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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What do labrador retrievers and NFL wide receivers have in common?

All opinions are my own and do not necessarily reflect those of Novo Nordisk.

A.  They’re both being studied via mobile tech to create their ethograms.

What’s an ethogram?  I had no idea until I saw this PLOSone paper on using inertial sensors such as accelerometers and gyroscopes to measure the movements and behaviors of dogs, so as to create an ethogram, or collection of behaviors and actions, characteristic of labrador retrievers and Belgian Malinois.  For scientists studying the behavior and action patterns of different species, building an ethogram is essential to studies of animal behavior.  Without a standardized, objective catalog of behaviors, it can be easy for the perspectives of the observer to get in the way.  And it can make comparisons of data among different researchers (or coaches, as we’ll discuss in a bit) difficult.

And just as I was mulling over how that study shows the power of technology for behavioral research, the latest issue of Sports Illustrated came in the mail and I read a short, fascinating article by Tim Newcomb about how eight NFL teams have signed up with the company Catapult to integrate small GPS sensors into practice and game uniforms. This data allows a more accurate, granular and comprehensive view of how different receivers, for example, play the game.  Basically, building the receiver ethogram (using the term rather loosely). Sadly, this article is currently only in the print issue and not online that I can find.  But it’s at newsstands now.  You can go pick one up.  I’ll wait.

So let me delve into each of these articles a little more.

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