Baseball analytics, arthritis, and the search for better health forecasts

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.

The best predictor was a first cousin of PECOTA. Continue reading

Add Big Data to the things the Supreme Court Justices know when they see them

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

Yesterday’s Supreme Court decision that searching cell phones from arrested individuals requires a search warrant has been described as “Bold. Landmark. Sweeping.” (I love Nina Totenberg’s reporting, by the way). Commentators have been discussing the precedent set for fourth amendment rights, digital privacy, comparison to historical judgements, civil liberties, yadda yadda yadda. Sure, that’s important and all, but let me add a different filter with which to view the decision.

The Supreme Court validated one of the fundamental premises of Big Data: that for many kinds of data at some point quantity becomes quality.

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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

The power law relationship in drug development

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

A few weeks ago a friend and I had the great opportunity to go see Nate Silver speak at the University of Washington. He’s a funny, engaging speaker, and for someone like me who makes his living generating and analyzing data, Silver’s work in sports, politics and other fields has been inspirational.  Much of his talk covered elements of his book, The Signal and the Noise, which I read over a year ago. It was good to get a refresher. One of the elements that particularly struck me this time around, to the point that I took a picture of his slide, was the concept of the power law and its empirical relationship to so many of the phenomena we deal with in life.

Nate Silver graph small

Figure 1: Slide from Nate Silver’s talk demonstrating the power law relationship in business–how often the last 20% of accuracy (or quality or sales or…) comes from the last 80% of effort.

Because I spend way too much time thinking about the business of drug development, I started thinking of how this concept applies to our industry and specifically the problem the industry is facing with creating innovative medicines.

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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

The fall and rise of the LEGO Kingdom: A review of “Brick by Brick”

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

When people ask me what I did growing up, they expect me to say “surf.” I know this because when I tell them what I did for fun their next question is always, “What, you didn’t surf?” I didn’t. Still haven’t learned. Instead I did a lot of the things boys all over the US did. I watched TV. I hung out at the mall and at fast food restaurants. And I played with LEGO.

The brick fundamentally hasn’t changed since I was a kid. My son has a bunch and the basic essence is still snapping things together with that satisfying “click,” and the gradual accretion of form and function from individual, generic elements. Kind of like how life evolves, you know? And yet at the same time LEGO has undergone great changes in packaging, themes, toy categories, and target audiences. Today it’s one of the most respected and recognized toy brands in the world. But something I hadn’t realized until reading “Brick by Brick” by David Robertson and Bill Breen is how close LEGO actually came to crashing and burning in the 90s and early aughts, before recovering to once again become a commercial powerhouse.

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The innovators dilemma in biopharma part 3. What would disruption look like?

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

h/t to @Frank_S_David, @scientre, and the LinkedIn Group Big Ideas in Pharma Innovation and R&D Productivity for links and ideas

Part 1 is here.

Part 2 is here.

In the previous parts to this series I’ve covered both why the biopharma industry is ripe for disruption, and what the markets might be that could support a nascent, potentially disruptive technology until it matures enough to allow it to supplant the current dominant industry players.  In this final part I’d like to ask what disruption would look like and provide some examples of directions and companies that exemplify what are, to my mind, these sorts of disruptive technologies and approaches. With, I might add, the complete and utter knowledge that I’m wrong about who and what specifically will be disruptive! But in any case, before we can identify disruption, it’s worthwhile to ask what are the key elements of biopharma drug development that serve as real bottlenecks to affecting  human health, since these are the elements most likely to provide an avenue for disruption. Continue reading