Cybermetrics

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

2013-06-17T17:55:00.002-07:00

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.

Click here to go to Tom McCreery's Baseball Reference page

Josh Hamilton's Strange Season

2013-06-12T18:36:00.000-07:00

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

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

2013-06-05T16:08:00.002-07:00

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.

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

2013-06-03T07:48:00.002-07:00

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.

How On-Base Percentage and Slugging Percentage Affect Winning

2013-05-31T19:04:00.002-07:00

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?

by Enoch on Mar 24, 2006 | 2:51 PM

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

by Cyril Morong on Mar 24, 2006 | 5:33 PM

Additional Comments

Can be found at Baseball Think FactoryThanks to Greg Tamer for the link.

"I don't set the rosters, I just make fun of the guy who does" - Rob Neyer

by Marc Normandin on Mar 25, 2006 | 3:08 AM

http://www.baseballthinkfactory.org/newsstand/discussion/beyond_the_box_score_cyril_morong_how_on_base_percentage_and_slugging_perce

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

2013-05-26T16:16:00.001-07:00

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.

Rabbit Maranville, Mr. RBI

2013-03-13T19:08:00.001-07:00

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?

Rich Gossage vs. Mariano Rivera

2013-03-11T18:49:00.001-07:00

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).

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

2013-02-17T18:42:00.004-08:00

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.

Musial and great power/contact hitters

2013-01-23T15:55:00.000-08:00

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

Bagwell Is A Clear Hall Of Famer Unless He Used PEDs

2013-01-11T15:43:00.003-08:00

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.

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

2013-01-10T16:44:00.000-08:00

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.

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

2013-01-09T12:03:00.001-08:00

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

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

2013-01-07T11:18:00.001-08:00

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

2013-01-07T10:37:00.000-08:00

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)

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

2013-01-05T09:58:00.002-08:00

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.

Rating the 2013 Hall of Fame Candidates Based on Win Shares by Bill Gilbert

2013-01-02T15:16:00.001-08:00

Click here to read it.

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

2012-12-31T18:25:00.002-08:00

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.

1974: The Year The MVP Voting Went To Heck

2012-12-20T15:51:00.002-08:00

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

Verlander For The Cy Young Award?

2012-11-11T13:51:00.001-08:00

I don't think you have use too many advanced stats or new formulas to make a case. Steve Kornacki comes up with a new metric to support Verlander. See Making case for Verlander. (Hat Tip: Baseball Think Factory) But I don't think that is really necessary.

Verlander led the league in IP and strikeouts and was 2nd in ERA to Price (2.56 vs. 2.64). Verlander had 238 IP while Price had 211.

He was 2nd to Weaver in WHIP (1.018 vs. 1.057).

He was 2nd to Weaver in hits per 9 IP (7.012 vs. 7.25). Price was 4th, Sabathia was not in the top 10.

He was 2nd to Sabathia strikeout-to-walk ratio (4.477 vs. 3.983). Price is not in the top 10. Weaver is not in the top 10.

He was 5th in HRs per 9 IP. Price is 4th. Sabathia is not in the top 10. Weaver is not in the top 10.

He was 3rd in strikeouts per 9 IP. Sabathia was 6th and Price was Price was 8th. Weaver is not in the top 10.

A reasonable objection to Verlander is that he might have been helped by his fielders. But look at how well he did in HR per 9 IP, strikeouts per 9 IP, and strikeout-to-walk ratio. So he did very well in "fielding independent" stats.

Now Verlander did not make the top 10 in walks per 9 IP (he had 2.3). Sabathia (4th, 1.98) and Weaver (7th, 2.147) did. But he beats both of them by a wide margin in IP. Weaver had 188 and Sabathia had 200.

His park could have helped him. But here is where one of the simpler sabermetric stats comes in handy. ERA+, which takes into account park effects and the league average. Verlaner led the league with 160 (meaning he was 60% better than the league average). Price was 2nd with 149. So a very solid lead for Verlander. Weaver had 134 and Sabathia had 124.

His teammate Scherzer led the league in strikeouts per 9 IP. But Scherzer did not make the top 10 in IP, HR per 9 IP, or ERA+ and was just 5th in strikeout-to-walk ratio.

Darvish finished 2nd in strikeouts per 9 IP, ahed of Verlander (who was 3rd, recall). But he only pitched 191 innings and did not make the top 10 in ERA+ or strikeout-to-walk ratio.

I have not mentioned Peavy, Sale, Harrison or Kuroda. But I think Verlander would also compare very well to those guys. Verlander seems to more consistently rank high in meaningful stats than all the rest. So he would be a great choice.

Did Darwin Barney And Brendan Ryan Go Into Fielding Slumps The Last Two Months Of The Season?

2012-11-04T16:56:00.000-08:00

As of August 1st, Barney had 3.3 defensive WAR (in 99 games) and Ryan had 3.2 (in 95 games). But Barney finished with a defensive WAR of 3.6, meaning he only got .3 in his last 57 games. Ryan also finished with 3.6, meaning he got .3 in his last 46 games. Does anyone know what happened? Did they make more errors? Did they get to fewer balls? Their pace sure fell off quite a bit. But they both finished tied for the 38th best fielding season ever with 13 other players.

Trout's 10.7 WAR Ties 20th Best Season Ever

2012-10-31T16:17:00.000-07:00

That is the Baseball Reference ranking. He is tied with Mays (1964) and Ted Williams (1946)

Click here to see their rank for position players.

The only players to ever have a better season were

Ruth
Hornsby
Yastrzemski
Bonds
Gehrig
Ripken
Wagner
Cobb
Mantle
Mays
Morgan
Musial

Per game (he played in 139 and in one game he was just a pinch runner), he had .0770 WAR. That would be 9th best ever. See Mike Trout's WAR Per Game Is Historically One Of The Best (So Far).

He also had, not surpringly, the best rookie season ever. See Does Mike Trout Already Have One Of The Top Ten Rookie Seasons Ever?

To see the top seasons ever in WAR from Fangraphs, go to Best WAR Seasons For Position Players-Fangraphs. (thanks to David Appelman) They give Trout 10.0 and that is tied for 83rd best every. But, recall he only played 139 games. The average games played for the top 30 is 150. If Trout had played that many and had racked up the same rate of WAR per game, he would have had 10.8, good enough for 43rd.

How Surprising Was Zito's Performance?

2012-10-20T11:22:00.001-07:00

He has not pitched well on the road this year or well against righties while the Cards hit very well at home and very well against lefties (more info below on this).

With the Giants down 3-1 in the series, he pitched 7.2 innings with 6 hits, 6 K's, 1 BB (intentional) and zero runs. That gives him a "game score" (a Bill James stat) of 72. Here is how it is calculated

1.Start with 50 points.
2.Add 1 point for each out recorded, so 3 points for every complete inning pitched.
3.Add 2 points for each inning completed after the 4th.
4.Add 1 point for each strikeout.
5.Subtract 2 points for each hit allowed.
6.Subtract 4 points for each earned run allowed.
7.Subtract 2 points for each unearned run allowed.
8.Subtract 1 point for each walk.

In 32 starts during the regular season, he had an average game score of 49.53 (I came up with an average game score of 46.4 in the NL this year, so Zito was not much better than that).

He is not a big game pitcher, either. His start against the Reds in the division series had a game score of 42. His previous 8 playoff starts averaged 55.13. Only 3 were 68 or higher with a high of 79 (way back in 2001).

It is not like he came on at the end of the season, either. His average game score was 51.38 in his last 13 starts with a high of 66 and only 4 60+ games.

He did have 4 starts this year with a game score of 72 or higher. But those were generally against below average hitting teams.

83 vs. Colorado (in Denver). Rockies OPS+ this year as 91
78 vs. Cubs at home. Cubs OPS+ was 85
74 vs. Braves in Atl. Braves OPS+ was 90
72 vs. Dodgers at home. Dodgers OPS+ was 90

(according to Baseball Reference, the NL league average OPS+ was 94, not 100 for some reason).

The Cardinals were tied for 1st in OPS+ this year with 107 (tied with Giants). Zito had an ERA+ this year of 84. He was hurt last year and only pitched 53 innings. In 2010, his ERA+ was 94.

The Cards had an OPS at home of .786 and .731 on the road while the league average was .718. They had .787 vs lefties but .835 vs. lefty starters. Zito allowed an OPS at home of .726 and .796 on the road. It was .823 vs. righties and .559 vs. lefties (the Cards started 5 righties, a switch hitter and 2 lefties).

Now in his career it is .705 vs. righties and .701 vs. lefties, so maybe righties don't especially bother him. But this year he gave up 1 HR per 9 IP, which was the league average. His SO/BB ratio was 1.63, far below the league average of 2.50. I doubt anyone could have expected his performance last night. He is hardly an ace or stopper.

The average game score by a lefty during the regular season against the Cards was 45.8. The highest by a lefty in Busch was 60 (Bumgarner). So Zito was well above both of those.

Only 3 Lefty pitchers during the regular season had a game score of 72 or higher against the Cards (out of 45 starts)

Johan Santana 90

Gio Gonzalez 82

Clayton Kershaw 79

In the Division series, Gio Gonzalez had 55 and 46. I don't think the Nats had any other lefty starters. Bumgarner had 22 in game 1. I think that is all the lefty starts agains the Cards in the playoffs besides Zito. Did I miss any?

Giancarlo Stanton's Amazing Power Season

2012-10-09T19:25:00.001-07:00

He hit 37 HRs in just 123 games, finishing 2nd to Ryan Braun's 41. Stanton also set the all-time record for most HRs in a season by a Panoramian (yes, all-time!) His SLG was .608 while his AVG was .290. That gives him an isolated power (ISO) of .318, which, as I will show below, is outstanding for a player his age.

But Marlins Park was not a good HR park this year. 113 HRs were hit there (by both teams) while 157 were hit in Marlin road games. So there were only 72% as many road HRs as at home. It is only one year, but that is a low HR park factor.

Stanton's ISO at home was .287 while on the road it was .357. He had 16 HRs at home in 247 ABs while on the road it was 21 in 202 ABs. I can't help wondering how he would do in a fair park. I don't know how symmetric Marlins Park is, so maybe righties were not hurt as much as lefties. But he has a big home/road differential.

The table below shows the top seasons in ISO relative to the league average by players who were 22 or younger who had at least 400 PAs. Williams gets 252 because .329/.130 = 2.52. That gets multiplied by 100

The rest of the top 15 include Bob Horner, Mel Ott, Jimmy Sheckard, Juan Gonzalez and Joe Jackson. So Stanton is in great company.

Stanton is one of just 3 guys to have an ISO of at least .275 in 2 different seasons at age 22 or younger. The next table shows all those seasons.

Other players with season of .250 or higher were Alex Rodriguez, Juan Gonzalez, Hal Trosky, Frank Robinson, Vladimir Guerrero, Bob Horner, Evan Longoria, Darryl Strawberry. Jimmie Foxx, Mel Ott, Joe DiMaggio and Ted Williams each had one more .250+ season to go along with those seen in the table.

Here are all the players who had 2 seasons of 30+ HRs at age 22 or younger

Albert Pujols
Alex Rodriguez
Bob Horner
Eddie Mathews
Frank Robinson
Giancarlo Stanton
Jimmie Foxx
Jose Canseco
Miguel Cabrera
Ted Williams

Pujols Had Worst September Ever

2012-10-07T18:31:00.001-07:00

His AVG-OBP-SLG in Sept were .269-.315-.420. Those numbers are a bit of a surprise and a bit of a disappointment for the Angels given that he was over 1.000 in OPS in July and August and had .977 in June. The table below shows his OPS+ for each month of his career.

At 104, his OPS+ is far below the next lowest ever for Sept (actually Sept/Oct). The next lowest was 143. It also looks like his 3rd lowest monthly OPS+ ever no matter what the month. In 8 of his 11 previous seasons, his Sept OPS+ was higher than what it was for the whole season. His worst differential had been -17 in 2003. Yet this year it was -37. So his Sept performance was very much out of character. Maybe a normal performance would not have helped the Angels as they finished 4 games out of the wild card.