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.
Continue reading

Advertisements

Could pro sports lead us to wellness?

Comment From Bill
St. Louis is being hindered in the stretch drive by some kind of GI bug passing through (so to speak) the team. Reports have as many as 15 guys down with it at once. That seems a lot, but given the way a baseball clubhouse works, my question is why don’t we see more of that? Answering that baseball players are fanatically interested in sanitation and hygiene ain’t gonna cut it, I don’t think…

12:10
Dave Cameron: They have access to a lot of drugs.

–comment from a chat at Fangraphs, September 24, 2014

So this comment caught my eye. Ever since I began following sites like BaseballProspectus.com and Fangraphs.com, and reading things like Moneyball, I’ve found myself thinking about efficiency and unappreciated or unexplored resources in different situations.

I realize this was a throwaway line in a baseball chat. But it piqued my interest because it seems to point out something that’s maybe underappreciated and understudied about how sports teams go about their business–specifically, the kinds of things they do to keep their athletes healthy.

My question is, does this represent a potential source of “Found Research” data that could help the rest of us reach wellness? Continue reading

Big data and baseball efficiency: the traveling salesman had nothing on a baseball scout

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

The MLB draft is coming up and with any luck I’ll get this posted by Thursday and take advantage of web traffic. I can hope! Anyway, Tuesday in Fangraphs I read a fascinating portrayal of the draft process, laying out the nuts and bolts of how organizations scout for the draft. The piece, written by Tony Blengino (whose essays are rapidly becoming one of my favorite parts of this overall terrific baseball site), describes all the behind the scenes work that happens to prepare a major league organization for the Rule 4 draft. Blengino described the dedication scouts show in following up on all kinds of prospects at the college and high school levels, what they do, how much they need to travel, and especially how much ground they often need to cover to try and lay eyes on every kid in their area.

One neat insight for me was Blengino’s one-word description of most scouts as entrepreneurs. You could think of them almost as founders of a startup, with the kids they scout as the product the scouts are trying to sell to upper layers of management in the organization. As such, everything they can do to get a better handle on a kid’s potential can feed into the pitch to the scouting director.

I respect and envy scouts’ drive to keep looking for the next big thing, the next Jason Heyward or Mike Trout. As Blengino puts it, scouts play “one of the most vital, underrated, and underpaid roles in the game.” While one might make the argument that in MLB, unlike the NFL or NBA, draft picks typically are years away from making a contribution and therefore how important can draft picks be?, numerous studies have shown that the draft presents an incredible opportunity for teams in building and sustaining success. In fact, given that so much of an organization’s success hinges on figuring out which raw kids will be able to translate tools and potential into talent, one could (and others have)  made the argument that scouting is a huge potential market inefficiency for teams to exploit. Although I’ll have a caveat later. But in any case, for a minor league system every team wants to optimize their incoming quality because, like we say in genomic data analysis, “garbage in, garbage out.”

As I was reading this piece, I started thinking about ways to try and create more efficiencies. And I started thinking about Big Data.  Continue reading

Drug development and the NFL draft

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

The NFL draft is happening as I am writing this post. And of the many draft-related pieces I’ve read in the past few days, one from Vox.com particularly stood out. The article, by Joseph Stromberg, describes research by Cade Massey and Richard Thaler (here and here) about the skewed and irrational choices often made by teams during trades of draft picks. In essence, teams are likely to pursue a strategy in trading up that suggests they believe they have a much greater ability to forecast the future performance of a given player than is actually the case. Put another way, rather than following a strategy of diversified risk, teams commit to a specific player that they feel they need to get, rather than simply seeing who’s available when they are scheduled to pick and choosing the best player on their draft board.

Historical analysis shows that the difference between various players drafted at the same position is often negligible; on top of that teams who aggressively trade down and gather more picks in the lower rounds generally do better in terms of the value they receive for the money they spend in salaries. One might argue this is an artifact in part of the NFL Rookie salary structure, but even without that, players taken in later rounds will always command smaller salaries. Getting similar value for less money is generally a good thing.

If you’ve read posts from this blog before you know where I’m going. Drafting NFL rookies sounds a lot like developing drugs. Continue reading

Can studies of bosses help us figure out how good sports managers are?

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

The world would be a simpler place, although maybe a much more boring and predictable one, if every aspect of performance could be measured directly. My completely unoriginal thought here is that one of the reasons sports appeal to so many people is because they provide clarity. In a confusing, complex world where the NSA is sucking up our information like a Dyson vacuum sucks feathers in a henhouse, and we’re told this is for our own good, clarity can be refreshing.

The simple view of an athlete’s performance is that all the accolades (or jeers), all the milestones (or flops), all the accumulated statistical totals (or lack thereof) are because of that athlete’s ability: his or her drive, passion, training, and natural ability. And that performance is measured via the statistics each sport collects and chooses to honor and promote. Performance is right there, what more do you need? What more could you want? Continue reading

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.

Continue reading

Biopharma should choose targets using a baseball-style draft

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

I was sitting around last evening checking out how the end of my fantasy baseball season is working out (for the record, first out of ten in one league and fourth in the league I wrote about here) and I starting thinking again about the parallels between baseball and drug development (which I previously wrote about here and here for example, and also Stewart Lyman has a nice piece on a similar theme here). And it hit me that there’s another way in which biopharma could take a  page from baseball: fantasy and Major League Baseball both.

Biopharma could institute a draft for drug targets.  And to explore this I’m going to employ the time-honored, not to mention trite and artificial, format of a series of questions and answers.

Continue reading