Finding Parallels in Baseball and Drug Development

3/1/13

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to set up mechanisms to broadly communicate the results, good and bad, and support and laud all of them, not just the ones that succeed.

Randomness and the unlucky bounce

Which brings me to a last insight from baseball.  Baseball is a probabilistic game.  The best players in the world get a hit three times out of 10.  Random factors, or at least factors so uncontrollable as to essentially behave like random factors, can influence the outcome of a game.  Just ask the Chicago Cubs. In Moneyball, Billy Beane is described as saying, “My s*** doesn’t work in the playoffs,” by which he meant that the team he built was designed to perform above average, on average, over the course of a 162-game baseball season. But the playoffs are fickle and any team can beat any other team in a best-of-5 or best-of-7 series.

We don’t appreciate how much randomness affects everything.  In The Drunkard’s Walk Leonard Mlodinow provides ample evidence about how little we really control everything around us, even though we might think we do.  He also shows the poor grasp people have on probabilities. In baseball the probabilities are made manifest in the statistics we track, and maybe that’s helped drive the adoption, finally, of better statistical tools.

Baseball is full of random happenings.  Adam Dunn hit 40 home runs a year (more or less) like a metronome for seven years, and then in 2011 was completely lost at the plate.  And then in 2012 he hit 41.  Drug development is full of randomness too.

Drug development sometimes seems to show a much more deterministic mindset. I blame the successes of the ’80s, when a whole raft of wonderful drugs entered the market, and lulled people into a sense that this kind of productivity could go on forever. Pipelines came to be viewed by companies and analysts alike as though they were treadmills steadily pushing new drugs forward, as though making drugs was like manufacturing widgets.  And yet, even companies that create widgets (albeit very large and complex widgets) have problems meeting their deadlines and come against unexpected issues.  How much more uncertain is drug development, which deals with trying to figure out how biology works?

What lesson can drug development companies take? Here there is one important difference:  even a failing baseball team often makes money, whereas a failing pharma company faces being bought or imploding.  On the other hand, poorly performing franchises in the Major Leagues have been threatened with being shut down, or at least moved, so perhaps there are still some parallels there. One key learning is the value of stability. Over-reaction to poor results can be deadly to the long-term health of a ballclub, or a company, as it can lead to the loss of talent due to mis-assignment of blame. Another key point is diversification of revenue streams.  Some of the best positioned ballclubs are there because they have worked hard to increase revenue beyond box-office sales and the occasional T-shirt purchase.  Similarly, some of the best positioned Pharma companies are diversified players like Roche and Johnson & Johnson.

Maybe the most important lesson is to realize in a random world there is no way to guarantee success in drug development, and therefore, the goal is to set up the best processes, with clear measurements and benchmarks; to evaluate constantly but to intervene rarely; to work on increasing the probability of success.  The 2001 Mariners won 116 games and still didn’t even make the World Series. And yet, few question that they were the best team that year by far.  The goal for the Mariners after that season was to evaluate how they got there, try to separate luck from skill, and attempt to replicate those elements that were under the control of the players and the front office.  That could be the approach taken in drug development as well.

Baseball, or the Movie Industry, or Oil exploration, or…

I’d love to delve into other concepts, like Value Over Replacement Player (VORP) and how we might apply that to drugs and scientists, but that could be a thought for another day.  As the drug development industry continues its struggle with how to carve out its future (because, you know, eventually there won’t be any more companies left to buy), it seems potentially fruitful to try and learn from other industries that have been faced with similar challenges.

Kyle Serikawa works as a Senior Research Scientist in Genomics for Novo Nordisk. He is a recent graduate and current board member for Leadership Tomorrow in Seattle. The views expressed on this post are his and do not necessarily reflect the views of Novo Nordisk. Follow @

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  • ~

    So, who’s Ichiro?

  • Matt

    Loved the article! I grew up near Seattle and finished undergrad at UW in Biochem. I moved to Baltimore for grad school and articles like this inspire me on what to consider in my future. Also I love Moneyball and the Mariners :D

  • Kyle Serikawa

    For anyone interested, there was a nice interview with the Director of Decision Sciences at the Houston Astros on the distinction between results and good process. http://www.fangraphs.com/blogs/index.php/qa-sig-mejdal-astros-director-of-decision-sciences/