys on 1st. Yet he had only 7 IBBs.

Take Don Baylor in 1978. He had 9 IBBs with 34 HRs and a .255 AVG. Less fearsome than Rice's numbers. He batted mostly 5th, but a good chunk came 4th, too. The 6th place hitters on the Angels had a .423 SLG, only .049 below Baylor's .472. So if you walk him, you don't gain much. Rice had an SLG of .600, so walking him saved you .183. Big incentive to give him a walk.

Now Baylor did bat 4th a good bit, too. So how did the 5th place hitters do? Some of that includes him. But if you take that out, they slugged .453. almost as good as Baylor. So again, IBBing him is no big gain.

28.5% of the time was there a man on 1st with Baylor up. That is less than Rice (30%) but not that much. With 1st base open, you will IBB a guy more. But gain the difference is not that great and with Rice you had a huge gain in SLG.

In 1979, Wayne Gross of the A's had 9 IBBs. He batted .224 with 14 HRs in 138 games. Jim Rice who batted .325 and had 39 HRs had 4 IBBs (and surely he had a fearsome reputation after winning the MVP award the year before). Gross tended to bat 3rd or 5th. The 4th place hitters on Oakland had a SLG of .406. 6th place hitters slugged .332. Gross slugged .367. It was hardly any advantage for A's opponents to walk him. Yet he more than double Rice's total.

Larry Walker's All-Around Ability And His Hall Of Fame Worthiness

Last year I tried to quantify who the greatest "all-around" players were. I tried to take into account hitting for power, hitting for average, speed and defense (I so far have not broken down defense into catching and throwing).

I used various formulas and tried to have everything adjusted for the league average and park effects (where possible). Besides a simple score of "all-aroundness," I also tried to calculate one that was "above replacement." That allows longevity to play a role. Below are the three posts I did on this. The last one is the best attempt, I think.

In Part 3, Walker had the 87th best "all-around" score out of 913 players with 5000+ PAs through 2011. Above replacement, he is 96th. So he is around being in the top 10%. He did have alot of assists, so if I could break down defense into catching and throwing, he might do a bit better.

If you think being an "all-around player" matters, perhaps this would make you want to vote for Walker more. That might be a reasonable argument. Maybe the "all-around" players are more likely to excel no matter what the circumstances or playing conditions.

So here are the links

Who Was The Greatest "All-Around" Player Ever? Another Quantitative Attempt

Who Was The Greatest "All-Around" Player Ever? Another Quantitative Attempt (Part 2)

Who Was The Greatest "All-Around" Player Ever? Another Quantitative Attempt (Part 3)

Saturday, January 5, 2013

Who was the youngest pitcher to ever win a game in major league baseball since 1900? Rogers Hornsby. Rogers Hornsby McKee, that is.

He was born in 1926 and named after the great 2B man. He pitched a few games during WWII and played in the minors for several years after that, although not as a pitcher since he hurt his arm. He is still alive. He hit 33 HRs one year and batted .357 for Baton Rouge in 1953. Here are links to some articles about him:

A career in a moment by Scott Fowler of the The Charlotte Observer. On thing it says is that in 2009 the Phillies "paid for McKee and his entire family to come to Philadelphia for alumni night and honored him on their field."

eBay Item of the Day: Named for Greatness by Jim Baker, featuring a picture autographed by McKee. Jim has lots of info about the game he won.

A short biography at Baseball Reference

Where Are They Now: Rogers McKee by Larry Shenk, writing for the Phillies link at MLB.com.

A baseball star -- for one day from Scott Fowler's blog.

Monday, December 31, 2012

Why Did The Yankees Finish 13.5 Games Behind The A's In 1931?

The A's were 107-45 for a .704 pct while the Yankees were 94-59 for a .614 pct. The A's outscored their opponents 858-626 (good for a 97-55 Pythagorean W-L record) while the Yankees outscored their opponents 1067-760 (good for a 100-53 Pythagorean W-L record). All data comes from Retrosheet and Baseball Reference.

The A's were 19-14 in 1run games while the Yankees were 19-20. The Yankees were 44-9 in games decided by 5+ runs, a .830 pct. The A's were 31-14 in games decided by 5+ runs, a .689 pct. So it looks like the A's were not especially great in close games or in lopsided games, while the Yanks did poorly in close games and great in lopsided games.

The A's and Yankees were also about even in their differentials in AVG, OBP and SLG. The following table shows this.


The A's had a batting average differential that was just .001 better. They were slightly better in SLG differential but that is offset by the Yankees having a better OBP differential. It seems like they should have had very similar records.

Maybe it was something else. Maybe the A's had better baserunning or reached on errors (ROE) alot more than their opponents. No, that is not the case, either, as the next table shows (the little i means Retrosheet does not have complete data on something).



Neither team had an especially big advantage in ROE or GDP, based on what is available. But the Yankees seemed to do alot better in stealing. The SB and CS for their opponents is about the same for both the Yankees and A's. But the Yankees stole alot more bases with a much better pct. So there is still no statistical smoking gun.

But the A's came through in the clutch alot more as the next couple of tables will show. RISP means withe runners in scoring position (Retrosheet notes that they don't have complete numbers on these splits).


It may be hard to take all that in, so the next table shows the differential for each team. But notice how well the A's hit with men on, with RISP and when it was Close & Late. Just incredible. The Yankees did not hit especially well when it was  Close & Late.


The advantages the A's have when it was  Close & Late are just astounding. They outslugged their opponents by .175. The Yankees, meanwhile, seemed, at best, to break even with their opponents when it was  Close & Late. Maybe this is the key to the 13.5 game margin.

The Yankees and A's split their 22 games at 11 wins each. But the Yanks outscored them 124-97, which should have given them 13.6 wins or a .620 pct.

The A's won 7 games by 1 or 2 runs while the Yanks won 4. The Yanks won 3 games by margins of 11, 12 and 12 runs while the A's won only one game by 10 + runs (12).

At the end of August, the Yankees were 17.5 games behind (actually in 3rd place-the Senators were in 2nd, 15.5 games out). They had  a Pythagorean W-L pct. of 0.625. The A's had about .670. The Yankees would have been about 5 games out, still with a chance to win the pennant. But they only had 3 more games left against the A's.

The Yankees actually went 21-6 after Aug. 31, outscoring their opponents 198-87. The A's were just 17-10, outscoring their opponents 137-120. They really had no need to try very hard that last month. So some of the mystery is solved with the September performance. But still, going into that month, the Yankees should not have been that far behind. The A's performance in the clutch is what gave them the big edge. This is probably due to luck.

Update Jan. 2: Although the A's had a better Pythagorean W-L pct. through August, it is still possible that the Yankees "out played" them. I don't have the OPS differential for each team through August, but I can show something similar based on each team's HRs, nonHRs and BBs and what they allowed in those stats.

I have previsously run regressions with the differentials of those stats on a per game basis to explain a team's winning pct. Here are the differentials each team had per game through August:

A's: 0.341, 1.22, 0.444
Yanks: 0.496, 0.512, 1.094

Here is the regression equation for the 1930s that I came up with

Pct = 0.496 + 0.145*HR + 0.035*NONHR + 0.025*BB

Now those are all differentials. If you plug in the values for the A's, you get .599. So they were a very good team through Aug. But the Yankees were better, at .613. So even before Sept, when the A's were able to coast, they still must have been timing things better than the Yankees, being 17.5 games ahead of them.

Again, I wish I had the OPS differential for the pre-Sept games. One problem with this lastest analysis is that it does not take into account what the nonHR hits were. It turns out the A's hit more 1Bs, 2Bs and 3Bs than their opponents while the Yankees just had more 1Bs. The Yankees actually had fewer 2Bs and fewer 3Bs.

So I assumed that the breakdown each team had for 1Bs, 2Bs and 3Bs as a percentage of all nonHRs was the same pre-Sept as it was for the whole season (this was done for those hits allowed, too).

Then I found each team's differential for 1Bs, 2Bs and 3Bs as well as HRs and BBs, but for pre-Sept. only. Then I calculated the linear weights value of those differentials. I had the A's being 177 runs better than their opponents while the Yankees were about 150 runs better.

So that makes the A's a better team pre-Sept. But those 27 runs are worth only about 2.7 wins. It should have been a close pennant race up to that time. So, again, it was pretty lucky for the A's that they had such great timing in getting or preventing hits.

Thursday, December 20, 2012

1974: The Year The MVP Voting Went To Heck

This is a family blog, so I have to say heck.

After checking data at Baseball Reference, 1974 looks like the first year that none of the top 3 vote getters in either league were among the top 10 in WAR. I did not find this happening in any year before, even if in only one league (if I missed one, let me know). At least one of the top 3 vote getters was in the top 10 of position players or pitchers in all previous years.

Here are the top 3 in each league from that year:

NL
Garvey
Brock
Mike Marshall (not in the top 10 for pitchers)

AL
Burroughs
Rudi
Bando

Now this is not a systematic analysis. Maybe other years were worse in terms of how well WAR was correlated with the points each guy got.

There were 3 times I found where the winner was not in the top 10 in WAR, but at least one of the other top 3 guys was

Frisch 1931
Cochrane 1934
Powell 1970 (Oliva was 2nd in voting and was in the top 10 in WAR but Killebrew (3rd) was not)

There were some cases of a winner tying for 10th (Boyer, Campanella). Konstanty was 7th among pitchers in 1950, but not in the overall top 10 (which combined pitchers and position players).

Now Frisch and Cochane were also managers. Maybe that influenced the voters.

In 1974, Reggie Jackson, Gene Tenace and Bert Campaneris all had more WAR than teammates Rudi & Bando.

In the NL, Garvey led his team in RBIs and batted over .300.  His teammate, Jimmy Wynn, beat him in WAR, 7.6 to 4.3. But Garvey beat him in AVG (.312-.271) and RBIs (111-108).

Brock set the then SB record with 118. Maury Wills won the award in 1962 when he set a record with 104, so I guess the voters thought Brock had to get some consideration. But Garvey's Dodgers won their division while Brock's Cardinals did not.

Mike Marshall set a record for games pitched with 106 and won the Cy Young award while pitching for the Dodgers.

Jeff Burroughs led the AL in RBIs with 118 (while also batting .301). But his Rangers did not win their division. Only one other player, Bando, had 100+ RBIs in the AL (he had 103).

Here is something I found about that year's MVP vote from the following site

http://www.mrpopculture.com/files/November%208,%201974.pdf
"Lou Brock, who finished second to Steve Garvey in the National League MVP voting, says he wouldn't accept the MVP next year if he stole a thousand bases. Brock, who had his best year ever, expected to get the award and even set-up a press conference. Brock beat Maury Wills' 12-year old record of 104 steals."
I found a NY Times article through my school's library that confirms the 1000 base quote and this is how I recall it. Brock also said the voters used "bad judgement" and his manager, Schoendienst, agreed.

Click here to see the MVP voting for both leagues in 1974

Click here to see the WAR leaders among position players in the NL in 1974

Click here to see the WAR leaders among position pitchers in the NL in 1974

Click here to see the WAR leaders among position players in the AL in 1974

Click here to see the WAR leaders among position pitchers in the AL in 1974