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Baseball Player Values (November 22, 2003)

Posted 10:19 p.m., February 4, 2004 (#28) - Cyril Morong
  Some of you may have seen my research on this. Ed Oswalt's stat is highly correlated with OPS over the time period. Here is my site on this. Also, sorry, I have not read much of what is above here.

http://hometown.aol.com/cyrilmorong/myhomepage/totalclutch1.htm

The Problem With "Total Clutch" Hitting Statistics (December 1, 2003)

Posted 10:13 a.m., December 1, 2003 (#1) - Cyril Morong(e-mail) (homepage)
  Tango, thanks for posting this. I look forward to any feedback.

The Problem With "Total Clutch" Hitting Statistics (December 1, 2003)

Posted 12:09 p.m., December 1, 2003 (#8) - Cyril Morong
  On the 6.4% not accounted for in the regression with OBP and SLG. If I could get the data broken down into singles, doubles, outs, GIDP, etc, the r-squared might go up even more. But Oswalt says that his stat is compared to the league average. I used the Lee Sinnins sabermetric encyclopedia to find relative OBP and relative OPS but I am not sure if I can get relative numbers for singles, doubles, outs, GIDP, etc. with the sabermetric encyclopedia. And all Oswalt shows is his PWV.

The remaining 6.4% could be from some clutch ability and it could be randomness. Any suggestions on how to test that? But as Willie Runquist has pointed out, we can never tell which of the outliers in some clutch hitting stat got there due to chance or from a real clutch hitting ability.

As for the top 100 players in PA. I chose them because it is more likely that randomness would be smoothed out. Some of those guys stuck around along time because they were good fielders. I did run a regression with only the bottom thirty of this 100 (15 were negative in PWV, 15 positive). The r-squared was .844. Then I did the top 30 and the r-squred was .867. Close, maybe there is some small effect of having used good hitters.

But why would all the good hitter still conform pretty closely to the regression line? Why are there still not some who go above what other good hitters do in the clutch?

I will try to run the regression again with more hitters, maybe doing a set of guys who are only negative in PWV. But it takes time to get the data from the Oswalt site matched up with what I generate from the sabermetric encyclopedia.

The Problem With "Total Clutch" Hitting Statistics (December 1, 2003)

Posted 12:25 p.m., December 1, 2003 (#9) - Cyril Morong
  The article from the print version of BusinessWeek was titled "Ball Park Figures You Can Bet On." That is a very strong endorsement. Will teams really make personnel decisions based on this stat more than on say, OBP and SLG? I don't think they should. I hope the White Sox don't. The article also mentioned two agents said they would consider using it. I don't think anyone should make financial decisions based on this. I wrote letters to them but heard nothing back.

It would take a several years of data for us to know if a guy truly has some clutch ability as indicated by this stat. That is, he consistently does better in PWV than is OBP and SLG would suggest. I think the burden of proof is on those who push the "total clutch" stat that it does tell us something we don' already know.

The Problem With "Total Clutch" Hitting Statistics (December 1, 2003)

Posted 2:35 p.m., December 1, 2003 (#15) - Cyril Morong
  Someone spoke of hijacking this thread. I thought metal detectors had been installed. Would somebody call the TSA before it's too late?

The Problem With "Total Clutch" Hitting Statistics (December 1, 2003)

Posted 2:58 p.m., December 1, 2003 (#17) - Cyril Morong
  In one of the STATS books they show that Babe Ruth was 0 for 16 in late and close games in the World Series. Those seem like very high leverage. But I would pinch hit Ruth before I pinch hit Mazeroski if it is the bottom of the 9th in game 7 of the WS and the score is tied.

The Problem With "Total Clutch" Hitting Statistics (December 1, 2003)

Posted 12:01 a.m., December 2, 2003 (#22) - Cyril Morong
  "The inclusion of such outliers increases the R^2 significantly."

Is this true in all regressions or just this one? I never heard this before. Why is it true? Do you mean outliers in terms of the independent or dependent variable? An outlier as an independent variable could also be far off the trend line. I think that would tend to lower the r-squared in a regression.

I used the top 30 and bottom thirty because someone mentioned that players who play long enough to get alot of PA's are good hitters. There were 15 of the 100 who had negative PWV's, so I added the first 15 above them to balance things off as in terms of negative and positive. Then I looked at the top 30 as a check against this.

You mention Ricky Henderson and his legs. But I only used the batting wins portion. Sorry that I did not mention that.

You ask the question if clutch performance is not the explanation, what is? I would rephrase the question as, what caused Grace to do better than expected while Sosa did worse? Grace had a special clutch ability is one answer. The other is randomness. How can we tell which one is right? Also, did Grace or Hernandez consistently do better in PWV than OPS would predict, season in and season out? Did they do so every year? I don't know if that data is available at Owalt's site. It seems the people coming up with these "total clutch" stats don't make all the data available for others to test them.

I ran the regression that you ran with the 46 guys from 110-119. The r-sqaured is low at .427. But the standard error is still pretty low, at .47 wins for a 700 PA season (a full season). Also, 37 of the 46 guys in your mini study are predicted to within .5 wins for a season of 700 PA's. That still seems pretty accurate for such a limited regression.

Of course, the better regression to run is the one with OBP and SLG as separate variables. Probably more players would be within .5 wins. Even better would be to break everything down into singles, doubles, triples, etc. But like I said earlier, it is not easy to put all of that together since I don't think you can generate relative outs with the sabermetric encyclopdia and the independent variables need to be relative to the league average since that is how Oswalt calculated his PWV.

I now have all players (284) with 5000 PA from 1972-2002. To do what FJM did with OPS but broken down by OBP and SLG, I had to figure who was in the middle. Not straightforward since a guy could be in the middle on OBP but not SLG. So I created a stat from 1.5*relativeOBP + relativeSLG. Then ran the regression with the middle 143 or so guys. The r-squared was .736. The standard error was .395 wins for a 700 PA season, still pretty accurate.

The Problem With "Total Clutch" Hitting Statistics (December 1, 2003)

Posted 9:47 p.m., December 2, 2003 (#29) - Cyril Morong
  Regarding comment #26, if you look at your regression with 83 players, sure the r-squared is only .74. But the standard error is not much more than for the 100 hitter regression. It goes from .441 to .466 for a 700 PA season. With just 83 guys, it is still less than half a win. Over 60 guys, almost 75%, are predicted to within half a win. So although the r-squared goes down alot, you still have a model that predicts well. A key thing is that the coefficient estimate for relative OPS doesn't change much when you remove the outliers. That shows the regression is not sensitive to who is in and out.

Also, and again, OPS is crude and it is better to run a regression with relative OBP and relative SLG, which I have already mentioned. I put OPS in the study because it is simple and being a single variable, I could put in a graph. And since I put in the graph I thought it would be good to put in the numbers for people to look at.

As for Phillips, someone, by chance is going to be in the tail. We cannot be sure if he is there becaue of clutch ability or luck.

The Problem With "Total Clutch" Hitting Statistics (December 1, 2003)

Posted 11:23 p.m., December 2, 2003 (#31) - Cyril Morong
  As a footnote to what Ted T. posted (#30), I ran a regression with the 32 players who ranged in OPS from 112 down to 108. The r-squared was only .068. But the standard error was still just .482 for 700 PA's.

Bases Batted Forward (December 3, 2003)

Posted 2:20 p.m., December 4, 2003 (#4) - Cyril Morong
  Has anyone checked to see what the correlation is between BBF divided by plate appearances and OPS over a long period of time? Has anyone run a regression for players with a large number of plate appearances that has BBF/PA as the dependent variable and maybe singles, doubles, triples, homeruns, walks, GDP, outs, sacs and SF's as the independent variables?

My hunch is that BBF/PA will be highly correlated with these other stats. At a team level, how much better does BBF predict run scoring than some other stat? Who are the best players in this stat?

Clutch Hitting: Fact or Fiction? (February 2, 2004)

Posted 11:01 p.m., February 5, 2004 (#67) - Cyril Morong(e-mail) (homepage)
  I have never done a monte carlo study, so I can’t comment on the method. But I did present research on clutch hitting at SABR32 in Boston. I used a method that Pete Palmer used for testing which players’ performance in the clutch was not due to chance. I looked at players who had 6000 or more plate appearances during the 1987-2001. I had 61 players and I looked at their OBPs when it was close and late and not close and late (OBP with intentional walks removed). I found 6 players were outside the range of + or – 1.96 in their Z-scores (Z = (CLUTCH AVG – NONCLUTCH AVG + EXPECTED DIFFERENCE)/SD, with SD being the standard deviation). Below are the players and their Z-scores. The first 3 are the possible clutch hitters and the last three are the possible chokers. But we would expect 1.5 players to have a Z-score of at least 1.96 and 1.5 to be below –1.96. So at most, we have 1.5 clutch hitters and 1.5 chokers. But we cannot be sure which guys are outliers because of some actual clutch ability (or choking) and which ones got their by chance (a poing Willie Runquist has made). In any case, there are not many clutch hitters here. I can email my handout from this presentation to anyone who wants it.

Edgar Martinez 2.216
Mark Grace 2.144
Tino Martinez 2.029
B.J. Surhoff -2.063
Travis Fryman -2.158
Ken Caminiti -2.390

Edgar Martinez and Tino Martinez could be labeled as power hitters. Also, the author of this study, AED, said that a .250 hitter won’t become a .400 hitter in the clutch, but a .285 hitter can become a .300 hitter. Suppose you get 150 at-bats in some clutch situation during a season. The .015 differential is only 2.25. That 150 at-bats is probably high since I think only about 15% of plate appearances come when it is close and late. 1 or 2 hits seems like a small difference and it suggests to me that the clutch hitting ability found here is very small. I also have some clutch hitting research at my website.

Clutch Hitting: Fact or Fiction? (February 2, 2004)

Posted 11:02 p.m., February 5, 2004 (#68) - Cyril Morong
  I think including players who had 1000 plate appearances overall and 250 in the clutch is still a pretty small number. This may only be 2 or 3 years of playing, and that may not be very long. I think more plate appearances would reduce the effect of randomness.

Clutch Hitting: Fact or Fiction? (February 2, 2004)

Posted 9:25 a.m., February 6, 2004 (#70) - Cyril Morong
  "What I've done is measure the cumulative probability of all data being created from randomness alone..."

Is this what the Monte Carlo method did?

Clutch Hitting: Fact or Fiction? (February 2, 2004)

Posted 6:08 p.m., February 6, 2004 (#75) - Cyril Morong
  In my study of players with 6000 or PAs during the 1987-2001 time period, only 5 of the 71 players had a close and late batting average that was .010 or more different than their non-close and late AVG. The biggest differential was for Tino Martinez at .028 (.297 vs. .269). In a 660 at-bat season, and with 15% of at bats coming when it is close and late, this amounts to about 2.77 hits (660*.15*.028). That seems pretty small for the very best clutch hitter.

How many games would this win? I guess we would have to look at the expected runs and wins tables. But close and late can mean alot of things. We don't have to have any one on base. So it might not be easy to estimate it that way.

In basic linear weights, a sinlge is worth .47 runs. Suppose we double its value for close and late (my guess is that it is high and I hope that it would offset the fact that I am only talking singles here). .94*2.77 is just 2.6 runs, or about a quarter of a win. (Again, I raised the run value of the single to try to take into account the extra value of close and late). This seems like a very small difference for the best clutch hitter. And almost everyone else is within .010, so for the vast majority of the players, their clutch hitting ability's impact on winning is very small.

The normal difference during the 1991-2001 period was -.012 (probably because of good relievers, especially on the same hand as the hitter). Even if we give Tino Martinez this extra .012, we are still talking a very small impact on winning.

Then there is the question of using this information. Player A has a higher OPS (or whatever) when the game is not close and late than player B, say 50 points. Player B has a higher OPS when it is close and late. How big does this differential have to be for us to want B on our team and not A? My guess is very big, mabybe bigger than anyone would get. A quick estimate based on my own research on team clutch is that player B's close and late OPS would have to be about 130 points better than A's. Maybe AED could tell us some pairs of players whose relative ranking we have to change based on clutch ability.

In my study, ever player had at least 711 at-bats when it was close and late. 22 were over 1000. I think if AED looked at only players like this, he would find less clutch hitting ability.

Also, at a team level, I think clutch play matters just about the same as non-clutch play. This was my research presentation at SABR33. It is posted at my site, there is a link above. One of the things I did was to have team winning percentage as a dependent variable and team OPS and opponents OPS as the independent variable. Then I broke OPS down into close and late and non-close and late. There was very little increase in the r-squared or explanatory power of the model. Almost all of the incrase was from breaking down opponent's OPS into close and late and non-close and late. That could be due to bullpen strength and not necessarily clutch ability.

Also, the coefficients on the non-close and late OPS were much higher than for close and late. So a .010 increase in non-close and late OPS would increase team winning percentage more than a .010 increase in close and late OPS. Now, there are more non close and late PA's but the close and late PAs are supposed to matter more, so at least in theory, the coefficient on the close and late OPS could have come in higher. But it did not.

Clutch Hitting: Fact or Fiction? (February 2, 2004)

Posted 7:49 p.m., February 6, 2004 (#77) - Cyril Morong
  I did not say that clutch play was not significant. Just that close and late OPS contributes no more to winning than non close and late OPS. In mya regression, the coefficient on close and late OPS was significant.

Clutch Hitting: Fact or Fiction? (February 2, 2004)

Posted 11:16 p.m., February 6, 2004 (#82) - Cyril Morong
  AED wrote: "The combined importance of the 15% of PAs that meet your "close and late" definition is 43% that of the other 85% of PAs."

How did you get 43%? What was in the numerator and what was in the denominator?