Is Ryan Howard The New Mickey Vernon? (Or Is His Career Really In Decline?)

Howard has had big drops in his OWP the last 2 years. OWP or offensive winning percentage is a Bill James stat that says what a team's winning percentage would be if it had a lineup of 9 identical players who all hit alike and they gave up an average number of runs. Since I got the data from the Lee Sinins Complete Baseball Encyclopedia, it is park adjusted. Here are Howard's OWP for each of the last 3 seasons with his age in parantheses:

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

So the declines are .102 and .093. For players who had long careers, such big back to back drops in OWP are somewhat rare, especially for someone under 30. Some stories I read using google news search indicate he is in better shape this spring and is hitting better than usual this pre-season. So maybe he has taken the necessary steps to stop the decline.

To see how unusual his declines are, I used a list of players that I have compiled before. This list includes all players who had 15+ seasons with 400+ plate appearances from age 20-40. Then I found all cases of players having back to back seasons of a drop in OWP of .075 or more. The tables below show all of these cases (more discussion of the tables below).

There are 22 such cases. But in only 3 of them, did the fall in OWP start before the age of 30. Those belong to Robin Yount, Jake Beckley and Mickey Vernon. Beckley's started at age 23 and neither of my biographical encyclopedia's mention anything. Same for Vernon's decline. Yount had shoulder problems during the 1984 and 1985 seasons and actually had surgery twice. But both Beckley and Yount are in the Hall of Fame. So if Howard can end up with 15+ seasons with 400+ plate appearances he has a 2 out of 3 chance of making the Hall of Fame.

If I had limited the study to declines of .093 or more, there were only 8 guys. The only one whose decline started before age 30 was Vernon. So who knew that he had something in common with Howard?

In the tables below, the numbers in red are the decline years. The year before the decline is there for each player for reference. Two guys had 3 straight years that fit the criteria. They were Jimmy Dykes and Willie Keeler. Yount and Honus Wagner had two such streaks. The average age at which the decline started was 33.95. 17 of the 22 cases started at age 33 or older.

There were 91 players with 15+ seasons with 400+ plate appearances and 22 back to back seasons of a drop in OWP of .075 or more. So nearly 25% of the 91 players had these big back to back declines. That makes it look like what has happened to Howard is not that rare. But what is rare is the age at which it has happened to him.

What happened to these guys in the third year? Did they finally rebound? Well, we know that Keeler and Dykes each had declines that fit the criteria in the third year. 5 players did not get 400+ PAs the next year. The average change in the third year, including Keeler and Dykes (who were the only declines), was a positive .078. 8 of the 17 had changes of +.100 or more. So Howard has a good chance to bounce back but also has a chance not to make it to 400 PAs. Mickey Vernon improved .295 in the third year.

Does Jim Bunning Belong In The Hall Of Fame?

This issue came up recently on the SABR list. One of the issues was why were pitchers from his era who seem to have been about as good he was not in. Then someone else mentioned that maybe the Veterans committee put him because he is a Senator. I posted some evidence on this. Basically it was references to research I had done in the past and seeing where Bunning ranked. I will put that post below, but first something new, although it is a simple, rough estimate of his value (I used Fielding Independent Pitching ERA or FIP ERA to find an imputed winning percentage for Bunning which is fairly high-it's all based on how good he was at strikeouts, walks and HRs). I find that there is some evidence for him being in the Hall, but I don't think it is all on his side. He had a great strikeout-to-walk ratio, which is one indicator of how good a pitcher is.

Below are the top 25 pitchers with 3000+ IP in strikeout-to-walk ratio relative to the league average. Bunning is 19th, which is very good. Mathewson is 1st. He had a strikeout-to-walk ratio of 2.96 while the league average was 1.29. Since 2.96/1.29 = 2.30, Mathewson gets a 230. Data came from the Lee Sinins Complete Baseball Encyclopedia.



I calculated his Fielding Independent Pitching ERA or FIP ERA. The idea is that a pitcher controls HRs, BBs and Ks and hits on balls in play not so much (if you have not heard of this, google Voros McCracken).

Here are the key calculations. HRs, BBs and Ks are per 9 IP.

(1) FIP ERA = Constant + 1.44*HR + .33*BB - .22*K

(2) The constant = League ERA - (1.44*HR + .33*BB - .22*K)

I used Bunning's stats and adjusted them to the AL stats of 2008. Bunning's Ks per 9 IP was 6.83 or about 25% above average. In the 2008 AL K/9IP = 6.36. Raising that 25%leaves 7.97. He walked 2.39 batters per 9 IP or about 26% fewer than average. In the 2008 AL BB/9IP = 3.32. Lowering that 26% leaves 2.47. So those numbers will get plugged into equation (1). The league ERA in the AL in 2008 was 4.35 and the constant for equation (1) works out to 3.21.

We still need to calculate his HRs per 9 IP. He actually gave up 372 HRs while the average was 346. So it looks like Bunning did poorly here. But he pitched in Tiger Stadium for part of his career where an above average number of HRs were hit. So I adjusted his HRs allowed in each season based on the HR park factors from the STATS, INC. All-Time Baseball Sourcebook. For example, if Tiger stadium gave up 20% more HRs than average in a season, I reduced his HRs for that year by 10% (only half of the 20% since he only pitched half his games there). In some of his years with the Phillies, the park factor was below average. After doing this for each of his seasons, his HR total came out to 349, or almost exactly average.

In the AL in 2008, there was just about 1 HR per 9 IP. So I used that for equation (1). With the HR, BB and K data done, I found a FIP ERA of 3.73 for Bunning (adjusted for the 2008 AL).

I then calculated what Bill James calls the Pythagorean winning percentage for Bunning if he pitched on an average team. It is

(runs scored squared)/(((runs scored squared) + (runs allowed squared))

For Bunning, adjusted to the 2008 AL, we get

4.35*4.35/(4.35*4.35 + 3.73*3.73) = .577

So Bunning pitching for an average team would have a .577 winning pct. For pitchers with 3000+ IP, he would be tied for 42nd (with Jack Morris). But I did not calculate the FIP ERA or Pythagorean winning percentage for anyone else. I am just assuming if I did it for everyone, just as many guys would move ahead of Bunning as would fall behind. 42nd is pretty good and seems high enough for a starter to make the Hall.

Now for the post to the SABR list.

I found the best fielding independent ERAs since 1920 (an imputed ERA based on walks, strikeouts and HRs with HRs being adjusted for park effects). Bunning ranked 13th among pitchers with 3000+ IP.

I found that he was 51st in Park-Adjusted Pitching Wins Above Replacement Level.

It also looks like he out pitched Koufax in neutral parks while they were both in the NL.

But he only had 257 Win Shares through 2001, tied for 291st. Not sure where that ranked among pitchers.

He ranks 67th in adjusted pitching wins in Pete Palmer's baseball encyclopedia.

Arbitration Wrap-up – 2009 (A Guest Post By Bill Gilbert)

Bill Gilbert has been involved in arbitration hearings and is the president of the South Texas chapter of SABR (aka the Rogers Hornsby chapter)

In 2009, 111 players filed for salary arbitration. Before players and clubs exchanged figures on January 20, sixty five of these players had agreed to contracts with their clubs. Of the remaining 46 players only three players actually went to an arbitration hearing, tying the low point set in 2005.


(editors note: the third column shows what the player wanted in $1,000s and the next column shows what the club offered)

It was the first time that the majority of the decisions went in favor of the players since 1996. Since the first hearings were held in 1974, the clubs have won on 280 occasions and the players have prevailed 207 times.

By my count, here is the breakdown of the 111 cases.

93 players signed one-year contracts

15 players signed multi-year contracts.

3 players had their salary determined at an arbitration hearing.

There are two situations where arbitration can come into play. By far the most common is the one involving players, under control of their clubs, with 3 to 6 years of major league service (MLS), plus the 17% most senior MLS-2 players, referred to as “super twos”. Of the 111 players who filed this year, 109 were in this category.

The other situation involves free agents. When a player with 6 or more years of major league service files for free agency, his club has the option of offering arbitration. A club must offer arbitration in order to get compensation in the form of draft picks if the player signs with another club. If the player accepts arbitration, he is no longer considered a free agent and he becomes bound to that club. If a player refuses arbitration, as most players do, he is a free agent who can sign with any club including the one he played for last year. Of the 24 free agents who were offered arbitration this year, the only two that accepted were Darren Oliver of the Los Angeles Angels and David Weathers of Cincinnati.

With the economic uncertainties this year, the market was more difficult to read. A number of free agents, such as Jason Varitek of Boston, Orlando Hudson of Arizona, Orlando Cabrera of the Chicago White Sox and Jon Garland of the Los Angeles Angels would have fared much better if they had accepted arbitration. Clubs also had difficult decisions to make and recognized that in a declining market, some players would likely be paid much more in the arbitration process than their market value. This led to non-tendering arbitration eligible players like Ty Wigginton of Houston, Willy Taveras of Colorado, Takashi Saito of the Los Angeles Dodgers and Tim Redding of Washington.

The arbitration process is designed to promote a settlement at a salary in line with that of other players with comparable performance and service time. Players eligible for arbitration for the first time receive a large increase in salary since they have no leverage in their pre-arbitration years when their salaries are under control of the clubs. Players who have been through the process before also generally receive salary increases depending on their performance in the preceding year.

Players that settle prior to hearings are frequently able to include performance bonuses, based on playing time, and awards bonuses in their contracts.

The big winners in the arbitration process this year were Nick Markakis of Baltimore and Ryan Howard of Philadelphia. Markakis, in his first year of arbitration eligibility, signed a six year contract for $66.1 million, which carries him through 3 years of arbitration eligibility and 3 years of free agency. Howard, who won at a hearing in 2008, received a three year contract for $54 million which takes him through his arbitration years.

The relatively quiet arbitration season this year suggest that the system is working as designed in achieving benefits for both sides. Players with 3 to 6 years of major league service receive salaries that are influenced by their market value and Clubs are able to retain the rights to these players through 6 years of major league service before they become eligible for free agency.

How Good Has Albert Pujols Been And How Good Will He Be?

This issue came up recently on the listserv of the Hornsby (or south Texas) chapter of SABR. Bill Gilbert wrote a report on Pujols, pointing out that he is now 4th all-time in SLG and 5th in OPS. But as he gets older, those ranks might slip. This got me thinking about how much a player might slip in percentage rankings since they might not be as good as they age.

So I looked at where some players (probably not a very scientifically selected group) ranked at early and late stages of their careers. I tried to find periods that parallel Pujols so far. But I was not always able to. In the table below, I either divided a players career roughly in half, or did the first 8 years and next 8 years 9since Pujols has 8 years so far). Then I also simply used when a guy really started to dropoff as a dividing line. The last 10 lines are all power hitting 1B men. Those guys I found by getting the top 10 all-time in SLG relative to the league average with 5000+ PAs using the Lee Sinins Complete Baseball Encyclopedia. That should give a group that is like Pujols (Greenberg and Mize don't appear due to WW II gaps).

Then I found each guys offensive winning percentage (OWP) for the given period. OWP is a Bill James stat that says what a team's winning percentage would be if it had a lineup of 9 identical players who all hit alike and they gave up an average number of runs. Since I got the data from the Lee Sinins Complete Baseball Encyclopedia, it is park adjusted. I also found where he ranked all-time for the stated years or up to a certain year. The normal PA minimum was 5000. But if a guy had, say, 4900 PAs in his first 8 years (or whatever the time periods was), I used that for both periods shown.



There is quite a variety of outcomes. Some guys fall quite a bit in the rankings due to a much lower performance in their 2nd half. Some actually did better and rose in the ranks. So based on this, where Pujols ends up is not clear.

I also took all the players who had 5000+ PAs before the age of 28 who also had at least 2500 PAs from ages 29-36. Of the 75 players in the first group, 49 made it into the2nd group. Only 16 of the 49 had a higher OWP from 29-36 than they did up to age 28. Roberto Clemente was the one real big gainer, .162 (from .531 to .723). The average change was a loss of .030. Only 8 of the 75 guys from the first group had 5000+ PAs from age 29-36. It seems like the chances Pujols will even get 5000+ PAs over the next 8 years is low. But there might be something I am missing here.

The other thing I did was to look at the normal performance trajectory as players age. I found all the players who had 15+ seasons with 400+ PAs up through 2005. Then I found the average OWP for each age from 20-40. The graph of that is below.



It looks like the ages 21-28 are symetric with the next 8 years. So the overall OWP is the same in each period. If that happens for Pujols, then he will not change much. Without getting into the details, I calculated his OWP will be .768 over the next 8 years based on what he has done and what the historical trends are. I project that if he plays until 40, he will end up with about a .755 career OWP, staying 10th (Musial is 11th at .752). For what is probably a more scientific treatment of aging and performance in baseball, see PEAK ATHLETIC PERFORMANCE AND AGEING: EVIDENCE FROM BASEBALL by J.C. BRADBURY.

Below is the current top 10 in career OWP with 5000+ PAs

1 Babe Ruth .852
2 Ted Williams .832
3 Barry Bonds .810
4 Mickey Mantle .801
5 Lou Gehrig .797
6 Rogers Hornsby .787
7 Ty Cobb .781
8 Joe Jackson .780
9 Dan Brouthers .770
10 Albert Pujols .769

One last thing about Pujols. His career really does not have the kind of rising arc that the historical trend shows. So we can't be sure how any of this applies to him. Here is the chart of his OWP by age:



If we take his .827 at age 28 and then project each year forward using the changes in the typical trend, he would get .830 at age 29, then starting at age 30 and going on through age 40, he would get

0.820
0.820
0.824
0.791
0.791
0.787
0.783
0.755
0.749
0.748
0.712

Roughly he will have an OWP of .784 over the the rest of his career.

Which Pitchers Improved The Most In 2008?

I used two different stats to determine this. The first was an imputed value for ERA based on HRs allowed, walks and strikeouts (sort of a poor man's DIPS ERA). The other was RSAA from the Lee Sinins Complete Baseball Encyclopedia. "RSAA--Runs saved against average. It's the amount of runs that a pitcher saved vs. what an average pitcher would have allowed." It is park adjusted.

For the first measure of imputed ERA, I ran a regression using all pitchers who had 100+ IP in either of the last two seasons. There were 284 cases. The resulting regression equation was

ERA = 3.16 + 1.37*HR + .365*BB - .226*SO

Those are all per 9 IP. Walks include HBP. Then I took only pitchers who had 100+ IP in both seasons, found an imputed ERA for each of them in each season, and then found their change. They were then ranked from lowest to highest. Lowest would be most negative, so those are the ones that improved the most. Here are the ten best:



Now the ten who declined the most.



Now the ten best using RSAA. It is on a per 9 IP basis.



So Sanatana allowed 1.08 runs more than average per 9 IP in 2007 and allowed .95 less than average in 2008. So that is a swing of 2.03, which was the best improvement.

Now for the ten who declined the most.



Gorzelany really had a miserable year, being worst in both methods. The first method only takes into account what the pitchers did (but it is not park adjusted). The second method is park adjusted but is not solely determined by the pitcher. Anyone who made both lists either really did alot better or alot worse than the year before. Interesting that Mussina made the best by the RSAA method. He was also 14th by the imputed ERA method. There were 93 pitchers in all. The correlation between the change calculated by the two methods is -.67 (that negative makes sense since a positive RSAA is good). The more runs you save, the more your ERA will be below average.

Positional Hitting Over Time (Part 2)

Part 1 was a few weeks ago (you can scroll down to see it). I looked at the slugging percentage (SLG) divided by the league average for all 8 every day fielding positions. Here I look at how many players in each decade were among the top 100 or 200 seasons at each position in offensive winning percentage (OWP). OWP is a Bill James stat that says what a team's winning percentage would be if it had a lineup of 9 identical players who all hit alike and they gave up an average number of runs. Since I got the data from the Lee Sinins Complete Baseball Encyclopedia, it is park adjusted. The PA minimum was 400.

The first table is the top 100. The second table has the top 200. After the tables is a little discussion then there are two more tables. In those tables I adjust the figures to account for the different number of teams in baseball at different times.





One general comment is that using the Sinins database, a player is listed at the position he played the most. Jimmy Dykes is a SS in a year he only plaed 60 games there. It might be better to only use seasons with 100+ games at a position. Maybe if I get time someday I will do that. Then guys like Dykes could be put into the utiltiy category. Musial had 3 at 1B, 1 in LF, 1 in CF and 4 in RF. He gets lost in the shuffle and deserve to be remembered because he is Polish.

1B-In the teens, the only one in the top 100 was Jack Fournier in 1915. As you might guess, Gehrig(7) and Foxx (6) dominate in the 1930s. Greenberg and Mize had 2 each. In the 1950s, the only one is Musial (he also appears as CFer in 1952). Thomas (6), McGwire (5) and Bagwell (3) are the big names that caused the surge in the 1990s.

2B-The first three decades are dominated by guys like Lajoie (9), Collins (10) and Hornsby (9). Hornsby had another in 1931 (and 2 at 3B in the teens). Then there is a big drought in the 1940s through the 1960s. Joe Morgan (6) and Rod Carew (3) are the big names in the 1970s. But Mike Andrews has one, too! In the 1990s, Alomar and Biggio each had 4.

SS-Honus Wagner has 9 in the first decade (and 2 in the teens plus 1 in RF in decade 1). Wagner has 9 of the top ten all-time. The only 2 in the 1920s were Dykes and Joe Sewell. Arky Vaughn has 6 in the 1930s (plus 2 in the 1940s). Boudreau leads with 4 in the 1940s. In the 1980s it was Trammell (4), Ripken (3) and Yount (3). Larkin had 5 in the 1990s. AROD has 2 in the 1990s and 6 in the 2000s (plus 3 at 3B). Jeter has 2 in each decade.

3B-In the teens, Baker has 4. But Hornsby has 2 more. The drought from the 1920s-1940 is incredible (maybe defense was considered more important). One of the few is actually Mel Ott in 1938 (he played 113 games at 3B). Mathews has 6 in the 1950s (and 2 in the 1960s), with Rosen getting 3. Minnine Minoso got 1! (68 games, more than either LF (44) or RF (42)). Dick Allen leads the 1960s with 4, Santo had 3. Schmidt had 3 in the 1970s and 5 in the 1980s. Brett was 2 & 3. Boggs had 5 in the 1980s (and 1 in the 1990s). Randy Ready had 1 in the 1980s. Chipper Jones had 5 in the 2000s and 2 in the 1990s. AROD has 3 in the 2000s.

LF-Not counting Bonds, Ruth and Ted Williams, Rickey Henderson has the highest ever for a LFer (.859, 1990). The only two from the teens are Sherry Magee and Ruth, who has 3 in the 1920s. Williams has 7 in the 1940s (he missed 3 years in the military, remember!) and 7 in the 1950s (missing 2 years to the military and in 1959 he had 331 PAs (although OWP = .555)). Keller had 4. 2 of the 5 in the 1960s are Carl Yastrzemski. Boog Powell is one! But intersting how the 1960-80s are light. Bonds has 9 in the 1990s and 6 in the 2000s.

CF-Besides Mantle, Cobb, Speaker and DiMaggio, the best ever was by Cy Seymour (.825, 1905). Cobb has 10 in the teens (and 3 in RF in decade 1) and Speaker has 7. DiMaggio has 4 in the 1940s and 2 in the 1930s. Maybe it is no surprise how incredible the 1950s are. Mantle had 8 and Mays had 5 (missing 2 years). Snider and Doby each had 3. Then there is the Musial season and one for Tito Francona. Mays had 6 in the 1960s while Mantle had 4. Aaron and Kaline each had 1. Then we have quite another drought. Griffey had 4 of those all in the 1990s. I can't imagine any reason for this. Has defense become more important for CF?

RF-In the first decade, Cobb and Flick each had 3. Honus Wagner had 1, too. Joe Jackson has 3 in the teens. Ruth has 6 in the 1920s (and 4 in the 1930s). Heilman had 5. Ott has 5 in the 1930s (and 1 in the 1920s and 2 in the 1940s). Musial has 4 in the 1940s. Aaron had 1 in the 1950s and 2 in the 1960s. Maybe he does not have more since he was so consistent. Frank Robinson had 4 in the 1960s. Only 2 guys since 1970 have as many as 3. Reggie Jackson and Shefield, 3 each.

C-Not many before 1920. Bresnahan had all 3 in the first decade. Dickey had 5 in the 1930s and Cochrane had 4. Hartnett had 2 in the 1920s and 2 in the 1930s. The only 1 from the 1940s was Lombardi and that was in a war year, 1945. Berra had 5 in the 1950s and Campanella had 3. Bench, Simmons and Tenace all had 3 each in the 1970s. Fisk had 2. Simmons has 1 more in the 1980s. Piazza had 6 in the 1990s plus 2 more in the 2000s.

For the tables below, I divided the absolute total for each position in each decade by the number of teams in MLB that decade. That got multiplied by 30. The first one has the top 100. The second has the top 200.

MVP Awards And Award Shares By Position

I used the baseball writers award given since 1931. I added up all the MVP awards and shares by regular postions. An award share is figured by dividing the points he got by the maximum possible points. The first I ever saw of this was by Bill James back in the 1980s. If you came in 2nd, but your points added up to 25% of the max (if you got all first place votes), you get a .25 share. Right now, the system is 14 for a first place vote, 9 for second and so on. It may have been different in earlier years. The last paragraph has more technical notes. Anyway, here are the awards by position since 1931

1B 26.1
2B 10
3B 14.35
SS 15
LF 21.67
CF 14.21
RF 20.09
C 13.74
DH 1.83

Now for the shares. BR lists the top 200 in MVP vote shares. But they include the different awards from before 1931. I removed any shares from those cases. Here how the positions ranked

1B 78.89
RF 68.30
LF 56.65
3B 34.82
SS 34.71
CF 33.18
C 23.86
2B 22.72
DH 9.30

Now some of the guys actually had quite a bit of their shares from before 1931 and only a little after. So I removed anyone who had any shares from before 1931 (so even now their post 1931 data does not count).

1B 67.68
RF 61.66
LF 52.88
3B 34.77
SS 33.72
CF 32.92
2B 21.48
C 20.05
DH 9.30

Which ever way I do it, it seems that the writers like to reward 1B men, RFers and LFers and don't like to reward 2B men and catchers. Maybe things would look better for 2B men if I had included pre 1931 info. There were 2B men like Hornsby, Lajoie, Frisch and Collins. But I was mainly interested in looking at who the writers like.

If a player split time between two or more positions, I divided up the award or share proportionately. If he played 25% of the time at one position and 75% at another, he got .25 for one and .75 for the other. I did not count time at any position that was less than 10% of the total. I used Baseball Reference for the data. I used innings played where possible and games other wise. If games added up to more than 154 or 162, I just had to suppose that the percentages still held. Players do switch between positions during the game. When innings are not known, it can add up to more than 154. For DH cases, I also used games. DH is never listed by innings played, just games.

Which Hitters Improved The Most In 2008?

The stat I used for this was "offensive winning percentage" or OWP. It is a Bill James stat that says what a team's winning percentage would be if it had a lineup of 9 identical players who all hit alike and they gave up an average number of runs. Since I got the data from the Lee Sinins Complete Baseball Encyclopedia, it is park adjusted. I included all players who had at least 300 plate appearances in both 2007 and 2008. The top 25 are below:



I was surprised to see so many players aged 30 or more (11) plus 5 more aged 29. I thought that it would be younger players who improved. Maybe the older guys fluctuate alot more so bigger improvements are possible. But the leader and the #6 guy were both 36. One guy was even 38. The number of players aged 33 or more equalled the number aged 24 or less. The next table shows how much these guys improved in more conventional stats.

Jim Rice vs. Jose Cruz

I thought this might make an interesting comparison since I belong to a chapter of SABR in Texas (the Austin one or Hornsby chapter). The point is not that Rice does or does not belong in the Hall of Fame, just that Cruz compares so well. Cruz got 2 votes in 1994. Those are the only votes he has ever gotten.

Career PA
Rice-9058
Cruz-8931

Career Offensive Winning Percentage
Rice-.593
Cruz-.611

Highest 3 year OWP
Rice-.698 (1977-79)
Cruz-.687(1983-85)

Full seasons with .700 OWP or better
Rice-2
Cruz-3

Full season means 400+ PAs.

Full seasons with .600 OWP or better
Rice-5
Cruz-8

Cruz had an additional season with 346 PA

Career Win Shares per 648 PA
Rice-20.17
Cruz-22.71

Since it takes about 3 WS to make 1 win in Bill James' system, Cruz was worth .85 more wins per season. See

http://us.share.geocities.com/cyrilmorong@sbcglobal.net/WSperPA.htm

Career Win Shares
Rice-282
Cruz-313

Seasons with 20+ WS (all-star type seasons)
Rice-7
Cruz-8

Seasons with 30+ WS (MVP type seasons)
Rice-1
Cruz-1

Best 3 Consecutive years in WS
Rice-90 (1977-79, 26-36-28)
Cruz-80 (1983-85, 30-29-21)

I have also attemtped to rank players by their value above replacement. I did two lists. One with a TPR or a BFW (from Pete Palmer) of -2 per 700 PAs as replacement level and one with -3. I divided each guy's career PAs by 700. Then I multiplied that times 2 or 3. That result got added to his career TPR to get career value over replacement. Here is the all-time ranking through 2004.

http://www.geocities.com/cyrilmorong@sbcglobal.net/REP.htm

For VAR using -2 TPR per season
Rice-44.8
Cruz-46.72

For VAR using -3 TPR per season
Rice-57.42
Cruz-59.48

Best 3 Consecutive years in TPR or BFW
Rice-10.3 (1977-79, 3.0-4.2-3.1)
Cruz-7.7 (1983-85, 2.8-3.6-1.3)

MVP award shares
Rice-3.15 (tied for 29th, 6 top 5 finishes)
Cruz-.96 (248th, Al Oliver is higher with 1.25, only 1 top 5 finish)

Positional Hitting Over Time

There are four graphs below. Each one shows the slugging percentage (SLG) divided by the league average for all 8 every day fielding positions. Each data point is a five year average. The first two graphs are the AL and the next two are the NL.

Some interesting trends:

-Shortstops have been rising quite a bit in both leagues since the 1970s.

-2B men started declining in the AL in the 1940s, then starting rising again in the 1960s. But even now they have not reached their earlier peak. They started declining in the 1920s in the NL but then started back up in the late 1950s.

-3B men started to rise in the AL in the 1920s and in the 1930s in the NL. But they have tailed off in the AL since 1980.

-1B men had a big spike in the AL in the 1920s and 1930s. In both leagues, in general, they have been high but have fluctuated.

-CFers seem to have been in decline since the 1970s in both leagues.

-LFers seem to have been in decline in the AL for some time but it does not seem that way in the NL.

Which Players Had The Most Surprising Walk Rates

A few years ago I noticed that Miller Huggins walked quite a bit yet did not seem to be much of a hitter. From 1904-1916 he lead the league 4 times in walks and was in the top ten 7 other times. So what kind of fearsome hitter was he that he got walked so much? His career batting average (AVG) was .265, not bad in the dead ball era. The league average was .260 during his career. His career slugging percentage (SLG) was .314 while the league average was .343.

But a better measure of power is isolated power (ISO) or SLG - AVG. It tells us extra bases per AB (after all, if a guy can get a single every time up, his SLG would be 1.000 yet he has now power). Huggins' ISO was .049 while the league average was .083. So I was very impressed that he knew the strike zone well enough and had so much discipline that he could walk so frequently yet not have much hitting ability in general.

I thought it would be interesting to come up with some kind of measure of this ability. At first I used walk rate divided by ISO. But his turned out to be unfair to many sluggers since ISO can go very high (theoretically as high as 3.000). Their walk rate would end up being divided by a very large number so their Walk rate/ISO would be low.

So I ran a regression. A player's walk rate (relative to the league average) was the dependent variable and his ISO (relative to the league average) was the independent variable. The idea is that power hitters would get walked more than other hitters. I used all players with 5000+ career plate appearances (885 players). The data comes from the Lee Sinins Complete Baseball Encyclopedia. Here is the regression equation:

Walk Rate = 61.5 + .428*ISO

(In the Sinins encyclopedia, a walk rate of 150, for example, means that the player walked 50% more than average). The r-squared was .159, meaning that only 15.9% of the variation in hitter's walk rates is explained by variation in ISO. But the t-value for the coefficient on ISO was over 12, so it was statistically very significant.

Then each player's walk rate was predicted using the equation and the difference between their actual rate and the predicted rate was found. Then all the players were ranked from highest to lowest by this difference. The table below shows the top 25.



So Roy Thomas did the best. His actual walk rate was 2.49 times the average but his ISO was only .55 or 55% of the average. The equation predicts that he would have a walk rate of 85.06 (or 85.06% of the league average). Since 249 - 85.06 = 163.94, his walk rate was that many points above expected and he had the highest difference. Miller Huggins does very well, coming in at number 7.

The players who walked the least (based on the equation) are below:



I also did something similar using SLG. Here are the best players followed by the worst.

Was Jim Rice A Feared Hitter?

This issue came up on the SABR list this week. Someone suggested that batters in the lineup slot ahead of him were helped by his presence. That is, since pitchers knew Rice was up next, they gave good pitches to the current batter. Did batting in front of Rice actually help anyone? I address this below but first I discuss Rice and intentional walks.

My recollection is that Rice was very feared and he was very imposing. So many HRs (and so many long ones) were probably the reason. But he only finished in the top 10 in IBBs 3 times in his career (thanks to Lee Sinins Complete Baseball Encyclopedia). A 5th a tied for 10th and a tied for 9th. Also, he was only tied for 12th in the AL in IBBs from 1975-89. Here are the leaders:

1 George Brett 187
2 Eddie Murray 131
3 Rod Carew 111
4 Ben Oglivie 95
5 Harold Baines 89
6 Wade Boggs 87
T7 Reggie Jackson 85
T7 Ken Singleton 85
T9 Don Baylor 82
T9 Don Mattingly 82
11 Carlton Fisk 78
T12 Kent Hrbek 77
T12 Jim Rice 77

I would expect a feared hitter to rank higher. There are lots of factors that go into IBBs. Maybe he always had someone good behind him (but these other guys might have, too). The guys ahead of him tend to be lefties or switch hitters. Maybe that is the reason (I think there is another interesting issue here about IBBs that I address below after I discuss if batting in front of Rice actually help anyone).

As for how batters in front of him did, I looked at 4 seasons, using Retrosheet, 1977-79 and 1983, arguably his 4 best years. I threw out 1979 since Rice batted 4th all year and Lynn pretty much was the only 3rd place hitter and Lynn did not bat anywhere else.

Let's start with 1977. Rice pretty much batted third. Below are the players who had a significant number of ABs batting both 2nd and in other slots. I show there ABs, AVG, SLG. First I show there stats batting 2nd (in front of Rice) and then the others (combining all ABs not in front of Rice)

Doyle (137-.219-.285) (318-0.248-.318)
Lynn (364-.253-.453) (133-0.278-.428)

Now 1978 (Rice was pretty much 3rd)

Burleson (76-.197-.263) (550-.255-.349)
Lynn (80-.275-.463) (461-.302-.497)
Remy (418-.280-.349) (165-.273-.352)

Now 1983 (Rice was pretty much 3rd)

Boggs (315-.352-.470) (267-.371-.506)
Evans (258-.225-0.419) (212-.255-.458)
Stapleton (54-.259-.352) (488-.246-.365)

It does not look like hitters did alot better in front of Rice than they did elsewhere.

I mentioned the leaders in the AL in IBBs from 1975-89 in my last post. I also just checked the NL. Below are the top 20 in each league. It looks like the AL only had 5 righties while the NL had 10. Also, the top 2 in the NL were righties while in the AL the highest ranked righty was tied for 9th. Seems like a big difference between the two leagues. Also looks like all 10 righties in the AL had more IBBs than the highest ranked AL righty (Baylor). Maybe it is jut a fluke. My apologies if I miss labeled anyone below. I put in R for the righties and nothing for lefites and switch hitters.

AL

1 George Brett 187
2 Eddie Murray 131
3 Rod Carew 111
4 Ben Oglivie 95
5 Harold Baines 89
6 Wade Boggs 87
T7 Reggie Jackson 85
T7 Ken Singleton 85
T9 Don Baylor-R 82
T9 Don Mattingly 82
11 Carlton Fisk-R 78
T12 Kent Hrbek 77
T12 Jim Rice-R 77
T14 Cecil Cooper 73
T14 Fred Lynn 73
16 Mike Hargrove 68
T17 Alvin Davis 67
T17 Robin Yount-R 67
19 Bruce Bochte 65
20 Buddy Bell-R 62

NL

1 Mike Schmidt-R 184
2 Dale Murphy-R 141
3 Dave Parker 139
4 Garry Templeton 134
5 Keith Hernandez 127
6 Ted Simmons 124
7 Jose Cruz 123
8 Bill Madlock-R 112
9 Tim Raines 110
10 Jack Clark-R 104
11 Andre Dawson-R 103
12 Steve Garvey-R 100
13 Gary Carter-R 98
T14 Ron Cey-R 96
T14 Pedro Guerrero-R 96
T14 Leon Durham 96
T14 George Foster-R 96
18 Darryl Strawberry 93
19 Ron Oester 92
20 Dan Driessen 91

Maybe Joe Gordon Does Belong In The Hall Of Fame

Gordon had 242 career win shares. Through 2001, that was tied for 334th among all players including pitchers. But he did miss two seasons due to the war. He missed 1944 and 1945. The two previous seasons he had 28 and 31 (although the competition in 1943 was not so good). In 1946 he only had 9, must have been hurt. In the next two years he had 25 and 24. Suppose we give him 50 for the two years missed. That brings him up to 292. That would be tied for 187 through 2001. Not too bad of a ranking. Good enough for the Hall? I don't know.

But in general, 2B men have an average wins shares per PA that is lower than other positions. Win Shares is supposed to allow us to compare players across positions. Gordon might deserve even more win shares. Maybe he deserves another 10-20. To see the data on win shares per PA for different positions, go to

http://us.share.geocities.com/cyrilmorong@sbcglobal.net/WSperPA.htm (Update Jan. 10, 2016: Here is the new, correct link   http://cyrilmorong.com/WSperPA.htm)

If we do give him all these extra win shares he gets close to the top 150 through 2001. I really don't know what type of adjustments to make for him, but I guess a good case could be made for him.

One other thing I thought of is that he was a right handed batter in Yankee Stadium. Win Shares uses runs created get offensive value. Runs created are adjusted for park effects but to the extent that I understand them, no adjustemt is made if a park favors lefties over righties. Gordon hit 69 HRs in home games at Yankee stadium and 84 in road games. You would expect more at home. In his Cleveland years, he had 50 both home and away. Perhaps, on balance, over his career, he was hurt by his parks.

I was a little surprised by his, and his only, selection. But it may be okay. Joe McCarthy said Gordon was the best all around player he ever saw.

Two Follow Ups: Underpaid Second Basemen And What Happens When Players Cut Down On Strikeouts

A recent report called Increase in MLB salary slowed in 2008 shows that only relief pitchers get paid less than second basemen. Here is the key exerpt:

"Among regulars at positions, designated hitters had the highest average at $7.5 million, followed by first basemen ($7.1 million), third basemen ($6.6 million), shortstops ($5 million), outfielders ($4.8 million), catchers ($3.7 million), second basemen ($3.5 million) and relief pitchers ($1.9 million)."

So second basemen are only half as valuable as designated hitters? Hard to believe. A few months ago I posted a study called Have Second Basemen Been Underpaid?. I found in regressions that, holding hitting performance constant and accounting for free agent/arbitration status, that being a second baseman had a negative effect on salaries.

For the other issue, two weeks ago, I posted Should Ryan Howard Try To Strikeout Less?. The basic idea was that from year to year, there was a positive correlation between player's change in strikeout frequency and change in contact average.

But a commentor named Vince at the The Sabernomics blog said:

"Could this just be a selection effect? If your strikeout rate rises and your contact rate falls, then you might get benched and not show up in the sample."

My response was:

"There were 267 players in 2005 who had 300+ ABs. 200 of them also had 300+ ABs in 2006. So it is possible that those 67 who did not make it to 300 in 2006 were benched for poor performance (which would include a low contact average).

But I took those 67 guys and found the ones who had atleast 100 ABs in 2006 (I think anything less is a small sample size). That left 41 guys. The correlation between their change in strikeout frequency and change in contact average was .037. So it was still positive for the ones who were “selected out” but not as strong an effect."

Do The Best Hitters Strikeout More Than Other Hitters (And Has This Changed Over Time)?

I found the correlation between strikeout frequency and offensive winning percentage (OWP) decade by decade. I started with the NL from 1910-1919 and the AL 1913-19 (there was a period after 1900 before this when strikeouts for batters was not compiled). I used players with 2000+ PAs in each decade or time period. Strikeout frequency was calculated two ways, per PA and per AB. So the table below has the correlation between OWP and strikeout frequency for each period. The PA column shows the correlation between OWP and strikeouts per PA and the AB column does the same for strikeouts per AB.



There seems to be quite a bit of fluctuation over time. I don't think I have any good reasons why. Most of the time the correlation is positive, meaning that the better hitters usually strikeout more that average. I am surprised that the correlations have come down since 1980 and that they are not as high today as they were in the 1960s and 1970s. This is because we have guys like Ryan Howard and Adam Dunn around.

It is also interesting that the 1930s were much higher than the periods right before and after. Same for the 1960s and 1970s. The table below shows the top ten batters in OWP for the 1930s and their strikeout rates.



The simple average of the two strikeout rates for these ten were 7.87% and 9.29% while the rates for the entire group in the 1930s were 6.94% and 7.79%. So the very best hitters struckout alot more than average then.

The next table shows the top ten in strikeouts per AB from the 1930s. The simple average of the OWP of these players was .631. Ruth was over .800 and Foxx and Greenberg were over .700 and three others were over .600.



In the AL 1913-19, the top ten in OWP had strikeout rates of 5.81% and 6.75% while the averages for the whole group were 6.97% and 7.99%. So in this period and league, the best hitters struckout alot less than average.

Should Ryan Howard Try To Strikeout Less?

You might think so. In both 2006 and 2007 he led the major leagues in "contact average." I define that as hits divided by (AB - K + SF). His contact average in 2006 was .448 and in 2007 it was .421 (although it fell to .372 in 2008). And he strikes out about 190 times a year. So more contact would mean more hits, right? Maybe, maybe not. I looked at this issue a few years ago with Strikeouts and the value of hitters.

Generally I found that when batters cut down their strikeout rates from year to year, they hit better. But I also found the effect was slight. Here is an exerpt:

"Using the data from the 2002-3 seasons, I ran a regression with change in AVG being the dependent variable and change in strikeouts per AB being the independent variable.

The equations was:

AVGChange = -.00036 - .274*(SO/AB)Change

This means that if a player cut his strikeouts down by 100, his hits would go up by 27.4. That is like saying on his additional ABs when he does not strikeout, he bats .274. This may not be impressive because for all of these players over the 2002-3 seasons, they already bat about .336 when they don't strikeout. Also, the r-squared was only .068, meaning that the regression explains only about 6.8% of the variation in AVGChange. So if there is any negative side to striking out, it is probably not too large."

This got me to thinking what happens to batter's contact average when their strikeout rate changes (something I had not looked at in this earlier study). I found all the hitters in baseball who had 300+ ABs in both 2006 and 2007 (190 plyaers). Then I calculated their strikeout rates (K/AB), their contact rates and how each one changed from 2006 to 2007. The correlation between the change in strikeout rate and the change in contact rate was .142. So if a batter's strikeout rate increased, his batting average while making contact also increased. Looking at the changes from 2005 to 2006 gave a .18 correlation.

Maybe this makes sense. If you swing harder, you strike out more. But a harder swing means the ball is hit harder, which should mean more hits. So combined with the earlier study, a player should be careful if he thinks he should make a big effort to strikeout less.

Which Players Had The Most Uncharacteristically Good Seasons? (adjusted for their age)

I did this last week but did not adjust for age. The key stat I used is offensive winning percentage, so read last week's post to understand it. The idea is to find out which player had a season that deviated the most from his norm or career average. But I did not take age into account. Player performance improves, then peaks, then declines. The typical peak may be as young as 25. So a player doing 100 points better than his norm at age 25 is not the same as doing 100 points better at age 38. To find the expected performance at a given age, I found the relationship between age and average OWP at each age using all players with 15+ seasons of 400+ PAs. That relationship is

OWP = -0.0008*AGESQUARED + 0.0474*AGE - 0.0574

This comes from regression analysis which had an r-squared of .95, meaning that 95% of the variation in an age's average OWP is explained by the equation. The standard error was .008 or pretty low. But as Bill James, Phil Birnbaum and probably many others have pointed out, averaging each player's OWP at a given age to predict career trends can have many problems. One is that as we get to older ages, there are not many players to use to get an average because so many players are not good enough to even play anymore. If those retired guys had kept playing, the average OWP for ages 39, 40, etc. would be much lower. So this equation will underestimate how unusual some seasons might have been for older players.

To predict a player's OWP at a given age, the above equation is used. But an adjustment is made based on his career norm, too. The average OWP by age for the group was .588. If a player had a .550 career OWP, then at any age his predicted OWP is adjusted down by .038 (a player with a career OWP of .638 would have each predicted OWP upped by .050). Once that was done, I found the 50 top seasons in terms of OWP above the prediction. The table below shows this. For example, Tommy Tucker in 1989 had an OWP of .783 at age 25. The equation predicts that he would have an OWP of .628. But his career OWP was .495, or .093 below the norm. So his adjusted prediction is .535. Since .783 - .535 = .248, his OWP was .248 better than expected. This was the highest positive difference ever (you will need to click on the table to see a larger version).

Barry Bonds' 2004 season at age 39 is number 31. His 2002 season is 54th, his 2001 season is 163rd and his 2003 season is 172nd. There were a total of 6319 season. So the four Bonds seasons from 2001-04 (ages 36-39) are all in the top 2.7%. He is the only player in the top 3% to have 4 seasons.

Which Players Had The Most Uncharacteristically Good Seasons?

Many fans know that Norm Cash batted .361 in 1961. He also had 41 HRs and 132 RBIs. Never batted .300 again (his last year was 1974) nor did he ever reach 40 HRs or 100 RBIs. Perhaps this is the most atypically good season ever. He clearly performed well above what ended up as being his career norms (was it the corked bat mentioned in the ESPN almanac? I recall that physicist Robert Adair said a corked would not really help).

Anyway, to study this, I looked at all players with 10+ seasons with 400+ PAs through 2005 (there were 504 players). I found the simple mean of their yearly offensive winning percentage or OWP (a Bill James stat that says what a team's winning percentage would be if all 9 batters were identical and you gave up an average number of runs). Since I used data from the Lee Sinins complete baseball encyclopedia, OWP is also park adjusted. Then I subtracted that mean from their best year. The following table shows the top 25 in terms of best minus average OWP. Cash's 1961 season was 25th. Another table follows that only looks at seasons since 1920.

Another Look At Consistency

Last week I had a post on which players had the most consistent careers. One measure I used was a player's yearly standard deviation in offensive winning percentage. Then I divided by the mean, thinking that high OWP hitters would fluctuate more. But Gerry Myerson suggested on the SABR-list that with an upper bound on OWP of 1.000, the best hitters won't fluctuate more than the worst. So I redid the list, which you can see if you click here. Actually, this time there are two lists, as there were last week. One ranks everyone just in standard deviation and the other is SD divided by number of years.

Which Players Had The Most Consistent Careers?

I don't know if anyone has ever proved that consistency has value. But I have compiled two lists which you can see here. I took all the players who had 10+ seasons with 400+ PAs through 2005 (there were 504 players). Then I found the standard deviation of their offensive winning percentage (a Bill James stat that says what a team's winning percentage would be if all 9 batters were identical and you gave up an average number of runs). Since I used data from the Lee Sinins complete baseball encyclopedia, OWP is also park adjusted. Then that SD is divided by the mean OWP. This is necessary because players with high OWPs will see bigger absolute year-to-year fluctuations.

But then I wondered if players with extra long careers would be penalized. The reason is that when you get older, your performance can tail off very quickly and those very low OWPs increase your SD. So you get penalized for longevity. Then also, your career OWP falls and your SD gets divided by a smaller number, lowering my measure of consistency because we get a bigger number now which means less consistency (the lower the SD/mean, the more consistent). So I created one more list where SD/mean was then divided by the number of years. For Hank Aaron, he jumped from 112th to 12th.

On the first list (SD/mean), Dom DiMaggio is first. He only had 10 400+ PAs seasons. Once I did the 2nd list, (SD/mean)/Years, Mel Ott jumped to first. Dom DiMaggio dropped to 8th. Guys that get hurt by the 2nd list are the guys who lost years to military service in WW II. They get divided by a smaller number.

Does Experience Affect Clutch Hitting?

In the Red Sox-Rays game yesterday, one of the announcers mentioned that Dioner Navarro batted .314 this year with runners in scoring position (RISP) while it was only .214 last year. He said that Navarro improved in the clutch due to experience. Maybe, maybe not. His overall average went from .227 to .295, also a big jump. Experience might make you a better hitter overall anyway.

But I had an article published on a similar topic in "By the Numbers," SABR's statistical bulletin several years ago. It was called Clutch Hitting and Experience (I know, not a real creative title). I only looked at one year, but I found that more experienced players did better, relative to their normal performance, in close and late situations than less experienced players. For example:

"ONE THING I DID NOT MENTION IN THE PAPER WAS THAT THE AVERAGE OPS FOR EXPERIENCED PLAYERS (2000 OR MORE PA) IN THE NONCLUTCH WAS .815 AND .808 IN THE CLUTCH, A DROP OF ONLY .007. FOR THE INEXPERIENCED PLAYERS, THEIR NONCLUTCH OPS WAS .792 AND NONCLUTCH WAS .741. A DROP OF .051, MUCH LARGER THAN FOR THE EXPERIENCED PLAYERS. THE DIFFERENCE IN DECLINES IS .044. THAT IS HIGH IN BASEBALL TERMS. THERE IS A RELATIVELY SMALL DIFFERENCE BETWEEN THE TWO GROUPS OF PLAYERS IN THE NONCLUTCH BUT A MUCH LARGER ONE IN THE CLUTCH SITUATIONS."

So it is possible that experience affects clutch hitting. But it was just one study over one year. If you know of any other studies on this, let me know. Also, I have a page called Clutch Hitting Links. There are links to lots of good stories and research. If you know of any that are not listed there, please let me know about that, too.

Something New In Clutch Hitting? A Couple Of Recent Articles

One was called Analysts: Tough to determine if there is such a thing as clutch by Paul White. In discussing the issue of whether or not some guys are clutch hitters, Reggie Jackson was mentioned. "Mr. October" had the following AVG-OBP-SLG 27 World Series games .357-.457-.755 (data from Retrosheet). But what about in 45 league championship series games? He had .227-.298-.380. Combining the two he has a .276 AVG and .521. Still good numbers but hardly stunning and why did he hit so poorly in the LCS? Lucky for him his teammates were doing well enough for him to make it into the World Series.

The article also mentioned Derek Jeter. Nike even has a shoe called the "Jeter Clutch." But in his career his AVG in close and late situations is .286 while his overall AVG is .317 (both through 2007). Generally players hit more poorly in when it is close and late because you face ace relievers and the pitcher is more likely to have the platoon advantage. But his differential is probably bigger than normal.

To read lots of other good articles on clutch hitting go to Clutch Hitting Links. One thing that is important to ask when we talk about clutch hitting is do teams make personnel moves even partly based on it? Have you ever heard of a team trading a .300 hitter because he hit poorly in the clutch or trading for a .250 hitter because he was good in the clutch?

What about Barry Bonds in the post season? It appeared that he was a bad clutch hitter until 2002, based on his past post-season performances. His averages in the LCS in 1990-2 were .167, .148, and .261. Did Dusty Baker decide to bench him in the 2002 playoffs because he was a bad clutch hitter? No. Obviously Baker, a big league manager, does not buy into clutch. For more on this kind if argument, go to Please, no more clutch hitting statistics!

The other article is called Clutch hitting is no accident. Apparently, Twins manager Ron Gardenhire thinks you can teach it. From this article:

"The Twins' .311 batting average with runners in scoring position is so much higher than any other major league team's — runner-up Baltimore is 24 points behind, at .287 — that it seems like a statistical fluke. In the Twins' case, the manager said, they can shorten their swings, watch for particular pitches, and use the entire field as a target. Under batting coach Joe Vavra, Gardenhire said, every Twins hitter sharpens his run-producing skills every day.

"It's execution — getting them over, getting them in. I think that's definitely a skill," Gardenhire said. "You work at anything long enough, you get a mind-set for what you're trying to do." The Twins didn't have that last season, when they batted .276 with runners in scoring position, 14th best in baseball."

We will have to see if the Twins continue to do so well with runners in scoring position next year. If they do, maybe other teams will adopt what they do and we will see them hit better in these situations, too. But I am not holding my breath. Pitches might start pitching differently then.

Have The Angels Been Lucky This Year?

The Wall Street Journal had an article about this recently called Baseball's Luckiest Team. It mentioned some things like their AVG with runners on base and how many more games they have won than expected. It also mentions how well their pitchers have done in stranding runners. But with runners on they allow a .264 AVG, 11th best in baseball. They are 5th in that in OPS allowed at .744. They are 10th in AVG allowed with runners in scoring position with .260.

So I decided to do my own analysis. First, I checked to see what their winning percentage should be based on their OPS differential using the equation

Pct = .5 +1.21*OPSDIFF

The table below shows how teams ranked in wins above those predicted using this formula (which is based on regression analysis I did a few years ago). With an OPSDIFF of .010, they should have a pct of .512 but they actually have .615! So they have won about 16 more games than predicted (over 161 games-I used that for all teams). You can click on the table to see a bigger image. After the table, I present another analysis which takes clutch situations into account.



I have some research called Does Team Clutch Matter in Baseball? I broked down performance into close and late and non-close and late. Then I ran a regression with OPS and opponents OPS in close and late and non-close and late situations as independent variables explaining pct. Here is the equation:

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

Then I predicted each team's pct and how many more games they won than predicted. The table below shows how the teams did and again the Angels are first in terms of doing better than expected. Maybe they are lucky.

Was Devon White A Good Leadoff Man As A Blue Jay?

Last Sunday one of the announcers on the TBS game said that Devon White became a good leadoff hitter when he came to the Blue Jays (I think it was Buck Martinez). Let's see if he was a good leadoff man during his Toronto years, 1991-5.

His SLG and OBP were .432 & .327 while the league average was .406 & .335. So his OBP was below the league average, probably not a good sign for a leadoff man. White did steal 31.11 bases per 162 games with 5.68 CS. The league averages were 13.58 & 6.67. (data from the Lee Sinins Complete Baseball Encyclopedia). So he was a better base stealer than the league average, but got on base less often. Since OBP is probably the most important stat for leadoff men, this is not a good sign.

The average leadoff man during those years in the AL had an SLG & OBP of .391 & .350 with 33.92 SB & 12.78 CS per 162 games. White has a slight edge in stealing due to his better success rate and a higher SLG but a big deficit in OBP. And SLG is not the key to being a good leadoff man.

I explained a fairly complex way of evaluating leadoff men a few months ago. You can read about it at Who are good leadoff men. The basic idea is that hit%, walk%, extra-base-hit%, SB per game and CS per game each has a run value based on which lineup slot you are talking about. As you might guess, walk% and SB% are very important for leadoff men, but less so for cleanup hitters where extra-base-hit% is more important. I had found these run values a few years ago using regression analysis.

Anyway, White's marginal run value as a leadoff man was 1.290 while for the average leadoff man it was 1.292. So he was below average. Not by alot, but that is not good.

Finally, I had come up with a simple statistical rating for leadoff men earlier this summerWho Are The Good Leadoff Men?. Here is the gist of it:

It seems obvious: Hitters who are fast and get on base alot. You also probably don't want someone who hits alot of HRs, since you want those guys to bat with runners on. So I tried to devise a stat that would capture this. Here it is:

(2B + 1.25*3B - HR + SB)/outs

In other words, how many times a player gets into scoring position per out. Since triples are worth about 25% more than 2B's according to run expectancy tables, I multiply them by 1.25. By dividing by outs, the ability to get on base is taken into account since if you make an out you don't reach base. Also, outs include caught stealing. By subtracting HRs I am saying that guys that hit alot of HRs, even though they may have other good leadoff traits, are "penalized" here, since they might be better suited to batting lower in the order.

Anyway, White ranked 11th among all AL players 1991-5 with 2000+ PAs. Considering that there are 14 teams and each one has a leadoff man, 11th is not that great a rank.

Cliff Lee vs. Roy Halladay

There was an interesting post on this at Battersbox: Lee vs. Halladay. One thing they mentioned is that the batters that Lee has faced this year have a collective OPS of .732 while it is .766 (OPS = OBP + SLG).

But how should this difference affect each guy's ERA? I did not see it mentioned or discussed there (my apologies if it was). So I will take a look at this issue.

Based on data from all major league teams from 2001-2004, here is the relationship between OPS and runs per game

R/G = 13.26*OPS - 5.29

For all teams this year it is

R/G = 12.07*OPS - 4.39

If we multiply 13.26 times .034, the difference in the OPS of their opponents, we get .45. If we use 12.07, we get .41. If we add that to Lee's ERA of 2.36, we get 2.77 or 2.81. Halladay is at 2.77. That makes things pretty even.

But what if we look at DIPS ERA, an ERA computed based only on things the pitcher controls himself like strikeouts, walks and HRs (DIPS means defense independent and was developed by Voros McCracken). Lee has a 2.85 DIPS ERA and Halladay has 3.06. So then Lee would jump well above Halladay in ERA.

Now we don't how good the pitchers were that these batters faced. Maybe the batters who have a collective OPS of .732 (the ones Lee has faced) faced unusually good pitchers. Probably not, but we do need to note it. If not, then this analysis gives the edge to Halladay. Halladay came into today with an edge of 14 in IP (224 to 210). He pitched 7 more today. Given that he will end up with more IP (it might not be as much as 21, though depending on how much each guy pitches from now on), Halladay has a case for the Cy Young award.

Technical note: The standard error in the OPS/Runs regression was about .15 in each case or about 24 runs a season. Certainly not the best estimators around but decent and OPS is the stat at issue here.

How Good Are Playoff Bound Teams At Preventing Homeruns?

Last week a commentator on a game (I think it was on TBS) said that the White Sox might have problems in the playoffs since they rely on HRs so much and pitchers in the playoffs are good at preventing HRs. So I looked at all the playoff teams in both leagues over the last three years and compared their HR rate allowed (HRs divided by batters faced) to the league average. Over that time, the NL playoff teams allowed about 1.5% fewer HRs than average. The AL teams allowed about 5.3% fewer HRs than average.

How might this impact the White Sox if they make it to the post-season this year? Suppose their season rate of hitting HRs is 1.5 per game (it is not quite that high, but close). Then even if that goes down 5.3% in the playoffs, that still leaves them with about 1.42 HRs per game. If a typical HR is worth 1.4 runs (using the linear weights value from Pete Palmer), the White Sox would lose about .112 runs per game (since .08*1.4 = .112). If a typical playoff team hit 1 HR per game, then that goes down to .947 a game in the playoffs. That would cost them about .074 runs per game.

Now the difference between what the White Sox lose and what the typical team loses is less than .04. Not very big. And the other teams will probably see something that they do better go down more than for the Sox and suffer bigger loss (like in stealing or walking or just plain hits-remember that the pitching staffs of playoff bound teams are probably better than average at other things than just preventing HRs). Then that brings the two teams even closer togther. The Sox reliance on the HR is not a big deal.

More On The Changing Historical Relationship Between Walks, HBPs and HRs

What I posted last week was something I posted on the SABR list last year. At that time, someone raised a question about this. Below is the question and how I responded, with a little more research. I think my basic finding is that there are not more HBP these days due to pitchers throwing faster.

"Cyril mentioned that current pitchers seem to be more willing to hit batters than pitchers in the past. How about since a lot more pitchers now pitch the ball around 90 MPH, it's harder for batters to get out of the way. Historically, have the pitchers leading the leagues in HB been hard throwers (more Ks) or poor control pitchers (more BBs)?"

I did some analysis on this although it is not exactly what John Lewis suggests. I took the top 500 pitchers in batters faced (seasonal data) from 1960-69 and 1997-2006. I ran a regression in each case in which the HBP rate was the dependent variable and the strikeout rate and the walk rate were the independent variables. Intentional walks were removed.

Here is the regression equation for the 1960s

HBP = .00387 + .0177*BB + .00186*SO

For the 1997-2006 period it was

HBP = .005 + .0031*BB + .00486*SO

The r-squared in the first case was just .013 and in the second it was .025. The r-squared tells us what percent of the variation in the dependent variable is explained by the model. So it is pretty weak. But the T-values for BBs and SOs in the first case were 2.44 and .44. So the walk rate is statistically significant. For the second period they were 3.32 and 1.13.

In the first period, a one standard deviation increase in BB rate increased HBP rate .000392. For the strikeout rate it was .00007. So if a pitcher increases his walk rate he increases his HBP rate more than if he increases his SO rate. For the second period these numbers were .00065 and .00022. So again, the walk rate has a bigger impact.

So all of this suggests that it is worse control in general that increases the HBP rate.

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Now another response to that question

The other day I discussed a regression relating HBP, BBs and SOs. I did that again but I added in HRs with the idea that a pitcher might be more likely hit a guy who hit a HR last time up (or the next guy). I again looked at both the 1960s and the last 10 years. Skipping the regression details (except to say the coefficient values and the r-sqaured values did not change much), the interesting thing I found was that HRs had a negative relationship with HBP in the 1960s but it was positive in the last 10 years. So in the 1960s, a pitcher who gave up more HRs hit fewer batters but today a pitcher who gives up more HRs hits more batters.

Having an increase in HR% of .01 over 1000 batters faced reduced HBP in the 1960s by about .23. In the last 10 years, they went up by .33. A 1 standard deviation increase in HR% in the 1960s decreased HBP by .15. In the last 10 years it increased HBP by .24 (again, over 1000 batters). The standard deviation of HR% in the 1960s was .0066. In the last 10 years it was .0075.

The T-value on HRs was not significant for either time period. But maybe the difference in their coefficients could be. Anyone know if you can look at two different regressions and run some kind of a test to see if the difference between coefficients from the regressions is significant?

I ran a regression which combined the two periods. There was a dummy variable for time period. It indicates that pitching in the last 10 years instead of the 1960s, holding everything else constant, means 2.5 more HBP per 1000 batters faced. The T-value was 8.98. In other words, highly significant.

I also ran a regression with the dummy variable and the dummy variable was multiplied by each of the other variables (HRs, BBs, SOs). In this case the dummy for time period was just about zero and not significant. The value of the HR*dummy coefficient was .055 (although the T-value was just 1.53 and about 2 is usually needed for significance). So I think the .055 value means that any given increase in HR% in the last 10 years would make the HBP rate go up by .055 more than in the 1960s. So over 1000 batters faced, if your HR% goes up by .01 (say you give up 10 more HRs) you would hit .55 more batters in the last 10 years than you would have in the 1960s.

The Changing Historical Relationship Between Walks, HBPs and HRs

Since I posted something on HBP's last week, I thought I would post a couple of items that I put on the SABR list last year. Here they are.

As many of you probably know, the HBP rate has been on a general increase for many years (since about 1980). But one thing that could account for it is that pitchers have poorer control than they used to (I am not saying that they do-just that it could be a reason for the rise in HBP rates). So I thought that it might be useful to look at the HBP-to-walk ratio over time. I created 4 graphs and they are at

http://www.geocities.com/cyrilmorong@sbcglobal.net/HBPWalks.doc

There is one graph for each league. The first one is the HBP-to-walk ratio using all walks and the second one excludes intentional walks (they were not officially recorded until 1955). I also started the NL in 1897 or so because it did not look like all of the HBP were recorded by then. The file is a Microsoft Word file so when you click on it you might be asked to open it in that program. You will have to say yes.

Both leagues were around .16 about 1900. That is, there were 16 HBP for every 100 walks. But by around 1940 or so, it was 4 (or fewer) HBP per 100 walks. For both leagues, the rate has been rising since 1980. This suggests to me that the higher HBP rates these days is not due to poor control. There may be other issues involved so we might not be able to conclude that.

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Yesterday I discussed the HBP rate relative to the walk rate and how HBP/Walks has risen over time. But I also thought about how HRs might affect this. If a player hits a HR, the pitcher might want to pitch inside more to that player or anyone else on that team. This could lead to more HBP. Maybe even sometimes pitchers intentionally try to hit someone because of HRs. So I looked at HBP/HR over time. Since 1920, in both leagues, the rate has pretty much stayed under .5. But, of course, control is an issue, too. So I figured out the non-intentional walk rate each season since 1955 for both leagues and then the historical average from 1955-2006 in both leagues.

For each league/season, I then divided the non intentional walk rate by the average over the 1955-2006 period. If a league/season had a rate that was 10% higher than the historical average, then they got a 1.10. The HBP/HR rate for that league/season was divided by 1.10. So I deflate the HBP/HR rate by 10% since that league/season's pitchers had control that was 10% worse than average, which could partly account for a higher HBP/HR rate. So I did that for all league/seasons. The new number is called the adjusted HBP/HR rate. I graphed this for each league since 1955. The two graphs are at

http://www.geocities.com/cyrilmorong@sbcglobal.net/HBPHR.doc

The file is a Microsoft Word file so when you click on it you might be asked to open it in that program. You will have to say yes.

What I see here, is that if you adjust for HRs and control (as measured by the walk rate), is that pitchers today seem pretty willing to hit batters. Does this mean that they are willing to pitch inside and that the high HBP rate is a side affect of that? We probably can't tell for sure since we don't have stats on how many pitches are thrown inside. But certainly pitchers today are willing to hit batters. In the AL, each of the last 6 seasons is above the historical average of my adjusted HBP/HR rate (which is about .27). In the AL, 5 of the top 6 seasons in the unadjusted HBP/HR rate were from 2001-05. 2006 was the 11th highest.

In the NL, the historical average of the adjusted HBP/HR rate is also about .27 and each of the last 6 years is above that. 6 of the 10 highest unadjusted HBP/HR rates were from 2001-06. One of the reasons I looked into this issues is that it came up at the most recent SABR convention. There was a panel on St. Louis baseball and the former player all said that pitchers today don't pitch inside enough, that they leave the ball out over the plate too much and that they are reluctant to hit, or be aggressive with guys who are hitting HRs. Based on what I have done, this does not seem to be true.

Are Good Pitchers More Likely To Hit Batters Who Hit Them Well?

I started wondering about this after last week's post on whether or not HR hitters are more likely to get hit by the pitch in recent times than they did in the 1950s and 60s. I took the top 10 in wins from 1960-69 and from 1998-2007. Then I found the correlation between their HBP% and HR%, OPS and SLG. For HBP% the formula was HBP/(HBP + AB). The other stats are calculated normally. My table below shows only 5 pitchers in the last 10 years since only 5 of them had faced 30+ batters in at least 50 ABs (those were the cutoffs I used). The data comes from Retrosheet. You can click on table to see a bigger version. A batter's record against a pitcher also includes cases not in the specified period. It includes their entire careers.

There may not be alot to learn here. Some guys have negative correlations and many are very low. The two who standout are Bunning and Mussina. According to the Lee Sinins Complete Baseball Encyclopedia, Bunning hit 160 batters while the average would have hit only 90. Relative to the league average, he was the 3rd most likely to hit a batter in the 1960s with 1000+ IP.

Mussina is very interesting. In his career he only hit 52 batters while the average pitcher would have hit 125. He was the 10th least likely to hit a batter relative to the league average in the last 10 years. Yet he has very high correlations on OPS & SLG. It seems like if a guy hit Mussina well, he was more likely to hit him. Yet Mussina has been very good at not hitting people in general. Has be been selectively and intentionally hitting certain guys? Of the 39 batters who have 50+ ABs against Mussina in the last 10 years, only 10 have been hit at least once. But their collective AVG against him is .321 (again, that is for their whole careers, not just the last 10 years). The other batters combined for only a .253 AVG. Getting back to the 10 who have been hit, they have collectively slugged .541 in their careers against Mussina.

Do Sluggers Get Hit By The Pitch More Than They Used To?

I found the correlation between HR frequency and HBP frequency for each decade since the 1950s. In one case the denominator was AB + HBP, in the other it was AB + BB + HBP. Here are the correlations for the first case, starting with the 1950s

0.029
0.119
0.088
0.222
0.186
0.17

Now for the second measure.

0.022
0.101
0.072
0.22
0.173
0.128

The correlations are higher in the 80s, 90s and the 2000s, meaning players who hit HRs more frequently are more likely to get hit by a pitch than in the the 50s, 60s and 70s. So when old-timers tell you something like "if you hit a HR off Bob Gibson, next time you got brushed back or put on your but," don't believe them. If that kind of thing was going so much, there would have been more hit batters (some of those brushbacks would be a little off the mark, so the pitch would hit you, not just come close). And the correlation would have been higher between HR hitting and getting hit back in those days. But they are higher now.

In fact, hitting a HR in the 1990s increased your chances alot more than hitting a HR in the 1960s. Here is the regression equation from the 1960s

HBP% = 0.0311*HR% + 0.0058

Now for the 1990s

HBP% = 0.0573*HR% + 0.0065

Since .0573/.0311 = 1.83, hitting a HR in the 1990s was 83% more dangerous in the 1990s than it was in the 1960s. And the T-value on HR% in the 1990s was significant (2.84) while it was not significant in the 1960s (1.52).

Predicting 2nd Half Winning Pct With First Half OPS Differential and Winning Pct

Earlier in the season, I did a post on which teams had the best OPS differentials. So I thought it might be interesting to see what has a higer correlation with second half (actually post all-star) winning pct: first half (actually pre all-star) winning pct or first half OPS differential? Using the data from ESPN, here are those correlations for the years 2000-2007. The first half pct is the first number and the 2nd is OPS differential.

0.384**0.498
0.384**0.34
0.708**0.669
0.612**0.708
0.625**0.607
0.327**0.297
0.226**0.237
0.444**0.361

Interesting that the correlations were much higher in 2002-4. Overall, it looks like first half pct does a slightly better job. The average correlation for the first half winning pct is 0.46375 and for first half OPS it is 0.46463. So a very slight edge for OPS.

I expected a bigger edge for OPS since it gives a good idea of a team's performance and pct can be more affected by luck in a short time span. Maybe it reflects how good the closer or bullpen is and that carries over from half to half.