Baseball, regression to the mean, and avoiding potential clinical trial biases

This post originally appeared on The Timmerman Report. You should check out the TR.

It’s baseball season. Which means it’s fantasy baseball season. Which means I have to keep reminding myself that, even though it’s already been a month and a half, that’s still a pretty short time in the long rhythm of the season and every performance has to be viewed with skepticism. Ryan Zimmerman sporting a 0.293 On Base Percentage (OBP)? He’s not likely to end up there. On the other hand, Jake Odorizzi with an Earned Run Average (ERA) less than 2.10? He’s good, but not that good. I try to avoid making trades in the first few months (although with several players on my team on the Disabled List, I may have to break my own rule) because I know that in small samples, big fluctuations in statistical performance in the end  are not really telling us much about actual player talent.

One of the big lessons I’ve learned from following baseball and the revolution in sports analytics is that one of the most powerful forces in player performance is regression to the mean. This is the tendency for most outliers, over the course of repeated measurements, to move toward the mean of both individual and population-wide performance levels. There’s nothing magical, just simple statistical truth.

And as I lift my head up from ESPN sports and look around, I’ve started to wonder if regression to the mean might be affecting another interest of mine, and not for the better. I wonder if a lack of understanding of regression to the mean might be a problem in our search for ways to reach better health.
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An Open Standard for APIs Could Lead us to Better Health

There’s a parable about the elephant and the rider that’s been used by Chip and Dan Heath, and that originated with Jonathan Haidt, to describe how humans make decisions. A person’s mind can be thought of as consisting of a rider, representing the rational part of human thinking, and the elephant she’s riding, representing emotion. Both of these play a role in how a person decides things, and many of us believe the rider–the rational part–is in charge. The rider taps the elephant with her guide stick, and the elephant obediently moves in that general direction or does a specific task, like carrying lumber from place to place.

Except that’s not how a lot of decisions actually get made. Instead, the elephant sees a bunch of bananas, or a herd of other elephants, or a nice cool river to bathe in, and goes that way instead. And the rider…well, the rider can’t do much about it except, after the fact, rationalize how she always wanted to go in that direction to begin with. Yeah, it was time for a bath, sure

This framing has stuck in my mind for years and it’s a really helpful way of looking at many of the odd things that people do or say, ranging from climate change denial, to believing genetically modified organisms are inherently evil, to smoking despite everything we know about the harms that result, to even saying that Paul Blart, Mall Cop II is really, you know, not that bad–really. And it also speaks to one of the more vexing problems we have in human health. Why do people keep doing things they really probably shouldn’t, and know they shouldn’t, if they want to stay healthy?

I’ve touched before on how the power of digital tools can help make it easier for us to make good decisions. OPower is doing this for power consumption and conservation, and with the advent of tools like Apple’s Healthkit and the proliferation of activity trackers, the time is right to do this for health. Continue reading