Go to end of page
Statements posted here are those of our readers and do not represent the BaseballThinkFactory. Names are provided by the poster and are not verified. We ask that posters follow our submission policy. Please report any inappropriate comments.
But what does he know about how much roids help major league baseball players? He's only a major league baseball player who took roids. It's not like he's a lawyer or Keith Law, or something.
I submit that if anything, the value of PEDs to a player has been drastically underpublicized as opposed to overblown.
"It’s unfortunate, but players, fans, and the media are still analyzing PED use like a 1950s baseball scout rather than using the mounds of data at our fingertips to appropriately measure what has occurred and is occurring
What the hell does Kapler know about how much roids help ballplayers?
Since there is a dummy variable for each player, the sum of a player’s residuals
across the years that he played is zero. Under the assumption that the errors, it,
are iid, they should lie randomly around the age-performance curve in Figure 1 for
each player. It is interesting to see if there are players whose patterns are noticeably
different. For example, if a player got better with age, contrary to the assumptions
of the model, one would see in Figure 1 large negative residuals at the young ages
and large positive residuals at the old ages.
Using OPS regression 1 in Table 1, the following procedure was followed to
choose players who have a pattern of large positive residuals in the second half
of their careers. First, all residuals greater than one standard error (.0757) were
recorded. Then a player was chosen if he had four or more of these residuals from
age 28, the estimated peak-performance age, on. There were a total of 17 such
players. In addition, for reasons discussed below, Rafael Palmeiro was chosen,
giving a total of 18 players. The age-performance results for these players are
presented in Table 3. The residuals in bold are greater than one standard error. The
players are listed in alphabetic order except for Palmeiro, who is listed last.
The most remarkable performance by far in Table 3 is that of Barry Bonds.
Three of his last four residuals (ages 37–40) are the largest in the sample period, and
the last one is 5.5 times the estimated standard error of the equation. Not counting
Bonds, Sammy Sosa has the largest residual (age 33, 2001) and Luis Gonzalez
has the second largest (age 34, 2001). Mark McGwire has three residuals that are
larger than two standard errors (age 33, 1996; age 35, 1998; age 36, 1999). Larry
Walker has two residuals that are larger than two standard errors (age 31, 1997;
age 33, 1999) and one that is nearly two standard errors (age 35, 2001). Aside
from the players just mentioned, 8 other players have one residual greater than two
standard errors: Albert Belle (age 28, 1994), Ken Caminiti (age 33, 1996), Chili
Davis (age 34, 1994), Dwight Evans (age 36, 1987), Julio Franco (age 46, 2004),
Gary Gaetti (age 40, 1998), Andres Galarraga (age 37, 1998), and Paul Molitor
(age 31, 1987).
There are only 3 players in Table 3 who did not play more than half their
careers in the 1990s and beyond: Bob Boone (1973–1989), Dwight Evans (1973–
1991), and Charlie Gehringer (1926–1941). Remember that the period searched
was 1921–2004, so this concentration is unusual. An obvious question is whether
performance-enhancing drugs had anything to do with this concentration. In 2005
Palmeiro tested positively for steroids, and so it is of interest to see what his age
performance results look like. He is listed last in Table 3. Palmeiro’s pattern looks
similiar to that of many of the others in the table. He has three residuals greater
than one standard error in the second half of his career, one of these greater than
two standard errors (age 35, 1999; age 36, 2000; age 38, 2002). In addition, his
residual in 2001 was .0750, which is very close to the standard error of .0757. He
was thus very close to being chosen the way the other players were. No other
players were this close to being chosen.
Since there is no direct information about drug use in the data used in this
paper, Table 3 can only be interpreted as showing patterns for some players that
are consistent with such use, not con?rming such use. The patterns do not appear
strong for the three pre-1990 players: Boone, Evans, and Gehringer. For the other
players, some have their large residuals spread out more than others. The most
spread out are those for Gaetti, Molitor, and Surhoff. Regarding Galarraga, four of
his six large residuals occurred when he was playing for Colorado (1993–1997).
Walker played for Colorado between 1995 and 2003, and his four large residuals
all occurred in this period. Colorado has a very hitter-friendly ball park. Regarding
the results in Table 3, there are likely to be different views on which of the patterns
seem most suspicious, especially depending on how one weights other information
and views about the players. This is not pursued further here.
From the perspective of this paper, the unusual patterns in Table 3 do not ?t the
model well and thus are not encouraging for the model. On the other hand, there
are only at most about 15 players out of the 441 in the sample for which this is
true. Even star players like Babe Ruth, Ted Williams, Rogers Hornsby, and Lou
Gehrig do not show systematic patterns. In this sense the model works well, with
only a few key exceptions.
No Ray, stupid cite. Only a moron would conduct such a study using OPS.
You can't make dangerous accusations about PEDs and completely ignore the other big benefits to players that are considered perfectly legal.
Yet the most glaring outliers in his study are admitted PED users.
Very few of the 18 outliers are "admitted PED users." McGwire, Caminiti, and who else? I don't doubt that the number of users is higher, but let's not sell statistical precision with rhetorical overreach.
Pitching leaders by ERA+ 1993 on (As before, using BB-ref's age leaderboards -- which include playing time)
And there are plenty of users who didn't produce glaring outliers, including those same players in other years. This is the problem. We can't start looking at who exceeding expectations and go "a-ha, steroids", but we have to evaluate how steroids are affecting all users.
it's the glaring outliers that really require some effort to explain away.
Explain away Hank Aaron's late-career surge.
23 - many players took steroids "once" to "help recover from an injury" or other such nonsense.
the author intentionally left Aaron out of the chapter on "Unusual Age-Performance Profiles"?
Can you reference some analysis that shows it was unusual beyond "he hit a lot of homers?""
Bill James: "moving from County Stadium to the Launching Pad"
Anyone who looks at Aaron's late career surge for two minutes will realize that it wasn't even on the same planet as Bonds's.
Fair enough... the "Known" PED users are Bonds, McGwire and Caminiti. Of course, I have made it clear that my opinion is that both Sosa and Gonzalez were very likely PED users and that probably affected the strength of my assertion since they were #2 and #3 on the list.
No Ray, stupid cite. Only a moron would conduct such a study using OPS.
Anyone who looks at Aaron's late career surge for two minutes will realize that it wasn't even on the same planet as Bonds's
Some entreprising sabermetrician should study this universe of players to see if there are any discerable trends rather than the converse of seeing if there are trends and then identifying players as tainted without proof of the underlying assumption.
Kapler comes from an academic family; IIRC his father is a theatre teacher, and his background is as much in the arts as in sports. Which doesn't augur for him having higher math skills either, I suppose, but he's not the stereotypical jock.
I think you're wrong, Ron. In this case, I think raw data (OPS) is preferable to normalized (OPS+). By using OPS+, you're scrubbing out the outliers by normalizing against league performance, a league in which there were quite a few other juicers who will dilute any outlier effect.
The problem is that is a circular argument. You assume that Sosa and Gonzalez used because they had anomolies in their aging trends. There is no independent variable being studied.
As I've said before, anybody who believe the offensive changes in 1993-1994 are due to steroids are abliged to believe a number of things that seem really questionable to me.
Do either Sosa or Gonzalez have aging trends that are really that out of whack?
Looking at OPS+, Sosa peaked at 32, then dropped off pretty quickly, with his last year at 38 (having missed his age 37 season). Gonzalez has his best season at 29, another very good one, then drops off such that his last season is at age 35. Not much different than a guy like McCovey, who peaked at age 31, with some very good years at 35 and 36 or Al Kaline, who had his best season by OPS+ at 32, then declined until his last season at age 39.
#44 If you don't believe that the changes in offensive levels from 1993 on are due to steroids it's bloody stupid to compare raw OPS from that period to raw OPS from the earlier period and think you're getting meaningful information about aging curves.
That is, unless video surfaces of a heavily attended Jose Canseco seminar "Steroids and You: Embrace the Longball".
46 It's somewhere around 90% changes in the ball (most specifically that the balls that made it into major league games were consistently at the very highest end of allowable liveliness). Most of the rest is changes in the parks. Expansion has very little to do with the change in offensive levels. Hitters were added at the same time as pitchers.
I am pretty sure that the entire point of the chapter I quoted from that study was to determine just that.
That's actually a really good point. There seems to be a need, however, to account for shifts in a player's expected baseline (new park, expansion, etc) and OPS+ does provide that, but maybe bites off more normalization than we need.
I do wonder at what point that the benefits of bulking up and steroid use became common knowledge among players. Caminiti started in 1996 to treat an injury and had no qualms about becoming a regular user afterwards. I'm guessing that the floodgates really opened after the 1998 HR chase.
The trouble I have with this is the idea that it took 10 years for steroids to go from fringey to widely used.
I remember hiding green M&Ms; in my socks so that I could take them during the game.
Don't fall into the trap of Andy's non-falsifiable dishonesty.
Andy's argument is the equivalent of saying smoking pot makes one become the president, because Bill Clinton smoked pot. You either call him out on the complete nonsense argument or he then moves on to the next argument, which would be that no rock musician has ever smoked pot because no rock musician has become president. And if you've let the argument get to the point at which the second nonsense argument is made, you've essentially let the clown car fill up and now you're following it to the circus.
Andy wants to argue the conclusion that steroids lead to drastic increases in play, without actually demonstrating that the supposition is true. And he would need a pretty good argument - the *fact* that players in the majors and minors, when caught for PEDs over the last decade, have not, as a group, underperformed projections in the years following the drug test or overperformed projections in the years before being busted, is a pretty big hurdle to jump over.
There were many factors that contributed to Bonds's late career surge, but given normal aging declines combined with Bonds's phenomenal spike in his late 30's numbers, it's extremely hard to conclude that steroids weren't among them.
especially when you don't know how much or how long
OPS or OPS+ is obviously a terrible way to measure steroid usage. Unless you think the drug increases IBBs.
Actually, why would you expect steroids to increase 1B? I think the cleanest method (for hitters!) is HR/FB, normalized for park, league, date (proxy for ambient T).
Also, I would NOT use "player seasons". I would do some sort of windowed average over 10-20 games or so and smooth it out
You must be Registered and Logged In to post comments.
Login to Join (0 members)
Page rendered in 0.9964 seconds, 74 querie(s) executed