Asterisks of Gall(?)

One of the biggest news items in the last week was the release of the Mitchell Report, which investigated the prevalence of steroid use in Major League Baseball. Pretty much everyone has had something to add to the discussion on performance enhancing drugs. I've been following the story as it develops and had some scattered comments of my own (unfortunately, grading lots of tests and finishing up for the semester prevents me from putting this in the usual essay format):

1) The Blame Game - People have been quick to vilify some of the players named in the Mitchell report as cheaters. For some of the bigger names, such as Roger Clemens, the buzz surrounds whether or not they should be inducted to the Baseball Hall of Fame or not. I am having a tougher time burning effigies of these players. Why? Because they aren't the real culprits behind baseball's "steroid era."

Here is my take. Suspicion of widespread use of steroids and other drugs was growing as early as the late 1980s and early 1990s. Major League Baseball did not take any steps as far as stringent testing and penalties until over a decade later. In the meantime, the use of performance enhancers likely lead to increases in wages and length of tenure for the average player. Given the low level of deterrence and potentially large expected benefits, players went for steroids and HGH, their preferences for health and discount rates notwithstanding. Perhaps there were even externalities from usage: a single player or groups of players performing better and accruing higher wage earnings likely upped the ante for everyone else to performance better, further tipping the cost/benefit balance over to usage.

In any case, the MLB did nothing for a really long time. Not only that, post the strike induced lock-out in the early 1990s, the game was essentially "saved" in the eyes of paying fans by the steroid induced home run chase in the summer of 1998. The MLB's inaction and their packaging of the game in terms of increased power hitting and speed likely created the right kind of incentives to induce the marginal player into trying to get the extra boost.

2) Asterisks - Putting asterisks on records held by suspected users of performance enhancing drugs seems like a difficult proposition to me. If the Mitchell Report is indeed the tip of the iceberg, and use of agents like anabolic steroids and HGH was (and is) widespread, it is hard to distinguish between "legit" and "cheater-induced" records and probably unwise to try and do so. Rather, interpretation of statistics should be left up to discerning fans.

Still like asterisks? Given the discussion in (1), try this one: MLB*.

3) "Steroids don't help you ______" - Some players and analysts claim that performance enhancing drugs do not really help you become a better baseball player. Two supporting arguments are generally cited. First, steroids and HGH do not help you develop the mechanics to hit a ball, work a pitch count, etc. Second, many of the players named in the Mitchell Report are scrubs - steroids didn't help them hit 50 home runs or clock 95 on their fastball.

Regarding the first point, here is a quote from Ken Caminiti (just one of a brilliant collection in http://www.baseballssteroidera.com):

"The stronger you get, the more relaxed you get. You feel good. You just let it fly. If you don't feel good, you try so hard to make something happen. You grip the bat harder and swing harder and that's when you tighten up. But you get that edge when you feel strong. That's the way I felt. I felt strong, like I could just try to meet the ball and -- wham! -- it's going to go 1,000 mph. Man, I felt good. I'd think, Damn, this pitcher's in trouble and I'd crush the ball 450 feet with almost no effort. It's all about getting an edge."

The second argument is really easy to deconstruct: we simply do not know how these players would have done had they not use steroids (assuming the allegations are true). The scrubs on the list may have never even made it to the major leagues without the extra jolt. Steroids likely provide a boost to players sitting at any level on the baseball skill distribution: they don't have to make you an all-star, but they may make you slightly better than you were before. In a competitive industry, this might be all the edge you need.

4) "The game of baseball has been denigrated" - Can anyone honestly tell me that this scandal, involving individuals cheating in order to perform better, is worse than the 1919 White Sox scandal and the match fixing scandal in international cricket where, in both cases, teams intentionally threw games? Sports have weathered these storms in the past. Baseball will be always popular and its heroes will come and go, steroids or not.

5) What to do now? - One the one hand, the Mitchell Report suggests stringent testing, harsher penalties and player education to stem the use of performance enhancing drugs in baseball. On the other hand, some analysts argue that it is impossible for testers to keep up with innovations in performance enhancers. After all, existing tests still cannot detect "the cream" and "the clear," substances Barry Bonds is suspected to have used. In fact, Marion Jones, who admitted using these products never tested positive, covering over 160 tests in sum. In this situation, these observers argue that legalizing these agents in the game and regulating their use with physician oversight might be a better way to go in terms of player health and leveling the playing field.

I'm not sure where I stand on this one, though I think I would lean towards trying to rid the sport of performance enhancing drugs. For one thing, I'm not really convinced by the "arms race" argument against more stringency. Following Gary Becker's seminal analysis on crime, suppose that the average player will use steroids if the expected benefits outweigh the expected costs. In this framework, expected costs are equal to the probability of being caught * the value of the costs themselves. Those who think we'll never catch steroid users believe the probabilities will be too low to give any bite to the costs. On the other hand, increasing penalties, publicly "outing" suspected users, and introducing uncertainty into Hall of Fame chances may jack up the costs enough to outweigh the lack of change in the probability. After all, do you think many players or managers are going to try and pull a Pete Rose now given all that he has been through? Social norms and pressure can be powerful and perhaps more stringency, then, could be a good policy to go with.

A thoughtful post by The Sports Economist provides more arguments for "cleaning up" baseball. One of the more interesting thoughts in that piece, which was alluded to earlier here, is that steroid use by a given MLB player has externalities: others will want to use it in order to compete and this will be true all the way down the pipeline. Where it becomes dangerous is at the level of younger people, who do not possess the resources to have a personal trainer monitor their use. This could have adverse health consequences both in the short and long run.

There is some evidence of noteworthy use of steroids in high school. This piece in JAMA suggests that almost 7% of high school senior athletes have used anabolic steroids. Of these, many started on their regimens as early as junior high. 7% seems like a pretty big number to me. What would be nice to have are figures relating likely steroid-driven adverse health events among this group and information on why these kids decided to initiate such regimens. That way we could get a sense of the extent to which externalities are operative in steroid use.

When Bad Inference Happens to Good People

I turned on ESPN yesterday night for some pre-game analysis on the Houston Texans-Denver Broncos contest. I wasn't paying too much attention until I heard the following statement: "One of Houston's keys to victory tonight is to give Ron Dayne 20-25 carries."

I did a double take. Ron Dayne? An NFL journeyman with decent size, but limited speed, who is best utilized in a platoon of running backs? It turns out that the analyst was basing his comment on the following fact: the Texans are 4-1 in the past two seasons when Ron Dayne gets more than 20 carries.

This is another example of people confusing correlation for causation. There are two (or maybe more) explanations for the Ron Dayne tidbit:

1) Ron Dayne is a game changing talent who, if given the ball, will more often than not win the contest for you.

2) Ron Dayne getting 20 carries or so is a symptom of things working right offensively for the Texans. When the Texans are firing on all cylinders, Dayne's rushing opportunities and totals may reflect the fact that linebackers and safeties are playing off the line of scrimmage and drop back into coverage, allowing Dayne to get his 5-10 yard runs, or that offensive line play is so dominant that Dayne is able to run clear through the woods.

I think the second explanation is probably the more likely one. After all, can you imagine a defensive coordinator thinking before a game, "wow, we need to get 8 in the box to stop Ron Dayne"? Ron Dayne is a good player, but he's not LaDainian Tomlinson or even Frank Gore - the 2007 version.

In any case, this innocuous episode reflects the danger of attributing causal stories to what are only correlations. Unfortunately, fates of entire policies have hinged on bad inference, that too in arenas less trivial than professional sports.

Time Inconsistency, Parking Tickets and Health Care

The New Haven police gifted me a parking ticket a couple weeks back. The payment scheme for the ticket works something like this: it's $15 if you pay it within two weeks or so, $30 if within four weeks and $60 thereafter. I still haven't paid my ticket, so I am looking at about $30 right now.

Am I just completely insane? Why didn't I pay my ticket on time? I could have saved at least 15 bucks. Well, as it turns out, I always did (and still do) intend to pay it sooner rather than later. But getting an envelope, writing a check and sending the thing out was (and is) kind of annoying process to me. Every day I would say, "I'll do it tomorrow."

This is what is called time (or dynamic) inconsistency. The basic idea is that one's current self and one's future self (say tomorrow, or next month) have differing ideas on what actions should be taken (present or future). This is more than a theoretical construct: its a pretty big deal in health economics. An example (taken from the above linked wikipedia page):

Each day smokers face a dynamic inconsistency: their best plan is to enjoy smoking today, but to quit tomorrow in order to get health benefits. However, the next day, the plan is the same; enjoy smoking today and quit tomorrow. This goes on, and they never give up, even though they plan to, hence the inconsistency.

You can easily see how this behavior is important in understanding other health behaviors, such as dieting and other weight loss efforts or perhaps even going to the doctor to get some anomaly checked.

If time inconsistency is a strong barrier to self-motivated health efforts, how does one get around this? Yale economist Dean Karlan and law professor Ian Ayres think they have an answer. The two have recently started a company called StickK. Here is the basic idea:

The company will have a Web site offering individuals hoping to reach a goal — anything from sticking to a diet to learning to ride a unicycle — legally binding contracts where they will pay a set dollar amount to charity if they fail in their endeavor.

The author of the book "The Undercover Economist," Tim Harford, is testing out StickK's methodology. He has paid a $1,000 so-called contract bond to the company, and has promised to donate 10% of this deposit to charity if he fails to complete 200 push-ups and 200 sit-ups every week.

"When I signed up to do this, I thought to myself, the contract bond isn't going to matter at all; what's relevant is that I've made the psychological commitment to do these press-ups and sit-ups," he said. "I was completely wrong. There's absolutely no way I would have done these press-ups and sit-ups for the past six weeks had it not been for the commitment bond."

I'm really curious to see a) how many people choose to enter binding contracts and b) how effective these are for the average consumer.

In the meantime, I think I'll pay my parking ticket...tomorrow.

Fashion Show Economics

I watched the Victoria's Secret fashion show a few nights ago. It was just fantastic. Here are a few interesting tidbits I picked up:

1) Heidi Klum is a good singer.

2) Only B-list celebrities appeared to be interested in actually showing up to the event. Ryan Seacrest? The Spice Girls? VS is offering this two day only promotion where any purchase of $60 dollars or more gets you a free Spice Girls CD. A neat little study for an undergraduate or intro stats class: using discontinuities in the sales promotion to identify causal effects, does the prospect of a Spice Girls CD induce more people to shop and buy at the store? Does it move the marginal $56 purchaser to buy the extra item that puts her over the top?

My priors tend towards the null on this one.

3) The most interesting feature of the show were the model biopics. Many of the models were discovered when they were 12-14 years old. I find that really amazing. How much certainty is there in forecasting whether a 13 year old will turn out to be a supermodel or not? I'm not sure where you can get data on this question, especially given the obvious selection bias - for every Selita Ebanks or Adriana Lima, there are probably 100s of others that you don't observe that don't make it.

To get around this, I thought about the compositional change in the popular clique between 7th and 12th grade in high school. The popular clique in any school is generally comprised of the "hot people," and is generally superficial enough to kick out people who move from hot to not as well as embrace people who move in the other direction. I estimate that about 68% of the popular clique in 7th grade continued to be popular in 12th grade. This larger inter-grade correlation can be explained by persistence in looks and social status as well as the bond of friendship, though its hard to pin down the relative contributions of these factors.

Even so, I think making predictions about a 13 year old is still really difficult. Of course there has to be some science to it: some people have a comparative advantage in discovering models and make a career out of doing so. Indeed, there are a lot of industry specific skills that are either innate or learned. If you've watched America's Top Model, it's easy to get a sense of this: Tyra and the other judges rate the contestants on a vector of different characteristics, where some of the elements are obvious and others not so much.

But at the end of the day, you just never know. A pharmaceutical company, for example, mines through a myriad of candidate molecules, finds the ones that are bioactive, and pushes those forward for further testing. The vast majority of these new chemical entities or compounds will fail, either to be refined or scrapped altogether. But some do make it, and the incentives are such that its worth pushing forward and leaving no stone unturned. After all, the next molecule you find might be worth billions.

I'm guessing there's a parallel to supermodels. As a model finder or agency, you don't know if your 13 year old will turn into John Abraham (the Indian one, not the guy on the Jets), on the one hand, or Atheendar Venkataramani, on the other. But you take the risk, and if it is indeed the former case, there are huge returns to be had. And, for a time, those returns might be increasing in the earlier you find the next great supermodel. Indeed, just as competitive forces push Merck and Pfizer into random jungles looking for even more random plants, the modeling industry probably evolved on a margin where those who moved first in finding younger and younger prospects were able to gain a leg up on their rivals.