“Given the advances in baseball statistics, one might hope it would be possible to deploy modern analytical tools to adjust today’s chemically enhanced stats and put them on a level playing field with those of yesteryear. But while number crunchers can tell you how to reorganize a batting lineup, they find it much tougher to interpret the effects of drugs.
The biggest challenge in attempting to measure the consequences of steroids is that it is impossible to know who used what, and when. Even if the supposedly anonymous results of all 104 players who tested positive in 2003 are revealed, there will still be more questions than answers. Steroids come in more combinations than Tony La Russa’s lineup cards, and they can further be paired with an array of workout programs and nutritional regimens.”
Furthermore, because both pitchers and hitters were evidently juicing, we shouldn’t expect either offensive or defensive performance to skyrocket, even if the drugs are powerful. Instead, if all players were taking the drugs we’d anticipate that the sport would reach a new steroid-based equilibrium that might not actually look all that different than the old one.
But not all players were taking the drugs. Even the most hyperbolic accounts of steroid use in baseball don’t allege that all, or even most, major league players were devotees of the cream, the clear, human growth hormone, etc..
Rosenheck realized that this lack of 100% adoption opened the door to a clever test of steriods’ impact, a test that didn’t depend on knowing or even guessing who the users were. He reasoned that if steroids provided a significant performance boost to a player, and if not all players took them, then we should expect to see the performance gaps between juicers and clean players get bigger — the juicers as a group would (unfortunately) pull away from those that were not taking drugs. More specifically, we’d expect to see increases in the standard deviation (a measure of spread) of all hitters’ batting averages, all pitchers’ walks+hits per inning pitched (WHIP), and other such statistics. And we’d expect to see these increases only during baseball’s alleged steriod era, which was commonly assumed to start in 1993 and ended with the start of a testing regimen in 2005.
Rosenheck has conducted these analyses, and found that
“performances by both hitters and pitchers were more spread out [between 1993 and 2004] than in any 12-year period since World War II. Although some of the difference was caused by adding new teams, expansion was much more rapid in the 1960s than the 1990s, and standard deviations were still lower back then.
None of this means that steroids are necessarily the cause of the separation. But the game’s fans are probably in no mood to write off the association as mere coincidence.”
It’s not automatically the case, of course, that a player with a high batting average or low WHIP between 1993 and 2004 was on the juice. Rosenheck’s analyses don’t prove who was taking drugs, and as he says they also don’t prove that steroids were responsible for the increased spread in performance. But I find it very interesting that what we see in the data is exactly what we’d expect to see if steroids were powerful, and were being used by some players. I’m pretty confident that this is not a coincidence.
My colleagues and I have documented that beginning in the mid 1990s corporate performance also started to diverge — the gaps between high-performing and low-performing firms in an industry started to widen (and have stayed wide ever since). We also found that the gaps were wider in industries that spent more on IT. This is exactly what we’d expect to find it IT were powerful, and if not all firms were equally good at adopting or exploiting it.
In a blog post a little while back I made an explicit analogy between baseball and business competition, building on the work of Stephen Jay Gould. Gould documented that standard deviations of baseball players’ performance had been declining steadily throughout the game’s history, and advanced a powerful hypothesis about why:
“Complex systems improve when the best performers play by the same rules over extended periods of time. As systems improve, they equilibrate and variation decreases.”
If Rosenheck’s analyses hold up, they strongly suggest that steroids in baseball were such a big deal that they amounted to a rule change in the game itself, a change significant enough to disturb a longstanding equilibrium. This change was a particularly unfair one since it only affected the players who took the drugs. Those who stayed clean were playing by the old rules, and evidently found themselves outperformed as a group.
One of the themes of my research and this blog is that advent of modern corporate technologies like enterprise systems, analytics, and Enterprise 2.0 tools is a big enough deal that it amounts to a rules change in the game of business. In sharp contrast to steroids in baseball, though, IT is legal, acceptable, and kosher in every way in business and competition. A-Rod is probably in for some serious abuse from the fans at Fenway and many other parks this year, but no one’s going to cry foul and hurl abuse at your company if you deploy technology.
In addition to being a performance enhancer, IT is similar to steriods in another way: effective use can be very hard to detect from the outside. Back acne and swelling craniums aside, it’s usually not easy to tell which athletes are taking drugs, exactly what types they’re on, and how much good it’s doing them. It’s also not always easy to determine what kinds of technology a company has, and how exactly they’re contributing to its overall value proposition.
But I have great confidence that companies in many sectors are using performance-enhancing tech effectively, thereby widening the gap between the best and the rest in the game they’re playing. My question is: why would any competitor turn their back on this perfectly legal and acceptable performance booster?