Baseball's "300" Hitters

What players have had both 300+ times reaching base (RB) and 300+ total bases (TB) in the same season? Not many. RB includes hits, walks and HBP. To see a list of all such occurrences, go to 300RBTB. It is in chronological order.

When you get there, the list on the left has all the players who did it. The list on the right shows some near misses, guys who had 280+ in each stat but did not make it. The tables also show each player's offensive winning percentage and RCAA or runs created above average, which is park adjusted since it is from the Lee Sinins Complete Baseball Encyclopedia. The most recent occurrence was Pujols in 2009, with 310 RB & 374 TB. The table below shows the leaders in such seasons.



The next table shows the very near misses, guys who had 297+ in both stats but not 300+ in both. Frank Thomas has one other very near miss. In 1995, when the season was only 144 or 145 games, he had 294 RB & 299 TB.



Now the breakdown by decade:

1890s 2
1910s 2
1920s 19
1930s 20
1940s 9
1950s 6
1960s 3
1970s 2
1980s 3
1990s 21
2000s 24

Did The Yankees Buy A World Championship In 2009?

That was the subject of a recent Wall Street Journal article by economist Andrew Zimbalist titled The Yankees Didn't Buy the World Series. On the surface, it would seem that they did. They ususally have the highest payroll and they signed three big free agents in the off season, 1B man Mark Teixeira and pitchers C. C. Sabathia & A. J. Burnett. Teixeira led the American League in home runs and runs batted in while the two pitchers both finished in the top 20 in earned run average and the top 11 in innings pitched.

But Zimbalist said:

"It's a little surprising, but the statistical relationship between a team's winning percentage and its payroll is not very high. When I plot payroll and win percentage on the same graph, the two variables don't always move together. In other words, knowing a team's payroll does not enable one to know a team's win percentage.

More precisely, depending on the year, I find somewhere between 15% and 30% of the variance in team win percentage can be explained by the variance in team payroll. That means between 70% and 85% of a team's on-field success is explained by factors other than payroll. Those factors can include front office smarts, good team chemistry, player health, effective drafting and player development, intelligent trades, a manager's in-game decision-making, luck, and more."

Some readers, however, disagreed, making some good points in the letters to the editor a few days later (see In Baseball’s World Series, Money Loads the Bases). The best point may have been made by Ira H. Malis who mentioned that the top 4 teams in salaries in the American League make up, on average, 60% of the teams that make the playoffs.

Economist T. Norman Van Cott makes a good point in support of Zimbalist, that long before the period of free agency, when players can sell their services to the highest bidder (with certain limits), the Yankees dominated baseball. But we need to recall that before 1965, a player coming out of high school or college could sign a contract with any team (but then the reserve clause kept them tied to that team forever). The Yankees had money advantage over the other teams in getting good players in the first place. They could offer bigger bonuses and the promise of often getting a World Series check and making business connections in New York.

Zimbalist also only analyzed the salary and win relationship one year at a time. I did something different last year, using team averages over many years. I found a stronger relationship between salaries and wins that Zimbalist did, that almost 50% of variance in team win percentage can be explained by the variance in team payroll Here is that post (Another look at salaries and wins).

Alot of people have looked at this. But I started thinking about it again after I came across some data at JC Bradbury's site. You can view that data here. The data shows how many games, on average, that teams won each year from 1986-2005. It also shows how much above or below the league average in total salary each team paid in percentage terms. Again, it shows yearly averages. Suppose a team was 10% above average one year and 30% above average another year, they would get 20 (if were just over two years).

What I did was to run a regression with average wins per year as the dependent variable and the average salary (SAL, the % above or below the league average) as the independent variable.

Here is the regression equation

Wins = 0.157*SAL + 80.22

The r-squared was .489 and the standard error was 3.89 wins. The T-value for SAL was 5.17. The .157 means that if you spent 10% more on salaries than the average team, you win 1.57 more games than the average team. A zero for SAL would mean that a team spent the average amount on salaries. A negative number means the team spent below the average salary level. The table below summarizes each team.



Tampa Bay, for example, on average, had a payroll that was 38.87% below the league average. They were predicted to win 74.12 but only 64.33 wins per game. If a team were to spend 100% more than average, it should win about 96-97 games a year. The Yankees had the highest payroll above average. They spent about 70% more than the average team. They were predicted to win 91.26 games a year but actually only won 90.24.

I think the results are fairly strong. 16 of the 30 teams were predicted to within 3 or fewer wins. Only 3 were off by 6 or more wins. I think what I did differently than JC Bradbury was to use the average annual values for each team, instead of each team's data for each year. By using the averages, I think the randomness from year-to-year is eliminated. A team can sign a big free agent and maybe one year he does not do well. Or you get lucky and some non-arbitration eligible young players do very well. So by averaging, some of the good and bad luck gets flushed out.

The graph below also summarizes the results. You can see that the relationship is strong.

Age And Performance Of Outfielders And First Basemen Who Had Long Careers

I found all the players who were primarily 1Bmen and/or OFers who had 15+ seasons with 400+ PAs and found their average RCAA at every age from 21-39. RCAA is runs created above average. It comes from the Lee Sinins Complete Baseball Encyclopedia. Here is how he defines it: “It’s the difference between a player’s RC total and the total for an average player who used the same amount of his team’s outs. A negative RCAA indicates a below average player in this category.” It is also park adjusted.

The graph below shows the averages at each age. It surprised me to see that there is no peak but a plateau from 25-29. I sure don't know why it would be like that for this group.



The table below gives the average for each age as well as the number of players at each age. Seems like a pretty stable number of players from 24-36.

Aging Patterns And Full-Time Players

In 2006 I wrote an article for Beyond the Boxscore called Player Aging Patterns Over Time. One thing I showed there was what percentage of the full-time players in any decade were a given age (I used 400+ PAs for full-time). Below is a graph of the distribution for 1991-2000. The trend line is the two year moving average. Usually 27 is the highest, with around 10% of the full-time players being that age.



Now for the 1961-1970 period. No reason why I picked these decades. Just wanted to show a sample.



The next graph as all the decades starting with 1901-10.



The table below shows the average of each decade's percentage for each age (it was just a simple average, so for age 27, for example, I just added its percentage from each decade and divided by 11, the number of decades I used although 2001-05 was only a half decade).

Should Andy Pettitte Make The Hall Of Fame?

This got discussed recently at Baseball Think Factory after Sean Forman wrote Pettitte Falls Short for the Hall of Fame for the NY Times. So here is my take on it.

I first looked at where he ranked all time in RSAA. That stat is from the Lee Sinins Complete Baseball Encyclopedia. It is "RSAA--Runs saved against average. It's the amount of runs that a pitcher saved vs. what an average pitcher would have allowed," including park adjustments. Pettitte now has 204 RSAA. That ranks him 77th all-time. Seems like too low of a rank to make the Hall. But he is 18th among lefites. Maybe left-handed pitchers have a tougher time than righties, so maybe the bar should be a little lower for them. It is not anyone's fault if they are left-handed. They could not have simply worked hard to become a righty. Of course, it also is the case that lefties simply have less value since there are many more right-handed batters. And maybe the Hall has to recognize how much value a pitcher had. But I will continue to show where Pettitte ranks among lefties.

Next I found the RSAA per IP for all pitchers with 2000+ career IP. Pettitte had .0697 (or . 63 runs per 9 IP). That was good enough for 58th. But among lefties, he was 13th. Then I found each pitcher's expected winning percentage using the Bill James pythagorean formula and assumed a league average of 4.5 runs per game. Each pitcher was given a number of games equal to his IP/9. That was multiplied by the expected winning percentage to get projected wins. I then subtracted from that the number of wins a replacement pitcher would have won. For that, I assumed a .400 winning pct. This process predicted that Pettitte would win 186.8 games while the replacement would win 130.06. So that gives him 56.74 WARP or wins above replacement pitcher. He ranks 87th in this WARP measure but is 20th among lefties.

But runs saved is partly determined by the fielders. So I created simple fielding independent ERA. I looked at all all pitchers with 2000+ career IP and used the following stats, all relative to the league average: ERA, HR, SO and BB. 100 is average. A number over 100 means better than average. I ran a regression with ERA as the dependent variable and the others as the independent variables. Here is the equation

ERA = 37.96 + .187*BB + .262*SO + .202*HR

Here are Pettitte's numbers:

BB 122
SO 103
HR 144

So, for example, he gave up 44% fewer HRs than the average pitcher (this comes from Lee Sinins Complete Baseball Encyclopedia). Plugging these numbers into the equation, Pettitte gets 116.85, meaning his projected ERA based on fielding independent stats is 16.85% better than the league average. But his actual ERA is 17% better, so he just happens to project well. Anwyay, he ranks 69th overall but is 20th among lefties.

I also computed a WARP using this predicted ERA in the manner described above. Pettitte ranks 89th while being 21st among lefties with 57.62.

The biggest think in his favor is ranking 13th in RSAA/IP for lefties. But some of his other ranks are pretty low. I think the Hall of Fame has about 219 players, of whom 71 are pitcers or 32.4%. If a team has 25 players and pitchers are 40% of the team, then the Hall should have pitchers (about 87). But if all the position players are deserving (not likely, but I will play along anyway), then about 38 more pitchers need to be in (109/257 is about .4). Only 15 of the pitchers were lefties and Pettitte does rank fairly high among lefties. And if there should be 109 pitchers, he seems to be in the top 109 all-time. Even if there should be 87, even his worst rank that I found is close to that. Of course, all of this assumes that there are no undeserving players or pitchers in right now.

A couple of other things. I thought maybe Pettitte got an advantage pitching at Yankee stadium above the normal park adjustments since he is a lefty and might face alot of righties there where they have a harder time hitting HRs. But from Retrosheet, he gave up a HR% (based on batters faced) at home of 1.94%. On the road it was 2.01%. That does not seem to out of the ordinary. But his HR% (based on ABs) vs. righties has been 2.18% while vs. lefties it has been 2.18% as well. It seems like it should be higher against righties because over the last three years in MLB left-handed pitchers have allowed a HR% of about .5 percentage points higher against right-handed batters. That points to him getting an advantage from Yankee Stadium, but then his home HR% does not seem to give him much of an edge. So I don't know what to conclude from that.

I also once created what I called the Pitcher’s Homerun/Walk Rating. It combined a pitcher's ability to prevent both HRs and BBs into one index rating. Pettitte was 23rd among pitchers with 2000+ IP from 1920-2006. Now it looks like he has slipped to 32nd. But that is out of 277 pitchers. Pretty darn good.

Starting Pitchers As Relievers Over Time

Many fans know that starters were often also used as relievers in the past. Lefty Grove, for example, only started 30 games the year he won 31 games (in 1931). He came in 11 times as a reliever. In 1930, he won 28 games while starting 32 and coming in to relieve 18 times.

On May 23, 1911, Christy Mathewson pitched a complete game victory giving up only 1 earned run. Then on May 26, he pitched the last 1 and 2/3 innings to get a win. When he came in in the 8th, the Phillies had two men on and had just scored 2 runs to tie the game. Then he got a double play. The Giants scored 2 in the bottom of the 8th and Mathewson pitched the 9th for the win, giving up no hits. The next day he pitched a complete game shutout.

But how often did starters pitch in relief in the past and how has this changed over time? I looked at the percentage of games pitched in relief by starters each decade starting with 1900-09. In each decade I found this % for the season leaders in games started. The number of pitchers in the leaders were 3 for each team in each year. I figured that each team would have at least 3 guys who started fairly often. But I also looked at the % for all pitchers who started at least 31 games (and at least 33 beginning in 1960). So the table below shows these percentages:



The first column shows the % of games pitched in relief by the leaders in starts. That would be the top 480 in games started in a season for the 1920s, for example. So in that group, 19.5% of their games were in relief. The next column shows the % of games pitched in relief by pitchers who started at least 31 games (up to the 1950s) or 33 games since the 1960s. The trends are pretty clear.

The graph below shows the percentages over time.

What Does The Past 3 Years Tell Us About The World Series? (updated)

I tried to use a tool of Tangotiger's called Marcel. There is a good chance I did not apply it correctly (I think I did, that is why it is updated) but I attempted to measure the skill level of the players using the last 3 years of performance with more recent years being weighted more and using a regression to the mean. Maybe in a day or two I will go through the numbers.

First I tried to generate an OPS relative to the league average for the 8 position players on each team. Then I took the simple average of that. For the Yankees, it was 10.13% above the league average. For the Phillies, it was 7.54% better. If we assume a league average of .750, then the Yankees would be at .826 while the Phillies would be at .807. I did not make any park adjustments and this might hurt Ibanez for his two years in Seattle.

For pitchers, I did the same thing using the FIP ERA from Fangraphs. Here are their ratios to the league average for the top 3 starters

Sabathia 0.751
Lee 0.829
Burnett 0.945
Martinez 1.052
Pettitte 0.907
Hamels 0.881


The Yankees have a big edge in the first and second matchups while the Phillies have the edge in the 3rd one. But Fangraphs has the same league average each year for both leagues. This may not be right, and if not, it would probably mean that the Yankees have an edge in all 3 slots. Then, as I mentioned yesterday, the Yankees were much better against lefties this year than the Phillies.

As I understand the Marcel method, the last 3 years have a weight of 5, 4, and 3. So the total is 12. Then the weight is

5/12 = .417
4/12 = .333
3/12 = .25

Now I think that assumes that the player has an equal number of PAs in each year. But I think those weights should be changed if PAs are not equal in each year. Let's take Jeter. Here are his PAs from 2007-9

695
648
706

The total is 2049. In each year, here are the %'s of the total for each year:

.339
.316
.345

Now how does that change the weight of 5, 4, 3? In 2009, he had a larger than expected pct (which is .333). The pct was .345/.333 = 1.036 times the expected value. So instead of using .416 for 2009, I used .416*1.036 = .357. Something similar was done for all the other players. Here are Jeter's OPS divided by the league average from 2007-9

1.11
1.02
1.14

So what is his ratio for the 3 years? 1.11*.339 + 1.02*.316+ 1.14*.345 = 1.097. Now the regression to the mean. First multiply the PAs from the 3 recent years (going backwards) by 5, 4 and 3. That gives 8207. But the regression to the mean involves two seasons worth of league average hitting. Each year is 600 PAs or 1200. So we have a denominator of 9407 (8207 + 1200). So Jeter's OPS relative to the league average is

(8207/9407)*1.097 + (1200/9407)*1 = 1.0846

Jeter's skill level means his OPS is 8.46% better than average. So I did this for each player on each team. I added their relative to the league average and then divided by 8. I did something similar for the starting pitchers.

Yankees vs. Phillies: Can OPS Tell Us Anything?

The table below shows the team OPS for both the Yankees and Phillies as well as the OPS their pitchers allowed.



So the Yankees have an overall advantage of .081 in OPS. I once found that winning pct = 1.26*OPSDIFF + .5. A team with that big of an OPS differential wins about 60% of their games. So that might be the Yankees probability of winning (although it may not be that simple-I actually came up with about a 72% chance for them to win if they have a 60% of winning each game). Of course, the Phillies did not have Lee or Martinez in their rotation all year. So the differences may not be thaat great in the series and we need to take that into account.

The next table shows the OPS of each pitcher in the rotation for the two teams, in what looks like will be the order for the series. Each pitcher's OPS is compared to the league average.



The next table shows which pitcher has the advantage in each matchup.



The Yankees have a big advantage in each of the first three games. My guess is that it will only be in game 4 that the Phillies have the advantage. Then the rotation starts up again. The Yankee hitters also had an OPS that was .076 better than the league average while the Phillies were only .042 better.

The next table shows some other breakdowns. It shows both hitting and pitching OPS for both teams, home and road and also the league averages for those respective stats.



The Yankees outhit their opponents at home by .129 in OPS. For the Phillies, it is only .037. On the road, these two stats are .082 and .012. So when in Yankee Stadium, the Yankees have an advantage of .117 (.129 - .012). Even in Philadelphia, the Yankees advantage is .045 (.082 - .037).

My guess is that park effects are not a big deal here. The simple average of the OPS in Yankee stadium was about 1.6% higher than in Yankee road games (I simply added what the Yankees hit and allowed at home and divided by 2, then did the same for road games and then the home number was divided by the road number-that is all probably not quite right since Yankee pitchers have more innings at home than Yankee hitters since they don't bat alot of the time at home in the bottom of the 9th). For the Phillies, this was 2.2% higher in home games. So, overall, not much going on with park effects.

Also notice that the Yankees hit .081 better than the league against lefties this year and they get to face 3 lefty starters.

The next table shows how the two bullpens faired compared to the league average bullpens.



So even here, the Yankees have an advantage.

Also, I once calculated that the team with home field advantage wins 51.52% of the time, if the two teams are of equal strength. The Yankees played in the tougher league (the AL has been winning most of the interleague games the past few years). And the other 4 teams in the AL East combined to finish 6 games over .500 outside their division this year. In the NL East, it was 28 games under. So it looks like the Yankees played in a much tougher division, too.

Will Barry Larkin Get Elected To The Hall Of Fame?

This is being discussed at Baseball Think Factory now. Click on Red Reporter: JinAZ: A HOF Case for Barry Larkin. I sure hope he gets elected. Sean Smith's Wins Above Replacement Rankings have him at 58th all time. Seems like a no brainer.

But what do the voters like? I created two models earlier this year. One is called Predicting Who Makes The Hall Of Fame Using A Logit Model. It gives him a probability of only about 17% of making it. The model took into account career average, number of 100 RBI seasons, all-star games, PAs, MVP awards, world series performance, getting 3000 hits and being a catcher.

The other model was called What Determines Vote Percentage In The First Year Of Hall Of Fame Eligibility? (Part 2). It said it would be 34.6% for Larkin. It took into account the same things as above plus getting 500 HRs, getting 500 SBs, gold gloves (but not being a catcher).

I sure hope my models are wrong. But this analysis was based on what the voters did from 1990-2009.

Some Very Old Sabermetric Classics That Are Online

Goodby To Some Old Baseball Ideas (from LIFE magazine 1954-contains some fairly advanced formulas)

If you really want to blow your mind, read this article from Fortune magazine in 1935 about a very early and very sophisticated

The Base in Baseball By Travis Hoke

Why the System of Batting Averages Should Be Changed (by FC Lane around the year 1917-has linear weights values-dedades ahead of its time-the man was a trained scientist)

Then his analysis of the value of walks is at

The Base on Balls

And links to more Baseball Magazine articles are at

Cyril Morong's Sabermetric Research

I posted the following at BTB a few years ago

The post below is the few pages from FC Lane's book called "Batting" that dealt with the batting order. Whether or not it matches up with some of the recent analysis on lineups I will leave up to readers. One expert mentioned that it was a good idea to bat Cy Williams 2nd. FC Lane was a great baseball writer and editor of Baseball Magazine in the early part of the 20th century. It comes to you through the miracle of scanning (well, it was a miracle that I figured out how to use the scanner-actually my wife who is a computer programmer showed me how-the miracle is that she stays married to me)

How the Batting Order "Colors" Batting

FC Lane on the Batting Order

Does Jimmy Rollins Have More Pop As A Left-Handed Batter?

One of the announcers last night, I think it was Buck Martinez, said that Rollins did. If I recall correctly, it was because he hit more HRs as a lefty this past season. He did hit 14 HRs vs. righties (when bats left-handed) and 7 vs. lefties. But, as many fans know, he also faced righties alot more. Here are his HR%'s vs. lefties and righties this year:

vs. lefties (as a right-handed batter) 4.02%
vs. righties (as a left-handed batter) 2.81%

Now for his entrire career.

vs. lefties (as a right-handed batter) 2.83%
vs. righties (as a left-handed batter) 2.33%

So it looks like he actually has more HR power as a right-handed batter, although it is pretty close for his entire career. Another way to look at "pop" is to use isolated power or SLG - AVG. Here are his 2009 figures:

vs. lefties (as a right-handed batter) .195
vs. righties (as a left-handed batter) .165

Now for his whole career.

vs. lefties (as a right-handed batter) .168
vs. righties (as a left-handed batter) .163

So, it looks like he has more power as a right-handed batter (but again, the edge is slight for his whole career). I don't think this is sabermetrics. I think it is just arithmetic. But it seems like announcer make this mistake alot when talking about lefty/righty stats. They look at raw totals instead of percentages, forgetting that there are fewer lefty pitchers than righties.

Even If There Really Are Clutch Hitters And We Can Tell Who They Are, Does It Significantly Affect Winning Or Affect Personnel Decisions?

There were some recent posts around the blogosphere on clutch hitting. As many times before, the discussion was mainly about whether or not it exists. Here are the links:

Overestimating the Fog by JC Bradbury at Sabernomics. This article got discussed at Baseball Think Factory. JC Bradbury also posted two other studies: Does Clutch Pitching Exist? and A Little Clutch Hitting Study. Phil Birnbaum had Doesn't "The Book" study pretty much settle the clutch hitting question?.

Bradbury's "Fog" article refers to an article from a few years ago by Bill James (JC has a link to it). Bill James suggested that our statistical methods might not be able to detect clutch hitting. JC has presents a different view.

Phil makes a refernce to "The Book" by Tom Tango, Mitchel Lichtman and Andrew Dolphin. Their basic finding was that there is clutch ability but it is very slight.

Now getting back to Bill James. He wrote an article a couple of years ago called Mr. Clutch: Big Papi, Chipper, Pujols come through when it counts. James said:

""Clutch" is a complicated concept, containing at least seven elements:

1. The score,
2. The runners on base,
3. The outs,
4. The inning,
5. The opposition,
6. The standings,
7. The calendar."

Then he showed how certain players did alot better in these cases than they normally do. But what he does not say in this article (it may be elsewhere), is how much differently all players hit in these situations than they normally do (that is, the league average differential). This information is necessary to see which players' clutch performance is statistically significant. I made a crude attempt at analyzing James' new measure of clutch in this post: Is David Ortiz A Clutch Hitter?. For differences from normal performance, I used those in close and late situations. It looked like his clutch performance was not significant. But I have not seen James post the clutch data for all players, so a complete analysis has not been done (maybe he has posted this on his site but I have not signed up to pay for it).

I did a study a few years ago called How Many Games Do Clutch Hitters Really Win?. I had two methods of seeing how many wins clutch hitters added above their normal hitting. In one method, only about 10% of the hitters I looked at were able generate as many .5 more wins a season than expected by hitting better in the clutch than they normally do. That assumes that this was thier true clutch ability. In the other method, only 3 out of 71 players added as many .5 wins (that table is partly cutoff now at the link).

Getting back to what "The Book" says, they show that the biggest clutch hitting skill of any player over the 2000-2004 period was .0018 on their wOBA stat (based on another formula they mention, I estimate that is about .004 in OPS). Their clutch situation was the 8th inning or later and the batting team is down 1-3 runs. I don't know what percentage of all plate appearances are made up by these situations, but for close and late situations (CL) it is 15%. My study, mentioned in the previous paragraph, found only a small number of hitters making much difference by their clutch performance and I made no "regression to the mean" adjustment to their clutch stats like "The Book" people did.

I assumed that if a guy's OPS was .050 higher in the clutch than otherwise, that was his true clutch ability. If "The Book's" clutch situation is also about 15% of the PAs (like CL), then I have to assume that their methods say that players add many fewer wins from their clutch performance than my method since my method has a top differential of .117 for Tino Martinez. That is, his OPS was that much higher in the clutch than otherwise. They have a biggest difference of about .004 in OPS, which probably creates very few extra wins. And that is the best they found.

Phil Birnbaum's post also mentioned how different kinds of hitters, like power hitters vs. singles hitters, hit differently in the clutch and whether or not it was due to a change in their approach with the game on the line. I did a study once called Do Power Hitters Choke in the Clutch?. It was inspired by a study by Andrew Dolphin (one of "The Book" people-I have a link to it at this study). I found mixed results, but maybe powers did do a little worse in the clutch than other hitters.

Finally, if clutch hitters are real, do teams make trades to get them? Do they offer those free agents more money? I would love to know if teams have ever done this. There is a study on this called Are Players Paid for "Clutch" Performance? by Jahn K. Hakes and Raymond D. Sauer. My guess is that teams never consider any clutch data when making personnel decisons. If that is the case, then effectively clutch is a non-issue.

The Percentage Of Batters Faced By Relief Pitchers Since 1953

The data came from Retrosheet. The graph below shows the % faced in the AL.



Now for the NL.



Now for both leagues in the same graph. The AL is the red line and the NL is the blue line.



This last graph shows the difference between the two leagues (NL - AL). In the first year, 1953, the NL had 0.288 while the AL had 0.259 for a difference of about .029. Then the next year the NL was .04 higher. It is intersting to see that there was one trend to about 1970 of the NL edge falling (actually turning negative in 1960 and staying there until 1970, except for 1962). Then there is a trend for at least 10 years of the NL rising relative to the AL. Then it generally declines until about 2000 and then it starts rising again. Maybe the DH plays some role here but it can't explain all of it.

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

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

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



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



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





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

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





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

Pujols wins triple crown

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

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

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

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

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

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

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

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

Now for Ted Williams

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

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

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

Now Musial

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

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

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

The following sites also discussed these issues

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

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

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

Yes, We Should Have Kept An Eye On The Rockies

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

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

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

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

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

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

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

167
144
124
136

Vernon's 4 years were

160
99
73
113

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

Clutch Hitting Over Time (1952-2008)

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

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



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



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





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

Some Interesting Stats On Gene Tenace

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Mariners Have Been Very Average This Year

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

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

OBP .313
SLG .400
OPS .713

Here is what their pitchers have allowed

OBP .312
SLG .395
OPS .712"

And

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

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

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

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

Now for runs allowed the equation is

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

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

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

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

Yogi Berra As A Clutch Hitter & Clutch Hitting Over Time

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

Post 1

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

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

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

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

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

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

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

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

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

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


Post 2

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

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

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

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

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

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

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

A Yogi Berra interview. He was asked:

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

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

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

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

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

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



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

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

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

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

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

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

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



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

How Good Was Al Simmons?

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

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

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

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

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

Click here to go to his Baseball Reference page.

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

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

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Is Chase Utley Undervalued By The Media?

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

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

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

Is Prince Fielder Good At Hitting "Late" HRs?

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

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

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

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

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

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

5.94
6.9
6.86

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

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

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

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

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

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

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

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

pct = 1.21*OPSDIFF + .5

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

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

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

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

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

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

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

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

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

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

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

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