This was first posted in May of 2009. Go to Why Isn't Steve Garvey In The Hall Of Fame?. It has generated comments every few months, so if people are interested I thought I would post it again. What I tried to show was that he seems to be the kind of player the writers like to vote in and that you could make a good case for him, that is, write an impressive plaque. But he has not made it. I don't think he was good enough, but the puzzle is why he has not made it.

Here is one slightly new tidbit. Last year I mentioned that Garvey had 6 200+ hit seasons. Through 2009, here are all the players who had 4 or more. Alot of them are in the Hall of Fame or will probably make it, or would have made it without doing something scandalous:

Pete Rose 10

Ty Cobb 9

Ichiro Suzuki 9

Lou Gehrig 8

Willie Keeler 8

Paul Waner 8

Rogers Hornsby 7

Derek Jeter 7

Wade Boggs 7

Charlie Gehringer 7

Steve Garvey 6

Bill Terry 6

Stan Musial 6

Jesse Burkett 6

George Sisler 6

Sam Rice 6

Al Simmons 6

Kirby Puckett 5

Chuck Klein 5

Tony Gwynn 5

Michael Young 5

Harry Heilmann 4

Jack Tobin 4

Roberto Clemente 4

Joe Jackson 4

Tris Speaker 4

Paul Molitor 4

Juan Pierre 4

Jim Rice 4

Joe Medwick 4

Heinie Manush 4

Vada Pinson 4

Lou Brock 4

Vladimir Guerrero 4

Lloyd Waner 4

Nap Lajoie 4

Rod Carew 4

## Friday, July 30, 2010

## Wednesday, July 28, 2010

### Close and late hitting vs. non-close and late hitting since 1950

The first link tells you the batting average (AVG) and isolated power (ISO) each year in the AL for both close and late hitting vs. non-close and late hitting since 1950. All data from Retrosheet.

AL Close and late hitting vs. non-close and late hitting

Now the same thing for the NL

NL Close and late hitting vs. non-close and late hitting

This next link simply shows the differences in each stat between the situations in both leagues. Numbers in red are positive or zero. Those pretty much stopped in the 1980s. That is, since 1990, AVG and ISO have pretty much been lower in the close and late situations than otherwise.

Yearly differences of each league

The graph below shows the annual difference in AVG in the AL.

Now for the annual differences in ISO in the AL

The next two graphs do the same thing for the NL.

AL Close and late hitting vs. non-close and late hitting

Now the same thing for the NL

NL Close and late hitting vs. non-close and late hitting

This next link simply shows the differences in each stat between the situations in both leagues. Numbers in red are positive or zero. Those pretty much stopped in the 1980s. That is, since 1990, AVG and ISO have pretty much been lower in the close and late situations than otherwise.

Yearly differences of each league

The graph below shows the annual difference in AVG in the AL.

Now for the annual differences in ISO in the AL

The next two graphs do the same thing for the NL.

## Sunday, July 25, 2010

### Was It Easier To Be A Good Clutch Hitter In The "Old" Days?

I first reported on this issue in a post last year Did The Increased Use Of Relief Pitching Cause A Decline In Clutch Hitting? Back in the 1950s and 1960s, as I show at this earlier post, hitting in non-close and late situations was not much better than in close and late situations. But, as I also showed, as the use of relief pitching grew, batting averages and isolated power started to decline, relatively, in close and late situations. So it looks like it might have been easier to hit well in the clutch in the "old" days.

Recently Tom Tango (aka tangotiger) had a post titled Best and Worst Clutch Hitters of the Retrosheet era .

Tom has a clutch stat based on WPA or "win probability added." The idea there is that every plate appearance by a hitter either increases or decreases his team's probability of winning. A HR with the score tied in the bottom of the 9th has more impact than one in the first inning with the score 10-0.

But Tom adjusts this by how often a hitter gets to hit in "high leverage" situations. Then that it is compared to what his WPA would be if he always hit in average leverage situations. I hope I got that right. But, of course, Tom explains it much better. That stat ends up telling us how many more games a player's team wins (or loses) because he hits better or worse in high leverage situations than he does overall. It is just called "Clutch."

To see if this stat changed over time, I took all the players with 4000+ PAs from 1950-2009 and found their Clutch/PA (758 players). Then I found the the year which was the mid-point of each player's career (the data all comes from Baseball Reference which showed the first and last year of each guy's career). Of course, that is not a perfect way to do it since that may not be finding the exact middle of a player's career in terms of PAs. But it is a reasonable approximation. Call this mid-point "Year."

Anyway, the correlation between Year and Clutch/PA is -.12. That is, as time goes on, batters are doing worse in the clutch. That makes sense given the increasing use and specialization of relief pitching. The -.12 is small, though. But, as time went on, there were more players reaching the 4000 PA minimum because there were more teams and in the early 1960s, the season grew to 162 games. So any correlation will have alot more guys from the later years when everyone was doing worse in the clutch. This waters down any correlation we might find (by the way, if you are interested, Yogi Berra, famous for being a clutch hitter, ranks 37th out of 758 players).

But if we look at the top and bottom 25 in Clutch/PA, we can see some interesting trends. The table below shows the top 25 along with their mid-point year.

If you look carefully at the mid-point years, you can see that there are more players from earlier years. But this will be summarized below. The next table shows the bottom 25.

It actually turns out that the top 25 has a disproportionate number of guys from earlier years and the bottom 25 has disproportionate number of guys from later years. The next two tables shows this. The first table shows what percentge or share of the 758 players comes from each decade.

The next table shows how many players were in the top 25 and the bottom 25 from each decade and the expected number based on the percentage from the table above. 2.18 is about 8.7% of 25, for example. Notice that the 1950s had 4 guys in the top 25 while its expected number is 2.18. The 1960s and 1970s also had more guys in the top 25 than expected. We can also see that the 2000s had none in the top 25 even though 4.49 were expected.

The 1950s, 60s and 70s did not have as many guys as expected in the bottom 25 while the 1990s and 2000s had more than expected. So it could be that it is harder to hit well in the clutch now than 4-5 decades ago. My guess is that this is due to relief pitching.

Update July 26:

I divided all the players into 6 groups since there are about 6 decades. And since 758/6 = 126.33, I looked at the top 126 and the bottom 126. The table below summarizes how many guys from each decade were in each group along with the expected number.

The 1950s don't have as many as expected in the top 126 and more than expected in the bottom. But the 1960s do have more in the top and fewer in the bottom. Same for the 1970s and 1980s. But the last two decades are very much under-represented in the top and very over-represented in the bottom. So this suggests it is harder to be a good clutch hitter in current times.

Recently Tom Tango (aka tangotiger) had a post titled Best and Worst Clutch Hitters of the Retrosheet era .

Tom has a clutch stat based on WPA or "win probability added." The idea there is that every plate appearance by a hitter either increases or decreases his team's probability of winning. A HR with the score tied in the bottom of the 9th has more impact than one in the first inning with the score 10-0.

But Tom adjusts this by how often a hitter gets to hit in "high leverage" situations. Then that it is compared to what his WPA would be if he always hit in average leverage situations. I hope I got that right. But, of course, Tom explains it much better. That stat ends up telling us how many more games a player's team wins (or loses) because he hits better or worse in high leverage situations than he does overall. It is just called "Clutch."

To see if this stat changed over time, I took all the players with 4000+ PAs from 1950-2009 and found their Clutch/PA (758 players). Then I found the the year which was the mid-point of each player's career (the data all comes from Baseball Reference which showed the first and last year of each guy's career). Of course, that is not a perfect way to do it since that may not be finding the exact middle of a player's career in terms of PAs. But it is a reasonable approximation. Call this mid-point "Year."

Anyway, the correlation between Year and Clutch/PA is -.12. That is, as time goes on, batters are doing worse in the clutch. That makes sense given the increasing use and specialization of relief pitching. The -.12 is small, though. But, as time went on, there were more players reaching the 4000 PA minimum because there were more teams and in the early 1960s, the season grew to 162 games. So any correlation will have alot more guys from the later years when everyone was doing worse in the clutch. This waters down any correlation we might find (by the way, if you are interested, Yogi Berra, famous for being a clutch hitter, ranks 37th out of 758 players).

But if we look at the top and bottom 25 in Clutch/PA, we can see some interesting trends. The table below shows the top 25 along with their mid-point year.

If you look carefully at the mid-point years, you can see that there are more players from earlier years. But this will be summarized below. The next table shows the bottom 25.

It actually turns out that the top 25 has a disproportionate number of guys from earlier years and the bottom 25 has disproportionate number of guys from later years. The next two tables shows this. The first table shows what percentge or share of the 758 players comes from each decade.

The next table shows how many players were in the top 25 and the bottom 25 from each decade and the expected number based on the percentage from the table above. 2.18 is about 8.7% of 25, for example. Notice that the 1950s had 4 guys in the top 25 while its expected number is 2.18. The 1960s and 1970s also had more guys in the top 25 than expected. We can also see that the 2000s had none in the top 25 even though 4.49 were expected.

The 1950s, 60s and 70s did not have as many guys as expected in the bottom 25 while the 1990s and 2000s had more than expected. So it could be that it is harder to hit well in the clutch now than 4-5 decades ago. My guess is that this is due to relief pitching.

Update July 26:

I divided all the players into 6 groups since there are about 6 decades. And since 758/6 = 126.33, I looked at the top 126 and the bottom 126. The table below summarizes how many guys from each decade were in each group along with the expected number.

The 1950s don't have as many as expected in the top 126 and more than expected in the bottom. But the 1960s do have more in the top and fewer in the bottom. Same for the 1970s and 1980s. But the last two decades are very much under-represented in the top and very over-represented in the bottom. So this suggests it is harder to be a good clutch hitter in current times.

## Thursday, July 22, 2010

### Don't Let Your Little Leaguers Grow Up To Be Right-Handed Power Hitters Who Strike Out Alot Because They Might Choke In the Clutch

This is prompted by a post by Tom Tango (aka tangotiger) titled Best and Worst Clutch Hitters of the Retrosheet era .

Tom has a clutch stat based on WPA or "win probability added." The idea there is that every plate appearance by a hitter either increases or decreases his team's probability of winning. A HR with the score tied in the bottom of the 9th has more impact than one in the first inning with the score 10-0.

But Tom adjusts this by how often a hitter gets to hit in "high leverage" situations. Then that it is compared to what his WPA would be if he always hit in average leverage situations. I hope I got that right. But, of course, Tom explains it much better. That stat ends up telling us how many more games a player's team wins (or loses) because he hits better or worse in high leverage situations than he does overall.

Nellie Fox is #1 with +13.4 wins since 1950. That is, by hitting better than he normally did in high leverage situations, he added 13.4 wins to his teams over his whole career. Sammy Sosa was last with -16.8 wins. That is, he hit worse in high leverage situations than he normally did and this cost his teams 16.8 wins over the course of his career. These two hitters maybe could not be more different and they may be good illustrations of what is going on with this clutch stat.

So let's call Tom's stat Clutch. That's what it is called at Baseball Reference. I took all the right-handed batters and left-handed batters since 1950 who had 4000+ PAs (653 players). Then I divided their Clutch stat by their PAs. I did the same thing for HRs and strikeouts. The I ran a regression with Clutch/PA being the dependent variable and HR/PA and SO/PA being the independent variables. I also added a dummy variable for being a righty (1 for righties and 0 for lefties).

Here is the regression equation

Clutch/PA = 0.0007 - .00025*Righty - .0169*HR/PA - .00157*SO/PA

All three variables seem to be significant. Here are the t-values:

Righty -6.31

HR/PA -10.49

SO/PA -3.06

R-squared is .314 (meaning that 31.4% of the variation in Clutch/PA across players is explained by the equation) and the standard error per 700 PAs is .33.

Mutltiplying -.00025*700 gives us -.172 (assuming 700 PAs is a full season). So simply being a righty means you will have a negative Clutch rating of -.172, meaning you will cost your team .172 wins. This could be because righties can't use the hole at first base with a runner on as well as lefties. When a runner is on first, it makes for a slightly higher leverage situation. Also, righties might have to face right-handed pitchers more often in high leverage situations than lefties face left-handed pitchers.

To see the impact of HRs and SOs, I found the standard deviation of HR/PA and SO/PA and then checked to see how much Clutch/PA would change with a one standard deviation increase in both stats. Here they are

HR/PA: .014

SO/PA: .0449

The coefficient on HR/PA was -.0169. That times .014 = -0.00024. But that times 700 PAs is about -.166. So being one standard deviation above average in HR/PA costs your team .166 wins per season. Maybe HR hitters cannot adapt well in high leverage situations since they generally just swing for the fences. But that is just a guess.

Something similar could be going on for guys who strikeout alot. The coefficient on SO/PA was -.00157. That times .0449 = -0.00007. That times 700 = -.049. So increasing your strikeout rate by one standard deviation costs your team .049 wins per season. Maybe guys who don't strikeout alot have better bat control and they can hit the ball the hole at first base better than average or they can adapt to the situation better.

Let's look at how all this affects Nellie Fox. He was a lefty, so he does not get the righty penalty. His career HR/PA = .003488. The average for all the players in the sample was .0268. So he was .0233 below that. To see the effect for the whole season, we multiply that first by -.0169, the coefficient on HR/PA from the regression equation and then times 700. This gives us -.0233*-.0169*700 = .276. So his lack of power added .276 wins to his teams each year.

What about for his entire career. He had 10,035 career PAs or 14.33 seasons. With 14.33*.276 = 3.96, Fox gets 3.96 clutch wins for his whole career just due to his lack of power.

For SO, Fox had a career rate of .0206. The average was .133. So he was .112 below that. Let's multiply that by -.00157 and then 700 to get .122 (-.00157 was the coefficient on SO/PA). It amounts to -.112*-.00157*700 = .122. So his ability to not strike out gave his teams .122 clutch wins per season. For his career that would be 1.76 Clutch wins. Then 3.96 + 1.76 = 5.72. Just by being a low HR, low SO guy added 5.72 clutch wins. That is nearly half his total.

For Sosa, we have a HR/PA rate of .06154 and a SO/PA rate of .233. Doing the same exercise as I did above for Fox has him with the following "clutch losses" per season due to his high HR rate and high SO rate:

HR/PA = .41

SO/PA = .11

Sosa had 9,986 career PAs or 14.14 seasons. His HR hitting cost him 5.81 clutch wins and his striking out cost him 1.54. And being a righty cost him 2.43 wins (14.14*.172 = 2.43). The .172 was how many wins a righty lost per year, as explained above. Then 5.81 + 1.54 + 2.43 = 9.78. That is more than half of his clutch losses.

All of this, is, of course, an approximation. The regression is not perfect, since the r-squared was only .314. But the variables all were significant and the F-stat was 98 (that is significant and it means that the 3 variables together probably explain some part of the dependent variable).

So Tom Tango's clutch stat is great in terms of what clutch stats should do but it may have some biases. But those biases might be ones teams should care about since HR hitting ability and SO avoidance ability are identifiable traits.

******************************************

I did a very different kind of study several years ago called Do Power Hitters Choke in the Clutch?. I have a link to a similar study by Andrew Dolphin. In this other study it did not look like they did choke. Also, here are some other comments I made at the tangotiger link:

I happened to have a list of players with 6000+ PAs from 1987-2001 with their OPS in close and late situations (CL) and their OPS in non-CL situations. I took the ratio of CL/nonCL. Tino Martinez did the best, with 1.095, meaning that his OPS in CL situations was 9.5% higher than nonCL. The correlation between CL OPS/nonCL OPS and SO/PA is -.364. So it looks like guys who strikeout alot have a little harder time doing well in the clutch

Also, if you go to the rankings, you can see that 10 of the 12 best players in maintaining their OPS in the CL were lefties or switch hitters

http://cyrilmorong.com/clutch.htm

And it looks like 8 of the bottom twelve are righties

Tom has a clutch stat based on WPA or "win probability added." The idea there is that every plate appearance by a hitter either increases or decreases his team's probability of winning. A HR with the score tied in the bottom of the 9th has more impact than one in the first inning with the score 10-0.

But Tom adjusts this by how often a hitter gets to hit in "high leverage" situations. Then that it is compared to what his WPA would be if he always hit in average leverage situations. I hope I got that right. But, of course, Tom explains it much better. That stat ends up telling us how many more games a player's team wins (or loses) because he hits better or worse in high leverage situations than he does overall.

Nellie Fox is #1 with +13.4 wins since 1950. That is, by hitting better than he normally did in high leverage situations, he added 13.4 wins to his teams over his whole career. Sammy Sosa was last with -16.8 wins. That is, he hit worse in high leverage situations than he normally did and this cost his teams 16.8 wins over the course of his career. These two hitters maybe could not be more different and they may be good illustrations of what is going on with this clutch stat.

So let's call Tom's stat Clutch. That's what it is called at Baseball Reference. I took all the right-handed batters and left-handed batters since 1950 who had 4000+ PAs (653 players). Then I divided their Clutch stat by their PAs. I did the same thing for HRs and strikeouts. The I ran a regression with Clutch/PA being the dependent variable and HR/PA and SO/PA being the independent variables. I also added a dummy variable for being a righty (1 for righties and 0 for lefties).

Here is the regression equation

Clutch/PA = 0.0007 - .00025*Righty - .0169*HR/PA - .00157*SO/PA

All three variables seem to be significant. Here are the t-values:

Righty -6.31

HR/PA -10.49

SO/PA -3.06

R-squared is .314 (meaning that 31.4% of the variation in Clutch/PA across players is explained by the equation) and the standard error per 700 PAs is .33.

Mutltiplying -.00025*700 gives us -.172 (assuming 700 PAs is a full season). So simply being a righty means you will have a negative Clutch rating of -.172, meaning you will cost your team .172 wins. This could be because righties can't use the hole at first base with a runner on as well as lefties. When a runner is on first, it makes for a slightly higher leverage situation. Also, righties might have to face right-handed pitchers more often in high leverage situations than lefties face left-handed pitchers.

To see the impact of HRs and SOs, I found the standard deviation of HR/PA and SO/PA and then checked to see how much Clutch/PA would change with a one standard deviation increase in both stats. Here they are

HR/PA: .014

SO/PA: .0449

The coefficient on HR/PA was -.0169. That times .014 = -0.00024. But that times 700 PAs is about -.166. So being one standard deviation above average in HR/PA costs your team .166 wins per season. Maybe HR hitters cannot adapt well in high leverage situations since they generally just swing for the fences. But that is just a guess.

Something similar could be going on for guys who strikeout alot. The coefficient on SO/PA was -.00157. That times .0449 = -0.00007. That times 700 = -.049. So increasing your strikeout rate by one standard deviation costs your team .049 wins per season. Maybe guys who don't strikeout alot have better bat control and they can hit the ball the hole at first base better than average or they can adapt to the situation better.

Let's look at how all this affects Nellie Fox. He was a lefty, so he does not get the righty penalty. His career HR/PA = .003488. The average for all the players in the sample was .0268. So he was .0233 below that. To see the effect for the whole season, we multiply that first by -.0169, the coefficient on HR/PA from the regression equation and then times 700. This gives us -.0233*-.0169*700 = .276. So his lack of power added .276 wins to his teams each year.

What about for his entire career. He had 10,035 career PAs or 14.33 seasons. With 14.33*.276 = 3.96, Fox gets 3.96 clutch wins for his whole career just due to his lack of power.

For SO, Fox had a career rate of .0206. The average was .133. So he was .112 below that. Let's multiply that by -.00157 and then 700 to get .122 (-.00157 was the coefficient on SO/PA). It amounts to -.112*-.00157*700 = .122. So his ability to not strike out gave his teams .122 clutch wins per season. For his career that would be 1.76 Clutch wins. Then 3.96 + 1.76 = 5.72. Just by being a low HR, low SO guy added 5.72 clutch wins. That is nearly half his total.

For Sosa, we have a HR/PA rate of .06154 and a SO/PA rate of .233. Doing the same exercise as I did above for Fox has him with the following "clutch losses" per season due to his high HR rate and high SO rate:

HR/PA = .41

SO/PA = .11

Sosa had 9,986 career PAs or 14.14 seasons. His HR hitting cost him 5.81 clutch wins and his striking out cost him 1.54. And being a righty cost him 2.43 wins (14.14*.172 = 2.43). The .172 was how many wins a righty lost per year, as explained above. Then 5.81 + 1.54 + 2.43 = 9.78. That is more than half of his clutch losses.

All of this, is, of course, an approximation. The regression is not perfect, since the r-squared was only .314. But the variables all were significant and the F-stat was 98 (that is significant and it means that the 3 variables together probably explain some part of the dependent variable).

So Tom Tango's clutch stat is great in terms of what clutch stats should do but it may have some biases. But those biases might be ones teams should care about since HR hitting ability and SO avoidance ability are identifiable traits.

******************************************

I did a very different kind of study several years ago called Do Power Hitters Choke in the Clutch?. I have a link to a similar study by Andrew Dolphin. In this other study it did not look like they did choke. Also, here are some other comments I made at the tangotiger link:

I happened to have a list of players with 6000+ PAs from 1987-2001 with their OPS in close and late situations (CL) and their OPS in non-CL situations. I took the ratio of CL/nonCL. Tino Martinez did the best, with 1.095, meaning that his OPS in CL situations was 9.5% higher than nonCL. The correlation between CL OPS/nonCL OPS and SO/PA is -.364. So it looks like guys who strikeout alot have a little harder time doing well in the clutch

Also, if you go to the rankings, you can see that 10 of the 12 best players in maintaining their OPS in the CL were lefties or switch hitters

http://cyrilmorong.com/clutch.htm

And it looks like 8 of the bottom twelve are righties

## Sunday, July 18, 2010

### Catcher Bengie Molina Hits For The Cycle. So How Slow Is He?

He hit only his 6th career triple in the game and that was after over 4,000 ABs. That sounds slow.

I have a theory that you can get a general idea of a guy's speed or base running ability by looking at his triple-to-double ratio. Some fast guys don't hit the ball hard enough or often enough to get many triples, so just using triples is not enough to gauge speed. And some guys who may not be that fast might get alot of triples more because they are good hitters.

But if you look at this ratio, it tells you how often a guy made it to third relative to how many times they had to stop at second. And if you get thrown out at third, you get a double. Fast guys will turn long hits into triples more often than slow guys who must stop at 2nd.

But Voros McCracken has a better way to do it. Take the following ratio: 3B/(2B + 3B). This makes it an average or a rate. It tells us what percentage of the time a batter was successful when he had a chance to make it to third with a triple instead of a double.

Let's see where Molina ranks in this stat. To do that, I found all the right-handed batters from 1960-2009 who had 4,000+ ABs. The table below shows the top ten and the bottom ten.

The average rate for righties was .110. That means that the average righty was 4.6 times more likely to get a triple instead of a double than Molina (.110/.024 = 4.62). The next table shows the top ten and bottom ten for lefties. Their average rate was .131.

The next table shows the top ten and bottom ten for switch hitters. Their average rate was .146 (I have no idea why it is higher than the lefties' rate).

I have a theory that you can get a general idea of a guy's speed or base running ability by looking at his triple-to-double ratio. Some fast guys don't hit the ball hard enough or often enough to get many triples, so just using triples is not enough to gauge speed. And some guys who may not be that fast might get alot of triples more because they are good hitters.

But if you look at this ratio, it tells you how often a guy made it to third relative to how many times they had to stop at second. And if you get thrown out at third, you get a double. Fast guys will turn long hits into triples more often than slow guys who must stop at 2nd.

But Voros McCracken has a better way to do it. Take the following ratio: 3B/(2B + 3B). This makes it an average or a rate. It tells us what percentage of the time a batter was successful when he had a chance to make it to third with a triple instead of a double.

Let's see where Molina ranks in this stat. To do that, I found all the right-handed batters from 1960-2009 who had 4,000+ ABs. The table below shows the top ten and the bottom ten.

The average rate for righties was .110. That means that the average righty was 4.6 times more likely to get a triple instead of a double than Molina (.110/.024 = 4.62). The next table shows the top ten and bottom ten for lefties. Their average rate was .131.

The next table shows the top ten and bottom ten for switch hitters. Their average rate was .146 (I have no idea why it is higher than the lefties' rate).

## Thursday, July 15, 2010

### Yes, They Sometimes Did Show Jubilation After Walk-Off HRs In The "Old" Days

Here is what was said at the Dallas Morning News blog by Guy Reynolds, Photo editor

The whole team did not go out to mob Henrich, as you can clearly see in the photo. See No jube at the plate? Archival photo shows walk-off home run in 1949 World Series. (Hat Tip: David Pinto's Baseball Musings)

I guess it depends on what you call old school (David Pinto's blog entry on this was titled "Old School Walk-Off"). This link shows Mazeroski’s series winning HR in 1960. I know it is different because it ended the series where as this one from 1949 was just the first game. But it looks like alot of the team came out to home plate to congratulate him as fans poured onto the field.

Mazeroski

Then there is Bobby Thomson’s series winning HR in the 1951 NL pennant playoff

Bobby Thomson

Again, it is a little different since it won a pennant.

But here is Dusty Rhode’s HR to win game 1 in the 1954 World Series. The whole team comes out to congratulate him at home plate and you also see Willie Mays jumping up and down as he rounds the bases. It is about 10 minutes long and the HR comes at the end, of course.

Dusty Rhodes

Here is a video that shows Eddie Mathews hitting a walkoff HR in game 4 in the 1957 World Series. It looks like a big celebration at home plate

Eddie Mathews

So sometimes in the old days they had big celebrations after walk-off HRs.

"I found this photo in the AP archives yesterday and wrote about the differences from 1949 and today on the Photography blog here . It's hard to believe that this shot was made as Tommy Henrich approached home plate after hitting a walk-off home run to win the first game of the '49 series 1-0. A World Series game! For some reason all the excessive exuberance shown today by players after every little thing bothers me. Seeing this old photo just made me smile."

The whole team did not go out to mob Henrich, as you can clearly see in the photo. See No jube at the plate? Archival photo shows walk-off home run in 1949 World Series. (Hat Tip: David Pinto's Baseball Musings)

I guess it depends on what you call old school (David Pinto's blog entry on this was titled "Old School Walk-Off"). This link shows Mazeroski’s series winning HR in 1960. I know it is different because it ended the series where as this one from 1949 was just the first game. But it looks like alot of the team came out to home plate to congratulate him as fans poured onto the field.

Mazeroski

Then there is Bobby Thomson’s series winning HR in the 1951 NL pennant playoff

Bobby Thomson

Again, it is a little different since it won a pennant.

But here is Dusty Rhode’s HR to win game 1 in the 1954 World Series. The whole team comes out to congratulate him at home plate and you also see Willie Mays jumping up and down as he rounds the bases. It is about 10 minutes long and the HR comes at the end, of course.

Dusty Rhodes

Here is a video that shows Eddie Mathews hitting a walkoff HR in game 4 in the 1957 World Series. It looks like a big celebration at home plate

Eddie Mathews

So sometimes in the old days they had big celebrations after walk-off HRs.

## Wednesday, July 14, 2010

### Update on Blue Jays and Astros Offenses

My first post on the Astros was Astros Offense On Record Setting Low Pace. Right now their OPS is .643 and the league average is .729. So .643/.729 = .882. That would be the 11th worst since 1969, as you can see from the table below.

They have been doing better lately. In June, the Astros had an OPS of .691 while the league average was .720. That is a ratio of .96. So far in July, it is .689/.733 for a ratio of .94.

The Astros have an OPS+ of 73 according to Baseball Reference. It takes park effects into effect as well as the league average (it is calculated a little differently than above). The lowest team OPS+ I found going all the way back to 1920 was 69 for the 1920 Philadelphia A's. So the Astros are close to that.

I am not sure what to make from the Astro's park ratings in the Bill James Handbook. For the years 2007-9, they have a run rating of 96, meaning that the runs scored in their park is 96% of the league average. But the rating for AVG is 101 and for HRs 108. So that indicates a slightly above average hitter's park. The walk rate is 98. That does not seem like enough to offset the HR and AVG ratings to say their park is a little hard on the hitters. The error rate is only 87. That might hold down the runs. My best guess is that when it comes to OPS, Minute Maid should be a little helpful to the Astros' hitters.

The Blue Jays have an isolated power (ISO) of .205 since their SLG is .445 and their AVG is .240. That is higher than the all-time record of .205 by the 1997 Mariners. Relative to the league average, it would be the third highest since 1900, at 138 (.205/.148 = 1.38). The league ISO in the AL this year is .148. The 1927 Yankees are the highest in realtive ISO at 153. My first post on this was Blue Jays On Record Power Pace.

They have been doing better lately. In June, the Astros had an OPS of .691 while the league average was .720. That is a ratio of .96. So far in July, it is .689/.733 for a ratio of .94.

The Astros have an OPS+ of 73 according to Baseball Reference. It takes park effects into effect as well as the league average (it is calculated a little differently than above). The lowest team OPS+ I found going all the way back to 1920 was 69 for the 1920 Philadelphia A's. So the Astros are close to that.

I am not sure what to make from the Astro's park ratings in the Bill James Handbook. For the years 2007-9, they have a run rating of 96, meaning that the runs scored in their park is 96% of the league average. But the rating for AVG is 101 and for HRs 108. So that indicates a slightly above average hitter's park. The walk rate is 98. That does not seem like enough to offset the HR and AVG ratings to say their park is a little hard on the hitters. The error rate is only 87. That might hold down the runs. My best guess is that when it comes to OPS, Minute Maid should be a little helpful to the Astros' hitters.

The Blue Jays have an isolated power (ISO) of .205 since their SLG is .445 and their AVG is .240. That is higher than the all-time record of .205 by the 1997 Mariners. Relative to the league average, it would be the third highest since 1900, at 138 (.205/.148 = 1.38). The league ISO in the AL this year is .148. The 1927 Yankees are the highest in realtive ISO at 153. My first post on this was Blue Jays On Record Power Pace.

## Monday, July 12, 2010

### Mercy! White Sox Storm Into First Place After Winning 8 Straight And 25 Out Of 30

It began with a 15-3 win over the Tigers on June 9. The Sox had 16 hits, including 3 HRs. They beat Detroit again the next day to take two out of three. In the last 30 games, the Sox have outscored their opponents 156-77. That gives them a Pythagorean winning pct of .804. In 30 games that would be 24.1 wins. All data is from Baseball Reference.

The Sox hitters have a .793 OPS in these games. The following formula shows the relationship between runs per game and OPS from 2001-04 (may not be the most accurate formula for this case, but I had it handy).

R/G = 13.27*OPS - 5.29

It predicts the Sox would score 5.22 runs per game. That is very close to what they have actually done (5.2). The Sox pitchers have allowed an OPS of .626. That would work out to about 3 runs per game or 90 total runs. They have actually only allowed 77.

The Sox OPS differential is .167. The next formula shows the relationship between OPS differential and winning pct.

Pct = 1.26*OPSDIFF + .5

This gives the Sox a pct of about .710. That would be only about 21 wins (which would still be very good). The big thing is that the Sox pitchers are allowing fewer runs than expected based on the OPS they have allowed. They must be doing well with runners on base in the last 30 games (but for the year they have allowed a .704 OPS overall while it is .741 with runners on). The Sox have out homered their opponents 34-17.

They have won or swept 9 of their last 10 series. The only series they lost was 2 out of 3 to the Royals a couple of weeks ago in KC. They avenged that with a 3 game sweep in Chicago, outscoring them 28-8. They beat the Tigers 2 out of 3 to start this run when the Tigers were in 2nd place. But the Tigers were recently in first place until yesterday. The Sox also swept the first place Braves and took 2 of 3 from the Rangers. And last week they swept the 2nd place Angels 4 straight in Chicago, outscoring them 19-5. In the five losses, the Sox have not lost by more than 2 runs, losing all 5 by a total of 9 runs.

Now if we can only get Oswalt from the Astros to take Peavy's place.

The Sox hitters have a .793 OPS in these games. The following formula shows the relationship between runs per game and OPS from 2001-04 (may not be the most accurate formula for this case, but I had it handy).

R/G = 13.27*OPS - 5.29

It predicts the Sox would score 5.22 runs per game. That is very close to what they have actually done (5.2). The Sox pitchers have allowed an OPS of .626. That would work out to about 3 runs per game or 90 total runs. They have actually only allowed 77.

The Sox OPS differential is .167. The next formula shows the relationship between OPS differential and winning pct.

Pct = 1.26*OPSDIFF + .5

This gives the Sox a pct of about .710. That would be only about 21 wins (which would still be very good). The big thing is that the Sox pitchers are allowing fewer runs than expected based on the OPS they have allowed. They must be doing well with runners on base in the last 30 games (but for the year they have allowed a .704 OPS overall while it is .741 with runners on). The Sox have out homered their opponents 34-17.

They have won or swept 9 of their last 10 series. The only series they lost was 2 out of 3 to the Royals a couple of weeks ago in KC. They avenged that with a 3 game sweep in Chicago, outscoring them 28-8. They beat the Tigers 2 out of 3 to start this run when the Tigers were in 2nd place. But the Tigers were recently in first place until yesterday. The Sox also swept the first place Braves and took 2 of 3 from the Rangers. And last week they swept the 2nd place Angels 4 straight in Chicago, outscoring them 19-5. In the five losses, the Sox have not lost by more than 2 runs, losing all 5 by a total of 9 runs.

Now if we can only get Oswalt from the Astros to take Peavy's place.

## Sunday, July 11, 2010

### Rating Hitters By Their Home Runs As A Percentage of Their Strikeouts, aka The "Splinter Score"

Derrick Gold and Joe Strauss recently came up with a stat they call the "splinter score." It is HRs divided by strikeouts. But neither HRs or K's are adjuted for era or the league average. See Bird Land 10@10: Pujols, Musial & the Splendid Splinter Scale. (hat tip: Baseball Think Factory)

Just about a year ago I posted an entry called Which Players Had The Best HR-To-Strikeout Ratios? Here is that post.

*************************************************

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

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

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

Just about a year ago I posted an entry called Which Players Had The Best HR-To-Strikeout Ratios? Here is that post.

*************************************************

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

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

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

## Friday, July 9, 2010

### Matt Garza vs. Lefty Grove

Below is a post from last year called Starting Pitchers As Relievers Over Time. Alot of people have been talking about starter Matt Garza coming in as a reliever the other day. But it was once fairly common for starters to pitch in relief. I don't claim to know all the reasons why the usage of pitchers has changed over time. But here is that post.

*********************************************************

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.

*********************************************************

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.

## Wednesday, July 7, 2010

### Update On Sammy Sosa's Clutch Hitting, 1998-2001

The first post on this is right before this one. It was generated by an announcer saying something like "Sosa hit alot of HRs in when the scored was one sided."

I thought of another way to look at this. In his career, Sosa had the following HR%'s in various situations. Data from Baseball Reference

Tie Game 0.0642

Within 1 R 0.0669

Within 2 R 0.0676

Within 3 R 0.0669

Within 4 R 0.0668

Margin > 4 R 0.0842

Ahead 0.0710

Behind 0.0710

So, yes, his % his much higher in games when his team was ahead by more than 4 runs or behind by more than 4 runs. He had 1139 ABs in those situations. What if he had had his "Within 4 R" HR% in the "Margin > 4 R" ABs? He would have had 76 HRs in those cases instead of the 96 he actually had. So he would lose 20 career HRs. That would still give him 589. Notice that his HR%'s in other cases are all pretty close together.

What about from 1998-2001? Using Baseball Reference again, here are his HR totals for each season followed by how many he hit in "Margin > 4 R" cases preceded by the totals for the 4 years

66/8

63/16

50/9

64/11

243/44

So 18.1% of his HRs were hit in "Margin > 4 R" cases. What about the entire NL for these years? Here is the same thing for the whole league

2565/356

2893/490

3005/478

2952/474

11415/1798

The league hit 15.75% of it's HRs in "Margin > 4 R" cases. What if Sosa had the same %? Well, 15.75% of 243 is about 38. He actually hit 44 in "Margin > 4 R" cases. So we should take 6 HRs away from him. That would leave him 237 for the whole 1998-2001 period, still an amazing total.

Now 38/237 = .16 or 16% of HRs in "Margin > 4 R" cases. If I dropped him down to 37/236, it would be .1568. So a loss of about 6 HRs is fairly accurate.

So, bottom line, if Sosa matched the league average in when he hit HRs according to run margin, he would not lose very many HRs. How could anyone fault him for this?

I thought of another way to look at this. In his career, Sosa had the following HR%'s in various situations. Data from Baseball Reference

Tie Game 0.0642

Within 1 R 0.0669

Within 2 R 0.0676

Within 3 R 0.0669

Within 4 R 0.0668

Margin > 4 R 0.0842

Ahead 0.0710

Behind 0.0710

So, yes, his % his much higher in games when his team was ahead by more than 4 runs or behind by more than 4 runs. He had 1139 ABs in those situations. What if he had had his "Within 4 R" HR% in the "Margin > 4 R" ABs? He would have had 76 HRs in those cases instead of the 96 he actually had. So he would lose 20 career HRs. That would still give him 589. Notice that his HR%'s in other cases are all pretty close together.

What about from 1998-2001? Using Baseball Reference again, here are his HR totals for each season followed by how many he hit in "Margin > 4 R" cases preceded by the totals for the 4 years

66/8

63/16

50/9

64/11

243/44

So 18.1% of his HRs were hit in "Margin > 4 R" cases. What about the entire NL for these years? Here is the same thing for the whole league

2565/356

2893/490

3005/478

2952/474

11415/1798

The league hit 15.75% of it's HRs in "Margin > 4 R" cases. What if Sosa had the same %? Well, 15.75% of 243 is about 38. He actually hit 44 in "Margin > 4 R" cases. So we should take 6 HRs away from him. That would leave him 237 for the whole 1998-2001 period, still an amazing total.

Now 38/237 = .16 or 16% of HRs in "Margin > 4 R" cases. If I dropped him down to 37/236, it would be .1568. So a loss of about 6 HRs is fairly accurate.

So, bottom line, if Sosa matched the league average in when he hit HRs according to run margin, he would not lose very many HRs. How could anyone fault him for this?

## Monday, July 5, 2010

### Was Sammy Sosa A Clutch Hitter From 1998-2001?

While watching the Ranger's broadcast of their game vs. the White Sox last night, one of the announcers said something about how Josh Hamilton's HRs are usually very important, like the one that put them ahead 2-1. Then he said something about Sammy Sosa like "in those years when he was hitting 60 HRs, he alot of them when the game was one sided."

I don't know if that is true. Here is one way to look at it. Sosa had a HR% in non-close and late situations of 10.12% during these years. In close and late (CL) situations, it was 8.85% (so he dropped off, but that is still much higher than most players in CL situations). But hitters generally have a lower HR% in CL situations. From 1991-2000, it was 2.99% in non-CL cases and 2.63 in CL situations, for a decline of about .0036.

So let's suppose that Sosa's differential should have been only .0036, then he should have had a 9.76 HR% in CL situations. He had 384 CL ABs. A 9.76 HR% would give him 37.47 HRs in CL situations. He actually had 34. So maybe he should have had 3.47 More HRs in those years, if he had not "choked" in the clutch. This is not a big deal.

Conversely, if we add .0036 to his .0885 CL HR% to get his expected non-CL HR%, we would have a HR% of 9.21%. In his 2,165 non-CL ABs, he would hit 199 HRs. He actually hit 219 in non-CL situations during those years. If we take away 20 HRs over these four years, he ends up with 233 instead of 253. That is still an average of 58.25 per season. Pretty incredible.

But we can easily imagine that Sosa had to face some very tough relievers in those CL situations, who might have been told to not give him much to hit. The following table summarizes his stats in various situations in each of the four seasons. Sept and Oct data are shown for 1998 & 2001 because in those years the Cubs were fighting for a playoff spot. In general, I think the numbers show that he hit very well with runners on or in CL situations or late in the season when the Cubs were trying to make the post season (they finished last in both 1999 & 2000). Sosa hit alot of meanigful HRs in these years. There is nothing misleading or deceiving about his performance or his stats.

To see what the normal clutch/non-clutch differentials are, go to General Clutch Data.

I don't know if that is true. Here is one way to look at it. Sosa had a HR% in non-close and late situations of 10.12% during these years. In close and late (CL) situations, it was 8.85% (so he dropped off, but that is still much higher than most players in CL situations). But hitters generally have a lower HR% in CL situations. From 1991-2000, it was 2.99% in non-CL cases and 2.63 in CL situations, for a decline of about .0036.

So let's suppose that Sosa's differential should have been only .0036, then he should have had a 9.76 HR% in CL situations. He had 384 CL ABs. A 9.76 HR% would give him 37.47 HRs in CL situations. He actually had 34. So maybe he should have had 3.47 More HRs in those years, if he had not "choked" in the clutch. This is not a big deal.

Conversely, if we add .0036 to his .0885 CL HR% to get his expected non-CL HR%, we would have a HR% of 9.21%. In his 2,165 non-CL ABs, he would hit 199 HRs. He actually hit 219 in non-CL situations during those years. If we take away 20 HRs over these four years, he ends up with 233 instead of 253. That is still an average of 58.25 per season. Pretty incredible.

But we can easily imagine that Sosa had to face some very tough relievers in those CL situations, who might have been told to not give him much to hit. The following table summarizes his stats in various situations in each of the four seasons. Sept and Oct data are shown for 1998 & 2001 because in those years the Cubs were fighting for a playoff spot. In general, I think the numbers show that he hit very well with runners on or in CL situations or late in the season when the Cubs were trying to make the post season (they finished last in both 1999 & 2000). Sosa hit alot of meanigful HRs in these years. There is nothing misleading or deceiving about his performance or his stats.

To see what the normal clutch/non-clutch differentials are, go to General Clutch Data.

## Friday, July 2, 2010

### Did Koufax Have The Best Peak Ever?

Dave Studeman brought this up topic up earlier this week at The Hardball Times with Koufax’s peak. I have done some research on a related note. It is not as sophisticated as what Dave has done since it is not clutch-based. But I did not find that Koufax had the best peak. This is after taking park effects and league averages into account. Also, I tried only using fielding independent stats. Here are the links:

Bert Blyleven: As Dominating as Sandy Koufax

How Good Was Sandy Koufax Outside of Dodger Stadium? ((I compared him to Gibson, Marichal and Bunning)

The Best Five-Year Pitching Performances Since 1920 Based on Fielding Independent ERA

The Best Five-Year Pitching Performances

Bert Blyleven: As Dominating as Sandy Koufax

How Good Was Sandy Koufax Outside of Dodger Stadium? ((I compared him to Gibson, Marichal and Bunning)

The Best Five-Year Pitching Performances Since 1920 Based on Fielding Independent ERA

The Best Five-Year Pitching Performances

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