What do the polio virus, baseball pitch choice and cancer have in common?
The answer, of course, is sequencing. But not in the “figure out the DNA” way (although that’s involved). Instead in the “what comes first” way. Confused? Read on!
A big perk of Seattle is proximity to great institutions of biomedical research like the University of Washington and the Fred Hutchinson Cancer Research Center. Ever since my graduate student days in genetics at UC-Berkeley I’ve enjoyed going to seminars–especially seminars that are outside my field of study. Very little beats a good seminar for giving you a quick, condensed view of the state of a field of research. A bad seminar…well…we all could use more sleep, right?
In early October, Raul Andino of UCSF came to the Hutch to talk about his work on viral evolution. His team has been examining a clever real-world system to track the evolution of viruses. The near-eradication of polio (one of the great public heath victories of the past century) has led to the curious problem that as of the middle of this year most new cases of polio arose as a result of vaccination efforts. The live, attenuated vaccine that’s used in the developing world can, in very rare cases, mutate in just the wrong ways in its host, leading to the creation of a virulent strain that can infect others. In the US we use an inactivated polio vaccine which requires several injections; in much of the developing world the oral polio virus is preferred due to its ease of administration, lower cost, and immunization profile. The Andino lab realized that by studying these isolated outbreaks, which all originated with the same, genetically identical progenitor, they could test a hypothesis about the adaptive landscape of virulence evolution. Continue reading →
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 →
One of the neat things about the people in the baseball research community is how willing many of them are to continually question the status quo. Maybe it’s because sabermetrics is itself a relatively new field, and so there’s a humility there. Assumptions always, always need to be questioned.
Case in point: a great post by Ken Arneson entitled “10 things I believe about baseball without evidence.” He uses the latest failure of the Oakland A’s in the recent MLB playoffs to highlight areas of baseball we still don’t understand, and for which we may not even be asking the right questions. Why, for example, haven’t the A’s advanced to the World Series for decades despite fielding good and often great teams? Yes there’s luck and randomness, but at some point the weight of the evidence encourages you to take a second look. Otherwise, you become as dogmatic as those who still point to RBIs as the measure of the quality of a baseball batter. Which they are not.
One of the thought-provoking things Arneson brings up is the question of whether the tools we use shape the way we study phenomena–really, the way we think–and therefore unconsciously limit the kinds of questions we choose to ask. His example is the use of SQL in creating queries and the inherent assumptions of that datatype that objects within a SQL database are individual events with no precedence or dependence upon others. And yet, as he points out, the act of hitting a baseball is an ongoing dialog between pitcher and batter. Prior events, we believe, have a strong influence on the outcome. Arneson draws an analogy to linguistic relativity, the hypothesis that the language a person speaks influences aspects of her cognition.
So let me examine this concept in the context of another area of inquiry–biological research–and ask whether something similar might be affecting (and limiting) the kinds of experiments we do and the questions we ask.
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…
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.
All opinions are my own and do not necessarily reflect those of Novo Nordisk.
It’s Fourth of July weekend in Seattle as I write this. Which means it’s overcast. This was predictable, just as it’s predictable that for the two months after July 4th the Pacific Northwest will be beautiful, sunny and warm. Mostly.
Too bad forecasting so many other things–baseball, earthquakes, health outcomes–isn’t nearly as easy. But that doesn’t mean people have given up. There’s a lot to be gained from better forecasting, even if the improvement is just by a little bit.
And so I was eager to see the results from a recent research competition in health forecasting. The challenge, which was organized as a crowdsourcing competition, was to find a classifier for whether and how rheumatoid arthritis (RA) patients will respond to a specific drug treatment. The winning methods are able to predict drug response to a degree significantly better than chance, which is a nice advance over previous research.
And imagine my surprise when I saw that the winning entries also have an algorithmic relationship to tools that have been used for forecasting baseball performance for years.