Trout Now One Of 13 Players With A WAR Of 4.8 Or Higher At Both Ages 20 & 21

With about one-third of the season left, he could get to a WAR of 7.0. No one has had 7+ WAR at both ages 20 & 21. He would be the first.

Here are the 13 players. The table also shows their career WAR and rank among position players. 10 of the previous 12 are in the top 50. The worst is 149th. He already has more WAR at both ages combined than all of these guys. And in only 236 games played.

Player	WAR	Year	Age	Career WAR	Rank
Al Kaline	8.3	1955	20	92.7	28
Al Kaline	6.6	1956	21
Alex Rodriguez	9.3	1996	20	115.5	12
Alex Rodriguez	5.6	1997	21
Frank Robinson	6.5	1956	20	107.1	18
Frank Robinson	6.9	1957	21
Johnny Bench	5	1968	20	75.2	46
Johnny Bench	6.1	1969	21
Ken Griffey	5.2	1990	20	83.7	34
Ken Griffey	7.1	1991	21
Mel Ott	7.4	1929	20	107.9	16
Mel Ott	6.4	1930	21
Mickey Mantle	6.4	1952	20	109.7	15
Mickey Mantle	5.3	1953	21
Mike Trout	10.9	2012	20
Mike Trout	4.8	2013	21
Rogers Hornsby	4.8	1916	20	127	9
Rogers Hornsby	9.9	1917	21
Sherry Magee	5.1	1905	20	59.1	122
Sherry Magee	5.6	1906	21
Ted Williams	6.7	1939	20	123.2	11
Ted Williams	6.3	1940	21
Ty Cobb	6.8	1907	20	151.1	4
Ty Cobb	6.2	1908	21
Vada Pinson	6.5	1959	20	54.2	149
Vada Pinson	5.7	1960	21

Here is the top 10 in WAR for ages 20 & 21 combined

Player	WAR	From	To	Age	G
Mike Trout	15.7	2012	2013	20-21	236
Alex Rodriguez	14.9	1996	1997	20-21	287
Al Kaline	14.9	1955	1956	20-21	305
Rogers Hornsby	14.7	1916	1917	20-21	284
Mel Ott	13.8	1929	1930	20-21	298
Frank Robinson	13.4	1956	1957	20-21	302
Ted Williams	13	1939	1940	20-21	293
Ty Cobb	13	1907	1908	20-21	300
Jimmie Foxx	12.6	1928	1929	20-21	267
Ken Griffey	12.3	1990	1991	20-21	309

How Great A Fielder Is Andrelton Simmons?

So far this year he has 3.3 defensive WAR, according to Baseball Reference. With just a little more than a third of the season left, it seems like he could end up with 4.9 or even slightly higher. Here are the top 3 seasons ever. This includes all positions

Terry Turner	5.4	1906	147 games
Art Fletcher	5.1	1917	151 games
Mark Belanger	4.9	1975	152 games

Simmons has played in 94 games this year. In his career, he has played in 143 games and has a defensive war of 5.7. That is comparable to what the guys above did. And simmons was 4th in the NL last year with a defensive WAR of 2.4 even though he only played in 49 games.

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 Martinez	2.40
Roger Clemens	2.63
Randy Johnson	2.75
Lefty Grove	2.77
Curt Schilling	2.82
Roy Halladay	2.86
Rube Waddell	2.99
Mike Mussina	3.07
Bret Saberhagen	3.08
John Smoltz	3.10
Greg Maddux	3.11
Bob Gibson	3.17
Dazzy Vance	3.18
Cy Young	3.20
Hal Newhouser	3.20
Kevin Brown	3.21
Walter Johnson	3.21
Andy Pettitte	3.22
Bert Blyleven	3.26
Kevin Appier	3.29
Kid Nichols	3.31
Dizzy Trout	3.31
Dennis Eckersley	3.33
Christy Mathewson	3.34
Rick Reuschel	3.34

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 Martinez	2.66
Roger Clemens	2.85
Curt Schilling	2.94
Randy Johnson	2.95
Rube Waddell	2.99
Roy Halladay	3.06
Lefty Grove	3.12
Bret Saberhagen	3.18
John Smoltz	3.18
Walter Johnson	3.18
Bob Gibson	3.19
Mike Mussina	3.20
Greg Maddux	3.23
Ed Walsh	3.25
Dazzy Vance	3.27
Christy Mathewson	3.30
Bert Blyleven	3.30
Hal Newhouser	3.31
Kevin Brown	3.32
Andy Pettitte	3.34
Eddie Plank	3.37
Cy Young	3.37
Rick Reuschel	3.37
Dennis Eckersley	3.38
Chief Bender	3.38

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.



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

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.

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

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+.

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

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.

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.

 

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

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

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.

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"

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?

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%

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.